Searching for Agents’ Best Risk Profiles

Abstract

The purpose of this research is to seek the best (highest performing) risk profiles of agents who successively choose among risky prospects. An agent's risk profile is his attitude to perceived risk, which can vary from risk preferring to risk neutral (an expected-value decision maker) to risk averse, or even a dual-risk attitude. We use the Genetic Algorithm to search in the complex stochastic space of repeated lotteries. We examine three families of utility (or value) functions: wealth-independent CARA and wealth-dependent CRRA, in which an agent's risk profile is unchanging, and the Dual-Risk-Profile (DRP) functions from Prospect Theory, in which the agent can be risk-averse (for gains) or risk preferring (for losses). Statistical analysis of the simulation results suggests that the best (profit-maximizing) CRRA functions are risk neutral, while the other functions remain slightly risk-averse. The most profitable are slightly risk-averse DRP functions.