From risk to fairness.

Kadcyla is a drug that extends the life of breast cancer patients by an average of 6 mo. It also happens to be incredibly expensive. The United Kingdom’s National Health Service sparked controversy when it refused to provide this drug to patients, citing its low cost effectiveness. Cases like this raise the question of how societies should make distributive decisions. Should we maximize utility or should we aim to improve the lives of the least fortunate, even if doing so is costly for everyone else? The influential philosopher John Rawls tackled this dilemma by framing fair distributive decisions as a kind of gamble (1). Rawls famously argued that we should choose the kind of society we would all prefer if our choice was made from behind a “veil of ignorance”—that is, under conditions of complete uncertainty about where we would end up. He held that people should make such choices by following a risk-averse “maximin” strategy of maximizing the minimum possible outcome for themselves and others. Echoing Rawls’s theory, new research by Kameda et al. (2) links risk and fairness by showing that preferences about risk and about distribution may arise from common psychological and neural substrates. In the experiments, participants made two kinds of decisions that at first glance seem rather different: distributive decisions involved allocating payoffs to anonymous others, whereas risky decisions involved choosing between lotteries affecting their own payoffs. However, most participants deployed similar strategies for both types of decisions. Those who followed a “Rawlsian” maximin strategy in their distributive decisions tended to adopt a similar strategy for risky decisions, avoiding lotteries with the lowest possible payoffs. Meanwhile, “utilitarian” participants maximized total welfare in their distributions and preferred lotteries that maximized expected payoffs. Participants’ diverse decision patterns thus appear to mirror the philosophical debate between Rawls and …