Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
The micro-macro link in DAI and sociology
MABS 2000 Proceedings of the second international workshop on Multi-agent based simulation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Game Theory and Decision Theory in Agent-Based Systems
Game Theory and Decision Theory in Agent-Based Systems
Understanding Climate Policy Using Participatory Agent-Based Social Simulation
MABS '00 Proceedings of the Second International Workshop on Multi-Agent-Based Simulation-Revised and Additional Papers
Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers
Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers
Simulation Analysis Using Multi-Agent Systems for Social Norms
Computational Economics
Proceedings of the 3rd international conference on Multi-agent-based simulation II
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
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In their model of network formation, Berninghaus et al. (Exp Econ 9:237---251, 2006; J Evol Econ 17:317---347, 2007) showed that a periphery-sponsored star network is a strict Nash equilibrium. To examine the validity of their result, they also performed a laboratory experiment with human subjects, and they found that a periphery-sponsored star network can be formed, but when broken down, a different star network forms. In this paper, after considering some factors explaining this phenomenon, we develop a simulation system involving these factors with artificial autonomous agents. Through simulations using this system, we try to explain the behavior of human subjects in the network formation experiment, i.e., the fact that a strict Nash equilibrium periphery-sponsored star network is broken down and a different periphery-sponsored star network is formed.