Performative Prediction on Games and Mechanism Design
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_ | 1866916616025931776 |
|---|---|
| author | Góis, António Mofakhami, Mehrnaz Santos, Fernando P. Gidel, Gauthier Lacoste-Julien, Simon |
| author_facet | Góis, António Mofakhami, Mehrnaz Santos, Fernando P. Gidel, Gauthier Lacoste-Julien, Simon |
| contents | Agents often have individual goals which depend on a group's actions. If agents trust a forecast of collective action and adapt strategically, such prediction can influence outcomes non-trivially, resulting in a form of performative prediction. This effect is ubiquitous in scenarios ranging from pandemic predictions to election polls, but existing work has ignored interdependencies among predicted agents. As a first step in this direction, we study a collective risk dilemma where agents dynamically decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We explore the resulting interplay between accuracy and welfare, and demonstrate that searching for stable accurate predictions can minimize social welfare with high probability in our setting. By assuming knowledge of a Bayesian agent behavior model, we then show how to achieve better trade-offs and use them for mechanism design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05146 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Performative Prediction on Games and Mechanism Design Góis, António Mofakhami, Mehrnaz Santos, Fernando P. Gidel, Gauthier Lacoste-Julien, Simon Machine Learning Computer Science and Game Theory Multiagent Systems Agents often have individual goals which depend on a group's actions. If agents trust a forecast of collective action and adapt strategically, such prediction can influence outcomes non-trivially, resulting in a form of performative prediction. This effect is ubiquitous in scenarios ranging from pandemic predictions to election polls, but existing work has ignored interdependencies among predicted agents. As a first step in this direction, we study a collective risk dilemma where agents dynamically decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We explore the resulting interplay between accuracy and welfare, and demonstrate that searching for stable accurate predictions can minimize social welfare with high probability in our setting. By assuming knowledge of a Bayesian agent behavior model, we then show how to achieve better trade-offs and use them for mechanism design. |
| title | Performative Prediction on Games and Mechanism Design |
| topic | Machine Learning Computer Science and Game Theory Multiagent Systems |
| url | https://arxiv.org/abs/2408.05146 |