A Course in Game Theory

By Martin J. Osborne, Ariel Rubinstein. 1995

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In Game & Decision Theory

Citations in the corpus

Shaking the foundations: delusions in sequence models for interaction and control (2021, background): “From game theory, we also know that hidden moves spawn information sets , i.e. game states that are indistinguishable (highlighted in Figure 3a) and hence do not admit the same state-speciﬁc actions that the expert would choose [17, 23].”
OpenSpiel: A Framework for Reinforcement Learning in Games (2019, background): “Games in OpenSpiel are represented as procedural extensive-form games [52, 63], though in some cases can also be cyclic such as in Markov Decision Processes [67] and Markov games [37].”
Stochastic Activation Pruning for Robust Adversarial Defense (2018, background): “Consider the defense problem from a game-theoretic perspective (Osborne & Rubinstein, 1994).”
Empirically Evaluating Multiagent Learning Algorithms (2014, background): “The payoff proﬁles achievable in Nash equilibrium of a repeated game are precisely the enforceable proﬁles (see, e.g., Osborne and Rubinstein (1994)).”
Multi-Agent Common Knowledge Reinforcement Learning (2018, background): “We refer the reader to Osborne & Rubinstein (1994) for general formal deﬁnitions of common knowledge.”
Optimal and Approximate Q-value Functions for Decentralized POMDPs (2008, background): “…is trivially sub-game perfect.4 To overcome this problem different refinements of the Nash equilibrium concept have been defined, of which we will mention the assessment equilibrium (Binmore, 1992) and the closely related, but stronger sequential equilibrium (Osborne & Rubinstein, 1994).”
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems (2012, background): “Deﬁnition 3 A joint policy π ∗ deﬁnes a Nash equilibrium in a Markov game iff, for each agent i : (3) Thus, no agent can improve its long-term gain by unilaterally deviating from a Nash equilibrium .”
Multiagent learning using a variable learning rate (2002, background): “Matrix games A matrix game or strategic game [21,22] is a tuple (n,A1.”
Finding Optimal Abstract Strategies in Extensive-Form Games (2012, unknown): “For a complete description see (Osborne and Rubinstein 1994).”
Game Theory in Wireless and Communication Networks (2008, methodology): “Because of the undesirable outcome of these Nash equilibria, an alternative solution based on Nash bargaining is introduced [376].”
Rational secret sharing and multiparty computation: extended abstract (2004, background): “As a consequence, many reﬁnements of Nash equilibrium have been considered in the game theory literature; these are attempts to identify the “good” Nash equilibria of a game (see, e.g., [17]).”
Social reward shaping in the prisoner's dilemma (2008, background): “In an iterated matrix game, two fixed history strategies constitute a subgame perfect equilibrium [5] if each strategy, for each possible history, chooses the action that maximizes its total expected discounted reward with”
Behavioral dynamics and influence in networked coloring and consensus (2011, background): “See Osborne and Rubenstein (4) for well known examples.”
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions (2020, methodology): “The Shapley Additive exPlanations (SHAP) method [19, 20] is based upon the Shapley value concept [20, 21] from game theory [22, 23] and can be rationalized as an extension of the Local Interpretable Model-agnostic Explanations (LIME) approach [8].”
Moral Decision Making in Autonomous Systems: Enforcement, Moral Emotions, Dignity, Trust, and Deception (2012, methodology): “The superscript i is used to express individual i’s partner [41].”