Self-stabilization
Distributed Algorithms
Analyzing Expected Time by Scheduler-Luck Games
IEEE Transactions on Software Engineering
Agents, Distributed Algorithms, and Stabilization
COCOON '00 Proceedings of the 6th Annual International Conference on Computing and Combinatorics
Self-Stabilizing Agent Traversal
WSS '01 Proceedings of the 5th International Workshop on Self-Stabilizing Systems
Easy Stabilization with an Agent
WSS '01 Proceedings of the 5th International Workshop on Self-Stabilizing Systems
Minority Games: Interacting Agents in Financial Markets (Oxford Finance Series)
Minority Games: Interacting Agents in Financial Markets (Oxford Finance Series)
Random Walk for Self-Stabilizing Group Communication in Ad Hoc Networks
IEEE Transactions on Mobile Computing
A network formation game for bipartite exchange economies
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Autonomics '08 Proceedings of the 2nd International Conference on Autonomic Computing and Communication Systems
Nash Equilibria in Stabilizing Systems
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
Analysis of an Intentional Fault Which Is Undetectable by Local Checks under an Unfair Scheduler
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
SSS'06 Proceedings of the 8th international conference on Stabilization, safety, and security of distributed systems
When consensus meets self-stabilization
Journal of Computer and System Sciences
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We consider a simple network model for economic agents where each can buy commodities in the neighborhood. Their prices may be initially distinct in any node. However, by assuming some rules on new prices, we show that the distinct prices will converge to unique by iterating buy and sell operations. First, we present a protocol model in which each agent always bids an arbitrary price in the difference between his own price and the lowest price in the neighborhood, called max price difference. Next, we derive the condition that price stabilization occurs in our model. Furthermore, we consider game (auction) theoretic price determination by assuming that each agent's value is uniformly distributed over the max price difference. Finally, we perform a simulation experiment. Our model is suitable for investigating the effects of network topologies on price stabilization.