A fuzzy model of reputation in multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions
IEEE Transactions on Knowledge and Data Engineering
A Trust/Honesty Model with Adaptive Strategy for Multiagent Semi-Competitive Environments
Autonomous Agents and Multi-Agent Systems
Existence of Risk Strategy Equilibrium in Games Having No Pure Strategy Nash Equilibrium
Agent Computing and Multi-Agent Systems
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To design intelligent agents for multi-agent applications, like auctions and negotiations, we need to first analyze how agents should interact in these applications. Game theory is a tool, which can be used. In game theory, decision-making often depends on probability and expected utility. However, decision makers usually violate the expected utility theory when there is risk in the choices. Instead, decision makers make decisions according to their attitudes towards risk. Also, reputations of other agents in making certain actions also affect decision-making. In this paper, we make use of risk attitude, reputation and utility for making decisions. We define the concepts of risk strategies, risk strategy equilibrium, and a formalized way to find the risk strategy equilibrium in infinitely repeated games. Simulations show that players get higher payoff by using risk strategies than using other game theoretic strategies.