An ethical minefield. Stepping from the worst to the best population ethical theories

About ten years ago, during my studies in moral philosophy, I encountered the area of population ethics. It is clearly one of the most tricky areas in ethics, a minefield of very counterintuitive conclusions. But it is also one of the most important areas, because it strongly influences very important decisions that deal with huge problems such as human extinction, animal farming and wild animal suffering. Population ethics tells us how much we should care about future people. As the number of those people can be huge, a lot is at stake with population ethics.

Population ethics is an extension of welfare ethics. Welfare ethics deals with problems where different situations involve people with different welfare levels. For example, in one situation, everyone is moderately happy, in another situation, those people are extremely happy, except for one person who is extremely miserable. Which situation is the best? A concern for justice implies that the first situation is preferable. Population ethics deals with problems where different situations involve different people having different welfare levels. The question of justice becomes much more tricky in population ethics.

In the early days of my PhD-research in moral philosophy, after many hours of work, I formulated my favorite population ethical theory. But a year later, around 2012, I learned that it was a mistake, as I discovered that my favorite theory contained counterintuitive implications. It was one of the first times that I had to change my mind, give up my favorite theory and destroy my own work. (I later learned that Ashem and Zuber called this theory ‘rank-discounted utilitarianism’ and defended it in their 2012 and 2014 articles, right at the time I changed my mind about this theory. Problems with this theory were mentioned by Budolfson and Spears.)

A few years later, in 2014, I defended my PhD-dissertation about animal ethics, which contained another population ethical theory that strongly relates to average utilitarianism (although as the name ‘positive number-dampened power mean prioritarianism with negative total utilitarianism’ suggests, a lot more complicated than average utilitarianism). But another few years later, I realized that this new theory was horrible again. So I had to start all over again, kill my darling a second time, and look for another favorite population ethical theory. I started drifting around from theory to theory.

In 2018, I came up with a new theory, which I called variable critical level utilitarianism (but perhaps is better called autonomous critical level utilitarianism, as explained below). I still consider it as a very good and promising candidate, but knowing how difficult it is to avoid very counterintuitive implications, I’m not so confident anymore. And a few weeks ago, I found a second very promising candidate, which I call minimax net-complaint theory.

In this article, I first explain what is wrong with my previous favorite candidate: average utilitarianism. I consider this theory, which I defended in my PhD-dissertation, as the worst of the population ethical theories that are still at least a bit reasonable. Next, I describe my two favorites (up to now): autonomous critical level utilitarianism and minimax net-complaint theory.

The worst reasonable theory: average utilitarianism

Standard population ethical theories start with a social welfare function, which is a function of individual welfares. For each situation we can calculate the social welfare, and the best situation is the one that has the highest social welfare. A common population ethical theory is average utilitarianism, where the social welfare of a situation is defined as the average welfare of everyone who exists, existed and will exist in that situation. You take the sum of everyone’s welfare, divide it by the number of existing people, and that quantity should be maximized.

This theory seems simple and elegant, and could be derived from a kind of Rawlsian ‘veil of ignorance’ thought experiment: imagine that you are behind a veil of ignorance, only knowing that you will be born in the world, but not knowing who you will be. You can become anyone who exists in the world, with equal probability. Now you can decide the welfare distribution of all existing people. If you want to maximize your expected welfare, which is reasonable, you end up with average utilitarianism.

However, I now consider average utilitarianism as the worst of all reasonable population ethical theories, because it faces several problems and counter-intuitive implications.

  • The dominance addition problem: increasing the welfare of extremely happy people by adding very happy people, is not good. Suppose in situation 1, a person has welfare level 100, whereas in situation 2, that same person has welfare 110 and an extra person exists who has welfare 84. Hence, the first person is happier, the extra person is also very happy, no-one is worse-off, and the total happiness almost doubles. Yet, situation 2 now has an average welfare of 97, which is lower than 100 and hence not preferred by the average utilitarian.
  • The dependence problem: whether or not it is good to add happy people, depends on the welfare of unaffected people. In taking the average, we have to include e.g. past generations, wild animals or even aliens on distant planets. Suppose in situations 1 and 2 described above, there exists an extra person who is unaffected by the choice between situations 1 and 2. That means in both situations, that person has the same welfare, say 100. The average welfare in situation 2 now equals 98, which is lower than the average in situation 1, which equals 100. Hence, in this case choosing situation 2 is not allowed, as in the dominance addition problem described above. However, suppose that unaffected person has welfare 10 in both situations. Now the average welfare in situation 2 equals 68, which is higher than the average in situation 1, which equals 55. If the unaffected person has a low welfare, adding the extra person in situation 2 is good. Hence, whether we decide whether procreation is permissible, we first have to know the welfare of everyone else who existed, exists and will exist in the universe. If we learn about the existence of a large population of unaffected people, a situation that was once better than another, might become worse.

Sadistic and repugnant conclusions. If the unaffected beings are much more numerous than the people whose existence and welfare we can influence, things become even worse for average utilitarianism.

  • The sadistic conclusion. If the welfare of the unaffected people is negative: adding extra people with negative welfare becomes good. Suppose there is a huge population of extremely miserable people, such that the average welfare is -100. Adding an almost as extremely miserable person with welfare -99 would slightly increase the welfare and hence would be good, even if all the other people are unaffected.
  • The repugnant conclusion. If the welfare of the unaffected people is positive but very small, then it would be good to strongly decrease the welfare of one person by adding a huge number of people with lives barely worth living (i.e. a very low welfare slightly above zero). Suppose the average welfare is 0,1, and one person has a high welfare of 100. Now thousands of extra people are born, all with very low welfare 0,2. But by adding those people, that one happy person gets a very low welfare of 0,2 as well. Everyone now has a very low welfare, but still, the average welfare increases slightly. Or, if we discover the existence of a huge population of aliens with lives barely worth living, humans would have a strong duty for extreme procreation, decreasing our welfare by adding lots of extra slightly happy people.
  • The sadistic repugnant conclusion. The above repugnant conclusion can become more sadistic: suppose by adding the thousands of extra people, that one happy person becomes extremely miserable, receiving a large negative welfare of -100. Even then, the average welfare increases slightly.
  • The reverse repugnant conclusion. If the welfare of unaffected people is positive and very high, adding people with high positive welfare is bad. If the average welfare is 100, adding people with welfare 90 lowers the average. This happens even when adding those people would increase the welfare of a person. Consider a slightly happy person who would become extremely happy when she has many very happy children. Increasing one’s welfare by adding many happy children would not be allowed according to average utilitarianism. Or, if we discover the existence of an extremely happy alien race, it is better for humans to stop procreating.
  • The reverse sadistic repugnant conclusion. The above reverse repugnant conclusion can become more sadistic: suppose the welfare of unaffected people is positive and extremely high (welfare +100), and one person is extremely miserable (welfare -100). We can make that person extremely happy (welfare +100) by adding a huge number of very happy people (with welfare 90). Now everyone is at least very happy, one person moves from extreme misery to extreme happiness, but the average welfare is lower, which is bad according to average utilitarianism.

We can try to avoid some of the above counter-intuitive implications, by looking for other population ethical theories. Average utilitarianism can be a starting point from which we can move towards other population ethical theories. For example, if we assume the existence of an infinitely large population of unaffected people, with a non-negative average welfare level, we arrive at critical level utilitarianism, where the average welfare of the unaffected people counts as a critical level.

In critical level utilitarianism, the social welfare function can be reduced to the sum of everyone’s relative welfare levels, whereby someone’s relative welfare is the welfare minus the constant critical level. When a person is added to the world and has a welfare above this critical level, the social welfare increases, but when the welfare is below the critical level, social welfare decreases.

A special version of critical level utilitarianism, is total utilitarianism, where the critical level is zero. This theory is susceptible to the repugnant and the sadistic repugnant conclusions.

Autonomous critical level utilitarianism

Critical level utilitarianism has many advantages. First, the social welfare function of critical level utilitarianism is simply a sum of terms, which means that the non-affected people do not matter, because they always have a constant contribution in this sum. As only the affected people count in the welfare function of critical level utilitarianism, we avoid the dependence problem. Second, as the critical level is non-negative, we avoid the sadistic conclusion. Third, when the critical level is not too low, we avoid the repugnant and sadistic repugnant conclusions. Fourth, when the critical level is not too high, we avoid the dominance addition problem and the reverse repugnant and sadistic repugnant conclusions.

Critical level utilitarianism has its problems, though. We face an arbitrariness of the choice of critical level. Some counter-intuitive implications can be avoided when the critical level is not too low, and other problems when the level is not too high. So the ideal critical level should be somewhere in the middle. However, with a medium critical level, both (sadistic) repugnant and reverse (sadistic) repugnant conclusions are still present to some degree. A low critical level gives one problem, a high critical level gives another problem, and a medium critical level gives us both problems, but with half of their strengths. Suppose we have a medium critical level, and a situation where one person suffers a lot. Suppose we can help that person, increase that person’s welfare to a high level, by adding a huge number of medium happy people with lives worth living, but welfare levels barely below the medium critical level. As they are below the critical level, that huge number of people negatively contributes to the social welfare function of critical level utilitarianism, which means we should not help that one miserable person.

My idea a few years ago, was to improve critical level utilitarianism by allowing a variable critical level. If we are faced with choices that lead to the repugnant or sadistic repugnant conclusion, we can set a high critical level in order to avoid those conclusions, and if we are faced with choices that lead to the reverse repugnant or reverse sadistic repugnant conclusion, we can set a low critical level instead. Or we can move a few steps further, by allowing complete freedom: if the choice of critical level is arbitrary, why not let people determine for themselves which critical levels they have? Autonomous critical level utilitarianism assumes that individuals can autonomously (freely) choose their own critical levels. Different individuals can have different critical levels. But also the same individual can choose to have different critical levels in different situations. And even the same individual in the same situation can choose to have different critical levels in that situation, depending on the other situations that are available. If another option becomes available, people may change their own critical levels.

The social welfare function is again the sum over all individuals of the differences of the individual welfare levels with the individual critical levels. The individual welfare level measures how strongly the individual prefers the situation, whereas the individual critical level captures the population ethical preferences of that individual.

The full flexibility in choosing critical levels may be unproductive, because people can choose to increase their critical levels to infinity. Suppose there are two situations and two people, the first person prefers situation 1 and the second prefers situation 2. To steer the outcome towards situation 1, the first person can set a very high critical level for himself in situation 2. But the second person can anticipate that and set a high critical level for herself in situation 1. Now the first person can choose an even higher critical level. This can quickly escalate, such that in both situations there is someone with an infinite critical level. In both situations, the social welfare function becomes minus infinity, and it becomes impossible to compare those situations and select the best situation that has the highest social welfare.

To avoid such problems of escalation and strategic manipulation, we can introduce some constraints.

First, individuals cannot choose a negative critical level. Choosing a negative critical level means that one is willing to be born with a negative welfare, and that is not rational.

Second, in each situation, the sum of everyone’s critical level cannot be higher than the maximum (over all available situations) of the sum (over all contingent people) of the positive welfares of contingent people. Available situations are those that are possible or feasible and can be chosen. Contingent people are people who exist in some but not in all the available situations, in contrast with necessary people who exist in all available situations. In each available situation, we can look at the contingent people who have positive welfare levels, and take the sum of those positive welfares. Finally, we can take the maximum over all available situations of those sums of positive welfares. If people choose high critical levels such that the sum of their critical levels becomes larger than this maximum, the excess critical level (the difference between the sum and the maximum) is simply not taken into account in the social welfare function.

Third, the necessary people who exist in all available situations, have to choose their critical levels from behind a Rawlsian veil of ignorance, as if they do not know which of the necessary people they will be.

Autonomous critical level utilitarianism often reduces to total utilitarianism, because in many cases, each available situation has some people who choose the maximum critical level. If all available situations have the same maximum critical level, those critical levels cancel each other out. Major exceptions are cases where total utilitarianism offers counter-intuitive implications such as the repugnant and sadistic repugnant conclusions. Consider the case of the sadistic repugnant conclusion: in one situation, one person has a high welfare and has no intention to choose a high critical level. In another situation, that same person gets a negative welfare, and a huge number of extra people are born with lives barely worth living. Those extra people are contingent people, because they did not exist in the first situation. We can take the sum of their welfare. Now, the first person, who is miserable, can set a very high critical level equal to the total welfare of the contingent people. It is as if the positive welfare of the contingent people no longer counts (because that part is subtracted by the critical level of the one person). As only the welfare of the one person counts, the first situation will be selected. In that situation, the contingent people do not exist, cannot complain and cannot set a positive critical level to steer the outcome back to the second situation.

Another implication of autonomous critical level utilitarianism, is that it is against animal farming, even if the farm animals would have a positive welfare. Consider a first situation, where humans exist but do not breed animals. These humans have a total welfare H. They can increase their welfare a little bit to H+, by breeding, raising, slaughtering and eating farm animals. Suppose those farm animals have a positive welfare overall (they live healthy happy lives on the fields and are painlessly killed). The total animal welfare is denoted as A. However, a third situation is available, where the same animals are not slaughtered, but live even longer happier lives on farm sanctuaries. Their welfare increases to A+. The animals are the contingent people, and they have maximum welfare in the third situation equal to A+. This means the animals can set a high critical level (equal to A+) in the second situation, to steer the outcome towards the third situation. But in the third situation, the humans have the lowest welfare, written as H-, because they can no longer enjoy eating meat, and now have to share resources with the animals and take care of them. So the humans set a maximum critical level equal to A+ in the third situation, steering the outcome towards the first. If in the first situation, the humans again choose a maximum critical level, all three situations contain maximum critical levels. These critical levels cancel each other, which means we end up with total utilitarianism, and this theory selects the third option. That third situation was not preferred by humans, so to avoid selecting that situation, it is reasonable for the humans in the first situation to set their critical level at zero. In the end, the first situation, without animal farming, is the best according to autonomous critical level utilitarianism.

Minimax net-complaint theory

If in a situation the necessary people set their critical level equal to the welfare of the contingent people, it is as if the welfare of the contingent people do not count. This brings us to a whole new category of population ethical theories: the person-affecting theories.

According to the person-affecting view, if a situation is better, it should be better for at least someone, and if a situation is worse, it should be worse for at least someone. Consider the repugnant conclusion again. In the first situation, a person is happy, in the second situation, that person has lower happiness and extra people with positive welfare exist. A total utilitarian says that the second situation is better, which means the first situation is worse. But there is no person existing in the first situation for whom the first situation worse. That is why total utilitarianism does not satisfy the person-affecting view.

Many person-affecting theories face a sadistic conclusion, because the contingent people do not count. Suppose we can increase your welfare a tiny bit, by adding a person to the world who has a very negative welfare. In the initial situation, you are worse-off and no-one is better-off, so the person affecting-view says that the initial situation is worse. A person-affecting theory would select the second situation where one person suffers a lot and another person is only slightly better-off.

This sadistic conclusion can easily be avoided by refining the person affecting theories, making them asymmetric. The asymmetry means that adding a life with negative welfare always makes things worse, but adding a life with positive welfare not always makes things better. In asymmetric person-affecting utilitarianism, the positive welfare levels of the contingent people are not included in the social welfare function, but the negative welfares are. If a contingent person has a negative welfare in one of the available situations, we have to consider that person as a necessary person: the situations where the person does not exist have to be treated as if the person exists and has welfare 0.

Autonomous critical level utilitarianism is asymmetric, because the critical levels are always non-negative. But it does not often satisfy the person affecting view. My second favorite population ethical theory is a more recent discovery that is also asymmetric, and satisfies the person-affecting view more often.

Suppose we have a number of available situations which we can choose. To say that a situation is better, we have to compare that situation with another situation. So let’s take two situations. Some people in the first situation would prefer the other situation, for example because they have a higher welfare in that other situation. Those people in the first situation can complain against choosing the first situation above the second. We can add up all the complaints in the first situation (against choosing the first above the second situation) and subtract the total complaints in the second situation (i.e. the complaints of all people in the second situation against choosing the second situation above the first). This is the net-complaint of the first situation relative to the second situation. For each pair of situations, we have a net-complaint that measures how much better people in those situations prefer one situation over the other. For each pair comparison, the person-affecting view is satisfied. This also means that each situation has many net-complaints relative to the other situations, one for each of the other situations. Now consider the maximum of those net-complaints of that situation. Finally, we look for the situation that has the lowest value of this maximum net-complaint. That situation is the best, according to minimax net-complaint theory: we have to minimize the maximum of the net-complaints. This selection of the best situation is closely related to the minimax Condorcet margins method in voting theory.

People can freely choose their complaints, just like people can freely choose their critical levels in autonomous critical level utilitarianism. If complaining results in the selection of a situation that is not preferred by the complainer (e.g. a sadistic repugnant conclusion), that person can choose not to complain in order to avoid selecting that situation. However, just as in autonomous critical level utilitarianism, we have to set constraints on the allowed complaints, otherwise people might choose negative or infinite complaints. Just like the critical level should always be non-negative, a complaint should always be non-negative. And the maximum complaint of a person in situation 1 relative to situation 2 equals the personal harm: the welfare of that person in situation 2 (assumed to be zero if the person does not exist in situation 2) minus the welfare of that person in situation 1. If a person does not exist in a situation, that person off-course has a zero complaint in that situation.

Note that this upper constraint on a complaint assumes that we can identify the same person in two different situations. But that brings us to a tricky area of personal identity. It is not always possible to clearly determine whether a person in one situation is the same person in another situation. Are you the same person as that person in a very different situation, that has your name and your parents, but a completely different life? Imagine after your birth you were raised in another country, speaking another language, having another education, having completely different life experiences, having other memories, having other atoms in your body, developing other preferences, having your genes altered, having chips implanted in your brain, and so on. There is no objective truth to this notion of personal identity: we cannot objectively determine which persons are identical. But, we can allow people to determine for themselves whether they identify with a person in another situation. In your actual situation, you can identify yourself with a person that lives in another situation, but with one condition: that person in the other situation should not already be identified by someone else in your actual situation. If you believe to be mister X in situation Y, and I also believe to be that person, then you and I have a problem. Only one of us is allowed to identify with mister X, unless we believe that you and I are the same person.

With this being said, we can look at an application of minimax net-complaint theory, that resembles the above example of animal farming. Consider an existential risk that can cause the extinction of humanity, such as extreme climate change. In the first situation, we do nothing against that risk, which means we are the last generation and humanity goes extinct. In this situation, we have welfare H. In the second situation, we partially mitigate that risk, which means humanity survives, but future generations face harsher living conditions due to the catastrophe. This would be the case of non-extreme climate change. As the current generation doesn’t face mass extinction, we are happier (welfare H+). The future generations could still have lives worth living, but with low welfare A. Those future generations are comparable to farm animals with net-positive lives in the above example of animal farming. But there is a third situation, comparable to the farm sanctuary situation mentioned above. We can increase our efforts, sacrifice our welfare, to mitigate the catastrophe and completely eliminate the risk, such that future generations would live very happy lives (welfare A+). Due to our efforts, however, we get a much lower welfare (H-), perhaps lower than in the first situation.

The tricky point is that our efforts to mitigate climate change have an influence on who will exist in the far future. If we change our behavior, we might have other children, and other grandchildren. That means the future generations in the second situation are not the same individuals who would exist in the third situation. If they would not exist in the third situation, the future generations in the second situation cannot complain against selecting the second situation, as long as they have a positive welfare in that situation. Concerning the current generation of humans, they can complain against selecting the first or the third situations, because they get a higher welfare in the second situation. Hence, the second situation has the lowest net-complaint, which should be chosen.

However, it is possible that some future people in the second situation identify themselves with future people in the third situation. Then they can complain against selecting the second situation, because their counterparts in the third situation are better-off. If they complain, we may have an intransitivity here that is a characteristic of person-affecting views: situation 2 is better than 1 (according to the current generation), situation 1 is better than 3 (according to the current generation), and situation 3 may be better than situation 2 (when future generations in situation 2 complain). This can be represented as a cycle with three nodes representing the three situations, and three directed links between the nodes, representing the net-complaints between situations. We can easily make this theory transitive, simply by cutting the weakest link in the cycle. The strength of a link is measured as the net-complaint. Hence, the situation with the lowest net-complaint will be selected. This is the minimax net-complaint theory.

Expressed mathematically, with minimax net-complaint theory, assuming everyone chooses their maximum allowed complaints, situation 2 is selected when A>max{A+-H-H++2H; A++H-2H++H}, i.e. when the welfare of the future generations in situation 2 is sufficiently high. Situation 3 is selected when H<min{(H++H)/2; 2H-H++A+-A}, i.e. when the welfare of the current generation in situation 1 is very low. Situation 1 is selected when H>max{(H++H)/2; 2H+-H-A++A}. The same goes of-course for the above animal farming problem: situation 2 with happy farm animals is the best only when those farm animals are sufficiently happy (i.e. when A is very high).

Summary

I have two favorite population ethical theories: autonomous critical level utilitarianism and minimax net-complaint theory.

Autonomous critical level theory selects the situation that has the highest sum of relative utilities (summed over all individuals existing in that situation), whereby a relative utility is the utility of a person in a situation minus a critical level chosen by that person in that situation. The critical levels capture the population ethical preferences of people and are constrained: an individual critical level cannot be negative, and the sum of critical levels in a situation cannot be higher than a maximum value which equals the maximum total welfare of contingent people who do not exist in all available situations. In many cases, this variable critical level utilitarianism resembles total utilitarianism, but it allows to avoid the most counterintuitive sadistic and repugnant conclusions of total utilitarianism.

Minimax net-complaint theory selects the situation that has the least amount of maximum net-complaint, whereby a net-complaint of a situation versus a second situation measures how much people in the first situation complain against selecting the first situation over the second situation, minus the complaints of people in the second situation against selecting the second situation over the first situation. The complaint of a person can be freely chosen, but should be between zero and the personal harm (the difference in utility of that person between the two situations).

Both theories avoid the counterintuitive implications such as sadistic and repugnant conclusions. When it comes to the animal farming problem, the situation without breeding of farm animals is selected, unless the farm animals are very happy and would not complain against them being used for consumption, or unless the situation with animal farming has a higher total welfare than all other available situations. When it comes to the existential risk problem, we should maximize risk mitigation and maximize the welfare of future generations, unless future generations have a sufficiently high welfare or would not complain when we do not maximize future welfare, or unless such level of risk mitigation is too costly for current generations (where ‘too costly’ is determined by all the welfare levels of both current and future generations in all possible situations).

Given two favorite theories, which one should we choose? One option is that we apply both theories and determine the optimal situations according to both theories. If the two optimal situations are different, we can have a democratic majority vote which one of them we should choose.

Geplaatst in Artikels, English texts | Tags: , | 1 reactie

Van radicale naar rationele ecologie, van laag naar hoog, van Shiva naar Dyson

De milieubeweging zit laag aan de grond en kan best opstijgen tot hogere niveaus. Dit kunnen we zowel figuurlijk als letterlijk interpreteren.

Ecologische stromingen

We beginnen met een figuurlijke interpretatie. Een sterke onderstroom binnen de milieubeweging is het radicaal ecologisme. Het woord ‘radicaal’ komt van ‘radix’, het Latijnse woord voor ‘wortel’. Net zoals een wortel graaft een radicale ecologist naar de grondoorzaken van de milieucrisis. Is het de vervuilende industrie? Of fundamenteler de technologie of het patriarchale-kapitalistische systeem? Of nog fundamenteler het mensdominante, antropocentrische wereldbeeld waarbij de mens boven de natuur staat? Binnen het radicaal ecologisme zijn er verschillende filosofieën, zoals de diepe ecologie dat zich verzet tegen het antropocentrische denken, de sociale ecologie dat strijdt tegen het kapitalisme en het ecofeminisme dat stelt dat de patriarchale onderdrukking van de vrouw door de man eenzelfde patroon kent als de onderdrukking van de natuur door de mens. De radicaal ecologische levensstijl kan samengevat worden in de slogan van de diepe ecologie zoals geformuleerd door de Noorse filosoof Arne Naess: “Eenvoudig in middelen, rijk in doelen.” Terug naar de basis, de eenvoud, de stilte en rust in de natuur. ‘Diepe’, ‘basis’, ‘wortel’: het radicaal ecologisme kent veel figuurlijke verwijzingen naar laag bij de grond.

Recent zien we een nieuwe wind waaien binnen de milieubeweging: het rationeel ecologisme. Hier geldt de slogan: “Doeltreffend in middelen, consistent in doelen.” De doelen van het radicaal ecologisme, zoals ‘natuurlijkheid’, ‘ecologische stabiliteit’ of ‘ongereptheid’, zijn vaak nogal ambigu, slecht gedefinieerd en tegenstrijdig. Als je let op het leed van wilde dieren in de natuur, dan is er niet veel sprake van stilte en rust. De radicaal ecologische voorkeur voor natuurlijkheid botst vaak met andere doelen zoals gezondheid en welzijn. En van ecologische stabiliteit of integriteit is niet echt sprake in de natuur. De zwakke plek van het radicaal ecologisme is dat de natuur zelf geen besef heeft van eigenschappen zoals stabiliteit en geen voorkeur heeft voor doelen en waarden zoals natuurlijkheid.

Het rationeel ecologisme probeert sterker te formuleren wat onze doelen precies zijn. Wat willen we precies met de natuur? Met welk doel moeten we onze relatie tot de natuur vormgeven? Wij kunnen wel waarden zoals natuurlijkheid, biodiversiteit of ongereptheid toekennen aan de natuur, maar de natuur zelf interesseert zich niet in die waarden. Het is zoals een esthetische waardering voor een mooi schilderij: dat schilderij zelf interesseert zich niet in schoonheid en kunst. Maar we kunnen ook waarden zoals welzijn toekennen aan mensen en dieren die afhankelijk zijn van en leven in de natuur. Die mensen en dieren zijn voelende wezens, wat wil zeggen dat ze zelf hun welzijn waarderen, zelfs al zouden wij hun welzijn onbelangrijk vinden. Daarom is het niet egocentrisch, arrogant, paternalistisch of chauvinistisch als we het welzijn van anderen waarderen. Dat is de reden waarom welzijn een cruciaal doel is binnen de rationele ecologie. Andere waarden zoals natuurlijkheid en biodiversiteit zijn dan enkel waardevol zolang ze welzijn bevorderen. De radicale ecologist durft al gauw de eigen esthetische voorkeuren voor natuurlijkheid of ongereptheid boven het welzijn van anderen, bijvoorbeeld wilde dieren, te plaatsen. In dat laten domineren van de eigen waarden boven die van anderen, zit een inconsistentie, want de radicale ecologist heeft een afkeer voor egocentrische dominantie.

Als we dan onze doelen hebben gekozen, gaat de rationele ecologist op zoek naar de doeltreffendste middelen. Rationeel-kritisch denken en wetenschappelijk bewijs zijn cruciaal om die middelen te vinden. Het radicaal ecologisme daarentegen beroept zich heel vaak op intuïtief denken en het subjectief aanvoelen van de natuur. Maar dat is irrationeel, want psychologen hebben al ruimschoots aangetoond dat intuïties vaak onbetrouwbaar zijn. We zijn vatbaar voor cognitieve biases. Buikgevoelens helpen niet om te evalueren of een nieuwe technologie risicovol is.

De wetenschappelijk onderbouwde, rationele, kritische manier van denken is kenmerkend voor de nieuwe stroming van het effectief altruïsme. In die beweging gaat men op zoek naar de effectiefste middelen om de wereld te verbeteren, om anderen te helpen. Binnen dat effectief altruïsme ontstond het effectief ecologisme, dat nauw verwant is met het ecomodernisme. Het effectief ecologisme en ecomodernisme zijn filosofieën binnen het rationeel ecologisme. De lagere buikgevoelens van de radicale ecologist en het hogere verstand van de rationele ecologist symboliseren opnieuw het verschil in positie: laag versus hoog.

Technologieën

Het onderscheid tussen laag en hoog zien we ook sterk aanwezig in de houding tegenover technologie. Waar de radicale ecologist eerder kiest voor low-tech, kiest de rationele ecologist eerder voor high-tech. Omdat het basismechanisme van een windmolen goed te begrijpen valt, en omdat windmolens al eeuwenlang bestaan, wordt een windmolen gepercipieerd als low-tech (hoewel de moderne windmolens diep vanbinnen behoorlijk high-tech zijn). Kernenergie daarentegen is ondoorgrondelijk en valt onder de high-tech.

High-tech vereist veel kennis en know-how, en die vind je vooral bij grote industriële bedrijven. Vandaar dat high-tech oplossingen voor milieuproblemen bijna altijd aangeboden worden door grote bedrijven. Een antikapitalistische radicale ecologist staat dan ook sneller wantrouwig tegenover high-tech oplossingen. Low-tech oplossingen daarentegen zijn zowat altijd kleinschalig en behoeven dus geen grote bedrijven.

High-tech oplossingen zijn vaak doeltreffender dan low-tech maatregelen. Dat zien we bij de covidpandemie. Mondmaskers, zeep en gezonde voeding zijn eenvoudige low-tech maatregelen, maar met die maatregelen kunnen we maar een beperkt aantal levens redden. De covidvaccins zijn daarentegen high-tech, want ze worden geproduceerd door grote farmaceutische bedrijven. Die vaccins zijn veel doeltreffender om de pandemie te bestrijden. Vaccintwijfelaars hebben een sterk wantrouwen tegenover die farmareuzen. Hun irrationele intuïties of buikgevoelens zeggen dat de vaccins onnatuurlijk zijn en daarom onveilig zijn. Dat wantrouwen en die buikgevoelens zijn ook kenmerkend voor de radicale ecologist. Als gevolg van die buikgevoelens maken radicale ecologisten net zoals vaccintwijfelaars keuzes die soms contraproductief of ronduit gevaarlijk zijn.

Het gebruik van low-tech vraagt vaak een sterkere gedragsverandering dan het gebruik van high-tech. De low-tech oplossing voor de gevaarlijke en vervuilende wagen met verbrandingsmotor, is de fiets, de high-tech oplossing is de propere en veilige zelfrijdende elektrische wagen. De low-tech oplossing om energiezuiniger te koken, is het gebruik van een hooikist en een deksel op de pot, de high-tech oplossing is het gebruik van een inductiefornuis. De radicaal ecologist kiest sneller voor een ecologische gedragsverandering. Je eigen gedrag veranderen en leven in vrijwillige eenvoud volgens de diepe ecologie, is misschien nog eenvoudig (want je hebt er bijvoorbeeld niet veel geld voor nodig). Maar anderen overtuigen om ecologischer te leven en minder te consumeren, is minder effectief. Vergelijk het met een pandemie waarbij sommige activisten proberen anderen te overtuigen om vrijwillig mondmaskers te dragen. Dan zullen er niet veel mondmaskers gedragen worden en zullen er meer mensen ziek worden. Omdat individuele, vrijwillige gedragsverandering van de hele bevolking zeer moeilijk is, richten radicaal ecologisten zich op een verandering van het politiek-economische systeem, waardoor iedereen als het ware gedwongen wordt om hun gedrag milieuvriendelijker te maken. Denk aan het voorstel van een steady-state economie zonder economische groei. Economische groei die de hoogte in gaat, dat botst met de radicaal ecologische voorkeur voor diepte. Politiek-economische systeemverandering is vergelijkbaar met de door de overheid opgelegde maatregelen om de groei van een pandemie te stoppen: fysieke contacten beperken, mondmaskers verplichten, bedrijven sluiten, reizen verbieden, avondklokken en lockdowns inroepen,… Dit is alvast effectiever dan rekenen op individuele vrijwillige gedragsverandering, maar ook deze systeemveranderende maatregelen roepen erg veel weerstand op bij de bevolking en zijn niet lang vol te houden. De meest effectieve oplossing voor een pandemie zijn de high-tech vaccins die komen van grote farmabedrijven.

Naast het verzet bij de brede bevolking is er nog een reden waarom louter radicaal ecologische maatregelen voor gedragsverandering, in het bijzonder minder consumptie en geen economische groei, minder effectief zijn: met die maatregelen kunnen we de ecologische impact wel wat verminderen, maar niet naar nul reduceren. De milieu-impact zal pas nul worden als we helemaal niet meer consumeren en helemaal geen economische activiteit meer hebben, en dat is onhaalbaar. Ook een maatregel voor bevolkingskrimp is minder effectief: enkel bij het volledig uitsterven van de bevolking zal de milieu-impact van die bevolking naar nul gaan. Maar met technologie kan men wel effectiever streven naar nul impact: men kan technologieën ontwikkelen die helemaal geen broeikasgassen uitstoten, geen afval produceren, geen uitputbare grondstoffen nodig hebben.

Volgens het ecomodernisme vormen moderne technologieën de meest effectieve oplossing voor milieuproblemen. Binnen het rationeel ecologisme pleit men dan ook eerst en vooral voor meer technologische innovatie en financiering van onderzoek en ontwikkeling van technologische oplossingen.  

Voedselproductiesystemen

Dat de milieubeweging laag aan de grond zit en moet opstijgen naar hogere niveaus, valt ook letterlijk te interpreteren als we kijken naar onze voedselproductie. Het radicaal ecologisme kiest vooral voor low-tech landbouw, zoals biolandbouw, agro-ecologie, permacultuur, zelfplukboerderijen en stadstuintjes.

Kenmerkend aan al die voedselproductiesystemen is dat ze heel grondgebonden zijn. De planten staan letterlijk met hun wortels in de aarde. En omdat de opbrengsten van biolandbouw lager zijn dan van conventionele landbouw, heeft men met biolandbouw nog eens extra grond nodig om evenveel voedsel te produceren.

Vaak werpen radicaal ecologisten tegen dat high-tech oplossingen niet effectief zijn omdat ze altijd weer nieuwe problemen genereren. Elektrische wagens vormen voor de radicaal ecologist geen oplossing omdat de batterijen gebruik maken van zeldzame metalen. Maar dergelijke generatie van nieuwe problemen zien we minstens even sterk aanwezig bij low-tech oplossingen zoals biolandbouw. Dat extra grondgebruik bij biolandbouw wil zeggen dat er meer natuur opgeofferd moet worden voor landbouw, wat niet goed is voor de biodiversiteit en de opslag van CO2. De biolandbouw maakt geen gebruik van kunstmest en kiest daarvoor sneller voor dierlijke mest en dus voor veeteelt. Dat creëert dan weer extra problemen: dierenleed, voedselvergiftigingsrisico’s door schadelijke darmbacteriën in de mest, methaanuitstoot bij de herkauwers, extra water- en grondgebruik voor de veevoeders, hogere risico’s van nieuwe zoönotische infectieziektes, en hogere vermesting van waterlopen (omdat dierlijke mest niet geoptimaliseerd is voor de groei van gewassen, waardoor veel meststoffen naar de rivieren doorsijpelen).

Bij low-tech voedselproductie worden we dus ook vaak geconfronteerd met moeilijke afwegingen of trade-offs. Biolandbouw heeft enkele voordelen, maar telkens ook ernstige nadelen. Daar tegenover staan sommige high-tech oplossingen die veel minder trade-offs kennen, zoals de innovaties op vlak van alternatieve eiwitten die een diervrije landbouw mogelijk maken. De nieuwe generatie vleesvervangers, en natuurlijk celkweekvlees, zijn high-tech. Minder veeteelt betekent zowel minder dierenleed, minder landgebruik, minder watergebruik, minder vermesting, minder verzuring, minder zoönotische infectierisico’s, minder antibioticagebruik, minder voedselvergiftigingen en minder methaanuitstoot. En als de energie voor de productie van het celkweekvlees nog eens van groene stroom komt, dan scoort dat celkweekvlees op alle vlakken beter. Het is een win-win-win voor het milieu, de dieren en de volksgezondheid. Biologische veeteelt vergroot daarentegen een aantal milieuproblemen omwille van extra broeikasgasuitstoot, extra vermesting en extra landgebruik.

De low-tech voedselproductie zoals biolandbouw en permacultuur staat in schril contrast met hypermoderne technologieën voor bijvoorbeeld precisielandbouw die gebruik maakt van drones en artificiële intelligentie. Maar high-tech voedselproductie gaat pas letterlijk de hoogte in door de ontwikkeling van indoor, verticale landbouw. De planten staan dan niet meer met hun wortels in de grond, maar groeien in geoptimaliseerde substraten, met geoptimaliseerd kunstlicht, in verticale flats (toegegeven, de planten kunnen ook groeien in kelders, wat niet bepaald in de hoogte is).

Waar biolandbouw extra land nodig heeft, heeft verticale landbouw zo goed als geen land meer nodig. En als we nog hoger vliegen, dan kan met high-tech het landgebruik helemaal naar nul teruggebracht worden. Denk hierbij vooral aan het neusje van de zalm: ruimtevaarttechnologie. De milieubeweging gebruikt vaak de metafoor van ruimteschip aarde: onze aarde is net als een ruimteschip klein en eindig en daarom moeten we er zuinig en voorzichtig mee omspringen. Het rationeel ecologisme neemt die metafoor iets meer letterlijk, door te kijken welke technologieën het beste werken in echte ruimteschepen. Als een technologie duurzaam is in een ruimteschip, is ze dat waarschijnlijk ook voor ruimteschip aarde.

Ruimtevaartorganisaties onderzoeken hoe men voedsel kan produceren in ruimteschepen, voor lange ruimtereizen. In die ruimteschepen gaat men natuurlijk geen gebruik maken van grond voor de planten, want dat zou veel te omslachtig zijn. Daarom denken die ruimtevaartorganisaties aan veel efficiëntere oplossingen, zoals precisiefermentatie. Fermentatie door bacteriën en gistcellen kennen we van bijvoorbeeld de productie van brood, kaas, yoghurt, tempeh en bier. Met precisiefermentatie kan men (soms door genetische manipulatie) eencellige organismen heel efficiënt elke gewenste voedingsstof op maat laten maken. Een echte hoogvliegertechnologie is air-based protein waarbij eencellige organismen in bioreactoren op basis van waterstof en CO2 in de lucht eiwitten en ander voedsel produceren. Dergelijke precisiefermentatie heeft ontzettend veel winsten.

Ten eerste zijn die eencellige organismen geoptimaliseerd om de voor ons noodzakelijke voedingsstoffen te produceren en kunnen ze dat veel efficiënter dan planten. De kostprijs van voedsel kan daardoor drastisch (mogelijks met een factor tien) dalen. Dat kan niet gezegd worden van biovoeding, dat meestal duurder is dan gangbare voeding.

Ten tweede kunnen die organismen CO2 uit de lucht capteren, wat positief is voor het klimaat.

Ten derde hebben die bioreactoren waarin de organismen groeien zo goed als geen land nodig. De problemen van bodemerosie, vermesting en verzuring van de bodem zijn dus van de baan.

Ten vierde is er ook geen dierenleed meer, zoals we dat nog wel kennen in de grondgebonden landbouw. Ook in bijvoorbeeld biolandbouw worden gronden vaak bewerkt en gewassen beschermd tegen plagen. Daarbij worden veel dieren (muizen, egels, mollen, vogels,…) gedood. Ook in de biolandbouw worden insecten bestreden, en het is niet uit te sluiten dat insecten pijngevoelig zijn en een pijnlijke doodstrijd kennen bij de gebruikelijke methoden om insecten te bestrijden. Met grondgebonden landbouw in open lucht streven naar nul dierenleed is zo goed als onmogelijk.

Ten vijfde is die grondloze voedingsproductie veel veerkrachtiger bestand tegen extreme rampen. In de transitiebeweging (Transition Towns), die deel uitmaakt van de milieubeweging – vreest men voor een grondstoffenschaarste en een piekoliecrisis. Veel van ons voedsel wordt geproduceerd met aardolie-energie. Om als samenleving veerkrachtig te zijn tegen de economische impact van piekolie, kiest de transitiebeweging daarom voor bijvoorbeeld lokale permacultuur en stadslandbouw. Dergelijke arbeidsintensievere voedselproductie zal de samenleving wel veerkrachtiger kunnen maken tegen een piekoliecrisis, maar niet tegen bijvoorbeeld extreme weersfenomenen. Neem in extremis een supervulkaanuitbarsting, de impact van een kleine asteroïde, een kernwapenoorlog of een plotse toename van mondiale bosbranden. Bij die gebeurtenissen kan er zoveel stof in de atmosfeer terecht komen, dat het zonlicht voor enkele jaren geblokkeerd wordt. Tegen dergelijke extreme fenomenen is permacultuur en biolandbouw niet opgewassen, maar ruimtevaart-voedselproductietechnologie wel. Technisch is het niet onmogelijk om pakweg 10 miljard mensen enkele jaren te voeden zonder zonlicht, maar die oplossingen zullen niet gebruik maken van landbouwgrond met fotosynthetische gewassen.

Rolmodellen

Als laatste kunnen we het onderscheid tussen radicale en rationele ecologie illustreren door twee representanten van die bewegingen: Vandana Shiva en Lisa Dyson.

Vandana Shiva geldt als een rolmodel voor het radicaal ecologisme: ze is een Indiase milieu-activiste en laureate van de Right Lifelihood Award (de ‘alternatieve Nobelprijs’). Als antiglobaliste verzet ze zich tegen het mondiale kapitalistische systeem, als ecofeministe behoort ze tot het radicaal ecologisme. Ze staat bekend als de “Gandhi van het graan”, omdat ze actie voert voor voedselsoevereiniteit en tegen het patenteren van zaden.

Shiva is vooral problematisch door haar verzet tegen genetisch gemanipuleerde gewassen (GGO’s), waarmee ze tegen de wetenschappelijke consensus ingaat. Dat verzet is gebaseerd op pseudowetenschap en is contraproductief, want volgens het rationeel ecologisme, effectief ecologisme en ecomodernisme kunnen GGO’s net wel belangrijke gezondheids- en milieuvoordelen bieden. Shiva beweert dat GGO’s in India de boeren armer maken en mee een oorzaak zijn van de zelfdodingen bij boeren, hoewel studies eerder het tegendeel uitwijzen. Ze beweerde ten onrechte dat GGO-zaden steriel zijn. Shiva zei dat de Indiase bevolking gebruikt werd als proefdieren voor GGO’s door de Amerikaanse voedselhulp na een orkaanramp in India, terwijl de Amerikaanse bevolking al jaren diezelfde granen en soja aten. Ze zei dat ze hoopte dat er geen GGO’s gestuurd worden naar de Indiase slachtoffers van de ramp. Ze riep de Zambiaanse overheid op om de voedselhulp, die bestond uit genetisch gemanipuleerde maïs, te weigeren tijdens een hongersnood in 2001. Ze voert actie tegen insectenresistente GGO’s die hogere opbrengsten en minder pesticidengebruik kennen dan gangbare gewassen in India. Het gebruik van genetisch gemanipuleerde aubergine in buurland Bangladesh resulteerde in een vermindering van bijna 40% in het gebruik van pesticiden, een stijging van meer dan 40% in opbrengsten en een stijging van $400 in jaarinkomsten van boeren per hectare. Die voordelen maken deze GGO’s erg gegeerd voor arme boeren. Maar door de acties van Shiva kent India een strenge regelgeving voor het gebruik van GGO’s, waardoor veel Indiase boeren illegaal GGO’s planten. Shiva verzet zich ook tegen gouden rijst: genetisch gemanipuleerde rijst die beta-caroteen aanmaakt waardoor miljoenen kinderen goedkoop en effectief geholpen kunnen worden tegen blindheid. De vertraging om gouden rijst te telen in India veroorzaakt jaarlijks een verlies van meer dan 100.000 gezonde levensjaren. Shiva’s verzet tegen GGO’s is contraproductief, want het leidt tot meer armoede bij boeren, meer pesticidengebruik, meer ondervoeding en meer blindheid. Ironisch genoeg uit Shiva ook felle kritiek tegenover Bill Gates, iemand die al wel miljoenen levens heeft gered (door vaccins), veel armoede heeft bestreden en klimaatverandering heeft beperkt (door investeringen in klimaatvriendelijke technologieën). Shiva’s ecofeministische opvattingen weerspiegelen een soort essentialistisch denken. Ze stelt bijvoorbeeld dat de huidige wetenschappelijk-technologische kennis te patriarchaal of mannelijk is. Maar wetenschap en technologie zijn gebaseerd op de natuurwetten, en deze wetten zijn genderneutraal. Er bestaat niet zoiets als mannelijke wetenschap. Tegen wetenschappelijke kennis en technologie zijn omdat ze ontdekt en uitgevonden worden door mannen, is seksistisch. Er zijn trouwens veel vrouwelijke onderzoekers in de biotechnologie. Emmanuelle Charpentier and Jennifer Doudna kregen de Nobelprijs chemie voor de uitvinding van een nieuwe genbewerkingstechniek die genetische manipulatie veel goedkoper en efficiënter maakt.

Vandana Shiva laat zien hoe sterk het radicaal ecologisme kan ontsporen. Tegenover Vandana Shiva kunnen we Lisa Dyson plaatsen als rolmodel voor het rationeel ecologisme. De Amerikaanse Dyson heeft enkele dingen gemeenschappelijk met Shiva: ze is ook een vrouw van kleur en ze doctoreerde ook in de theoretische natuurkunde, waarna ze een carrière koos met als doel de wereld te verbeteren en verduurzamen. Maar in tegenstelling tot Shiva, die koos voor grondgebonden, low-tech landbouw, koos Dyson voor grondloze high-tech voedselproductie: ze richtte het bedrijf Air Protein op, dat hydrogenotrofe bacteriën gebruikt die sneller dan planten CO2 in de lucht opnemen en omzetten in eetbare eiwitten. Daarnaast richtte Dyson het bedrijf Kiverdi op, dat biotechnologie gebruikt voor de productie van milieuvriendelijkere materialen en plastics. Het contrast met Shiva kan niet groter zijn.

Het is duidelijk, het radicaal ecologisme zit laag aan de grond, graaft zich in het zand, is tegen groei, terwijl het rationeel ecologisme als een raket de hoogte in schiet.

Geplaatst in Blog | Tags: , | 1 reactie

Cost-effectiveness distributions, power laws and scale invariance

The cost-effectiveness of policies, actions and interventions measures how much good is done (how many lives are saved, greenhouse gases are mitigated, diseases are cured, education levels are improved,…) per dollar investment. Cost-effectiveness is often difficult to measure, but sometimes an estimate is possible, and when it does, we often see a very skewed distribution: a small minority of interventions is much more (sometimes orders of magnitude more) cost-effective than the vast majority of interventions. In contrast with a normal distribution, which has a symmetric bell-shape, a skewed or lopsided distribution is asymmetric and has a long tail. A positive skew means that there are a few very high outliers which drive up the mean, such that the mean is higher than the median. Household income has a skewed distribution, with a small minority of super-rich people driving up the mean income level. If body length would have a skewed distribution instead of a symmetric normal distribution, we would see many dwarfs and a few giants.

In this article I briefly describe power-law distributions, a class of skewed distributions that show scale invariance. Next I show that some cost-effectiveness distributions are close to power-law distributions, I argue why the skewness of cost-effectiveness is very important for effective altruists, and I explain a hypothesis that power-law distributions often occur in hierarchical classification schemes that have a fractal notion of scale invariance.

Power-law distributions

An important class of skewed distributions are power-law distributions. These are defined by the property that the probability of a random variable X being larger than a value x is proportional to a negative power of x. Formally:

P(X>x)=C/xp ,

with P(X>x) the probability that the variable is larger than x, and C and p constant parameters.

There are many examples of power-law or Pareto distributions, such as the income distribution of the richest people (with parameter p close to 1,16).

Power-law distributions have a unique property of scale invariance. Multiplying the argument x with a constant gives back the same probability distribution (after proper normalization of the probabilities). This means that if you zoom in on a piece of the distribution, you get the same shape as the whole distribution.

Power-law distributions relate to Zipf’s law. Consider a random sample of N elements taken from a power-law distribution. Rank these elements from high to low. The n-th element (i.e. the element with rank n) has value xn. Then we have the share of elements with values higher than xn equal to the probability P(X>xn), i.e.:

n/N=P(X>xn)=C/xnp.

Writing xmax =(CN)1/p, we get Zipf’s law (the value of an element is proportional to the highest value, with a negative power of the rank of the element):

xn =xmax/n1/p.

The power parameter p can be determined by taking logarithms:

p=-log(n)/log(xn/xmax).

Cost-effectiveness distributions

It seems that distributions of cost-effectiveness are usually very skewed and closely follow a power-law distribution. Three examples demonstrate this

1. The Disease Priorities Control Project (Laxminarayan, R., e.a. 2006, Advancement of global health: key messages from the Disease Control Priorities Project. The Lancet, 367(9517), 1193-1208), which measures Disability Adjusted Life Years (DALYs) averted per dollar invested in health interventions related to high-burden diseases in low-income and middle-income countries. (I took the mid-range values of the 30 most cost-effective interventions in figure 1 of that study.)

2. The Education Global Practice and Development Research Group study (Angrist, N., e.a. 2020, How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric.  Policy Research Working Paper 9450), which measures the Learning-Adjusted Years of Schooling (LAYS) per dollar invested in education interventions in low-income and middle-income countries. (I took the 30 most cost-effective interventions in figure 5 of that study.)

3. Greenhouse gas emissions reductions (Gillingham, K., & Stock, J. H., 2018, The cost of reducing greenhouse gas emissions. Journal of Economic Perspectives, 32(4), 53-72), which measures the amount (ton) of CO2-equivalent avoided per dollar static costs of climate policies. (I took the mid-range, non-negative values of the 20 most cost-effective interventions in table 2 of that study.)

We can take the logarithm (with base 10) of the ratio of the cost-effectiveness of the n-th most effective intervention to the cost-effectiveness of the most effective intervention (i.e. log(xn/xmax)), and plot this against the logarithm of the rank (i.e. log(n)). If the data points are on a straight line, the data follow a power-law distribution.

The figure shows that the cost-effectiveness distributions of the most effective interventions and policies in education, health and climate change, are close to power-laws, as they are almost on straight lines (note however that the data do not allow to reject the possibility that the cost-effectiveness distributions follow another skewed, long-tailed distribution, such as the lognormal distribution). The (log-likelihood estimate of the) power parameter p equals 0,7 for the education interventions, 1,04 for the health interventions and 1,8 for the climate policies.

In the figure, we see on the vertical axis that the logarithm of cost-effectiveness relative to the most effective intervention ranges up to -2 (for climate policies) and almost -3 (for health policies). This means that the top intervention is 2 or almost 3 orders of magnitude (i.e. a factor 100 and almost 1000) more cost-effective than the least effective intervention. The majority of interventions is more than an order of magnitude less cost-effective than the top intervention.

Why the skewness of cost-effectiveness is important

If the cost-effectiveness distribution of interventions is very skewed, there are two important implications.

First, the information about which interventions are top effective, is very valuable. An effective altruist donor who wants to do the most good by donating to charities, should be willing to pay a lot in order to learn what are the top charities and top interventions. For example, suppose there are 10 interventions, and one of them is 100 times more effective than the other 9. The top intervention does 100 units of good per dollar, whereas the others cause 1 unit of good per dollar. Suppose you have 10 dollars to spend. You can give 1 dollar to each of the 10 interventions, which means you cause 100×1+1×9=109 units of good. Similarly, picking an intervention at random and giving the 10 dollars to that intervention causes 109 units of good in expectation. Without information about effectiveness, you cannot cause more good than 109 units in expectation. But suppose you can pay some money in return for the information which intervention is the best. Paying 8 dollars for this information is worthwhile: the remaining 2 dollar can be spend on the top intervention, causing 2×100=200 units of good, almost twice as much as what you can do without information. You can even pay up to 8,9 dollars for this information, almost 9 times more than what is left to spend on the top intervention. This shows how valuable the information is.

With a power-law distribution, the lower the parameter p, the higher the value of information of cost-effectiveness is and the more important it is to look for more effective interventions. For example, if p=2, and the currently known most effective intervention has cost-effectiveness x, the probability of finding an intervention that is 10 times more effective than x is 1% of the probability of finding an intervention that is barely more cost-effective than x, because P(X>10.x)=C/(10.x)2=P(X>x)/100. But if p=1, the probability of finding an intervention that is 10 times more effective than x is 10% of the probability of finding an intervention that is barely more cost-effective than x. (Sidenote: the expected value of the cost-effectiveness x, given that the new discovered intervention is more effective than the most effective known intervention, is E(x|X>xmax)=C.xmax-p+1.p/(p-1). This expected value approaches infinity when p goes to 1. In reality, there is no infinitely effective intervention, which means the power-law distribution is truncated at the highest value.)

The second important implication, is that if the distribution is very skewed and there may be unknown interventions that are more effective than the interventions you already know, it is worthwhile to look for those unknown interventions and estimate their effectiveness, because it is likely that you find a new intervention that is vastly more effective than the most effective of the known interventions. For example, suppose there are 100 possible interventions, 90 of them cause 1 unit of good per dollar, 9 cause 10 units of good and 1 causes 100 units of good. If you only know about one intervention, that intervention most likely causes 1 unit of good. If you look further, after discovering 6 new interventions, you have probability higher than 50% that one of them causes 10 units of good. Looking further, after discovering 50 interventions, you have probability 50% that one of them is the big winner causing 100 units of good. The more skewed the cost-effectiveness distribution, the more important it is to look further for a more cost-effective intervention. In the explore-exploit tradeoff, a skewed distribution means that one should explore more to look for even better interventions, instead of exploiting the currently known best intervention.

Two notions of scale invariance

There is no well-understood mechanism that explains why many cost-effectiveness distributions are close to a power law. Perhaps it is related to a second type of scale invariance that we see with many power law distributions.

The first scale invariance, mentioned above, relates to rescaling the variable X. Consider the range of interventions with cost-effectiveness between 1 unit and 10 units of good per dollar. Now 10 times more money becomes available, such that with those same interventions, 10 times more good can be done. If the new distribution of how much good can be done with 10 dollar is the same as the old distribution of how much good can be done with 1 dollar for interventions that had a 10 times higher cost-effectiveness (i.e. between 10 and 100 units of good per dollar), then the cost-effectiveness distribution is scale invariant.

But there is a second scale invariance, that relates to hierarchical classification schemes. In a hierarchical classification scheme, a set of elements is subdivided in subsets, which are again subdivided in subsubsets, and so forth. This results in a vertical hierarchy of subdivisions, with the complete set as one group at the top and all the separate elements as different subgroups at the bottom. Each level of subdivisions consists of subgroups. Suppose each of the subgroups at the same level has a property (that is determined by similar properties of their subsubgroups at a lower level). Then we can rank the subgroups, from the highest to the lowest value of that property, and look at the distribution of the property. If the distributions are of the same type for all levels in the hierarchy, there is a scale invariance.  

As a concrete example, consider the ways to subdivide the world population in subpopulations. One such division is according to countries, and the population size is a property of a country. So we can consider the distribution of country population sizes. This distribution is close to a power law (with a few outliers, such as India), having estimated power parameter p = 0,9. Now we can consider a country and subdivide it in cities (as if that country is the world and the cities are countries), and look at the distribution of population sizes of the cities in that country. For many countries and many years, the sizes of (the largest) cities also closely follow power law distributions (e.g. for US cities, p=1,02). The power law can also appear on other levels, such as city streets or states (the 35 most populated  states in the US roughly follow a power law with p=1,3). Hence, we have a hierarchy of power law distributions. This is a fractal pattern with self-similarity: the distribution at the lower scale of cities is similar to the distribution at the higher scale of countries (and perhaps other scales, such as city streets).

A second example is biological taxonomy. We can divide the set of all living beings in subsets of kingdoms (e.g. animals), which can be further subdivided in phylums (e.g. chordates), which can be subdivided in classes (e.g. mammals), which can be subdivided in orders (e.g. primates), which can be subdivided in families (e.g. great apes), which can be subdivided in species (e.g. humans), which can be subdivided in subspecies (e.g. homo sapiens sapiens), which can finally be subdivided in individuals (e.g. you). At a taxonomic rank (a level in this hierarchy), such as the rank of species, we can observe a power law distribution of abundance (number of individuals belonging to the taxon). For example, the species abundance distribution shows the number of individuals per species. This is a skewed distribution, close to a power law with average parameter p=1,1. A small minority of species are much more abundant than the vast majority of species.  

A third example brings us closer to the cost-effectiveness distribution of health interventions. The Global Burden of Disease measures the shares of healthy life years lost (i.e. DALYs) by several diseases. At the highest level, the set of diseases can be divided in major groups such as non-communicable diseases, injuries, nutritional diseases and infectious diseases. At a second level, the group of non-communicable diseases can be subdivided in e.g. cancers (neoplasms), cardiovascular diseases, chronic respiratory diseases, metabolic diseases (diabetes), mental disorders and other non-communicable diseases. Next, cancers can be subdivided into e.g. skin cancer, lung cancer, breast cancers and many others. Skin cancers can be subdivided into melanoma and squamous-cell carcinoma. For many diseases, at many levels, we see DALY distributions close to power laws. For example, at the second level, the distribution of non-communicable diseases has an estimated parameter p=1,2. A level lower, the cancers also have an estimated parameter p=1,2 (whereas e.g. the cardiovascular diseases have p=0,5).

With the above examples, we can hypothesize that power law distributions are common in hierarchical classification schemes. But like diseases, interventions can also be classified in such hierarchical classification schemes. We can subdivide the set of altruistic interventions in e.g. longtermist versus neartermist interventions. Neartermist interventions can be subdivided according to biological taxonomic groups, such as human welfare interventions versus animal welfare interventions. Human welfare interventions can be subdivided according to objective such as health. Human health interventions can be further subdivided according to geography (e.g. country), disease (e.g. skin cancer) or treatment (e.g. prevention campaigns). In the end, we can investigate e.g. the cost-effectiveness of a skin cancer prevention campaign in the US.

Whether hierarchical classification schemes are more likely to exhibit power laws, is an open question for future research. But perhaps it offers (a part of) an explanation why cost-effectiveness distributions are skewed like power-law distributions. (Sidenote: cost-effectiveness distributions can also resemble skewed lognormal distributions. The multiplicative central limit theorem says that the limiting distribution for the product of many independent positive random variables with finite variances, is lognormal. Hence, if the cost-effectiveness is determined by a product of a lot of random factors, we may see a lognormal distribution. Many skewed distributions, such as household income or city sizes, appear to be lognormal in the bulk and power-law in the tail.)

Hierarchical classification schemes, arbitrariness and effectiveness

Hierarchical classification schemes may not only be associated with power-law distributions, but they are also a cause for concern for effective altruists. As argued elsewhere, such classification schemes may generate a moral illusion or cognitive bias of arbitrary group selection, which influences our cause prioritization and makes us less effective.

Nationalists prioritize helping people who are born in their own country. But why their own country and not their own city or their own planet? The choice for the country, i.e. a specific level in the hierarchy, is arbitrary. Similarly, speciesists prioritize helping individuals who belong to their own species. But why their own species and not their own race or their own phylum? Picking the level of species instead of other levels in the biological hierarchy, is arbitrary. Similarly, many people want to support a charity that targets a certain type of diseases, such as cancers. But why prioritizing cancers and not e.g. skin cancers or non-communicable diseases?

Arbitrary group selection bias means preferring or prioritizing an arbitrary group (e.g. the group of fellow countrymen, the group of humans or the group of cancer patients) at an arbitrary level in a hierarchical classification. Sticking to one’s own preferred country, species or disease, is not only arbitrary, but it prevents us from taking opportunities to do more good. One can often do more good by helping individuals who live in other countries, who belong to other species or who have other diseases than cancer. Nationalist and speciesist inclinations make us less effective, and they are a cause of discrimination. The same goes for an inclination to prioritize a certain disease such as cancer. Impartiality or cause-neutrality means that in order to be more effective, one should only look at the top level in the hierarchical classification, i.e. consider the whole world (instead of a specific country), all beings (instead of members from a specific species), and all diseases (instead of a specific type of diseases such as cancers). Especially when we see power-law distributions in cost-effectiveness related to hierarchical classification schemes, an arbitrary group selection bias is a serious cause of a loss of effectiveness.

For important comments on this article, see the comments section on the Effective Altruism Forum

Geplaatst in Artikels, English texts | Tags: , | Een reactie plaatsen

Rational altruism and risk aversion

Let’s start with a very hypothetical but tricky dilemma. The importance of this dilemma will become clear at the end.

Suppose I give you a choice between two options. If you choose option A, I will save one person. If you choose option B, I will flip a fair coin, heads means I will save 104 people, but tails means I will kill 100 people. The expected number of people saved in the second option is 104 times a probability of 50% minus 100 times a probability of 50%, which equals 2. Does this mean that option B is twice as good as option A? I expect most people have the same intuition as I have, which says that option A should be chosen. This is due to a risk aversion: we do not want to run a risk of losing e.g. 100 peoples lives. However, when it comes to rational or pure altruism, one could argue that option B is the best.

First, we face scope neglect.[1] You can see the difference between saving one person and saving no-one. But when I save a group of 104 people, you don’t see the difference with saving a group of 100 people, unless you take the effort to count. Saving 104 people feels as good as saving only 100 people. The flip-side of me saving 104 people, is me killing 100 people, and if saving 104 people is equally good as saving 100 people, saving that many people cannot morally outweigh killing 100 people. Option B does not seem to be any good in expectation.

However, pure altruism implies doing what other people want, and not imposing your own values or preferences on others against their will. The 104th person does not share your scope neglect judgment that saving his or her life has zero added value. That last person wants to be saved as much as if he or she were the first person to be saved. Saving an extra person has always the same moral value, no matter how many people are already saved. (This is different from money: an extra dollar has less value for you, the more money you already have.)

Second, we have an act-omission bias. Killing a person means causing the death of a person. But there are many interpretations of what counts as a cause. Letting someone die (not saving someone) can also count as a necessary and sufficient cause of the death of that person. In this broader interpretation of ‘cause’, both killing someone (an act) and letting someone die (an omission) causes the death of a person. The difference between killing and letting die is the reference situation: if I kill someone, we compare that situation with a reference situation where I did not act (e.g. where I was not present), which means the person would not have died. If I let someone die, we take as reference situation the situation where the person died anyway (in my absence). So if I save 104 people in option B, it means I could have saved 104 people in option A as well. If I only save one person in option A, I let 103 people die, which means I cause the death of 103 people.

However, pure altruism implies not imposing your own interpretations and preferences on others who you help. I may use a narrow interpretation of ‘cause’, in which killing causes the death but letting die does not cause the death of a person. But who is to say that my interpretation of a cause is the right one? I may value the act of killing as worse than the omission of letting die, because I prefer to take as reference situation the situation where I am absent or do not act. But who is to say that my choice of reference situation is the right one? Other people may not share my valuation of acts and omissions and my preference for the reference situation where I am absent. Other people could prefer another reference situation, which means they locate the dividing line between acts and omissions elsewhere. For them, they do not want to die and it may be irrelevant whether the cause of death was my act or my omission. This can be seen in option A where I let 103 people die. I didn’t tell you why they died, but the reason could be because they are killed by someone else (or something else such as a falling rock or a virus). So I let those people die because those people are killed. In option B, when the coin falls heads, I simply prevent that killer from killing 104 people. That is how I save those people.

Third, there is a framing effect.[2] This is related to the previous point about the reference situation. Suppose option A remains the same: I save one person for sure. But option B now becomes a different option B’ where I throw a 104-sided dice. Only if it gives the number 1, I will save 104 people, otherwise I do nothing. In expectation, option B’ saves one person, which is as good as option A. Still, being risk averse, I prefer option A, because I do not want to run the very high risk (probability 103/104, which is close to 99%) that no-one is saved. But suppose in a different situation, you have to choose between options C and D. Option C means I cause the death of 103 people for sure, by letting them die (letting them be killed). In option D, I throw the same dice, and if it gives the number 1, no-one dies. A lot of people now are risk seeking: they prefer option D, because they want to avoid the certainty that 103 people die. In option D, there is still a non-zero probability that no-one dies. But as you may have noticed: options A and C are exactly the same, as are options B’ and D. The reason why options A and C appear to be different, is the choice of reference situation. In option A, when we consider one person being saved, the reference situation is where everyone dies. In option C, the reference situation is where no-one dies.

Again, pure altruism implies not imposing your own preferences (e.g. your preferred reference situation) on others. Other people don’t care about your preferred reference situation or how you frame the problem: they just want to be saved.

Fourth we have a personal contribution preference. If you choose option B, you may be unlucky by having me kill 100 people. But suppose next to you there are nine other people with whom I play this game. Everyone can chosen between options A and B. If you all choose option A, then I save 10 people for sure (one for each of the 10 players). If you all choose option B, you can calculate that with 62% likelihood, the group of 10 players saves at least 20 people. If the group becomes larger, that likelihood approaches 1. You may cause the death of 100 people, but someone else may cause the rescue of 104 people. To make it simple, suppose that the first 100 people saved are the ones that would have died by your outcome. Hence, together, you and that other person have saved 4 people. You didn’t save anyone, but that other person saved 4 people.

However, from a pure altruistic perspective, your feelings and preferences do not count. You may feel bad because you didn’t save anyone, but other people in your group saved many people. And for the people who are saved, it doesn’t matter whether they were saved by you or by anyone else. You may have a preference to save someone personally, but that preference is not shared by the ones who can be saved. When the group of players is small or when you are the only player, you may have a preference for option A, but when the group is large, you may be convinced by the above reasoning that option B is better. However, the way how your preference for option B depends on the group size (e.g. the minimum required group size for you to choose option B), is not necessarily the same way how the people that can be saved prefer option B. They always prefer the option that gives them the highest likelihood of being saved, independent of the size of the group of players. Hence, even when you are the only player, from a rational altruistic perspective, you should make the same choice that you would make when there are thousands of players (and vice versa: the choice that you make in the case of many players should be the same as when you are the only player).

Fifth, we have a zero-risk bias.[3]  Let’s change our initial dilemma. You have the choice between option A* in which with 90% probability I kill 100 people and with 10% probability I save one person, and option B* in which with 95% probability I kill 100 people and with 5% probability I save 104 people. In both options, the likelihood of 100 people being killed is very high (90% and 95%), whereas the likelihood of saving at least one person is very low (10% and 5%). The difference between 10% and 5% does not seem to matter that much. But the big difference is the number of people who are saved, when we are in the lucky situation that people are saved instead of killed. In option B*, 104 times more people will be saved than in option A*. That is why my intuition says to pick option B*.  

But note that options A* and B* are in some sense similar to options A and B. The choice between A* and B* can be describes as a two-stage game. In the first stage, I throw a 10-sided dice and when it lands at a value higher than 1 (which means 90% probability), I kill 100 people and the game ends. But when the dice gives a 1, you are lucky: I don’t kill anyone (yet), but instead you enter the second stage of the game in which I let you play our initial game to choose between options A and B. In this two-stage game, both options A* and B* involve the risk that 100 people are killed, so this risk is unavoidable. But if in the first stage of the game the dice gives the value 1, you can avoid the risk of 100 people being killed by choosing option A in the second stage. Hence, if you have a zero risk bias and you are lucky that you may enter the second stage of the game, you choose option A that doesn’t involve a risk of people being killed by me. You want to minimize risk, but choose option A* instead of option B* reduces the risk of 100 people being killed from 95% to 90%, which seems a bit futile. In contrast, choosing option A instead of B reduces the risk from 50% all the way to 0%. You can eliminate the risk completely. Not choosing A* because the risk reduction seems futile, is an example of futility thinking.[4]

This two-stage game is an example of Allais paradox[5]: my intuition says to choose B* above A*, but to choose A above B. This is strange, because option A is simply the same as option A*, but only considering the second stage of the game. If I consider the whole two-stage game, I prefer B*. But the first stage of the game is irrelevant, because that doesn’t involve making a choice. So we can equally consider only the second stage of the game that does involve making a choice (between options A and B). In that case I do not prefer B and hence I should not prefer B* either. From one perspective, I prefer B*, from another perspective, I prefer A*.

However, pure altruism implies not imposing your own perspective on others. Whether you look at the two-stage game as one whole game, or you consider the second stage separately, is not something that other people care about. Therefore, you should look at the choice between A and B in exactly the same way as the choice between A* and B*, which means you have to avoid zero risk bias.

Sixth, we do narrow bracketing[6], which is related to the above two-stage game. Suppose that next to the game where you can choose between options A and B, you can play a second game between options X and Y. Choosing X means I will kill 100 people, choosing Y means I throw a fair coin, heads means I kill 202 people, tails means I kill no-one. According to the abovementioned framing effect, many people are risk seeking when it comes to risky losses. That means many people choose option Y above X: in Y there is at least a non-zero probability that no-one is killed. When it comes to risky gains (lives saved), most people are risk averse, which means they choose option A above B: In option A there is certainty that at least someone is saved. Now we can combine the two games, which means there are four options. Option AX (choosing A in the first and X in the second game) means a certain death of 99 people. Option AY gives 50% probability that 201 people are killed and 50% probability that 1 person is saved. Option BX means a 50% probability of killing 200 people and a 50% probability of saving 4 people. Finally, option BY, which is not so relevant in this argument, gives 25% probability of killing 302 people, 25% of killing 100, 25% of killing 98 and 25% of saving 104 people. Now we see an irrationality: we prefer A above B and Y above X, but the combination of AY is dominated by the combination of BX. No matter what the outcomes of the coin tosses, BX is always better than AY.

Again, pure altruism implies not imposing your own perspective on others. Our preference of A above B depends on our perspective: if we consider the first game separately, we prefer A, but if we consider the first game as part of a bigger game, we prefer B. For the people who want to be saved, it shouldn’t matter whether we consider the two games as one whole, or whether we ‘narrow bracket’ and consider the two games separately.

In summary, pure altruism means that we should not impose our own values, preferences, interpretations and perspectives on others who simply want to be saved. If you want to help others purely altruistically, choosing option A instead of B would be irrational. Nevertheless, after the above long and detailed reasoning, it still feels intuitively wrong to choose B. This is a kind of moral illusion: a persistent intuitive moral judgment that is inconsistent with other, stronger moral judgments.

What lesson should we draw from this moral illusion? Of course, in reality we do not face a dilemma like the choice between options A and B, where some outcomes may seriously harm instead of help others. If we would face such a choice, we may still follow our moral gut feeling and choose option A, i.e. the option that has the least risk of causing harm. But when it comes to effective altruism, where the goal is to effectively and impartially help others purely altruistically, we should try to be at least a little bit more risk neutral instead of risk averse, especially in the many usual cases where we have to choose only between helping others in non-harmful ways.

More concretely, instead of supporting specific projects that directly help others and produce small but certain beneficial outcomes, it is worthwhile to focus more on risky bets such as scientific research, entrepreneurship, advocacy and policy change. Scientific technological research is high risk high impact: a small chance that the research is fruitful and results in a new useful technology, but when it does, the technology can do a lot of good. Similarly, a small chance that a start-up that develops a new technology will make it and succeed, but when it does, the start-up can become big and produce a lot of good with the new technology. Policy change is hard and has low probability of being successful, but when it does, it can have a huge positive impact. Investing is high risk high reward and the expected return on investment is higher when investments are riskier (the higher expected return of a risky investment compared to a safe investment, is the risk premium). This means that investing is interesting as a strategy of earning to give, which involves earning a higher income to donate more to charities.

Consider a group of effective altruists who decide to become risky investors. Many investors will lose and get very low returns and hence will not be able to donate much to charity. But a small minority will win and earn a huge return that can be donated to charity. For an effective altruist, it doesn’t matter who of the group wins and is able to donate the money. If each altruist wants to feel personal satisfaction from a personal donation to a charity, the altruists will choose safe investments such that they are sure that each of them can donate at least something. But this means that the total return of this group of altruists, and hence the total amount donated, will be lower. The group of effective altruists who choose the risky investments, is in the end able to donate much more to charity, even if an individual effective altruist of this group is very likely to have a negligible contribution.


[1] Desvousges, W. Johnson, R. Dunford, R. Boyle, K. J. Hudson, S. and Wilson K. N. (1992). Measuring non-use damages using contingent valuation: experimental evaluation accuracy. Research Triangle Institute Monograph 92-1.

[2] Tversky A. & Kahneman D. (1981). The Framing of decisions and the psychology of choice. Science 211 (4481): 453–458.

[3] Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk, Econometrica, 47, 263-291.

[4] Unger, P. K. (1996). Living high and letting die: Our illusion of innocence. Oxford University Press, USA.

[5] Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica: Journal of the Econometric Society, 503-546.

[6] Rabin, M., & Weizsäcker, G. (2009). Narrow bracketing and dominated choices. American Economic Review, 99(4), 1508-43.

Geplaatst in Artikels, English texts | Tags: , , , , | 2 reacties

Big is beautiful. On the dangers of too much distrust in big institutions and high-tech solutions.

Imagine a covid19 pandemic without big institutions such as governments, tech companies and pharmaceutical companies. We would rely on personal, voluntary behavioral change such as social distancing and washing hands, added with low tech solutions such as face masks. These personal voluntary measures could not control the pandemic. With Big Government, imposed lockdowns could curb the pandemic, but these come at high societal and economic costs. Luckily, the costs are mitigated thanks to Big Tech: new information and communication technologies such as online video conferencing made the lockdowns and quarantines much more bearable. But still, government interventions remain costly. Enter Big Pharma: they can create a solution that makes personal behavior change and governmental lockdowns superfluous. Who would have thought that within one year after the beginning of the pandemic, there are already five covid-vaccines on the market after being developed and tested on thousands of volunteers, and first evidence (of Israel) appeared of plummeting new infections after vaccinations? Without Big Pharma, this would not have been possible, because only Big Pharma has the capacity and capital to do the necessary research and development of vaccines. Within a year, vaccines became likely by far the most cost-effective measure to fight the pandemic.

Distrust in big institutions

However, as the antivaccination movement demonstrates, we see an increasing distrust in big pharmaceutical companies. Within the environmental movement, there is a similar distrust in Big Agri and Big Tech, as demonstrated by the opposition against genetically modified organisms (GMOs) sold by big agribusinesses (e.g. Bayer-Monsanto) and nuclear power plants owned by big energy corporations. The anti-capitalist, anarchist, degrowth and localist movements criticize Big Money, Big Finance and Big Government.

As written almost 50 years ago by Ernst Schumacher in his book Small is Beautiful, the criticism against big institutions is not unjustified. There is a positive correlation between the size of an institution and its monopoly market power. In absence of free competition, big institutions earn a monopoly profit or economic rent, which is both unfair and inefficient. Big institutions have also financial means to influence other people, including scientists, which results in financial conflicts of interest. However, positive economies of scale can make big enterprises more efficient. And also small organizations are susceptible to lying, cheating and creating serious conflicts of interest.

Rationalization and the excess in arguments against an issue

The criticism against big institutions is not always rational. One indicator that a criticism is rather a rationalization instead of a rational position, is the excessive number of claims and arguments presented against the issue. The more arguments are given, the more suspicious we should be, because normally, when something is problematic, it has only one or a few real problems. It is unlikely that something can be criticized on dozens of fronts.[1] It is unlikely that a toxic chemical causes dozens of different diseases. It is unlikely that a government policy fails in dozens of different ways. Hence, the fact that many arguments and claims are made against an issue, is cause for concern.

Consider as a first example the many arguments and claims made against the covid19 policy: lockdowns are bad because they cause massive suicides from social isolation, PCR-tests are too unreliable because they have false positive test results, vaccines are unnecessary because chloroquine medicines and vitamin D supplements are better alternatives, vaccines are unsafe because of unknown long-term side-effects, mainstream media is unreliable because of political influences, scientists such as epidemiologists and virologists are untrustworthy because of conflicts of interest, philanthropes such as Bill Gates who invest in vaccines are bad because they desire to control the population, mobile phone warning apps are dangerous because they violate privacy, face masks are unhealthy because they increase CO2 inhalation, hand washing is unhealthy because that makes our immune systems lazy, government policy is ineffective because governments do not properly campaign for healthier lifestyles that prevent covid19,…

Next, consider the many arguments against nuclear power: it creates radioactive waste, it causes nuclear disasters, it distorts incentives for investments in renewable energy, it requires depletable uranium as a fuel, it causes human rights violations in uranium mines, it is too costly, it cannot be insured against risks, it causes high electricity prices, it increases the probability of nuclear wars with atomic bombs, it is incompatible with decentralized small-scale energy production,…

Third, consider the many arguments against GMOs: they increase pollution from pesticides, they genetically contaminate wild plants, they exploit farmers by making them more dependent on big agribusiness, they decrease farmers profits, they increase litigation and lawsuits against farmers, they have unknown dangerous consequences for biodiversity, they decrease crop diversity, they cause farmer suicides in developing countries, they are unnecessary because organic solutions already exist, they are unhealthy because they contain toxic chemicals, they cause allergic reactions, they are improperly tested, they are sold by money-hungry companies,…

For each of the three examples above, I could easily collect at least ten claims made by people who are against vaccines, nuclear power and GMOs. Not unsurprisingly, almost all of the claims and arguments can be debunked or countered (see e.g. about vaccines, nuclear power and GMOs).

The more arguments are presented against a problem, the more likely we have a situation of rationalization. People perceive something as a problem and present a first argument against that problem. If that first argument is debunked, one may look for another reason why the thing is bad, because the gut feeling that it is bad is still present. This second argument is usually weaker and more easy to debunk, because if it were stronger, it would have been more likely that one would have given that stronger argument in the first place. If the second argument is debunked, one might look for a third reason that is even weaker. And so on. These extra reasons are rationalizations: attempts to justify the premise that the thing is bad. That premise is based on underlying processes (feelings, perceptions or intuitions) that are not easy to express in moral arguments or principles. In the above examples of vaccines, nuclear power and GMOs, the underlying process could (partially) be a sense of distrust in big institutions.

Why distrust in big institutions can be harmful

Concerns of monopoly power and conflicts of interest may be justified, but do not generally warrant the observed high distrust in big institutions. Economist Tyler Cowen argued in his book Big Business that big corporations are underrated and under-appreciated. I am also worried that a distrust in big institutions can be dangerous or counterproductive. The reason is that high technological solutions (that can only be efficiently produced at large scale by big corporations) are often more effective than e.g. individual behavioral change measures or small-scale low-tech solutions. When it comes to vaccines and covid19, the distrust in Big Pharma is clearly dangerous, but the same goes for other technologies such as GMOs and nuclear power.

In the future, we may face similar opposition to new technologies. Consider animal rights and animal welfare. In the past, I volunteered at vegan outreach street actions, which involved talking to people in the hope to persuade them to go vegan. For me it was easy (not costly) to go vegan, but investing resources in persuading others to voluntarily individually change their behavior happened to be much more difficult and less effective. As a comparison, what would have happened if instead of government interventions and vaccines, people tried to stop the covid pandemic by individually talking to strangers and asking them to voluntarily change their behavior (such as not giving hands, keeping 1,5 meter distance from others,…)? Imagine street activists offering face masks to passers-by to persuade them to voluntarily wear those masks in public places. That would not be effective at all to fight the pandemic. A more systemic change such as government intervention (e.g. a lockdown) is more effective, but most effective are technologies such as vaccines. Similarly, government regulation against animal farming would be more effective than street activism, but probably most effective would be the development of meat alternatives such as cell-based (clean) meat. However, even in the animal rights movement there is opposition against cell-based meat. Reading the Clean Meat-Hoax-website, I guess this opposition is largely due to a distrust in Big Food, as big food companies are likely to sell the most cell-based meat to customers.

Recently I wrote about my personal paradigm shift from soil-based low-tech food to air-based high-tech food. People offer many arguments against conventional farming and propose small-scale low-tech organic farming as the better alternative. However, as explained above, an overdose of arguments against conventional farming is cause for concern. Organic farming has its own downsides and many of the presumed benefits of organic farming are absent. Even if everyone voluntarily chose to eat organic food from small-scale farms, we would still cause a lot of harm to e.g. wild animals. It might even cause more harm, by having a less efficient food production. Only with very high-tech food technologies can we drastically reduce harm and improve human and animal health and welfare. Similarly, but more generally, instead of sticking to low-tech solutions, some transhumanists are looking for very high-tech solutions (such as genetic engineering and nanotechnology) to eliminate all unwanted suffering of sentient beings. This ambitious ‘hedonistic imperative’ goal to abolish suffering will never be achieved with low-tech solutions or individual behavioral change.

If a technology is very effective (in terms of minimal costs and maximal benefits), there will be a very high demand. For many technologies, that means high production rates are required to meet that demand. Only big companies are able to massively produce and sell those technologies. And small start-up companies that produce technologies that are highly sought after, are likely to grow fast and become very big. All Big Tech, Big Agri and Big Pharma companies started small but produced and sold a technology that was high in demand. Think of personal computer software, high yield crops, and lifesaving vaccines. That is why we see a positive correlation between the size of a company and the desirability of a technology. That positive correlation explains why a distrust in big institutions can be harmful.


[1] After a long search, I only encountered a few problems that are problematic in many ways: animal product consumption, unwanted pregnancies, migration restrictions and privatized economic rent.

Geplaatst in Artikels, English texts | Tags: , , , , , , , | Een reactie plaatsen

De begrensde reddingsboot. Reactie op de boekbespreking van Open Grenzen

Als veganist, atheïst en ecohumanist zit ik vaak op dezelfde lijn met Floris van den Berg. Verschillende van zijn boeken heb ik reeds besproken. Maar hier en daar hebben we meningsverschillen. Iemand met een andere mening geeft ons een mogelijkheid om onze mening bij te sturen. Aangezien we het vaak mis hebben, is van mening veranderen erg belangrijk. Daarom loont het de moeite om open te staan voor de meningen van anderen wanneer die verschillen van de eigen mening. Maar meestal komt een tegenpartij met ongeldige drogredenen af, waardoor het niet interessant meer is om voor die andere mening open te staan. Met iemand als Floris van den Berg is dat anders: aangezien hij reeds mijn fundamentele waarden deelt en in het verleden geldige redeneringen gaf, is de kans kleiner dat hij bij een meningsverschil de bal totaal misslaat. Uit zijn bespreking van mijn boek Open Grenzen denk ik dat het thema migratie ons grootste meningsverschil is. In deze reactie ga ik focussen op zijn argumenten waarvan ik denk nog in staat te zijn ze te weerleggen.

Theorie versus praktijk

Van den Berg citeert Maarten Doorman: “In theorie is er geen verschil tussen praktijk en theorie. In de praktijk wel.” Deze uitspraak kunnen we eens analyseren. Wat wordt er bedoelt met theorie en praktijk? De uitspraak is correct, als theorie betekent: het geval waarbij iedereen rationeel is, de argumenten voor open grenzen begrijpt en de achterliggende kosmopolitische waarden (zoals antidiscriminatie) erkent. In mijn boek wijs ik op het belangrijkste risico van open grenzen: racisme bij autochtonen. “Door irrationele denkfouten over migratie kan de autochtone bevolking zich keren tegen immigranten en kunnen ze stemmen op racistische, extreemrechtse partijen. […] Het belangrijkste bezwaar tegen het meer openstellen van grenzen, is het risico op een backfire effect, waardoor het racisme en de xenofobie bij de bevolking kunnen toenemen.” (p. 88-89) Daarmee erken ik de praktijk, waarbij autochtonen dus niet rationeel handelen of niet de juiste waarden delen. In zijn boekbespreking wijst Van den Berg op drie problemen van immigratie die dit punt ondersteunen.

Ten eerste zegt hij: “Als iedereen die dat wil, zo maar naar Europa zou kunnen komen – dus niet alleen arbeidsmigranten, maar alle economische vluchtelingen en asielzoekers – zou het sociaal economisch systeem ineenstorten.” Dat het economisch systeem zou ineenstorten bij immigratie, wanneer de economische studies wijzen op sterke positieve effecten van migratie op de economische welvaart en groei, is erg twijfelachtig. Maar misschien komen onze sociaal-economische instituties (bv. economische vrijheid, eigendomsrecht, muntstabiliteit en een goede rechtsstaat) in verval door immigratie? Daar heb ik in mijn boek niets over geschreven, maar na publicatie van het boek wel extra onderzoek naar gedaan. Het is heel moeilijk om de effecten van immigratie op instituties te meten, maar sommige recente studies doen een verdienstelijke eerste poging (Clark e.a. 2015; Padilla & Cachanosky; 2018). De conclusies die ik hieruit trek: er zijn geen aanwijzingen van een negatieve impact, en licht bewijs voor een positieve impact, van immigratie op institutionele kwaliteit in de ontvangende landen. Zelfs massa-immigratie, zoals gebeurde in Israël, kan een relevant positieve impact hebben op instituties (Powell e.a., 2017). Maar er is een uitzondering of kanttekening: polarisering binnen de bevolking kan wel een negatieve impact hebben op de kwaliteit van instituties in ontvangende landen waar instituties zwak zijn (Roupakias & Dimou, 2020). Hier zien we het verschil tussen theorie en praktijk. In theorie is iedereen antiracist en gaan autochtonen geen polarisering in de hand werken. Maar in de praktijk kan immigratie polarisering wel aanwakkeren, bijvoorbeeld wanneer autochtonen racistisch gaan doen. Gelukkig zien we dat in landen met gezonde instituties er geen relevante negatieve impact is van immigratie op instituties. De vraag is of instituties in West-Europa gezond dan wel fragiel zijn.

Ten tweede haalt Van den Berg Ruud Koopmans aan, die “laat ook zien dat de integratie qua liberale opvattingen onder tweede en derde generaties moslims niet toeneemt.” De meeste literatuur die ik tegenkwam, laat zien dat tweede en derde generaties van immigranten een minder sterke religieuze beleving hebben dan hun (groot)ouders. In het boek schrijf ik: “Eigenlijk bieden open grenzen een extra voordeel in de strijd tegen het islamisme. Als moslims naar het seculiere Westen migreren, worden ze geconfronteerd met de humanistische verlichtingswaarden en gaan ze die waarden sneller overnemen. Jongeren van geïmmigreerde moslims in West-Europa blijken in vergelijking met hun ouders minder belang te hechten aan geloof, religieuze voorschriften minder streng na te leven, minder in de Koran te lezen, minder een hoofddoek te dragen, minder vijandig te zijn tegen homo’s, meer de evolutietheorie te aanvaarden en meer liberale denkbeelden over te nemen (Kalter, 2018; van de Pol en van Tubergen, 2014; van der Bracht, 2015).” Maar er is – alweer in de praktijk – een uitzondering op dit proces van assimilatie (bv. Fleischmann, 2011): wanneer allochtone moslims zich gediscrimineerd of sociaal uitgesloten voelen door de autochtone bevolking, kunnen ze radicaliseren en een sterker geloof in de Islam ontwikkelen (omdat ze de seculiere samenleving dan meer als een bedreiging voor hun identiteit gaan beschouwen). Opnieuw dragen autochtonen hier een verantwoordelijkheid: als ze racistisch gaan doen tegen immigranten, wordt het een zichzelf-vervullende voorspelling. Wil je radicalere moslims? Zorg er dan voor dat ze zich gediscrimineerd voelen. Het sterkste wapen tegen dergelijke radicalisering, zijn intergroep vriendschappen: seculiere autochtonen die vriendschapsbanden aangaan met geïmmigreerde moslims.

Een derde voorbeeld vormen de islamfundamentalistische terroristische aanslagen. Hoewel er bij die aanslagen relatief veel minder slachtoffers vallen dan bij andere risico’s in de samenleving (van het verkeer tot de overdadige vleesconsumptie), stelt Van den Berg: “Het gevaar van terrorisme zit hem niet zozeer in het risico zelf slachtoffer ervan te worden, maar in de ondermijning van de democratische rechtsorde zelf.” Maar na alle terroristische aanslagen de afgelopen decennia, zie ik nog steeds geen aanwijzingen van een ondermijning van de democratische rechtsstaat. Hoewel, indirect kan terrorisme wel de rechtsstaat aantasten, namelijk door hoe autochtonen op dat terrorisme reageren. Het terrorisme kan leiden tot een verrechtsing van de samenleving, waardoor populistische politici en extreem rechtse politieke partijen terrein winnen. En die partijen vormen een bedreiging voor een liberale rechtsstaat. In theorie gaan mensen niet verrechtsen door immigratie en gaat immigratie veel meer welvaart en economische groei creëren zodat er nog meer middelen beschikbaar komen voor een verdere versterking van de rechtsstaat. In de praktijk kunnen autochtone kiezers na terroristische aanslagen irrationele politieke keuzes maken die de rechtsstaat ondermijnen.

Reddingsboot

Het wordt tijd om het argument van de reddingsboot erbij te halen. Van den Berg schreef: “Het is als met een reddingsboot: als je onbeperkt mensen op een reddingsboot aan boord neemt dan komt er een moment waarop de boot overbelast raakt, zinkt en iedereen ten onder gaat.” Het is in deze context een nogal pijnlijke analogie, aangezien er omwille van de gesloten grenzen van Europa jaarlijks honderden opvarenden sterven op overvolle reddingsbootjes op de Middellandse Zee. Maar goed, hiermee bevestigt hij wat lijkt op een wetmatigheid: in discussies over immigratie waarbij de migratiecriticus bijvoorbeeld gelooft dat het land vol is, zal die persoon de analogie van de reddingsboot gebruiken. In het boek anticipeer ik op dat argument (p.52): “Of neem de situatie van een reddingssloep die gaat kapseizen als alle drenkelingen aan boord klimmen.”

De economische argumenten tonen aan dat de reddingsboot nog helemaal niet vol zit. De economie is namelijk een bijzondere reddingsboot: des te meer volk op de boot komt, des te groter, steviger en luxueuzer de boot wordt. De drenkelingen die aan boord gehaald worden, dragen bij aan de extra capaciteit van de boot (hoe dat komt, wordt besproken in mijn boek Open Grenzen, maar bv. Ng (2019) gaf ook een zeer heldere uitleg).

Het voorbeeld van de reddingsboot is een goede illustratie voor het principe van getolereerde partijdigheid. Van den Berg verwoordt dit als volgt: “Scherp gesteld: als er een boot omslaat en er dreigen kinderen te verdrinken en ik geef prioriteit aan het redden van mijn eigen kind boven het redden van jouw kind, dan is dat gerechtvaardigd, omdat ik kan begrijpen dat jij precies hetzelfde zou doen in deze situatie, namelijk eerst jouw eigen kind proberen te redden. Maar als partijdigheid getolereerd is in gezinsverband, zou partijdigheid op basis van natie dan niet ook gerechtvaardigd kunnen zijn? Als er iemand in nood is, schiet de overheid idealiter te hulp. Als er iemand een ongeval heeft, komt de ambulance – niet die uit België of Duitsland, maar uit Nederland.”

Ja, dergelijke partijdigheid voor landgenoten is te tolereren en is consistent met het anti-willekeurprincipe, zoals ik in het boek beargumenteer: “Zo ook mogen we bij hulpverlening partijdig zijn ten voordele van onze dierbaren, ook al zijn alle dierbaren binnenlanders. We kunnen tolereren dat bui­tenlanders wel en wij niet aanspraak kunnen maken op hulp en sociale voorzieningen in het buitenland.” (p.61) “Net zoals het antiwillekeurprincipe nog niet wil zeggen dat je puur onpartijdig iedere hulpbehoevende op aarde evenveel moet helpen, zo wil open grenzen nog niet zeggen dat we iedereen moeten toelaten tot onze sociale zekerheid. We kunnen bijvoorbeeld prioritaire toegang verlenen aan diegenen die bijdragen leveren aan de sociale zekerheid, en hun dierbaren.” (p.53) Met deze laatste groep worden in de praktijk landgenoten aangeduid.

Cruciaal bij het principe van getolereerde partijdigheid, is (p.61): “Deze getolereerde partijdigheid geldt uitsluitend als het gaat om het verlenen van hulp [bv. het redden van drenkelingen] of het geven van cadeaus, niet als het gaat om het veroorzaken van schade of het schenden van rechten.” Bij de gangbare reddingsbootanalogie stellen we de vraag of men de plicht heeft om drenkelingen te redden, en zo ja, welke drenkelingen we eerst moeten helpen. Dus stel jij hebt een boot, er is nog plaats aan boord, en je besluit om drenkelingen te gaan redden. Links van je boot zijn drenkelingen met dezelfde nationaliteit als jij, rechts van de boot zijn drenkelingen met een andere nationaliteit. Stel dat je met je boot naar links vaart om je landgenoten te redden. Ja, als je voorkeur wil geven aan je landgenoten, is dat partijdigheid. Als je gaat zeggen dat je je landgenoten verkiest omdat zij moreel superieur zijn, dan maak je je schuldig aan racisme, dus dat mag niet. Maar als je je landgenoten eerst redt omdat je voor hen meer ‘feeling’ voelt of met hen een sterkere sociale band hebt, of omdat je landgenoten bijgedragen hebben aan de bouw van je boot (zoals landgenoten bijdragen aan de sociale zekerheid) waardoor de boot niet enkel van jou is maar van al je landgenoten, dan kunnen we dat tolereren.

In deze context stelt Van den Berg de vraag: “Maar – en dit is de crux – waarom geven we wel om de mensen die het gelukt is om onze landsgrens te bereiken en niet om al die mensen die om welke reden dan ook niet zijn gekomen maar wel hulp kunnen gebruiken? Vergelijk het met hulp aan een drenkeling. Stel je hebt een boot en je ziet drenkelingen in het water. Je helpt iedereen die op eigen kracht de boot kan bereiken aan boord, maar degenen die het niet lukt om de boot te bereiken laat je achter en die zullen verdrinken. Hoe moreel is dat?” Maar hier zien we dat deze analogie tussen gesloten grenzen en partijdigheid op de reddingsboot niet opgaat. Dat de reddingsboot gesloten grenzen heeft, wil niet zeggen dat men drenkelingen achterlaat. Nee, het wil zeggen dat men drenkelingen die aan boort klauteren terug het water in duwt.

Een correctere analogie gaat dus als volgt. Stel er is een lege reddingsboot en die is van niemand. Jij klimt als eerste aan boord en haalt ook je landgenoten mee aan boord, prima. Maar dan ga je die boot voor jezelf en je landgenoten claimen, hoewel niemand van jullie die boot gemaakt of rechtmatig gekocht heeft (net zoals niemand de grond van een land gemaakt heeft). Dat wordt al discutabeler, een beetje vergelijkbaar met diefstal. Maar goed, je kunt het nog erger maken. Er is namelijk nog veel plaats over op de boot en er zijn nog drenkelingen in het water die niet jouw nationaliteit hebben. Dat je besluit om niet naar hen toe te varen of hen geen reddingsboeien toe te werpen, tot daaraan toe. Misschien heb je teveel spierpijn om een boei te gooien of heb je niet genoeg brandstof over om naar die drenkelingen te varen. Maar dat je besluit om drenkelingen die aan boord klimmen met kracht tegen te houden en terug het water in te duwen, terwijl die boot niet van jou is, dat kan echt niet. Dan grijp je actief in en veroorzaak je schade. Want als jij er niet was geweest, dan konden die drenkelingen nog wel zichzelf redden. Door ze actief tegen te houden, verhinder je de fundamentele vrijheden van anderen om zichzelf te redden. Dat laatste is nu net wat we doen bij een beleid van gesloten grenzen. Dat je geen ontwikkelingshulp stuurt naar arme landen, tot daaraan toe. Misschien heb je te weinig geld. Maar dat je mensen uit die landen actief verhindert om zichzelf te helpen (door in je land te gaan werken), wanneer je die mensen met kracht tegenhoudt aan de grens (of wanneer je geld geeft aan grenspatrouilles), dat is ontoelaatbaar. Dan beroof je anderen het fundamentele recht op werk.

Tot slot bereiken we een apotheose, wanneer Van den Berg voorstelt om ontwikkelingshulp te verhogen: “Momenteel wordt zo’n 0,7 procent van het BNP aan ontwikkelingshulp uitgegeven. Dat is niet bepaald hoog – 15 tot 20 procent zou billijker zijn.” In de reddingsbootanalogie is ontwikkelingshulp het analogon van het gooien van reddingsboeien. Ja, de mensen aan boord zouden wel wat meer reddingsboeien mogen gooien. Maar tja, ze klagen al snel over spierpijn, weet je. En als ze meer reddingsboeien gooien, moeten ze ook nog boeien gaan bijmaken, en daar hebben die opvarenden ook niet zoveel zin in. In theorie is het allemaal mooi, maar in de praktijk is het niet zo simpel om hen te overtuigen om een 21 keer hogere inspanning te leveren (vergelijkbaar met het opkrikken van ontwikkelingshulp van 0,7% naar 15%). Waar de mensen aan boord wel zin in hebben, is het terugduwen van drenkelingen die op punt staan aan boord te klimmen. Als ze nu eens gewoon die drenkelingen niet tegenhouden, dan kruipen die drenkelingen aan boord, gaan ze massaal extra reddingsboeien maken en met al hun kracht werpen naar de resterende drenkelingen (hun familieleden) in het water.

Dat is wat er gebeurt bij open grenzen: de immigranten dragen massaal bij aan de economie, creëren extra economische groei (waarbij de wereldbevolking zelfs bijna dubbel zo rijk kan worden), en gaan een deel van de centen die ze verdienen als geldschenkingen terugsturen naar hun familie in de herkomstlanden. Vandaag, met de migratiebeperkingen, liggen die geldzendingen (remittances) van immigranten al drie keer hoger dan de totale officiële ontwikkelingshulp. De migranten hebben dus al de autochtonen voorbijgestoken als het gaat om ontwikkelingshulp. Autochtonen mogen inderdaad wat meer geld geven aan effectieve goede doelen voor de armste landen, maar hen daarvan overtuigen is in de praktijk nog niet zo simpel. Wat voor de autochtonen een veel lagere inspanning vergt, is dat ze stoppen met hun inspanningen om immigranten tegen te houden. Meer hoeven ze niet te doen; ze moeten gewoon wat minder inspanningen leveren. Dan gaan die immigranten zelf vanzelf massaal veel ontwikkelingshulp geven. En de autochtonen worden er ook nog eens rijker door, waardoor ze een extra centje over hebben om ook te doneren aan goede doelen. Als zowel de geldschenkingen als het BNP zo sterk stijgen, dan gaat de totale hoeveelheid financiële hulp voor de armste landen wel wat hoger kunnen zijn dan pakweg 20% van het huidige BNP. En dat met minder inspanningen. Althans in theorie. In de praktijk bestaat het risico dat autochtonen dingen gaan doen die de samenleving in gevaar brengen. Het toelaten van drenkelingen op de reddingsboot klinkt ook mooi in theorie. In de praktijk gaan enkele opvarenden die het eerst op de boot klommen, harder en harder aan de boot beginnen schudden des te meer drenkelingen aan boord klimmen. Die opvarenden tegenhouden, is de grote uitdaging. Als we daarin slagen, verzoenen we theorie en praktijk.

Appendix: enkele verduidelijkingen

Om af te sluiten wil ik enkele vragen beantwoorden die Van den Berg stelt. Allereerst zegt hij dat ik beter zou moeten definiëren wat een arbeidsmigrant inhoudt. In het boek geef ik een brede definitie: iemand die toegelaten is op de arbeidsmarkt van het gastland. Dat zijn dus ook nieuwkomers die nog geen werk hebben gevonden, maar wel toegelaten worden om te werken (dus werkloze werkzoekende legale immigranten). De twee belangrijkste groepen immigranten die niet behoren tot de arbeidsmigranten, zijn de kinderen van immigranten (wegens verbod op kinderarbeid), en de ‘illegalen’ (meestal vluchtelingen en asielzoekers die geen verblijfsvergunning en dus ook geen werkvergunning hebben). Van zodra asielzoekers werk mogen zoeken, vallen ze onder mijn definitie van arbeidsmigrant.

Van den Berg zegt dat: “Wanneer Bruers zijn argument uitbreidt naar immigratie in het algemeen zijn er grote bezwaren aan ongelimiteerde en ongereguleerde immigratie.” Maar het verhaal van open grenzen is geen verhaal van ongereguleerde migratie. In mijn boek schrijf ik: “Het openen van grenzen is niet hetzelfde als het afschaffen van grenzen. Denk aan gemeentegrenzen: die bestaan, maar ze zijn open.” (Open Grenzen, p.13) Dat gemeentegrenzen open zijn wil niet zeggen dat er ongereguleerde migratie is: “Jij mag verhuizen naar een naburige gemeente, dus tussen jouw huidige en je toekomstige gemeente is de grens open. Maar je moet je wel registreren in die nieuwe gemeente, dus is die grens niet afgeschaft.” (p.13) Van den Berg schrijft zelf: “Wie in een gemeente gaat wonen moet zich er inschrijven en er gemeentebelastingen betalen en zich aan de in de gemeente geldende regels houden.” Dergelijke maatregelen zoals een registratieplicht zijn een vorm van regulering van migratie. Je zou kunnen zeggen dat Van den Berg hier een stroman drogreden maakt, door mijn standpunt over “immigratie in het algemeen” te vertekenen tot een foutieve karikatuur over “ongereguleerde immigratie”, en vervolgens dat foutief voorgestelde standpunt aan te vallen.

Daaropvolgend vraagt Van den Berg: “Maar als ik mijn tent opsla in een gemeente en zeg dat ik werk zoek (ook al spreek ik de taal niet), wat dan? En als duizenden mensen hun tenten opslaan in een gemeente, wat dan te doen?” Maar dat is een gangbaar fenomeen. Denk aan de plattelandsvlucht: duizenden mensen die van landelijke gemeenten migreerden naar een stedelijke gemeente. Die migratiestroom, wat nu sterk gaande is in bijvoorbeeld China, veroorzaakt een sterke economische groei in de steden. Nu ja, in plaats van een tent hebben de immigranten gewoon een woning. En mensen met zeer diverse normen en gewoonten, zoals zowel joden, protestanten, neopaganisten, anarchisten, autisten, gothic metal fanaten, computernerds, nouveau riche en laaggeschoolden mogen van de ene gemeente verhuizen naar de andere. Taal is geen criterium: ik mag verhuizen naar een gemeente in de Belgische Oostkantons, ook al spreek ik geen Duits.

De migratie tussen gemeenten biedt ook antwoorden op andere vragen die Van den Berg stelt: “Maar wat moet er, volgens Bruers, gebeuren indien immigranten zich niet houden aan de wetten en basale conventies (als handen schudden in het precoronatijdperk)? Moeten immigranten bestraft worden net als iedere burger die de wet overtreedt, of moeten ze in het geval van overtreding worden teruggestuurd? Hoelang blijf je eigenlijk immigrant? Zijn tweede generatie immigranten nog immigranten of worden ze nog gezien als migranten? Op deze vragen gaat Bruers helaas niet in.” Dat ik in het boek niet diep inga op deze vragen, is waarschijnlijk omdat de antwoorden evident zijn. Als een nieuwkomer in de gemeente zich niet houdt aan de wetten, wordt die persoon niet gewoon teruggestuurd naar de gemeente van herkomst, maar wordt die net als andere inwoners bestraft. Aan welke basale conventies men zich moet houden in een liberale gemeente, is open voor discussie. Niemand heeft de plicht om handen te schudden bij een ontmoeting.  Een immigrant blijft een immigrant zo lang die persoon in een ander land woont dan het land van herkomst of geboorte. De kinderen van immigranten die in het ontvangende gastland of -gemeente geboren worden, tellen niet als immigranten. Ik ben in Herentals geboren nadat mijn ouders daar zijn gaan wonen. Zij zijn immigranten voor Herentals, maar ik niet. Nu ik in Antwerpen woon, ben ik emigrant van Herentals.

Over het houden aan wetten ga ik in het boek wel in. Bijvoorbeeld schrijf ik (p.59): “Dus het opsluiten van delinquenten is dus nog wel toegelaten [bij open grenzen]. Indien er bij de immigranten een grote bevolkingsgroep is die delinquent gedrag vertoont en het onmogelijk is om al die delinquente immigranten in kleine binnenlandse gevangenissen op te sluiten, kan het nog wel interessant zijn om die delinquente immigranten naar een plaats te sturen afgebakend met gesloten grenzen, zodat ze niet meer uit die ‘openluchtgevangenissen’ naar de vrije wereld kunnen migreren.” Deze en andere uitspraken in het boek tonen aan dat ik erken dat een aantal immigranten onverantwoord gedrag vertonen dat niet te tolereren is. Van den Berg wijst hier op de tolerantieparadox: om tolerantie in de samenleving te vrijwaren, moeten we intolerant zijn tegenover intolerantie. Ik erken in het boek dat er ook veel intolerantie is bij immigranten (en zeker bij de Islamisten onder hen), en de context, waarin ik spreek over bv. gevangenissen, zou wel duidelijk moeten maken dat ik die intolerantie niet tolereer.

Bronnen

Clark, J. R., Lawson, R., Nowrasteh, A., Powell, B., & Murphy, R. (2015). Does immigration impact institutions?. Public Choice, 163(3), 321-335.

Fleischmann, F. (2011). Second-generation Muslims in European societies: Comparative perspectives on education and religion. Doctoraat Utrecht University.

Kalter, F. e.a. (2018). Growing up in a diverse Europe. Oxford University Press.

Ng, Y. K. (2019). Immigration Typically Makes Existing Residents Better Off. In: Markets and Morals. Cambridge: Cambridge University Press.

Padilla, A., & Cachanosky, N. (2018). The Grecian horse: does immigration lead to the deterioration of American institutions?. Public Choice, 174(3), 351-405.

Powell, B., Clark, J. R., & Nowrasteh, A. (2017). Does mass immigration destroy institutions? 1990s Israel as a natural experiment. Journal of Economic Behavior & Organization, 141, 83-95.

Roupakias, S., & Dimou, S. (2020). Immigration, diversity and institutions.

Van de Pol, J. & van Tubergen, F. (2014). Inheritance of religiosity among Muslim immigrants in a secular society. Review of religious research 56(1).

Van der Bracht, K. (2015). First and second generation migrant religiosity in Europe. Doctoraat Universiteit Gent.

Geplaatst in Artikels | Tags: , | Een reactie plaatsen

My top ideas of 2020

Now that 2020 came to an end, it is time to look back and reflect on my top ideas of last year.

Top charities

In 2020, I focused on effective giving: donating to the most effective charities. I increased my personal donations to 50% of my net, after-tax income and made a list of my top three charities: Wild Animal Initiative, Animal Ethics and the Good Food Institute. The first two charities are promoting research in welfare biology, how to help nature to improve the welfare of wild animals. The third charity promotes the development of new animal-free food. Wild Animal Initiative and the Good Food Institute were top recommended animal charities of Animal Charity Evaluators in 2020.

Veganmodernism

I came to the conclusion that veganmodernism could be our final strategy for meat abolition. Veganmodernism focuses on research and development of new animal-free food, using modern technologies, instead of doing traditional vegan outreach, consumer behavioral change and corporate pressure campaigns.

I estimated the extreme cost-effectiveness of cell-based meat R&D. In a medium estimate, funding 1 dollar of cell-based meat R&D could spare the suffering of 100 vertebrate farm animals and the killing of 1000 vertebrate land and sea animals. This is at least an order of magnitude more effective than other animal advocacy actions. Due to that article, someone donated 5000 dollar to New Harvest, an organization who, like the Good Food Institute, funds research and development in cellular agriculture. I also made an infographic to demonstrate why cell-based meat will be better than animal-based meat.

As a result of these reflections on the importance of alternative protein such as cell-based meat, I co-founded the Leuven Alt. Protein Project, a university-based initiative by the Good Food Institute.

Cell-based meat or clean protein is also very beneficial for the climate, as I learned about a second big climate benefit of animal-free agriculture: the carbon sequestration potential. With a global animal-free food production, less agricultural land is required. Roughly 7 million km² could be reforested spontaneously (without having to plant trees). Those new grown forests could absorb more than half of the total amount of CO2 ever emitted from burning fossil fuels. I argued that clean protein and clean energy R&D, or more generally clean technology innovation is the most cost-effective climate action. Funding 1 dollar of clean meat R&D could avoid 1 ton CO2-equivalents in the atmosphere. That is more cost-effective than other climate actions.

Harm-free agriculture

As agriculture is one of humanities most harmful activities, I am looking for effective means to minimize agricultural harms. Animal-free agriculture is necessary but not sufficient. We have to be much more ambitious and move towards zero harm by exploring the possibilities of high-tech, soil-free (landless) agriculture such as vertical farming and air-based food. Lisa Dyson, CEO and founder of Air Protein, became my new role model, as she exemplifies my personal paradigm shift from radical ecology with soil-based low-tech food to rational ecology with air-based high-tech food. I included my affiliation with radical ecology in my by now long list of personal mistakes.

In general, I realized once more how important scientific research, development and innovation of new technologies is. We need more research in welfare biology, clean protein, clean energy and soil-free agriculture.

Happy New Year!

Geplaatst in English texts | Een reactie plaatsen

De irrationele logica van vaccintwijfelaars

Wat vaccintwijfelaars in essentie geloven (opiniestuk gepubliceerd op VRT)

Het afgelopen jaar zagen we hoe naast een virus ook desinformatie zich snel kan verspreiden. We zagen opstoten van coronatwijfel en weerstand tegen coronamaatregelen. Nu we D-day tegen corona hebben bereikt en de vaccinatiecampagne wordt uitgerold, kunnen we een opstoot verwachten van vaccintwijfel. De volksgezondheid komt in gevaar als niet voldoende mensen zich laten vaccineren.

Hoe we het best vaccintwijfel bestrijden, is een vraag voor psychologen. Als filosoof wil ik hier met een gedachtenexperiment het kritisch denken aanwakkeren en blootleggen wat vaccintwijfelaars in essentie geloven.

Stel we hebben een nieuw vaccin. In tegenstelling tot het gekende injectievaccin – de prik – is dit een inhalatievaccin: mensen krijgen het binnen via ademhaling. Injectievaccins worden in laboratoria ontwikkeld, maar het inhalatievaccin heeft een ingenieus mechanisme: het produceert zichzelf in de lichamen van gevaccineerde mensen. Onze lichamen worden letterlijk omgebouwd tot vaccinfabriekjes. De uitgeademde lucht draagt onzichtbaar kleine vaccindeeltjes naar andere mensen die het inademen. Zeer handig en goedkoop: zo worden andere mensen vanzelf gevaccineerd. Geen kosten voor naalden en dokters meer. En we hoeven niet te discussiëren over al dan niet verplichting: met het zelfproducerend inhalatievaccin in omloop worden mensen gedwongen gevaccineerd. We ontsnappen er niet aan, tenzij we stoppen met ademen.

Nog een voordeel van het inhalatievaccin, voor diegenen die Big Pharma wantrouwen: de commerciële farmaceutische bedrijven verdienen er geen cent aan. Het inhalatievaccin is niet ontworpen door Big Pharma, werd niet gepatenteerd, en wordt niet gemaakt in fabrieken. Puur natuur.

Het injectievaccin en het inhalatievaccin hebben een gelijkaardige werking: ze stimuleren ons immuunsysteem. Helaas heeft het inhalatievaccin enkele ernstige nadelen. Ten eerste kwam het zo snel in omloop, dat er zelfs geen enkele veiligheidstest werd uitgevoerd en niet werd geëvalueerd door onafhankelijke gezondheidswetenschappers en regulerende instanties. Ten tweede is er gebrekkige transparantie: het heeft geeneens een bijsluiter met veiligheidsinstructies en mogelijke bijwerkingen. Ten derde is er geen controle op de dosis. We kunnen dus zonder het te weten een gevaarlijk hoge dosis van het vaccin inademen. Het injectievaccin heeft daarentegen een geoptimaliseerde dosis. Ten vierde kent het inhalatievaccin gevaarlijke additieven: chemische stoffen die het zelfproducerende proces bevorderen. Een spike-eiwit zorgt dat de vaccindeeltjes diep in onze lichaamscellen binnendringen, en een ribonucleïnezuur beïnvloedt de genetische werking van onze cellen. Onze lichaamscellen worden genetisch gemanipuleerd om vaccindeeltjes te produceren. Helaas leidt dat – in tegenstelling tot het injectievaccin – tot celdood. Het inhalatievaccin kan daarom heel ernstige bijwerkingen en zelfs de dood veroorzaken.

De vraag van dit gedachtenexperiment is: welk vaccin verkies je, het gevaarlijke inhalatievaccin of het veel veiligere injectievaccin? Maar het is geen gedachtenexperiment. Het inhalatievaccin bestaat reeds. Het is reeds in omloop, en het noemt Sars-cov2. Het is het virus zelf. De beste manier om dit inhalatievaccin uit omloop te halen, is met een injectievaccin.

Vaccintwijfelaars zijn tegen het injectievaccin en verkiezen daardoor de facto eigenlijk dat inhalatievaccin. Waarom? Willen ze absoluut niet dat farmaceutische bedrijven geld verdienen, wat ze kunnen met de verkoop van het injectievaccin, en vinden ze dat belangrijker dan de volksgezondheid? Vinden ze dat vrijheid enkel geschonden wordt wanneer een vaccin doelbewust wordt toegediend door een dokter, wat het geval is bij het injectievaccin maar niet bij het inhalatievaccin? Bij het inhalatievaccin worden mensen wel gedwongen gevaccineerd, maar niet doelbewust, en misschien vinden vaccintwijfelaars doelloosheid (sommigen noemen het natuurlijkheid) belangrijker dan gezondheid?

Het gedachtenexperiment toont de ongeldigheid aan van de geopperde bezwaren van vaccintwijfelaars. Hun keuze voor het gevaarlijkere inhalatievaccin, dat iedereen wordt opgedrongen, schendt onze gezondheid, welzijn en vrijheid. Patiënten die sterven aan het inhalatievaccin, verliezen al hun vrijheid. De anti-vaccinatiebeweging is een opvallend voorbeeld van irrationaliteit, waarin goede mensen met goede morele waarden, verkeerde keuzes maken die indruisen tegen hun eigen morele waarden.

Geplaatst in Blog | 1 reactie

Clean technology innovation as the most cost-effective climate action

Main takeaways

Funding research and development of clean technologies, in particular clean protein and clean energy, is probably the most cost-effective method to avoid dangerous climate change. Clean protein is animal-free protein such as cell-based meat and fermentation-based dairy and eggs that can replace their animal-based counterparts. Clean energy includes carbon neutral energy sources that can replace fossil fuels and do not emit greenhouse gases or toxic chemicals, as well as enabling energy infrastructure (e.g. grids, batteries, carbon capture and storage facilities).  

The three factor framework for focus area selection, often used in effective altruism, is useful for back-of-the-envelope cost-effectiveness estimates. According to this framework, the effectiveness is the product of three factors: scale (importance), solvability (tractability) and neglectedness. The three factor framework can be applied in two different ways, resulting in two cost-effectiveness estimates. The results are pretty robust in the sense that for each technology, both cost-effectiveness estimates have the same order of magnitude.

According to very conservative/pessimistic assumptions (likely underestimating the cost-effectiveness), clean protein R&D-funding has a lower-bound cost-effectiveness of roughly 1 ton CO2 avoided per dollar; whereas clean energy has a lower-bound cost-effectiveness of roughly 0,1 ton CO2 avoided per dollar. The latter is also the (very conservative) estimate by a 2020 Founders Pledge report. Hence, in the short run, clean protein is likely more effective than clean energy R&D (although the back-of-the-envelope calculations might be too rough to draw a strong conclusion on this). However, in the long run, clean energy becomes crucial: without it, climate targets cannot be met. Clean protein is not sufficient to avoid dangerous climate change. As the development of clean protein that can replace animal protein is probably easier than the development of clean energy that can replace all fossil fuel energy, we can also expect that clean energy R&D has more room for more funding than clean protein R&D, which means that its cost-effectiveness is likely to remain very high for a longer time than clean protein R&D.

Clean protein and clean energy innovations are more cost-effective than e.g. Gold Standard carbon offsetting projects (around 0,05 ton CO2 per dollar), buying emission permits (less than 0,05 ton CO2 per euro for European ETFs), planting trees (0,03 ton CO2 per euro in Belgium), paying the social cost of carbon to the poorest people who are most affected by climate change (a meta-analysis estimate of 0,02 ton CO2 per dollar, a refined estimate of 0,002 ton CO2 per dollar) or current technology direct air carbon capture and storage (0,001 ton CO2 per dollar). The clean tech cost-effectiveness estimates are in the same order of magnitude as other carbon reduction methods that are considered to be very cost effective, such as forest protection (preventing deforestation by paying local communities for ecosystem services, which has a cost-effectiveness around 0,4 ton CO2 avoided per dollar) and reducing human population growth (preventing unwanted pregnancies by voluntary family planning, with a cost-effectiveness around 0,14 ton CO2 avoided per dollar). However, these other cost-effective carbon reduction methods have limited room for more funding, which means as carbon offsetting methods they cannot be universalized: not everyone can offset their own carbon emissions with such offsetting methods. When more people fund such carbon offsetting methods, their cost-effectiveness will strongly increase. There are not enough forests to protect and unwanted pregnancies to avoid. Avoiding carbon emissions from burning forests is not enough to meet climate targets. Reducing the human population is not enough to move to zero carbon emissions, unless the population goes to zero. Only with new, carbon-neutral technologies we can move all the way to zero carbon emissions.

Clean tech innovation has a lot of room for more funding, is likely to stay very cost-effective, and has other important co-benefits. Clean protein reduces farm animal suffering, environmental impacts (e.g. water pollution, biodiversity loss) and public health risks (from antibiotic resistant bacteria and new zoonotic diseases), whereas clean energy reduces public health risks (e.g. air pollution from burning coal).  

Directly funding clean protein and clean energy R&D is possible by donating to e.g. New Harvest and the MIT Energy Initiative. But cost-effectiveness could increase further (perhaps with a factor of 10) by leveraging funding, for example by advocating for more government funding of clean tech innovation, offering strategic support to clean tech companies or facilitating clean tech in other ways. Supporting clean protein advocacy is possible by donating to the Good Food Institute. Clean energy advocacy can be supported by donating to Let’s Fund (which supports the Clean Energy Innovation Program at the Information Technology and Innovation Foundation) and the Founders Pledge Climate Change Fund (which supports the Clean Air Task Force, Carbon180 and TerraPraxis as of 2020).

Calculation methods

Two back-of-the-envelope cost-effectiveness estimates are possible: the acceleration and the contribution estimate.

The acceleration estimate assumes that the new technology will be developed anyway and will enter the market sooner or later, but an extra unit of R&D-funding accelerates the research such that we have the new technology sooner, such that atmospheric greenhouse gases can be reduced or avoided sooner. If the old technology emits X ton of CO2 each day, and one dollar funding accelerates the new, clean technology with Y days, the cost-effectiveness of R&D-funding equals X times Y ton CO2 per dollar. Once the new technology enters the market at competitive prices, consumers instead of philanthropic funders will pay for the production costs as well as the marketing and advertisement of the new technology.

The contribution estimate assumes that the new technology requires a fixed amount of R&D-funding to enter the market and has a fixed total greenhouse gas reduction potential, but a unit of funding has a fixed share of the total funding requirement, such that it offers a certain contribution to the total greenhouse gas reduction. If one dollar R&D-funding has a share S of the total funding requirements, and the total greenhouse gas reduction potential of the clean technology is T ton of CO2, the cost-effectiveness of R&D-funding equals S times T ton CO2 per dollar.

Clean protein

In terms of animal suffering reduction, clean protein R&D, and in particular cell-based meat, is more cost effective than other animal welfare actions. But clean protein is also top effective in terms of greenhouse gas reduction.

First estimate: acceleration of clean protein development

Scale of the problem: in a business-as-usual scenario, the global annual greenhouse gas emissions from livestock will increase to 10 billion ton CO2-equivalents, which equals 320 ton CO2e per second. (Current livestock emissions are 8 billion ton CO2-equivalents per year. Global protein consumption will increase, but will be slightly offset by replacement of animal protein with currently available animal-free protein).  

Solvability of the problem: 1%. This is a very conservative (low) estimate, composed of two factors: the tractability of research and the tractability of adopting the new technology. Concerning the tractability of research, I assume a 10% probability that extra clean protein R&D-funding is not superfluous, i.e. that accelerated R&D is not ineffective. Concerning the tractability of the new technology adoption, I assume a 10% probability that clean protein will eliminate livestock farming (i.e. that consumers switch from animal-based meat to clean, cell-based meat). The latter means that with 90% likelihood, consumers still prefer animal-based meat above cell-based meat, or that cell-based meat is expected to reduce animal farming with 10%. This is a much lower estimate than most predictions.

Accelerating R&D may be ineffective, because the extra research can be too soon or too late. Funding clean protein research thousand years from now will be too late, because by that time all research will have already been done, or everyone will already eat vegan. But starting clean protein research thousand years ago would have been too soon, because in those days people did not have research technologies such as computers to make progress in clean protein development. Similarly future new research technologies make current research obsolete. Examples of important new research technologies are powerful artificial intelligence and gene editing. Consider how CRISPR-Cas9 gene editing drastically accelerated genetic engineering. Years of genetic engineering research before the discovery of this CRISPR gene editing technology can be done much faster now with CRISPR. Similarly, years of work on the protein folding problem can now be done quickly with artificial intelligence. Suppose with current research technologies, developing a new clean technology takes twenty years, and current extra funding can speed up the development of the clean technology with one year. Hence, we have the clean technology in nineteen years. However, suppose after ten years, a new research technology is invented, that accelerates the research, such that ten years of research can now be done within one year. That means with this new research technology, we will have the clean technology in eleven years. The extra funding to shorten the development from twenty to nineteen years becomes superfluous, because we have the clean technology in eleven years anyway.

Neglectedness of the solution: extra funding of clean protein R&D accelerates the development of clean meat such that it enters the market (at more competitive price and quality than animal-based meat) by 0,32 seconds per dollar. This assumes in the business-as-usual scenario, clean protein has a constant annual global R&D-funding in the order of 100 million dollar per year (e.g. cell-based meat had investments around  70 million dollar per year in 2019). The lower this amount of annual funding, the more neglected is the research and the more effective is an extra dollar of funding. For example, if current annual funding was only one dollar, adding an extra dollar of funding doubles the R&D efforts and accelerates the research with one year (because the research funded with one dollar next year could be done this year).

The cost-effectiveness of clean protein development is the product of scale, solvability and neglectedness: 320 ton CO2e avoided per second x 1% x 0,32 seconds per dollar = 1 ton CO2e per dollar. This is a conservative, lower-bound estimate.

Second estimate: contribution to clean meat development

The above estimate is based on the annual greenhouse gas emissions of animal farming, expressed in tons of CO2-equivalents. However, these livestock-related greenhouse gases contain both long-lived gases (CO2 and N2O) and short-lived gases (methane) and it is very tricky to add them up in one measure scale (it is like adding up a stock measure with a flow measure). Hence, the notion of CO2-equivalents can be criticized. The second estimate considers long-lived and short-lived livestock related greenhouse gases separately.

When considering animal farming, the long-lived gases, especially CO2, relate to the carbon opportunity cost: the lost potential of CO2 sequestration from forest restoration of agricultural land. Animal farming requires a lot of agricultural land, and this land is no longer available for carbon sequestration by reforestation. Switching to clean protein (e.g. cell-based meat, vegan food) decreases agricultural land use, because clean protein is much more land efficient than animal protein. The available land can be reforested.

Scale of the problem: the carbon opportunity cost of animal farming is 750 billion ton CO2. This is the amount of CO2 that can be absorbed by the trees and soils of abandoned farmland, through spontaneous reforestation. This estimate also corresponds with another study, which calculates a carbon opportunity cost of 5 ton CO2 per year per person who eats animal protein (e.g. an average omnivorous diet) instead of clean protein (e.g. an average vegan diet). Multiplying this personal carbon opportunity cost by 7,5 billion people and a 20 year carbon sequestration of forests, gives the estimated 750 billion ton CO2. The carbon opportunity cost of animal farming is huge: it is more than half of the total amount of CO2 that was added to the atmosphere and oceans from the burning of fossil fuels since the industrial revolution.

Solvability of the problem: 10-11 contribution per dollar. Assume 10 billion dollar R&D-funding is required to have clean protein at competitive market prices (for example 100 years at current yearly funding rate of 100 million dollar per year, which is likely an overestimation). The bigger this required funding, the more difficult (less tractable or solvable) is clean protein research. Once it enters the market, assume a 10% probability that clean meat will eliminate animal farming (that consumers switch from animal-based meat to clean meat). This 10%, divided by 10 billion dollars, gives the total solvability estimate.

Neglectedness of the solution: assume a 10% probability that extra research funding will not be crowded out. When considering a personal contribution to the development of a new technology, there is a risk of funding crowding-out: other people will likely decrease their funding. Suppose if you invest an extra dollar, someone else disinvests a dollar. That means your extra investment is completely crowded-out by someone else’s disinvestment. In this case, your personal contribution increases, but the total amount of funding and hence the likelihood of the development of the new technology stays the same. From a consequentialist perspective, your extra contribution is futile. In reality, extra funding will not always completely crowd out funding by others. This crowding-out effect relates to neglectedness: the more neglected the research, the less crowding-out will occur. It also relates to the additionality: the less crowding-out, the more additive R&D-funding becomes, i.e. the more R&D really increases with an extra unit of funding. Here, I assume a very high crowding-out level of 90%. This gives a conservative, low estimate of the cost-effectiveness.

The product of scale, solvability and neglectedness gives a lower-bound cost-effectiveness of 0,75 ton CO2 per dollar.

Next to the long-lived greenhouse gases and the carbon opportunity cost of animal farming, we have to consider the short-lived greenhouse gases, and in particular the methane emissions from farm animals. Clean protein (especially cell-based beef and animal-free dairy) has a huge methane mitigation potential.

Scale of the problem: future livestock methane emissions, when kept constant at the current level, contribute to the same global temperature increase as a one-off emission of 200 billion ton CO2. This estimate equals the global temperature increase due to global methane emissions at constant current level (0,3 Kelvin) times the fraction of global methane emissions coming from livestock (30%) divided by the global temperature increase per ton CO2 emitted (0,5 picokelvin).

Solvability of the problem: 10-11 contribution per dollar, as above.

Neglectedness of the solution: 10% probability of extra research funding not being crowded out, as above.

The product of scale, solvability and neglectedness gives a lower-bound cost-effectiveness of 0,2 ton CO2e per dollar.

Adding up the cost-effectiveness of carbon sequestration and methane mitigation due to clean protein, the total cost-effectiveness is around 1 ton CO2e per dollar, similar to the acceleration estimate above.

Clean energy

We can make similar back-of-the-envelope cost-effectiveness estimates for clean energy R&D.

First estimate: acceleration of clean energy development.

Scale of the problem: 34 billion ton CO2 per year annual energy related CO2 emissions from burning fossil fuels.

Solvability of the problem: 10% probability of effectiveness. This corresponds with a high estimate of 90% that extra clean energy R&D is superfluous in the sense that it is either too soon (e.g. it will be overtaken anyway by new research technologies such as artificial intelligence) or too late (e.g. fossil fuel reserves will all be burned before new clean energy technologies enter the market).

Neglectedness of the solution: 22 billion dollar per year annual global funding for clean energy R&D.

The product of scale, solvability and neglectedness gives a lower-bound cost-effectiveness of 0,15 ton CO2 per dollar.

Second estimate: contribution to clean energy development

The second estimate is based on the amount of unused fossil fuel reserves when those fossil fuels are replaced by clean energy.

Scale of the problem: the total CO2 emissions from burning all reported fossil fuel reserves would be 1,6 trillion ton CO2.

Solvability of the problem: 5.10-13 contribution per dollar. This corresponds with a 50% fraction of reported fossil fuel reserves not used due to clean energy replacement, divided by 100 billion global funding required for replacement of fossil fuels by clean energy. The latter equals 22 billion dollar per year annual global funding of clean energy R&D times 50 years of required research. (As clean energy has more R&D-funding than clean protein, its expected development time is shorter. But as development of clean energy to replace all fossil fuels is likely more difficult than developing clean protein, its development time is not too much shorter.)

Neglectedness of the solution: 10% probability of extra research funding not being crowded out.

The product of scale, solvability and neglectedness gives a lower-bound cost-effectiveness of 0,08 ton CO2 per dollar.

Both acceleration and contribution estimates are around 0,1 ton CO2e per dollar funding of clean energy R&D, an estimate similar to the Founders Pledge.

Geplaatst in Artikels, English texts | Tags: , , , , , | Een reactie plaatsen

From Shiva to Dyson: a paradigm shift from radical ecology with soil-based low-tech food to rational ecology with air-based high-tech food

In the past decade, I made a paradigm shift in thinking about ecology and sustainable food production. I shifted from radical ecology to rational ecology. These two paradigms are represented by two women of color: Vandana Shiva and Lisa Dyson. When it comes to food production, this paradigm shift is a move upward: from Shiva’s low-tech, soil-based food to Dyson’s high-tech, air-based food.

Vandana Shiva and Lisa Dyson have three things in common. First, they are both women of color (not like me: I’m a white man). Second, they have a PhD in theoretical physics (just like me). Third, they are environmental activists focusing on sustainable food and agriculture (pretty much like me).

I considered Vandana Shiva as one of my environmental heroines, as I was fond of her ecofeminism. However, in later years, after changing my mind about GMOs, I became much more critical towards her views. I think Vandana Shiva has harmful beliefs.

Vandana Shiva is against GMOs, including golden rice and Bt-eggplant. However, these GMO crops are very beneficial in terms of improving health, economic welfare and sustainability. According to one estimate, the absence of Golden Rice in India causes a loss of more than 100.000 healthy life years every year. The use of Bt-eggplant in Bangladesh resulted in an almost 40% reduction in pesticide use, a more than 40% increase in yields and a $400 increase in yearly farmer profits per hectare. The health benefits due to the decreased pesticide use save a few million dollars per year.

Furthermore, Vandana Shiva made misleading claims about terminator genes and untrue statements about GMOs causing increased Indian farmer suicides. There is no evidence that GMOs are the cause of farmer suicides. The number of suicides has even decreased slightly after the introduction of Bt-cotton GMOs in India. There are indications that GMOs in India contribute to rural development and poverty reduction (also among the poorest farmers and women).

As a result of her GMO-opposition, which goes against the scientific consensus of GMO-safety, Shiva was against international food donations to Zambia (2001) and Orissa-India (1999) during mass-famine events. The opposition to such food aid can be considered as believing that unfounded GMO-risks are worse than people dying from starvation. Shiva’s pseudoscientific beliefs about GMOs can be very harmful.

Finally, Shiva’s ecofeminist views reflect a kind of essentialist thinking that I disagree with. For example, she states that current scientific-technological knowledge is too patriarchal or masculine. But science and technology are based on the laws of nature, and these laws are gender neutral. There is no such thing as male science. Being against science and technology because they are discovered and invented by men, is sexist.  

Considering the above, Vandana Shiva fell from grace. Interestingly, someone else became my new woman of color theoretical physicist environmental activist heroine: Lisa Dyson. When doing research on how to minimize harm, I learned about the importance of land-free food. Shiva’s solution to our food production problems, consists in buying local organic food. But the health and environmental sustainability of local organic food is very much in dispute. Most importantly, organic food is soil-based, requiring a lot of land. Taking the welfare and harms of wild animals also into consideration, land occupation generates many problems.

Lisa Dyson, on the other hand, is doing research on gas-based or air-based food production. Hydrogenotrophic bacteria can be genetically modified to produce all kinds of protein, oils, carbohydrates and other essential nutrients, made from air by using gases such as hydrogen and CO2 as inputs. Hence, this potential technology is carbon negative (having net negative greenhouse gas emissions) and requires almost no land, water, pesticides and soil fertilizers. It can even be applied in space stations. This space-age technology offers clear environmental benefits. Dyson founded the company Air Protein to develop air-based meat.

Shiva prefers local, low-tech food production, which includes soil-based organic food and permaculture, whereas Dyson looks for extremely resilient high-tech food production, which includes air-based and fermentation-enabled food. That food production is extremely resilient, because it can even help feeding the world population in situations of extreme climate catastrophes. That will not be feasible with Shiva’s soil-based, small-scale, organic, agro-ecological permaculture. The fact that Dyson is a woman also offers a nice counterexample to Shiva’s belief that high-tech science solutions are too masculine.

Shiva’s and Dyson’s different attitudes towards food production technologies reflect two different paradigms. Shiva is a representative of the radical ecology paradigm that includes my earlier position as an environmental activist: deep ecology, ecofeminism, low-tech, low consumption. Coincidentally, the word ‘radical’ comes from the Latin word ‘radix’, which means ‘root’. As roots are low and growing in the soil, Shiva’s low-tech soil-based food represents radical ecology. But after learning about effective altruism and rational (scientific) skepticism with its focus on rationality and critical thinking, I turned towards a new paradigm, of which Dyson is a representative. I could call this paradigm rational ecology, to contrast it with radical ecology. This rational ecology paradigm highlights the importance of high-tech solutions such as air-based food, and is common in circles of effective environmentalism and ecomodernism.

Lowering consumption (especially of luxury products), which is part of the radical ecology paradigm, remains important, but it is not sufficient. Improving production, which is part of the ecomodernist paradigm, is necessary as well. Using our limited resources, time and money to do scientific research to improve production is likely to be more effective than using those resources to convince people to lower their consumption. That is why the environmental movement has to shift upwards, from Shiva’s low-tech radical ecology paradigm with soil-based food, to Dyson’s high-tech rational ecology paradigm with air-based food.

Geplaatst in English texts | Tags: , , , , , , | Een reactie plaatsen