Games with Payments between Learning Agents
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915791794864128 |
|---|---|
| author | Kolumbus, Yoav Halpern, Joe Tardos, Éva |
| author_facet | Kolumbus, Yoav Halpern, Joe Tardos, Éva |
| contents | In repeated games, such as auctions, players rely on autonomous learning agents to choose their actions. We study settings in which players have their agents make monetary transfers to other agents during play at their own expense, in order to influence learning dynamics in their favor. Our goal is to understand when players have incentives to use such payments, how payments between agents affect learning outcomes, and what the resulting implications are for welfare and its distribution. We propose a simple game-theoretic model to capture the incentive structure of such scenarios. We find that, quite generally, abstaining from payments is not robust to strategic deviations by users of learning agents: self-interested players benefit from having their agents make payments to other learners. In a broad class of games, such endogenous payments between learning agents lead to higher welfare for all players. In first- and second-price auctions, equilibria of the induced "payment-policy game" lead to highly collusive learning outcomes, with low or vanishing revenue for the auctioneer. These results highlight a fundamental challenge for mechanism design, as well as for regulatory policies, in environments where learning agents may interact in the digital ecosystem beyond a mechanism's boundaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Games with Payments between Learning Agents Kolumbus, Yoav Halpern, Joe Tardos, Éva Computer Science and Game Theory Artificial Intelligence Multiagent Systems Theoretical Economics 91A05, 91A06, 91A10, 91A20, 91A40, 91A80 F.0; I.2; I.2.6; J.4 In repeated games, such as auctions, players rely on autonomous learning agents to choose their actions. We study settings in which players have their agents make monetary transfers to other agents during play at their own expense, in order to influence learning dynamics in their favor. Our goal is to understand when players have incentives to use such payments, how payments between agents affect learning outcomes, and what the resulting implications are for welfare and its distribution. We propose a simple game-theoretic model to capture the incentive structure of such scenarios. We find that, quite generally, abstaining from payments is not robust to strategic deviations by users of learning agents: self-interested players benefit from having their agents make payments to other learners. In a broad class of games, such endogenous payments between learning agents lead to higher welfare for all players. In first- and second-price auctions, equilibria of the induced "payment-policy game" lead to highly collusive learning outcomes, with low or vanishing revenue for the auctioneer. These results highlight a fundamental challenge for mechanism design, as well as for regulatory policies, in environments where learning agents may interact in the digital ecosystem beyond a mechanism's boundaries. |
| title | Games with Payments between Learning Agents |
| topic | Computer Science and Game Theory Artificial Intelligence Multiagent Systems Theoretical Economics 91A05, 91A06, 91A10, 91A20, 91A40, 91A80 F.0; I.2; I.2.6; J.4 |
| url | https://arxiv.org/abs/2405.20880 |