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Exponential Utility Functions Learn to be Risk Neutral
created with NetLogo
view/download model file: RA-CARA-EU-3l2p.nlogo
This GA program searches for the highest-scoring agents ("punters") as they choose among three random 2-prize lotteries, using highest expected utility as their criterion. The utility functions are exponential (CARA, wealth-independent), and "best" means highest scoring after the chosen lottery has been realised.
100 agents are chosen with random gammas (the risk-aversion coefficients). Each agent chooses a thousand times. The agents with the highest average payoffs become the parents of the next generation of agents. This continues for many (max 1050) generations.
The only two things to vary are the maxiumum absolute prize in the lottery (up to 100), and the GA's mutation rate, which helps avoid local maxima.
Notice how the GA takes several generations before the average fitness (black) starts to rise rapidly to the max (green) line, which happens as the mean gamma tends to zero = risk neutral (or EV decion making).
This section could give some ideas of things for the user to try to do (move sliders, switches, etc.) with the model.
This section could give some ideas of things to add or change in the procedures tab to make the model more complicated, detailed, accurate, etc.
This section could point out any especially interesting or unusual features of NetLogo that the model makes use of, particularly in the Procedures tab. It might also point out places where workarounds were needed because of missing features.
This section could give the names of models in the NetLogo Models Library or elsewhere which are of related interest.
Thanks to Nigel Gilbert for his implementation of the GA in NetLogo (3), and to Luis Izquierdo for his help with NetLogo.