Robust stochastic stability

A strategy profile of a game is called robustly stochastically stable if it is stochastically stable for a given behavioral model independently of the specification of revision opportunities and tie-breaking assumptions in the dynamics. We provide a simple radius-coradius result for robust stochastic stability and examine several applications. For the logit-response dynamics, the selection of potential maximizers is robust for the subclass of supermodular symmetric binary-action games. For the mistakes model, the weaker property of strategic complementarity suffices for robustness in this class of games. We also investigate the robustness of the selection of risk-dominant strategies in coordination games under best-reply and the selection of Walrasian strategies in aggregative games under imitation.Learning in games, stochastic stability, radius-coradius theorems, logit-response dynamics, mutations, imitation

Interdomain routing and games

We present a game-theoretic model that captures many of the intricacies of \emph{interdomain routing} in today's Internet. In this model, the strategic agents are source nodes located on a network, who aim to send traffic to a unique destination node. The interaction between the agents is dynamic and complex -- asynchronous, sequential, and based on partial information. Best-reply dynamics in this model capture crucial aspects of the only interdomain routing protocol de facto, namely the Border Gateway Protocol (BGP). We study complexity and incentive-related issues in this model. Our main results are showing that in realistic and well-studied settings, BGP is incentive-compatible. I.e., not only does myopic behaviour of all players \emph{converge} to a ``stable'' routing outcome, but no player has motivation to unilaterally deviate from the protocol. Moreover, we show that even \emph{coalitions} of players of \emph{any} size cannot improve their routing outcomes by collaborating. Unlike the vast majority of works in mechanism design, our results do not require any monetary transfers (to or by the agents).Interdomain Routing; Network Games; BGP protocol;

The Logit-Response Dynamics

We develop a characterization of stochastically stable states for the logit-response learning dynamics in games, with arbitrary specification of revision opportunities. The result allows us to show convergence to the set of Nash equilibria in the class of best-response potential games and the failure of the dynamics to select potential maximizers beyond the class of exact potential games. We also study to which extent equilibrium selection is robust to the specification of revision opportunities. Our techniques can be extended and applied to a wide class of learning dynamics in games.Learning in games, logit-response dynamics, best-response potential games

Distributed Computing with Adaptive Heuristics

We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best replying" to others' actions, and minimizing "regret", that have been extensively studied in game theory and economics. We explore when convergence of such simple dynamics to an equilibrium is guaranteed in asynchronous computational environments, where nodes can act at any time. Our research agenda, distributed computing with adaptive heuristics, lies on the borderline of computer science (including distributed computing and learning) and game theory (including game dynamics and adaptive heuristics). We exhibit a general non-termination result for a broad class of heuristics with bounded recall---that is, simple rules of behavior that depend only on recent history of interaction between nodes. We consider implications of our result across a wide variety of interesting and timely applications: game theory, circuit design, social networks, routing and congestion control. We also study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics. We believe that our work opens a new avenue for research in both distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion of v1. Revised version will appear in the proceedings of Innovations in Computer Science 201

Endogenous Networks in Random Population Games

Population learning in dynamic economies has been traditionally studied in over-simplified settings where payoff landscapes are very smooth. Indeed, in these models, all agents play the same bilateral stage-game against any opponent and stage-game payoffs reflect very simple strategic situations (e.g. coordination). In this paper, we address a preliminary investigation of dynamic population games over `rugged' landscapes, where agents face a strong uncertainty about expected payoffs from bilateral interactions. We propose a simple model where individual payoffs from playing a binary action against everyone else are distributed as a i.i.d. U[0,1] r.v.. We call this setting a `random population game' and we study population adaptation over time when agents can update both actions and partners using deterministic, myopic, best reply rules. We assume that agents evaluate payoffs associated to networks where an agent is not linked with everyone else by using simple rules (i.e. statistics) computed on the distributions of payoffs associated to all possible action combinations performed by agents outside the interaction set. We investigate the long-run properties of the system by means of computer simulations. We show that: (i) allowing for endogenous networks implies higher average payoff as compared to "frozen" networks; (ii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s) or not; (iii) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.Dynamic Population Games, Bounded Rationality, Endogenous Networks, Fitness Landscapes, Evolutionary Environments, Adaptive Expectations.

Concurrent Lexicalized Dependency Parsing: The ParseTalk Model

A grammar model for concurrent, object-oriented natural language parsing is introduced. Complete lexical distribution of grammatical knowledge is achieved building upon the head-oriented notions of valency and dependency, while inheritance mechanisms are used to capture lexical generalizations. The underlying concurrent computation model relies upon the actor paradigm. We consider message passing protocols for establishing dependency relations and ambiguity handling.Comment: 90kB, 7pages Postscrip

A Cognitive-based scheme for user reliability and expertise assessment in Q&A social networks

Q&A social media has gained a great deal of attention during recent years. People rely on these sites to obtain information due to the number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradictory answers, causing ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. In this work, we propose a novel approach for estimating the reliability and expertise of a user based on human cognitive traits. Every user can individually estimate these values based on local pairwise interactions. We examine the convergence performance of our algorithm and we find that it can accurately assess the reliability and the expertise of a user and can successfully react to the latter's behavior change. © 2011 IEEE