Even after controlling for observable traits such as IQ, married men earn more. Bryan Caplan suggests there are three economic explanations for this male marriage premium:

1. Ability bias: Qualities that make a man attractive to a woman are also attractive to employers.
2. Human capital: Marriage makes men more productive.
3. Signalling: Marriage signals to employers that a man has desirable traits.

Caplan notes a study that argues that ability bias accounts for less than 10 per cent of the premium. However, Caplan suggests that ability bias accounts for 50 per cent of the premium, while signalling accounts for less than 10 per cent. That leaves human capital to fill the gap.

I would make a different split, as there are two other candidate explanations. Each derives from the evolutionary biology insight that females seek high-quality genes, which they cannot directly observe, and they desire resources for their offspring.

The first explanation is also a signalling explanation, but in this case relates to signalling by men to women. When women are seeking a mate and trying to assess male quality, a man’s income (often displayed in the form of conspicuous consumption) is one of the most obvious signals. Income and conspicuous consumption are not perfect signals of quality, and there is likely to be noise in the signal through sources such as luck and men lying, but it is a useful proxy. Given that income is not a perfect signal of quality, female use of income as a proxy for quality would result in a higher marriage premium than would be expected on the basis of quality alone.

In relation to resources, women care about income in itself as they can direct those resources to raising offspring. Even if the female knows that he is an idiot who received his wealth through inheritance, that inheritance still has some value.

As a result, I would suggest that human capital accounts for a smaller proportion of the marriage premium than Caplan suggests, with mate signalling and resource acquisition filling the gap. I would also put a slightly different bent on the ability bias explanation by noting that women seek in men many qualities that employers favour, not only because are these are desirable attributes in a marriage partner, but because employers favour them and they can increase income.

Having made these claims, I should address the study by Ginther and Zavodny that suggests the ability bias and resource acquisition explanations have little power in explaining the male marriage premium. They use the interesting approach of comparing normal marriages with “shotgun” marriages, where a child was born within seven months of the date of marriage. If the likelihood of shotgun marriage occurring is not related to income or ability due to their unforeseen nature, we would expect no marriage premium to exist in those cases. However, the authors show that shotgun marriages also have an income premium and the premium is not significantly different from that in normal marriages. As a result, the premium must not be due to underlying quality.

The authors’ finding rests on this clever shotgun wedding mechanism, but ultimately it is flawed. One obvious problem noted by the authors is that many shotgun weddings were planned before the conception or would have occurred regardless of the pregnancy. In those cases, the pregnancy is not the primary reason for the marriage or only affected its timing.

The authors also address another problem, which is the issue of whether the occurrence of a shotgun wedding is correlated with the man’s income. First, they note studies that show that a man’s income does not affect the chance of a couple legitimating a birth. They also ask whether men who marry following a premarital conception are more desirable than men who do not, and note that there is no substantive difference in quality or income.

This explanation misses an important element. The decisions to mate with a man in the first place, to take less caution with contraception, and to keep the child are all likely to be contingent on the quality of the male and their income. If women prefer to mate with high quality, high-income men before marriage and to keep their children, shotgun weddings will not provide an independent comparison.

To disentangle these effects, the first place I would look is income of men before and after marriage. My rough understanding of the literature is that they are highly correlated, which is not what you would expect in a pure signalling to the employer model.

Another interesting area we could look at is the quality of the wives. We need to remember that these are two-sided markets. If women are attracted by income, and men buy the best they can afford, we would see a strong correlation between husband income and wife quality. We would expect to see this correlation under the signalling to females, resource acquisition and ability bias explanations. Under the signalling to the employer theory, we would see no link between male income and wife quality, whereas the human capital explanation would be only dependent on productive qualities of the wife, such as conscientiousness. We would also find that the male marriage premium is smaller at the low-quality female end of the scale.

*Caplan has also written a post on the marriage penalty for women.

From Daniel Kahneman’s Thinking, Fast and Slow:

Much is therefore at stake in the debate between the Chicago school and the behavioral economists, who reject the extreme form of the rational-agent model. Freedom is not a contested value; all the participants in the debate are in favor of it. But life is more complex for behavioral economists than for true believers in human rationality. No behavioral economist favors a state that will force its citizens to eat a balanced diet and to watch only television programs that are good for the soul. For behavioral economists, however, freedom has a cost, which is borne by individuals who make bad choices, and by a society that feels obligated to help them. The decision of whether or not to protect individuals against their mistakes therefore presents a dilemma for behavioral economists. The economists of the Chicago school do not face that problem, because rational agents do not make mistakes. For adherents of this school, freedom is free of charge.

Kahneman’s comment could also be applied to the belief that markets will always deliver an optimal outcome. Obviously, there are further layers of debate beyond the basic question of whether people are rational or markets fail. Kahneman is only addressing the first stage of libertarian denial.

From When the Sleeper Wakes (1910):

“What of the yellow peril?” he asked and Asano made him explain. The Chinese spectre had vanished. Chinaman and European were at peace. The twentieth century had discovered with reluctant certainty that the average Chinaman was as civilised, more moral, and far more intelligent than the average European serf, and had repeated on a gigantic scale the fraternisation of Scot and Englishman that happened in the seventeenth century. As Asano put it; “They thought it over. They found we were white men after all.”

To make his point that socioeconomic status is a major driver of educational outcomes, Dan Pink made the following chart. SAT scores are on the vertical axis, and family incomes on the horizontal axis.

Sidestepping questions of what this correlation actually means, is there any plausible scenario that would result in a different relationship?

If we assume, as many are using the chart to assert, that parental income “buys” higher SAT scores, then we will see SAT scores rising with income. The same result would be expected if those at the bottom of the socioeconomic scale receive poor educations.

However, if we assumed the world was a perfect meritocracy and everyone had the same opportunities, we only need mild assumptions about correlations between parent and child talent and personality to get the same result. Those correlations could be through genetic or cultural transmission. If children resemble their parents in any way, and income and SAT scores reflect those underlying dispositions, we will see a positive relationship.

The only way we would see no relationship between parental income and SAT scores would be if there was zero correlation between parent and child traits, and we lived in a perfect meritocracy. I don’t believe that anyone can substantiate either of those claims. The question is one of degree.

So, in some senses, the chart does not tell us anything, and I am surprised that anyone might have expected any other result (not to mention that this has received plenty of attention in and since The Bell Curve.)

In Tim Harford’s discussion in Adapt of the benefits to experimentation , Harford notes that experimentation by individuals is often at great potential cost. As when a species evolves, what appears to be beneficial experimentation at a societal level involves frequent failure to survive by individuals. While Harford suggests that people should consider experimenting in a manner that avoids failure that threatens survival, the reality is that many people take risks with a large downside.

Harford’s discussion reminded me of a section in Daniel Kahneman’s Thinking, Fast and Slow, where Kahneman notes the economic benefits of optimism. Entrepreneurs often engage in “optimistic” behaviour, in that they vastly overestimate the probability of business success. Kahneman writes:

The chances that a small business will survive for five years in the United States are about 35%. But the individuals who open such businesses do not believe that the statistics apply to them. A survey found that American entrepreneurs tend to believe they are in a promising line of business: their average estimate of the chances of success for “any business like yours” was 60% — almost double the true value. The bias was more glaring when people assessed the odds of their own venture. Fully 81% of the entrepreneurs put their personal odds of success at 7 out of 10 or higher, and 33% said their chance of failing was zero. …

The optimistic risk taking of entrepreneurs surely contributes to the economic dynamism of a capitalistic society, even if most risk takers end up disappointed.

They do not consider their conduct to be risk seeking, as the odds they have calculated suggest they believe they are taking a reasonable bet. Kahneman notes that we need optimistic people to run these experiments, as they are responsible for the economic dynamism in a capitalist society.

The penchant for entrepreneurial activity in people suggests that such optimistic and apparently risk-seeking behaviour may have evolutionarily foundations. Take the historical case where members of a clan left for new territories with significant potential for death. Those who departed were usually on the fringe of the group or faced severe resource constraints. As a result, even if a move to a new frontier was likely to end badly, the low benefits to staying and the potential upside of departing make the departure worthwhile. Or consider the evolutionary bonanza for the early settlers to eastern Canada and the north-eastern United States, who often experienced order of magnitude population increases within five generations of arrival. Many moved from being fringe members of societies they left to comprising significant portions of the new populations.

This brings me to a post by Bryan Caplan on the benefits of meekness. Caplan suggests those at the bottom of the social ladder are “dysfunctionally assertive” and would benefit from being meeker. Caplan states that advice to be assertive often comes from those with power who can afford to be assertive. When someone with low-status stands-up, it may be disastrous.

If we ignore whether there is evidence of the lower classes being more assertive, it raises the question of whether assertive behaviour of low-status people is, on average, costly. There may be a low expected monetary pay-off to an assertive act as they lose jobs or other privileges, but what are the costs and benefits in dimensions that matter? Is the person balancing a meek and certain passage into genetic oblivion, versus a risky shot at reproductive success? We should not derive the optimal strategy by a simple cost-benefit analysis in monetary terms. When facing the end of the genetic line, strategies with an expected negative monetary and social pay-off, but high variance, may be the preferred path.

This issue is similar to a paper I posted about concerning risk-seeking behaviour by those without a mate. A utility function measured in terms of mates leads to significantly different behavioural predictions than one in financial terms.

Natural selection operates through heritable variation in traits and differential reproductive success due to those traits. Many combinations of genes and mutations are failures, but the variation in traits creates a natural experiment in which highly evolved solutions to the environment can develop.

In his excellent book Adapt: Why Success Always Starts with Failure, Tim Harford applies this evolutionary concept to business, war, accidents and other human pursuits. How did on-the-ground experimentation lead to a better outcome in Iraq? How does Google or W.L. Gore & Associates develop new ideas? Harford’s argument is that by allowing low-level experimentation, solutions to highly complex problems are more likely to be found than through top-down decree.

Unlike much of the work in areas such as evolutionary economics, which use an evolutionary analogy to describe business activities or other social phenomena, Harford moves beyond the descriptive and asks how these processes can improve policy, reduce accidents and improve business outcomes.

For example, Harford’s encourages more government experimentation. Politicians tend towards large, sweeping plans, which can have unintended consequences and allow little opportunity for alternative approaches to be examined. If governments were more tolerant of failure (the lack of tolerance a function of the electorate as much as politicians), they could allow many options to be tried, with the best and most successful then applied on a broader scale. Harford also questions whether government should offer prizes or alternative incentive mechanisms to encourage private sector solutions where existing incentives such as patents have credibility issues.

One of my favourite sections of the book was Harford’s discussion of accidents. Most of the problems Harford examines in the book are complex and “loosely coupled”, which allows experimentation with failure. But what if the system is tightly coupled, meaning that failures threaten the survival of the entire system? This concept reminded me of work by Robert May, which undermined the belief that increased network complexity led to stability.

The concept of “normal accidents”, taken from a book of that title by Charles Perrow, is compelling. If a system is complex, things will go wrong. Safety measures that increase complexity can increase the potential for problems. As such, the question changes from “how do we stop accidents” to how do we mitigate their damage when they inevitably occur? This takes us to the concept of decoupling. When applied to the financial system, can financial institutions be decoupled from the broader system so that we can let them fail?

Climate change is also addressed, as Harford takes on the soft target of the well-meaning environmentalist. Decisions as to which options are most “environmentally friendly” are inevitably problematic as it is impossible for someone to understand the full network of cause and effect underlying their decision. In deciding which type of coffee is most environmentally friendly, how do you consider the inputs to the coffee, the cup, the building in which it you purchased it and the manner in which the barista got to work? As Harford points out, it is only through the decentralised price system that this information can be reliably provided to the consumer, while also providing incentives for the less well-meaning to change their behaviour.

Normally I am indifferent to criticisms of the well-meaning environmentalist, as the people who mock Harford’s environmentalist are often those who oppose measures to introduce a carbon price. Thankfully, Harford takes the relatively rare option of pointing out the flaws of a piecemeal approach in a complex world but providing an option to address the problem.

Harford also demonstrated his strong understanding of evolution by including the concept of survivability in his recommendations for how to implement his evolutionary approach to problems. While some people talk about evolution being for the good of the species, it is actually only good for those individuals that survive. In future generations, the survivors are the species. Drawing on experiments involving the adaptation of guppies in response to predation, Harford writes:

Adapting is not necessarily something we do. It may well be something that is done to us. We may think of ourselves as Professor Endler, but we’re actually the guppies. No individual guppy adapted, but some guppies avoided being eaten and some did not. …

As the pike cichlid closes in for a meal, it’s little consolation to the polka-dotted guppy that its failure is helping clear space for a thriving population of pebble-coloured nieces and nephews. A struggling entrepreneur is just as unlikely to be comforted by the thought that the failure of her start-up is part of a wealth-generating process of creative destruction. …

[U]nlike Amazon, or geniuses like Mitchell or Capecchi, or a pebble-coloured guppy, we don’t all get it right first time. Fortunately we have something that guppies do not: the ability to adapt as we go along.

Most of the individuals are toast. As a result, to apply the evolutionary ideas to your own life, you should experiment more, but you want to undertake experiments that you can survive. You are only one guppy.

In the New York Times profile of Justin Wolfers and Betsey Stevenson, Motoko Rich writes:

Still, data alone can’t explain everything in life. Before Matilda arrived, Ms. Stevenson reviewed research on children and their effect on adult happiness. “I was really put off by the fact that people with kids were less happy,” she said.

But at their home last month, their delight in their daughter was clear.

Mr. Wolfers has written about the joys of fatherhood: “It’s visceral; it’s real; it’s hormonal and it’s not in our economic models.”

On the back of this profile, some people are suggesting that there are circumstances where we need to throw out the equations. KJ Dell’Antonia writes:

Ms. Stevenson and Mr. Wolfers are a relief: finally, academic recognition that not every human action is, or needs to be, rational. Of course our children don’t necessarily repay us for our efforts every minute of every day (witness the laptop shooting father in my last post). But whatever indefinable thing those children do offer (joy, love, purpose) resists quantification.

Are you only rational when you maximise what the economists say you should?

More importantly, much of what children do offer, a vehicle for 50 per cent of your genes, is easily quantifiable. Evolutionary biologists have been quantifying the results of reproduction for a century.

If we accept that “joy, love, purpose” resist quantification, does that intangible joy we feel when we buy a new iPhone mean that we cannot quantify the purchase (consumption) of it? Of course not – the purchase is clearly quantifiable. In the same way, the hard to measure drivers behind our urge to have children do not erase the very real, quantifiable results. And there is a growing science on those urges.

Ultimately, Wolfers identifies the problem when he states, “it’s not in our economic models”. It is time to start putting the biology in.

The title of this post comes from a 2002 paper by Paul Zak and Kwang Woo Park. The title is mildly deceptive, as the paper has many elements and ideas crammed into it beyond population genetics. The model described by the authors includes working, consumption, saving, marriage, genetic diversity, sexual selection, intelligence, beauty, education, the Flynn effect, family size effects and more. While many of these elements deserve consideration, this is ultimately the paper’s weakness. Even though most of the assumptions are reasonable and well supported in the literature, the resulting mix is hard to disentangle, with a few factors dominating the results.

Zak and Park built an age-structured model in which agents’ cognitive ability and beauty are genetically determined, with human capital a function of cognitive ability and education. The model agents are paired with potential partners over a series of rounds, and decide whether they will marry their potential partner based on their beauty and human capital (together called “pizzazz”). In each round, they weigh the potential benefits to marriage, including increased income if their partner has higher human capital, the joy of marriage due to their partner’s pizzazz and the potential partners in future matching rounds. If they agree to marry, they then decide family size, trading off consumption (in both their young adulthood and old-age) and children. These interactions drive the population dynamics and economic output in the economy.

The agents do not seek to maximise biological fitness directly. As I have posted before, this is a workable choice if you can offer a link between the factor that the agent seeks to maximise and their fitness. However, where agents gain utility from a basket of outcomes that are linked to fitness to varying degrees, this creates a fitness trade-off between the activities. In the case of Zak and Park’s model, the agents trade-off marriage, children and consumption of goods. As a result, an agent with lower preference for consumption of goods relative to children would have a biological advantage and could invade the population.

Zak and Park run their agents through a number of simulated scenarios. In the baseline scenario we see a typical evolutionary biology result – a preference for pizzazz increases the accumulated human capital and beauty of the population, reducing its variance. Low pizzazz people are rejected, causing them to disappear from the gene pool. Beauty increases towards its upper bound, while human capital can continue to accumulate. It is the increase in human capital that drives long-term growth. The base-line simulation generated a one per cent growth in human capital per generation over 40 generations, which the authors suggest is a reasonable approximation of the last 800 years.

In an increased inequality scenario, higher variance in traits for half of the population results in low growth as there are low marriage rates and reproduction. The increased inequality results in less pairings where each agent meets the others’ criteria. The population shrinks, with output bottoming out and eventually picking up as agents become less choosy and start to reproduce. Total output is largely a function of population size. A similar effect is seen when there is a bimodal distribution in beauty. Initially there are low marriage and reproduction rates as high pizzazz individuals reject those with low pizzazz. This causes lower population, with associated reduced total output, which recovers once people become less choosy. Greater genetic diversity also decreases marriage rates, population and output.

In each of these scenarios, output is largely tied to population size and the degree to which agents are willing to mate with whomever they are paired. As a result, the authors’ discussion of economic output is effectively a discussion of population size. Only in the pandemic scenario, where different segments of the population are eliminated, is any commentary on per capita income made. In that case, the authors note that there is little effect of the pandemic on the output of survivors.

The last scenario explored by the authors is love. Love increases the probability that agents agree to marry despite more dissimilar levels of pizzazz. This scenario makes for massively increased output with increased population. The authors see this as illustrating the benefits of the right balance  between diversity an assortive mating. Assortive mating drives an increase in average pizzazz, but if too strict, population plummets as no one is willing to mate with the agent they are randomly paired. They call this balance the “The Goldilocks Principle”.

This conclusion is interesting, but I am not sure that is the world we are in.  Many women may be increasing their standards as their income increases, but per capita income is also increasing despite lower marriage rates. The demographic revolution was not primarily due to lower pairing, but due to the timing of pairing and lower fertility within pairs. Assortive mating is relatively easy in our assorted world. A university educated person is likely surrounded by other people of similar quality.

Apart from a near identical paper that Zak and Park released in 2006 (with an extra section on inequality), there has been little further work in the footsteps of this model. I suspect this is because the paper has too many elements, and that there is more reward in using simpler models to explore each element in-depth.

Several times over the last few years, I have come across someone willing to claim that risk aversion is a bias or that standard economics cannot explain it (such as this claim by David Sloan Wilson – although he mistakenly named it the Allais paradox).

Yesterday, I saw the other half of the equation. I was looking for reviews of Richard McKenzie’s Predictably Rational?: In Search of Defenses for Rational Behavior in Economics,when I came upon an article by McKenzie containing a snapshot of the argument from his book. He writes:

[C]onsider one of the main experiments that behavioralists Kahneman and Amos Tversky use as evidence for the limitations of perfect rationality as a behavioral premise. They offer their subjects two options: Option A is a “sure thing,” carrying a payoff of, say, $800. Option B is a gamble with an expected payoff of $850: The subjects have an 85-percent chance of receiving $1,000 and a 15-percent chance of getting nothing. The behavioralists report that a “large majority” of subjects choose Option A, in spite of its having an expected value $50 lower than Option B. According to behavioralists, this majority choice demonstrates a form of “bounded rationality.” In other words, the subjects’ rational decision making is impaired by mental constraints on information processing and calculating capacity, not the least of which is risk aversion (with risk aversion evident in people heavily favoring Option A).

McKenzie then provides a “rational” explanation for the phenomena:

 I have repeated this exact choice experiment with my fully employed and executive (business-seasoned) MBA students for several years at the start of their first class—before we discuss rationality, decision making, or any microeconomic concepts and lines of analysis. Just as Kahneman and Tversky report, a “large majority”—between 70 and 85 percent—of my MBA students choose Option A, the sure thing. But would conventional economic thinking fail to predict such an outcome? Not really. As Dwight Lee explained four decades ago (and economists in earlier epochs have presumed), expected value is not all that matters for rational decision making. What the behavioralists miss is that variance in outcomes is also consequential in assessing options. Option A has no variance; Option B has a substantial variance, with the outcome ranging from zero to $1,000. Hence, for many choosers, Option A can be more valuable than Option B. Indeed, if expected value were all that mattered, people would never buy insurance. Is the purchase of insurance irrational?

In economics, the trade-off between expected value and variance is often discussed through the concepts of expected utility and risk aversion. As utility is assumed to diminish with higher rewards, the expected utility of a gamble is less than the utility of the expected outcome with certainty. People balance risk and reward.

But while McKenzie is correct to claim that the experimental result may not be irrationality, I am not aware of anyone active in the field who claims that it is irrational. In regard to McKenzie’s example, Kahneman and Tversky did not claim that the preference for a sure outcome of lower expected value is irrational. It is the universality of the risk-reward trade-off that they challenged. Kahneman and Tversky noted that if framed as a potential loss (an 85 per cent chance of a $1,000 loss versus a sure $800 loss), the experimental subjects became risk seeking. They prefer the gamble. The subjects prefer both higher variance and a higher expected loss. A simple model of expected utility does not show this result, nor does an intuitive explanation of a variance-value trade-off.

Kahneman discusses the history, implications and flaws of expected utility theory in more detail in his fantastic book, Thinking, Fast and Slow. (He also points out the flaws with the alternative approach of prospect theory). His discussion of expected utility is one of the best there is, and shows that he does not consider the existence of a trade-off between variance and expected outcome to be irrational in itself. It is the lack of consistency in how people make this trade-off that is the interesting result.

Despite the article, I will still read McKenzie’s book. The chapter list looks good, including some consideration of the evolutionary foundations of behaviour. and it has the odd good review. The title also plays to my dislike of calling behavioural biases and heuristics “irrational”. I will assume for the moment that the article is not representative of the book.

Postscript: After writing this post, I found this on Less Wrong.

In The Folly of Fools: The Logic of Deceit and Self-Deception in Human Life, Robert Trivers asks “Is economics a science?”  He answers:

The short answer is no. Economics acts like a science and quacks like one – it has developed an impressive mathematical apparatus and awards itself a Nobel Prize each year – but it is not yet a science. It fails to ground itself in underlying knowledge (in this case, biology).

Trivers notes the cost of this:

[T]he first piece of reality they should pay attention to – and this has been obvious for some thirty years now – is biology, in particular evolutionary theory. If only thirty years ago economists had built a theory of economic utility on a theory of biological self-interest – forget the beautiful math and pay attention to the relevant math – we might have been spared some of the extravagances of economic thought regarding, for example, built-in anti-deception mechanisms kicking in to protect us from the harmful effects of unrestrained economic egotism by those already at the top.

Take the use of utility in economics. Economists assume that economic agents maximise utility. But what is utility? Trivers makes the point that biology has a theory, which is over one hundred years old, of what utility is. The concepts of reproductive success, or more particularly, inclusive fitness provide the answer. Rankings between goods by an economic agent could be assessed against this fitness objective. Trivers notes this might not always give the answer, but it is pointless to miss the obvious linkages.

Trivers also has a short shot at the invisible hand metaphor. He notes that biology has hundreds of examples of where the pursuit of self-interest can have dramatic negative effects on group wellbeing. This reflects the recent arguments of Robert Frank.

Trivers saves some sharper criticisms for behavioural economics, where he makes a point I have made before on this blog:

One recent effort by economics to link up with allied disciplines is called behavioral economics, a link with psychology that is most welcome. But as usual, economists resolutely refuse to make the final link to evolutionary theory, even when going through the motions. That is, even those economists who propose evolutionary explanations of economic behavior often do so with unusual, counterlogical assumptions. For example, a common recent mistake (published in all the best journals) is to assume that our behavior evolved specifically to fit artificial economic games.

This point is fair, as many interpretations of experimental games ignore the environment in which the relevant traits may have evolved. For much of our evolutionary history, humans lived in small bands where one-shot games with anonymous strangers would have been rare. For example, we might interpret punishment in the ultimatum game to indicate that people having an innate sense of fairness for which they are willing to bear a cost. However, this could equally be interpreted as a strategy that would maximise personal fitness in a small band through the repeated encounters the two people are likely to have. It may not be a sense of fairness driving their action, but rather pure self-interest.

We should be careful, however, not to take this critique too far. As a reading of Daniel Kahneman’s Thinking, Fast and Slow demonstrates, the findings of experiments are often shown to apply though many real-life situations. A methodological limitation does not imply that we cannot learn anything. We did not evolve to play economic games, but it is an evolved human that is playing them.

The growing use of experimental games by evolutionary biologists reflects this, which was my main takeaway from the Social Decision Making: Bridging Economics and Biology conference last year. While it seems that evolutionary biologists are a few years behind economists in obtaining some results, their (generally) superior methodologies and use of evolutionary biology as the starting framework for the experiments gives me some confidence that they will draw the required links.

As a final note, Trivers also writes chapters in which he makes a similar point about the lack of biology in anthropology, psychology and psychoanalysis. One observation by a biologically inclined anthropologist friend of Trivers describes the situation. “[T]hey think we’re Nazis and we think they are idiots”. That is a fair summary of where we are at.