The situation is more important than a person’s disposition. This message permeates through Philip Zimbardo’s The Lucifer Effect: Understanding How Good People Turn Evil, and while I disagree with some of the implications that he draws from this message, Zimbardo’s case is compelling.

Zimbardo builds the book on the Stanford Prison Experiment, one of the most cited psychological experiments. Zimbardo and his colleagues selected a group of “psychologically normal” young men and randomly assigned them roles as guards and prisoners in a role-play that they would conduct in the basement at Stanford University. Almost as soon as the experiment started, the guards started to abuse the prisoners and create more intricate methods of psychological torture. Ultimately, Zimbardo terminated the two-week experiment on day six due to fears for the mental health of the participants. The experiment now stands as the prime example of how good people can go bad due to the situation they are in.

The first half of The Lucifer Effect gives a blow-by-blow account of the experiment, with a chapter allocated to each day from the Sunday through to the experiment’s termination on the Friday morning. The detail of the experiment is enthralling, and, for me, there were some interesting new factoids.

The first was that the abuse, while clearly terrifying to the prisoners, never breached the guidelines set down by Zimbardo and his colleagues. There was no physical violence and the participants in the experiment acted as though the prohibition was a clear constraint. It would be interesting to conduct the experiment with less upstanding citizens to have seen if that constraint would have held.

The second was how Zimbardo and the others who ran the experiment played roles within the experiment, and how they were also transformed by these roles. Zimbardo was the prison superintendent and the others acted as wardens. It did not take long for them to fall into these roles, nudging guards to be tougher, implying to prisoners that they had a limited right to leave the role-play and manipulating outsiders to create the impression that the prisoners were well treated. Others who became involved in the experiment such as a prison chaplain, pro bono lawyer and the prisoners’ parents all quickly fell under the spell of the experiment and acted as though the experiment was real. The power of the situation extended beyond the experimental subjects.

The Stanford Prison Experiment, and Zimbardo and others’ subsequent experimental work, makes clear that situational factors are important. But in placing the emphasis on the situational assessment, Zimbardo is somewhat blind to the role that people’s disposition plays in forming the situation faced by others. For example, when a couple of guards were clearly reluctant to push the prisoners, it was a nudge from the experimenters (in their role as wardens and superintendent) that caused them to be more aggressive. The more passive guards were also motivated by the actions of the most aggressive on their shift.

Another result of people’s disposition forming part of the situational environment for others is the potential for feedback loops. People with bad dispositions create a situation in which good people might do bad things, creating a permanently negative situation for all people. How can this feedback loop be broken?

The second half of the book seeks to place the Stanford Prison Experiment in a broader context. A chapter on power, conformity and obedience (including a description of Stanley Milgram’s almost as famous electrocution experiment) examines how people are unwilling to oppose power or breach norms. The next chapter on dehumanisation and deindividuation discusses how depersonalising people can lead to people treating each other as less than human.

The book then moves to an analysis of the events in Abu Ghraib prison in Iraq. Zimbardo acted as an expert witness for one of the Abu Ghraib accused, Chip Frederick. Frederick was one of the seven low ranking people who faced the full brunt of prosecution for the abuse. As the military framed it, they were the “bad apples” in the barrel.

Zimbardo provides an extended discussion of the environment in which the accused existed and the strong effects of this situation (which make the Stanford prison seem a bit soft). He then details the abuses. Zimbardo’s argument, based on the events in the Stanford Prison Experiment, was that the situation drove the conduct, not Chip Frederick’s disposition. And Zimbardo’s argument is strong as he paints a compelling picture of how the situation faced by the people working in the Abu Ghraib would have affected them. If the prison had been under better control with clear, strong instruction from above, the abuses are unlikely to have happened.

This leads to where Zimbardo’s and my views on the right punishment for his client part company. Zimbardo argues that as the situation was the larger driver of Chip Frederick’s conduct, the military tribunal should not hold him to be as culpable and his punishment should reflect this.

Zimbardo’s position reflects the first of two ways to treat people who, while not of a “bad disposition”, do bad things due to situational forces. The other way is to recognise that due to the power of situational forces, we need to strengthen the incentives for people to fight against them – and that comes in the form of harsher penalties. The framework for punishing someone forms part of the situational forces. This is a similar argument to that crafted by Steven Pinker in The Blank Slate, who notes that it is equally credible to argue for harsher prison sentences if people are genetically predisposed to crime as it is to argue that they should be held less culpable.

The other question that arises through this, and in Zimbardo’s discussion of heroism later in the book, is why some people fall under the spell of the situational forces and others don’t. Are they distinguished by disposition? Would harsher penalties mean that even more people would refrain from abusing the prisoners? These questions are not answered.

Zimbardo then presents the case for those higher up to the command ladder to be held culpable for the Abu Ghraib abuses. It is obvious that many higher ranking officers, civilian contractors and the CIA either explicitly or implicitly ordered the abuse, making the failure to fully prosecute many of them disgraceful. But Zimbardo takes his case to the top – to Dick Cheney and George W Bush. I am sympathetic with his argument. Their implicit approval of torture through their treatment of the Geneva Conventions and practice of renditioning, among other things, were significant factors in the situational forces faced by those lower down the chain. An interesting question is whether it was situational forces that drove Cheney and Bush’s conduct? It’s not easy to fit in with your conservative base and Republican buddies if you are soft on the enemy. Do we simply need the strong spectre of punishment or consequences at all levels?

Zimbardo closes the book with a plea for heroism. He suggests a range of ways in which people can prepare themselves to act heroically, such as humanising others and questioning authority at the right times.

When it comes to why people do act heroically, strangely (to me), Zimbardo reaches a conclusion that, like those who commit evil, heroes are normal people. This may be true, but the question then becomes why they resist the situational forces and avoid committing evil acts, and then take action at personal risk to themselves? Perhaps this should be the topic of Zimbardo’s next book.

From 100 years ago, Scientific American calls for more research into human evolution:

Mendelian principles have no doubt long been followed by professional animal breeders in an empirical way, but only within recent years have enough data been accumulated to show that they apply with equal force to human beings. We know enough about the laws of heredity, we have enough statistics from insane asylums and prisons, we have enough genealogies, to show that, although we may not be able directly to improve the human race as we improve the breed of guinea pigs, rabbits or cows, because of the rebellious spirit of mankind, yet the time has come when the lawmaker should join hands with the scientist, and at least check the propagation of the unfit. Prizes have been offered to crack trotters for beating their own record, $10,000 for a fifth of a second, all for the purpose of evolving a precious two-minute horse. Yet we hear of no prizes which are offered for that much worthier object, the physically and intellectually perfect man.

The Journal of Economic Perspectives has an excellent article by Beauchamp and colleagues titled Molecular Genetics and Economics (ungated pdf here). It is a nice contrast to another article in the same issue, Charles Manski’s bashing of the heritability straw man.

The authors argue that “genoeconomics”, the use of molecular genetics in economics, has the potential to supplement traditional behavioural genetic studies and build an understanding of the biology underlying economically relevant traits. They note that behavioural genetics, particularly research into heritability, has produced compelling evidence of the link between economically important characteristics and DNA. Molecular genetics is an “exciting tool” that can now be turned to this area.

However, potential pitfalls mar the way forward. These pitfalls are beautifully illustrated by a study that the authors undertook in which they sampled over half a million single-nucleotide polymorphisms (SNPs) from each of 7,500 people. An SNP is a DNA sequence variation where a single nucleotide differs between people. They then searched for SNPs associated with educational attainment. They found a large number of associations, many passing significance tests of 10^-6. Passing this test suggests that there is a one in a million chance that the association is by chance (of course, there were 500,000 chances). If they took this result to the right journal, they might have had their study published and got some headlines about “the education gene”.

However, the authors took the 20 most significant associations from the first sample and checked them against the SNPs from another sample of 9,500 people. In the second sample, none of these 20 SNPs significantly affected educational attainment, even using a weak five per cent significance test. This showed that the results from the first sample were spurious.

The authors noted some important lessons from this. The first is that given the low sample size of many studies, the probability of a true association being discovered among the noise is minute. The studies are underpowered – power being the probability that an association between an SNP and the trait of interest will be found when there is a relationship. The fact that almost all SNPs reported in the literature can explain very little of the variation in most traits exacerbates this problem as the studies are trying to detect small effects. For example, no marker has been found to predict more than one per cent of the variation in height between people. As a result, very large samples are required to find true associations and sort them from the noise.

For example, with a five per cent threshold test for significance and an SNP that explains 0.1 per cent of variation in a trait, you need a sample of 4,000 subjects before the association has a 50 per cent chance of being found. Yet, in a 500,000 SNP panel there are likely to be thousands of false positives that meet the five per cent significance level.

If the significance test is increased to by a factor of one million to 10^-8, which is appropriate given the huge number of potential associations being tested for in a 500,000 SNP panel, the need for a large sample size increases. For an SNP that explains 0.1 per cent of variation in a trait, the study will need a sample of around 25,000 to have a 50 per cent chance of detecting the relationship. If the SNP explains 0.01 per cent of the variation, a sample size of 200,000 results in only a 20 per cent chance of finding the relationship. However, the more stringent significance test reduces the number of false positives – it is just that the reduced number of false positives comes at the cost of power, which must be compensated for by increased sample size. At this time, there is little useful genetic data available in samples of this size.

Beyond the power issue, the authors identified publication bias as a problem. Papers which find interesting relationships are more likely to be published, which creates incentives for data mining and the write-up of results that are interesting but not robust. It is not easy to find a publisher for a paper that shows no relationship. This paper by Beauchamp and colleagues is the exception that proves the rule. To get their negative finding published they turned it into an analysis of the broader use of genetics in economics.

They do note, however, that data mining in genoeconomics is not in itself bad. It is when it is not accompanied by robust methodologies and stringent review processes that the problems arise.

Beauchamp and colleagues close their paper by noting some benefits of the genoeconomics enterprise. They endorse the use of genetic information in policy, even where the causal mechanisms are not known. They give the example of targeting children with markers for dyslexia with alternative teaching methods. This is a good long-term goal, but we will want to have SNPs explaining more variation in traits before this will be useful. For now, family history or information about siblings and twins is more useful information. How much of that  information is being used now?

More interestingly, they suggest that this genetic data could be used as a control variable in other economics studies. If it is known that, say, income varies with certain SNPs, those SNPs might be used as a control in a study of how certain environmental factors affect income.

Their last suggestion is that the information obtained from genoeconomics could be used to understand variation in policy response across people. Compared to the standard economic assumption that everyone is the same, this might be the most radical effect of the genoeconomics enterprise.

The  heritability straw man has copped another bashing, this time in the Journal of Economic Perspectives. In it, Charles Manski picks up an old line of argument by Goldberger from 1979 and argues that heritability research is uninformative for the analysis of policy.

Manski starts by arguing that heritability estimates are based on the assumption that there is no gene-environment correlation. Manski writes:

The assumption that g and e are uncorrelated is at odds with the reasonable conjecture that persons who inherit relatively strong genetic endowments tend to grow up in families with more favorable environments for child development.

Any review of discussions of heritability, whether in the peer-reviewed literature or the blogosphere, will show that his claim is generally false. The proviso that the heritability estimate is only relevant to the existing environment is usually threaded through any discussion of heritability.

It is true that gene-environment covariance can affect estimates of heritability. Yet this does not mean that existing estimates have no value, nor that there are not methods that seek to account for the covariance. For example, the use of comparisons between misdiagnosed identical twins and actual identical twins allows for bounded estimates of heritability to be developed (pdf).

Manski’s broader claim, adopted directly from Goldberger, is that even if you knew the heritability of a trait, it tells you nothing about social policy. Manski uses Goldberger’s eyeglasses example as an illustration:

Consider Goldberger’s use of distribution of eyeglasses as the intervention. For simplicity, suppose that nearsightedness derives entirely from the presence of a particular allele of a specific gene. Suppose that this gene is observable, taking the value g = 0 if a person has the allele for nearsightedness and g = 1 if he has the one that yields normal sight.

Let the outcome of interest be effective quality of sight, where “effective” means sight when augmented by eyeglasses, should they be available. A person has effective normal sight either if he has the allele for normal sight or if eyeglasses are available. A person is effectively nearsighted if that person has the allele for nearsightedness and eyeglasses are unavailable.

Now suppose that the entire population lacks eyeglasses. Then the heritability of effective quality of sight is one. What does this imply about the usefulness of distributing eyeglasses as a treatment for nearsightedness? Nothing, of course. The policy question of interest concerns effective quality of sight in a conjectured environment where eyeglasses are available. However, the available data only reveal what happens when eyeglasses are unavailable.

Manski and Goldberger may be correct that the heritability estimate is uninformative as to the efficacy of distributing eyeglasses, but it is useful in assessing other policy responses to the problem and the trade-offs between them. Is it possible to prevent the eyesight loss in the first place? Is that policy cheaper and more effective than eyeglasses? If the heritability estimate was zero, you would look to the environmental causes and ask whether the eyesight problem is more appropriately dealt with by addressing the cause rather than by distribution of eyeglasses.

There is no shortage of other areas where heritability estimates might add value. Heritability estimates can inform whether it is an effective use of resources to make sure that everyone has a university degree or is over six-foot tall. Is everyone putty in the hands of the policy maker, or are there some constraints? On a personal level, Bryan Caplan’s use of heritability in Selfish Reasons to Have More Kids is a useful input to his parenting strategy.

For me, the most salient example of the usefulness of heritability research comes from examination of the heritability of IQ among children. Among high socioeconomic status families, the heritability tends to be high. Among low socioeconomic status families, it is significantly lower. This suggests that there is significant room to improve the outcomes of the children at the bottom of the socioeconomic ladder in the early years of their life (assuming those changes have effects that persist into adulthood). Increasing heritability of IQ might be evidence that environmental disadvantages are being ameliorated and opportunity equalised.

The latter part of Manski’s paper turns to the use of genes as covariates in statistical regressions. Regression identifies statistical association and not causation, which appears to be an important point in attracting Manski to this use. Noting the wealth of data being created and the possibility of observing changes in the effect of genes as the environment changes, Manski considers that these regression exercises may assist in examining how genes and environment interact.

I don’t disagree with Manski, but at present, genome association studies have plenty of issues. First, there is the missing heritability problem. To date, the magnitude of the identified effect of genes on most traits accounts for a miniscule proportion of the trait’s heritability. This points to the important role played by heritability research to provide direction to research on genes as covariates. It also indicates that until these genes are found, heritability estimates will be more informative for social policy.

A second issue is that with 30,000 odd genes and the ability to test so many of them for correlation with traits, many are found to have a statistically significant relationship through chance. As blogged about recently by Razib, this is shown when people seek to replicate earlier results – such as when it was found that most reported genetic associations with general intelligence are probably false positives (pdf).

Finally, genome based research is now feeding back into estimates of heritability. From a recent paper:

We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits.

Despite all the critiques about methodology, most new studies confirm that the old “methodologically poor” heritability estimates were in the right ballpark. The problem is not that the estimates are not useful, but rather that they are not used.

In Benoit Debreuil’s Human Evolution and the Origins of Hierarchies, which I reviewed last week, the opening chapter contains the interesting argument that egalitarian hunter-gatherer societies were not built on a wish for equality. Dubreuil writes:

Egalitarian social arrangements must build on what Boehm (1999: 66) called an “egalitarian ethos,” which is culturally constructed and transmitted and does not straightforwardly result from our passion for equality. This does not mean that equality does not matter per se. People arguably have a certain preference for equality, which, at the level of cultural evolution, might create a bias in favor of more egalitarian social organizations, albeit indirectly by making cooperation among equals more efficient. However, because our egalitarian motives are limited in intensity, action often comes under the influence of more powerful passions: lust, greed, fear, or hatred. Consequently, the emergence of egalitarian social outcomes depends on other factors that are not directly connected with equality, such as the various motivational and cognitive mechanisms related to social norm and sanction.

Dubreuil’s conclusion comes from a review of the literature in experimental economics (which forms the basis of the first chapter of the book), where he examines what motivations drive people to punish behaviour by other players. Many of these experiments suggest that norms about fairness are a stronger driver of punishment that equality. For example, punishment for unequal distribution of resources was much increased where the distributor stated their desire for an unequal distribution.

Dubreuil does not suggest that inequality aversion is completely irrelevant, and points to experiments where players are willing to cut the income of top earners and augment that of bottom earners. However, arguments can be crafted to suggest even these actions are driven by other motives. The punishment of top earners might be driven by envy, while augmentation of bottom earners might be based on the desire to encourage cooperation and build alliances.

Despite arguing that inequality aversion is not the main driver of our actions, Dubreuil does consider that inequality can have harmful effects. It can reduce cooperation and trust. It can change people’s sensitivity to sanction by others. And in many ways, Dubreuil’s arguments match my own intuitions about inequality. Even if concern about inequality is not the primary driver of our actions (such as the Occupy movement), it affects important elements of human interaction such as trust and cooperation.

I missed it when it first went up, but over at The Crux, Discover’s new group blog, Razib Khan has pointed to a couple of interesting papers on the heritability of fertility. As natural selection acts strongly on fertility and the traits that affect it, you might expect that the heritability of fertility would be low as variation is eliminated. But change the environment, and heritability can increase drastically. Razib writes:

Quebec is also an ideal “natural experiment.” It began as a frontier society with a small founding population on the order of thousands, but now has a population of over 8 million. …

One study http://www.pnas.org/content/108/41/17040.abstract focused on a 140-year period on an island in the Gulf of St. Lawrence. Over five generations, the island’s population increased by a factor of 10 through natural increase, while the average age of first reproduction declined from 26 to 22. Just as menarche and menopause are heritable (variation in genes explain much of the variation in the outcome of the trait), so too age at first reproduction seems heritable. …

Another group focused on the differences between core and frontier populations in the Saguenay–Lac-Saint-Jean region of Quebec. It turns out that the majority of the modern population descends from those in the frontier zone, not the core. Women on the frontier were ~20% more fertile, and fertility as a trait was heritable on the frontier but not in the core. (On the frontier, there was a connection between relatives and how many offspring they would have, in direct proportion to the amount of genes they shared. E.g., two sisters tended to have similar numbers of offspring, while there was no correlation between strangers.) …

Changes in the environment can dramatically change the power of selection on traits. While a trait may not be heritable in one environment, it may be in another. Considering the changes in environment to which humans have been exposed over the last couple of hundred years, there must be many traits now experiencing significant selective pressure. I recommend that you read the whole post by Razib.

Benoit Dubreuil’s Human Evolution and the Origins of Hierarchies seeks to explain two historical transitions in social hierarchies in human (and pre-human) history. The first is the transition from dominance hierarchies, such as those lived in by our common ancestor with the chimpanzee, to egalitarian social relationships. The second is from those egalitarian relationships to the large-scale, state-based hierarchies we see today.

The assumption as to the existence of the first transition is largely taken from the work of Christopher Boehm (Hierarchy in the Forest: The Evolution of Egalitarian Behavior). I have not read Boehm’s work yet, so I will leave this assumption alone apart from noting that when talking about the end of dominance hierarchies, this is not to suggest that there are no hierarchies. Rather, they are based on cooperation, coalitions and other socially based arrangements, as opposed to raw strength and aggression. As noted in the work of Napoleon Chagnon, egalitarian societies can have very unequal distributions in areas such as access to mates.

Dubreuil’s argument is that each transition was driven by the evolution of the human mind. Dominance hierarchies were destabilised as early humans gained the ability to cooperate and coordinate to bring down dominant individuals. The re-emergence of hierarchies, although different in form, was driven by the cognitive changes associated with the behavioural modernisation of humans. The institutions necessary for efficient enforcement of norms in a large-scale society require cognitive abilities that humans did not have at the time of their earlier egalitarian societies.

On one level, the argument behind the second transition must be right. The common ancestors of humans and chimpanzees, and many earlier humans, simply did not have the cognitive firepower to create the institutions underlying a modern state. The more particular claim of Dubreuil that the emergence of state-based hierarchies are directly linked to these cognitive changes faces some challenges due to the gap in time between the emergence of behaviourally modern humans (as identified by evidence such as from the fossil record) and the establishment of agriculture and its associated large-scale societies. One potential reconciliation is that humans continued to evolve beyond the time when he (and most anthropologists) would consider them to be behaviourally modern. As I would claim, humans have continued to evolve through to today.

Dubreuil’s thesis does create an interesting challenge for those that claim that human nature supports egalitarian social structures. If the departure from those structures was driven by the evolution of the mind, has human nature changed such that these egalitarian relationships are no longer stable or feasible? It may be possible to argue that an egalitarian society is the preferred social arrangement, but the argument must be more sophisticated than simply claiming that egalitarian structures were the predominant evolutionary environment faced by early humans.

Dubreuil’s book started life as a thesis, and to some extent, it still feels like one. The first two chapters, in which Dubreuil establishes the foundations of norm enforcement in humans, form an excellent literature review on experiments about cooperation, fairness, punishment and envy. It is worth grabbing the book for these alone. In the other three chapters, Dubreuil crafts the argument. Dubreuil convinced me of the general idea, but with issues such as the timing of the transition to modern behavior, there is some way to go before the finer points of his argument will be convincing.

In an evolutionary sense, resource inequality affects survival and access to mates. While the current “Occupy” debates about growing inequality and the power of the 1 per cent are very much focused on the resource issue, the underlying reason people have an innate aversion to the unequal distribution of power (or, more particularly, their being at the wrong end of that distribution) comes back to these evolutionary factors. But to what extent are survival and reproduction actually affected by the inequality being protested?

In the case of survival in a developed country, the “we are the 99 per cent” is a useful benchmark, but in this case it is the 1 per cent at the bottom compared to the 99 per cent above. Only those at the very bottom likely to have their survival through their reproductive years threatened. Survival is not at the core of the debate.

When we consider the issue from a reproductive perspective, the issue becomes more interesting. I would expect that the increased share of income to the top 1 per cent increases their ability to attract mates. This might be through the ability to support more mistresses and to engage in serial monogamy, with the increased wealth allowing second and third wives to be obtained more easily despite advancing years. There would also be some effect on mate quality.

However, I am not sure that the next 10 per cent of the income or wealth distribution are significantly harmed by this, except for the possibility of a small decline in quality. They still have the means to attract mates – and will successfully do so. It is not until we get to men in the bottom, say, 20 or 30 per cent of the income or wealth distributions that we find a group that is suffering.

Inequality in income growth must be one factor in this, but the increase in the status and income of women over the last 50 years (a reduction in inequality), together with growth in the welfare state, has also reduced the benefit for many women of pairing with low-income men. However, I am not sure that low-income men form a significant part of the Occupy movement (or even support it). Maybe they should be the ones protesting in the street?

Or maybe they already are protesting. High levels of single men without access to mates is linked to crime, gambling, risk taking and other social problems – the protest takes a different form. We might be about to see this happen in China.

As a last note, I recently pulled out an essay by Napoleon Chagnon titled “Is Reproductive Success Equal in Egalitarian Societies?” (from a 1979 volume edited by Chagnon and Irons) in which Chagnon nicely captures this issue across time:

Polygyny is widespread in the tribal world and has probably characterised human mating and reproduction for the greater fraction of our species’ history. Given that natural selection by definition entails the differential reproduction and survival of individuals, this fact of life – this inequality – is of considerable importance. This raises the question of the utility of viewing human status differentials largely, if not exclusively, in terms of material resources and the relationships that individuals in different societies have to such resources. That the relationship between people and control over strategic resources is central to understanding status differences in our own highly industrialised, materialist culture is insufficient reason to project these relationships back in evolutionary time and to suggest that all human status systems derive from struggles over the means and ends of production. Struggles in the Stone Age were more likely over the means and ends of reproduction.

Although not as obvious, today’s struggles are over those same ends and means.

I have finally finished reading Francis Fukuyama’s The Origins of Political Order: From Prehuman Times to the French Revolution, several months after my initial comments.

Of the grand history books I have read this year (the others being Ian Morris’s Why the West Rules for Now and Niall Ferguson’s Civilization: The West and the Rest), I found Fukuyama’s to be the most convincing. The focus on self-interest as a motivating factor of the individual actor, which is in turn underpinned by biological considerations, creates a more plausible story than one which talks of nations as actors. A nation acts subject to the motivations of its parts.

Many of Fukuyama’s explanations might be considered to be just-so stories, and they might be wrong, but they feel as though they have a plausible foundation. In describing historical events with an effective sample size of one, it is hard to do better.

In the closing chapters, Fukuyama states his four biological factors that underlie the origins of political order. These are:

• Human beings never existed in a precocial state.
• Natural human sociability is built around two principles, kin selection and reciprocal altruism.
• Human beings have an innate propensity for creating and following norms and rules.
• Human beings have a natural propensity for violence.

I noted some of Fukuyama’s more interesting statements on the first two factors in a earlier post, but his observations on the last two are also interesting. On the third, Fukuyama states:

Rules can be rationally derived by individuals calculating how to maximize their own self-interest, which requires that they enter into social contracts with other individuals. Human beings are born with a suite of cognitive faculties that allow them to solve prisoner’s-dilemma-type problems of social cooperation. … The ability to make and obey rules is an economizing behavior in the sense that it greatly reduces the transaction costs of social interaction and permits effective collective action. …

The propensity of human beings to endow rules with intrinsic value helps to explain the enormous conservatism of societies. Rules may evolve as useful adaptations to a particular set of environmental conditions, but societies cling to them long after those conditions have changed and the rules have become irrelevant or even dysfunctional.

On the propensity for violence, he writes:

It is important to resist the temptation to reduce human motivation to an economic desire for resources. Violence in human history has often been perpetrated by people seeking not material wealth but recognition. Conflicts are carried on long beyond the point when they make economic sense.

From Charles Manski in the latest Journal of Economic Perspectives (pdf):

Someone reading empirical research relating human genetics to personal outcomes must be careful to distinguish two types of work. An old literature on heritability attempts to decompose cross-sectional variation in observed outcomes into unobservable genetic and environmental components. A new literature measures specifific genes and uses them as observed covariates when predicting outcomes. I will discuss these two types of work in terms of how they may inform social policy. I will argue that research on heritability is fundamentally uninformative for policy analysis, but make a cautious argument that research using genes as covariates is potentially informative.

From the same edition, Beauchamp and colleagues address the following question (pdf):

How, if at all, should economists use and combine molecular genetic and economic data? What challenges arise when analyzing genetically informative data?

I’ll post my thoughts on these articles when I have had a chance to digest.