Gambit: Software Tools for Game Theory

532 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Empirically Evaluating Multiagent Learning Algorithms (2014, methodology): “This primarily involves running the MALT “engine”; however, MALT calls GAMBIT’s (McKelvey et al., 2004) implementation of Lemke-Howson (Lemke and Howson, 1964) when an algorithm needs to ﬁnd the set of Nash equilibria for a game instance, and CPLEX when an algorithm needs a maxmin strategy.”
Simple search methods for finding a Nash equilibrium (2004, methodology): “In the implementation of Lemke-Howson in Gambit (McKelvey et al., 2004), the first action of the first player is selected.”
Robust game theory (2006, methodology): “The Lemke-Howson and simplicial subdivision algorithms form the backbone of the well-known game theory software Gambit [36].”
Predicting human behavior in unrepeated, simultaneous-move games (2013, methodology): “Specifically, we used Gambit (McKelvey et al., 2007) to compute QRE and to enumerate the Nash equilibria of games, and computed maximum likelihood estimates using the Nelder–Mead simplex algorithm (Nelder and Mead, 1965).”
Classifier evaluation and attribute selection against active adversaries (2010, background): “Although there are computational game theory tools to find subgame perfect equilibria for finite games (McKelvey et al. 2007), searching for subgame perfect equilibria is a hard problem in general (Basar and Olsder 1999).”