Efficient Inverse Multiagent Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910835931086848 |
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| author | Goktas, Denizalp Greenwald, Amy Zhao, Sadie Koppel, Alec Ganesh, Sumitra |
| author_facet | Goktas, Denizalp Greenwald, Amy Zhao, Sadie Koppel, Alec Ganesh, Sumitra |
| contents | In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which we develop polynomial-time algorithms to solve, the former of which relies on an exact first-order oracle, and the latter, a stochastic one. We extend our approach to solve inverse multiagent simulacral learning in polynomial time and number of samples. In these problems, we seek a simulacrum, meaning parameters and an associated equilibrium that replicate the given observations in expectation. We find that our approach outperforms the widely-used ARIMA method in predicting prices in Spanish electricity markets based on time-series data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Inverse Multiagent Learning Goktas, Denizalp Greenwald, Amy Zhao, Sadie Koppel, Alec Ganesh, Sumitra Computer Science and Game Theory Artificial Intelligence Machine Learning Theoretical Economics In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which we develop polynomial-time algorithms to solve, the former of which relies on an exact first-order oracle, and the latter, a stochastic one. We extend our approach to solve inverse multiagent simulacral learning in polynomial time and number of samples. In these problems, we seek a simulacrum, meaning parameters and an associated equilibrium that replicate the given observations in expectation. We find that our approach outperforms the widely-used ARIMA method in predicting prices in Spanish electricity markets based on time-series data. |
| title | Efficient Inverse Multiagent Learning |
| topic | Computer Science and Game Theory Artificial Intelligence Machine Learning Theoretical Economics |
| url | https://arxiv.org/abs/2502.14160 |