This is the second installment in a short series about inequality. The first covered the evolutionary origins of inequality and its relationship to interpersonal violence. Today’s extends this logic to the broader problem of risk-taking, of which violence is a part.
As I argued previously, reproductive variance was the first inequality—all other forms that matter to us, like income inequality, do so because they have historically been related to reproductive variance. Those with more resources, for instance, had more babies that survived to reproduce; when possible, those babies also tended to inherit their parents’ resources, starting the cycle anew. These chronic effects on reproductive success have imposed selection pressures on the human mind to compete optimally for resources.
One rather broad strategy for competition revolves around risk-taking. “Risk” can mean many things but a widely used definition refers to outcome variance (there’s that “v” word again): when two options have the same average outcome, the riskier of the two is the one that is more variable. For example, I have several routes to take from work to my favorite lunch spot, Wayne’s Smoke Shack. As you can see from the Google map below, each of the three routes (one in blue and two in grey) average a nine-minute drive. The blue route, with its numerous traffic controls, will generally take the advertised nine minutes from office to Shack, give or take only a few seconds. However, the two grey routes run along the much-maligned US 36, a highway under massive reconstruction that is prone to numerous arterial blockages. Often, it can take only seven minutes to get your grub; occasionally, though, it takes closer to fifteen. The blue route is thus the least variable of the three, and so it is also the least risky.
Come lunchtime, it’s not obvious which route to take. All three average the same amount of time, sure, but what if I’m in a hurry? Being a true barbecue joint, when food sells out at Wayne’s, it sells out for the day. Let’s say it’s 12:10 and the ribs reliably sell out at 12:18. I can opt for the blue route and arrive in precisely nine minutes, but that would be one minute too late. If I take one of the grey routes instead, I might get there in fifteen minutes thanks to heavy traffic, but then I’m really no worse off than if I had taken the blue route — no ribs either way. If I take one of the grey routes and traffic happens to be light, however, I can get there just in time for the last rack of the day. Though the average drive times are all the same, the risky option is the best one when a nine-minute commute won’t cut it.
Just like reproductive variance, outcome variance pervades everything. Violence entails risk — you could walk away a winner with spoils or crawl away a loser with broken legs — as does dating, cheating on your taxes, and, of course, gambling. Because of this breadth, social scientists have had some difficulty wrapping their heads around risk. Many think of it as an aspect of personality: some people are risk seeking, others risk averse. Still others see it as a broader phenomenon: we are risk seeking when trying to avoid losses and risk averse when pursuing gains, because we value gains and losses differently.
There is certainly good empirical support for these views but the work of Sandeep Mishra is beginning to complicate the picture. Inspired by an evolutionary theory of risk-sensitivity, Mishra has argued that a key reason to seek out risk is need— the difference between where we are in life and where we ought to be. I need ribs for lunch today, and only the grey routes will suffice.
In a series of experiments (see here, here, and here), Mishra and his colleagues have shown that need underlies risky decision-making. But I want to focus here on a recent study of his with collaborators Pat Barclay and Martin Lalumière, in which they led their participants to gamble by creating an inequality among them of what biologists call “condition” or “quality.” Specifically, participants were given false feedback about their intelligence relative to their peers, generating a transparent inequality among them. And while it may not be the case that more intelligence is always better, there is little doubt that it can offer a significant advantage now and again.
The researchers asked participants to choose between pairs of monetary payoffs. In each pair, one option was always a sure thing and the other was a gamble of the same average value. For instance, participants could be asked to choose between (1) a certain $3 and (2) a 10% chance of earning $30 versus a 90% chance of earning bupkis — which, over successive gambles, amounts to $3. Thus, their choices indicated their willingness to take a risk. Before doing this task, however, participants were given an intelligence test of sorts. Crucially, participants were never given their actual results. Instead, one third of the participants were randomly assigned to learn that they were smarter than average (the “competitively advantaged” participants) whereas another third was randomly assigned to learn that they were less intelligent than average (the “competitively disadvantaged” participants). The final third took the “intelligence test” but were given no feedback at all (the control participants). Thus, the researchers experimentally devised an apparent inequality in intelligence among (two thirds of) the participants. The effects of this manipulation on gambling decisions are summarized by the figure below.
The results are striking: participants who were informed that they were on the low end of the intelligence spread made risky choices twice as often as those informed that they were on the high end and still more often than those who had no new information about their relative intelligence at all. A follow-up experiment also demonstrated that giving the competitively disadvantaged participants the intelligence test a second time around and then providing them with feedback that they scored above average reverses the effect on gambling.
The implications of this work are significant. Risk-taking is not inherently irrational: we have evolved to take risks when the gap between where we are and where we need to be — inequality, that is — is too large to close by other means. Thus, our lives play out with loaded dice: those at the top can avoid risk whereas those at the bottom have no alternative. Lotteries are not a tax on the stupid; they are the only chance, however slim, some people have. Sadly, violence may follow the same logic.
Cover image by Diacritica.