On Nash-equilibria of approximation-stable games

Pranjal Awasthi, Maria Florina Balcan, Avrim Blum, Or Sheffet, Santosh Vempala

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 languageEnglish
Pages (from-to)1014-1020
Number of pages7
JournalCurrent Science
Issue number9
StatePublished - 10 Nov 2012
Externally publishedYes

Bibliographical note

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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.

FundersFunder number
National Science FoundationCCF-0830540, CCF-0953192
Office of Naval ResearchN00014-09-1-0751
Air Force Office of Scientific ResearchFA9550-09-1-0538


    • Behaviour prediction
    • Nash equilibrium
    • Self-interested agents
    • Stable games


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