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Wednesday, January 7, 2015

The origin of risk aversion

By Ruixun Zhang, Thomas J. Brennan, and Andrew W. Lo
PNAS 2014 111 (50) 17777-17782; published ahead of print December 1, 2014, doi:10.1073/pnas.1406755111

Abstract

Risk aversion is one of the most basic assumptions of economic behavior, but few studies have addressed the question of where risk preferences come from and why they differ from one individual to the next. Here, we propose an evolutionary explanation for the origin of risk aversion. In the context of a simple binary-choice model, we show that risk aversion emerges by natural selection if reproductive risk is systematic (i.e., correlated across individuals in a given generation). In contrast, risk neutrality emerges if reproductive risk is idiosyncratic (i.e., uncorrelated across each given generation). More generally, our framework implies that the degree of risk aversion is determined by the stochastic nature of reproductive rates, and we show that different statistical properties lead to different utility functions. The simplicity and generality of our model suggest that these implications are primitive and cut across species, physiology, and genetic origins.

Significance

Risk aversion is one of the most widely observed behaviors in the animal kingdom; hence, it must confer certain evolutionary advantages. We confirm this intuition analytically in a binary-choice model of decision-making—risk aversion emerges from mindless decision-making as the evolutionarily dominant behavior in stochastic environments with correlated reproductive risk across the population. The simplicity of our framework suggests that our results are likely to apply across species. From a policy perspective, our results underscore the importance of addressing systematic risk through insurance markets, capital markets, and government policy. However, our results also highlight the potential dangers of sustained government intervention, which can become a source of systematic risk in its own right.

The entire article is here.