|
|
|
|
| LEADER |
01540 am a22001573u 4500 |
| 001 |
137667.2 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Jaakkola, Tommi
|e author
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
|
| 700 |
1 |
0 |
|a Garg, Vikas
|e author
|
| 245 |
0 |
0 |
|a Local aggregative games
|
| 260 |
|
|
|c 2021-11-08T19:43:38Z.
|
| 856 |
|
|
|z Get fulltext
|u https://hdl.handle.net/1721.1/137667.2
|
| 520 |
|
|
|a © 2017 Neural information processing systems foundation. All rights reserved. Aggregative games provide a rich abstraction to model strategic multi-agent interactions. We introduce local aggregative games, where the payoff of each player is a function of its own action and the aggregate behavior of its neighbors in a connected digraph. We show the existence of a pure strategy e-Nash equilibrium in such games when the payoff functions are convex or sub-modular. We prove an information theoretic lower bound, in a value oracle model, on approximating the structure of the digraph with non-negative monotone sub-modular cost functions on the edge set cardinality. We also define a new notion of structural stability, and introduce 7-aggregative games that generalize local aggregative games and admit e-Nash equilibrium that is stable with respect to small changes in some specified graph property. Moreover, we provide algorithms for our models that can meaningfully estimate the game structure and the parameters of the aggregator function from real voting data.
|
| 546 |
|
|
|a en
|
| 655 |
7 |
|
|a Article
|
