Title :
Learning to be risk averse?
Author :
Marks, Robert E.
Author_Institution :
Econ., Univ. of New South Wales, Sydney, VIC, Australia
Abstract :
The purpose of this research is to search for the best (highest performing) risk profile 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. We use the Genetic Algorithm to search in the complex stochastic space of repeated lotteries. We find that agents with a CARA utility function learn to possess risk-neutral risk profiles. Since CARA utility functions are wealth-independent, this is not surprising. When agents have wealth-dependent, CRRA utility functions, however, they also learn to possess risk profiles that are about risk neutral (from slightly risk-averse to even slightly risk-preferring), which is surprising.
Keywords :
bankruptcy; decision making; genetic algorithms; risk analysis; stochastic processes; CARA utility function; CRRA utility functions; bankruptcy; decision making; genetic algorithm; risk averse; risk neutral; risk profile; stochastic space; Computational modeling; Decision making; Economics; Educational institutions; Genetic algorithms; Java;
Conference_Titel :
Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference on
Conference_Location :
London
DOI :
10.1109/CIFEr.2014.6924096
