Adaptive agent bidding strategy based on stochastic modeling

Sunju Park; Edmund H. Durfee; William P. Birmingham

doi:10.1145/301136.301181

Adaptive agent bidding strategy based on stochastic modeling

Sunju Park, Edmund H. Durfee, William P. Birmingham

Department of Business Administration

Research output: Contribution to conference › Paper › peer-review

36 Citations (Scopus)

Abstract

For a dynamic, evolving multiagent auction, we have developed an adaptive agent bidding strategy (called the p-strategy) based on stochastic modeling. The p-strategy takes into account the dynamics and resulting uncertainties of the auction process using stochastic modeling, and avoids the shortcomings of stochastic modeling by adaptively deciding when to use the model and when not to. Our experiments show that the p-strategy outperforms other candidate bidding strategies in a continuous double auction regardless of the status of the auction and the demography of the agent population.

Original language	English
Pages	147-153
Number of pages	7
DOIs	https://doi.org/10.1145/301136.301181
Publication status	Published - 1999
Event	Proceedings of the 1999 3rd International Conference on Autonomous Agents - Seattle, WA, USA Duration: 1999 May 1 → 1999 May 5

Conference

Conference	Proceedings of the 1999 3rd International Conference on Autonomous Agents
City	Seattle, WA, USA
Period	99/5/1 → 99/5/5

All Science Journal Classification (ASJC) codes

Engineering(all)

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10.1145/301136.301181

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Adaptive agent bidding strategy based on stochastic modeling

Abstract

Conference

All Science Journal Classification (ASJC) codes

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