Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity - Bruce Edmonds
Since we primarily have humans in mind in this exercise we wish for our software agents to at least capture some of the known qualitative characteristics of humans. In particular we are interested in agents:
Each notional week, the new population of models is produced using a genetic programming (GP) algorithm (Koza 1992). In GP each `gene' is a tree structure, representing a program or other formal expression of arbitrary complexity. A population of such genes is evolved using a version of crossover that swaps randomly selected sub-trees and propagation. Selection of genes for crossover and propagation is done probilistically with a likelihood of selection in proportion to its fitness.
I have slightly modified this here by only using some tree crossover but with a high degree of propagation and also some new random genes introduced each time. Then the best model is selected and used to determine first its communicative action and subsequently whether to go to El Farol's or not. Thus the evolution of mental models is a rough representation of learning.
The cross-over operation is not very realistic but does as a first approximation. For a critique of cross-over and further discussion of the philosophy of agent design for the purposes of the credible modelling of human agents, see . This model of learning fits into the wider framework of modelling economic learning as modelling described in .
Generated with CERN WebMaker