- 1 - Risk Neutral is Best for Risky Decision Making

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) function from Prospect Theory, in which the agent can be risk averse (for gains) or risk preferring (for losses). Analysis of the simulation results reveals that the best function for risky decision-making is the risk-neutral linear function.