Abstract
One of the goals of algorithmic game theory is to develop efficient methods for predicting the behaviour of self-interested agents in a given scenario, or game. A common approach is to attempt to compute an (approximate) Nash equilibrium, a behaviour such that agents have little incentive to deviate. Yet this computation appears to be quite difficult in general. In this work, we define and study games that are approximation-stable, meaning that all approximate equilibria predict similar behaviour. By analysing their properties, we show that finding approximate equilibria is substantially easier in such stable games.
Original language | English |
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Pages (from-to) | 1014-1020 |
Number of pages | 7 |
Journal | Current Science |
Volume | 103 |
Issue number | 9 |
State | Published - 10 Nov 2012 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022, Current Science. All Rights Reserved.
Funding
ACKNOWLEDGEMENTS. This work was supported in part by NSF grants CCF-0830540 and CCF-0953192, ONR grant N00014-09-1-0751,and AFOSR grant FA9550-09-1-0538.
Funders | Funder number |
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National Science Foundation | CCF-0830540, CCF-0953192 |
Office of Naval Research | N00014-09-1-0751 |
Air Force Office of Scientific Research | FA9550-09-1-0538 |
Keywords
- Behaviour prediction
- Nash equilibrium
- Self-interested agents
- Stable games