Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Simulation Analysis Using Multi-Agent Systems for Social Norms
Computational Economics
| Hi-index | 0.00 |
In the previous study on models of network formation and laboratory experiments, Berninghaus, Ehrhart, Ott and Vogt in 2007 showed that there are some differences between the real human behaviour in the laboratory experiments and the theoretical prediction based on the concept of Nash equilibrium; although a peripheral sponsored star network is theoretically proved to be strict Nash equilibrium, the experimental results showed that there were some players who deviated from the equilibrium strategies. As an efficient tool for explaining such differences, this paper provides a simulation system using artificial adaptive agents for behavioural analysis of the human subjects. The decision making and the learning mechanism of the agents are constructed on the basis of neural networks and genetic algorithms. Through the simulation analysis using the constructed system, it is shown that the differences between the experimental results and the theoretical prediction are explainable in terms of the long-term view and the adaptivity of human behaviour.