Evolution of Risk Preferences

This paper presents a simulation, made with a genetic algorithm (GA), in order to shape risk attitudes in a very simple environment of lotteries such as the Machina triangle. GA models the process of learning and imitation from successful traders in real world economics in a satisfactory way. An overlapping generations case is used where bankrupted agents exit the market and are replaced by new-entrants that select their models with probabilities that can be proportional not only to instantaneous economical success, but also to longevity in the market or to financial stability (different kinds of fitness in the GA). The genetic code of the agents is nothing else but a permutation (the order of preference) on a finite number of lotteries forming a grid on the Machina triangle, with the possibility of winning or loosing fixed amounts. The resulting indifference curves are shaped graphically, moreover measuring how far they are from the ideal risk neutral case. The main result is that imposing the simple condition of the bankruptcy benchmark, with instantaneous wealth as fitness, is enough to induce stochastic dominance. JEL Classification Numbers: C61, D81, D83.