Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Classifier fitness based on accuracy
Evolutionary Computation
Simulation Analysis Using Multi-Agent Systems for Social Norms
Computational Economics
| Hi-index | 0.00 |
In this paper, we discuss the development of a simulation system with artificial autonomous adaptive agents that select one out of a given pair of binary lotteries, as represented by probability distributions over two outcomes. The agent's decisions are made by a learning classifier system, and after classifying the information of a given pair of binary lotteries, the agent chooses one of them. The condition part of a classifier consists of two types of conditions: the conditions identifying probabilities and payoffs of a given pair of binary lotteries, and the conditions identifying characteristics of the lotteries known by several models that describe the behavioral regularities of choices under risk. We compare the result of the simulation with that of the experiment by Selten et al. (Theory Decis 46:211---249, 1999), and demonstrate the similarity between them. From the similarity, we consider a mechanism of human choices under risk. Finally, we examine the possibility of controlling a subject's preference with respect to risky events using the lottery ticket procedure in laboratory experiments.