This page was automatically generated by NetLogo 4.0. Questions, problems? Contact email@example.com.
The applet requires Java 1.4.1 or higher. It will not run on Windows 95 or Mac OS 8 or 9. Mac users must have OS X 10.2.6 or higher and use a browser that supports Java 1.4. (Safari works, IE does not. Mac OS X comes with Safari. Open Safari and set it as your default web browser under Safari/Preferences/General.) On other operating systems, you may obtain the latest Java plugin from Sun's Java site.
created with NetLogo
view/download model file: RA1000-3lot2p.nlogo
Following George Szeps' paper in Complexity 1997, I thought I'd take Nigel Gilbert's GA code for NetLogo 3.1.1 and put a fitness function in which agents choose between two lotteries, based on the calculated means and variances of the two lotteries and the agent's Constant Absolute Risk Aversion coefficient, gamma, after which the chosen lottery would be realised and the prize added to (or subtracted from) the agent's fitness. Thanks to Nigel Gilbert and Luis Izquierdo -- Robert Marks
Each agent is modelled as a binary string, which translates to a value of gamma, the CARA coefficient. Each agent chooses among three randomly constructed 2-prize lotteries, based on their Certainty Equivalents (which would be correct for a Gaussian lottery) based on the prizes, probabilities, and the agent's gamma. The cumulative payoffs over 100 selection (positive and negative) are each agent's fitnesses. There are 100 agents per generation.
This section could explain how to use the model, including a description of each of the items in the interface tab.
Notice the rapid plateauing of the mean (black) fitness across agents each
Notice the convergence of the mean, maximum, and minimum (resp., black, green, red) gammas per generation to zero.
Notice the correlation between falls in the minimum fitness (red) per generation and divergences of the maximum or minimum (or both) gammas from zero: too risk preferring is bad on average, and too risk averse is also bad.
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.
See Nigel Gilbert's GA in NetLogo 3.1 at http://cress.soc.surrey.ac.uk/s4ss/code/NetLogo/axelrod-ipd-ga.html
Thanks to Nigel Gilbert for his GA code in NetLogo, and to Luis Isquierdo for his assistance.