Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
How do Autonomous Agents Solve Social Dilemmas?
PRICAI '96 Proceedings from the Workshop on Intelligent Agent Systems, Theoretical and Practical Issues
Learning to cooperate in multi-agent social dilemmas
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Strategies of cooperation in distributed problem solving
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Equilibrium analysis of the possibilities of unenforced exchange in multiagent systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using partial global plans to coordinate distributed problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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The Tragedy of the Commons involves a community utilizing a shared resource (the "commons") which can sustain a maximum load capacity beyond which its performance degrades. If utility received is proportional to the load applied on the system, individuals will maximize their applied load. Such greedy behavior will eventually lead to the total load exceeding the capacity of the commons. Thereafter, individuals will get less for adding more load on the system, which signifies a social dilemma. We develop a distributed solution approach to the tragedy of the commons that require individuals in the society to adapt their aspirations and apply loads based on their own aspirations. An aspiration level corresponds to the satisficing return for an individual, which is adjusted based on experience. In our model, individuals choose the load applied on the system based on their aspiration levels, thereby affecting the stability and performance of the "commons". We evaluate two different aspiration and load adjustment policies as well as effects of asynchronous decision making on the stability and performance of populations of varying sizes. Interesting results include mitigation of free-riding for larger populations. We also develop a mathematical model to predict the convergence time for such populations and verify the predictions experimentally.