Socially conscious decision-making
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
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
A Trust/Honesty Model with Adaptive Strategy for Multiagent Semi-Competitive Environments
Autonomous Agents and Multi-Agent Systems
An Adaptive Strategy for Resource Allocation Modeled as Minority Game
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
An adaptive strategy for minority games
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Expected utility maximization and attractiveness maximization
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
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Two key properties defining an intelligent agent are reactive and pro-active. Before designing an intelligent agent for any multi-agent system, we need to first understand how agents should behave and interact in that particular application, which can be done by modelling the application as a game . To analyze these games and to understand how decision-makers interact, we can use a collection of analytical tools known as Game Theory . Risk strategies is a new kind of game-theoretic strategy. Simulations in previous work have shown that agents using risk strategies are reactive as well as pro-active and thus have better performance than agents using other models or strategies in various applications. However, research on risk strategies has been focusing on formalization, application, and games having pure strategy Nash equilibrium. In this paper, we analyze a game having no pure strategy Nash equilibrium. We find that risk strategy equilibrium may exist even the game does not have pure strategy Nash equilibrium. We then summarize general conditions for the existence of risk strategy equilibrium. Simulation shows that agents using risk strategies also have better performance than agents using other existing strategies in a game having no pure strategy Nash equilibrium.