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Published on: June 10, 2021
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Behavioral Analytics for Myopic Agents
Yonatan Mintz1, Anil Aswani2, Philip Kaminsky2
1Department of Industrial and Systems Engineering, University of Wisconsin - Madison, 53706.
Summary
This study introduces a behavioral analytics approach to optimize agent incentives within budget constraints. The method effectively reduces program costs by up to 60% while maintaining efficacy.
Area of Science:
- Multi-agent systems
- Behavioral economics
- Optimization
Background:
- Coordinators in multi-agent systems face budget limits and unknown agent utility functions.
- Agents' decisions depend on unknown prior states, inputs, and parameters.
Purpose of the Study:
- To develop a behavioral analytics framework for optimizing agent incentives.
- To address coordinator challenges of budget constraints and unknown agent utilities.
Main Methods:
- Modeling agent decision-making processes.
- Estimating agent model parameters using data to predict future decisions.
- Optimizing incentives through a decomposition scheme with a master integer program.
Main Results:
- The proposed framework adaptively optimizes incentives as new information is gathered.
- Incentives computed are proven to be asymptotically optimal.
- A simulation for a personalized weight loss program showed cost reductions up to 60% with maintained efficacy.
Conclusions:
- The behavioral analytics approach offers an effective solution for incentive optimization in constrained multi-agent systems.
- This framework significantly reduces costs compared to adaptive heuristics.
- The approach is validated through a successful application in personalized health programs.
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