The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design.multi-agent learning; auction markets; design economics; agent-based computational economics

Markov evolution with inexact information

stochastic processes;game theory;information

Sequential Sampling Equilibrium

This paper introduces an equilibrium framework based on sequential sampling in which players face strategic uncertainty over their opponents' behavior and acquire informative signals to resolve it. Sequential sampling equilibrium delivers a disciplined model featuring an endogenous distribution of choices, beliefs, and decision times, that not only rationalizes well-known deviations from Nash equilibrium, but also makes novel predictions supported by existing data. It grounds a relationship between empirical learning and strategic sophistication, and generates stochastic choice through randomness inherent to sampling, without relying on indifference or choice mistakes. Further, it provides a rationale for Nash equilibrium when sampling costs vanish

Deception in Game Theory: A Survey and Multiobjective Model

Game theory is the study of mathematical models of conflict. It provides tools for analyzing dynamic interactions between multiple agents and (in some cases) across multiple interactions. This thesis contains two scholarly articles. The first article is a survey of game-theoretic models of deception. The survey describes the ways researchers use game theory to measure the practicality of deception, model the mechanisms for performing deception, analyze the outcomes of deception, and respond to, or mitigate the effects of deception. The survey highlights several gaps in the literature. One important gap concerns the benefit-cost-risk trade-off made during deception planning. To address this research gap, the second article introduces a novel approach for modeling these trade-offs. The approach uses a game theoretic model of deception to define a new multiobjective optimization problem called the deception design problem (DDP). Solutions to the DDP provide courses of deceptive action that are efficient in terms of their benefit, cost, and risk to the deceiver. A case study based on the output of an air-to-air combat simulator demonstrates the DDP in a 7 x 7 normal form game. This approach is the first to evaluate benefit, cost, and risk in a single game theoretic model of deception

Learning in evolutionary environments

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Institutions Are neither Autistic Maximizers nor Flocks of Birds: Self-organization, Power, and Learning in Human Organizations

In this work we shall attempt an excursus across fundamentally different streams of modern interpretations of the “ primitive entities” constituting the social fabrics of economic systems. Behind each specific interpretative story, there is a set of ceteris paribus assumptions and also some fictitious tale on a 'once upon a time' reconstruction of the theoretical primitives of the story itself. Pushing it to the extreme, as we see it, there are in the social sciences two archetypal (meta) tales. The first says, more or less, that 'once upon a time' there were individuals with reasonably structured and coherent preferences, with adequate cognitive algorithms to solve the decision-action problems at hand, and with self-seeking restrictions on preferences themselves. They met in some openings in the forest and, conditional on the technologies available, undertook some sort of general equilibrium trading or, as an unavoidable second best, built organizations in order to deal with technological non-convexities, trading difficulties, contract enforcements, etc. In the alternative tale, 'once upon a time' there were immediately factors of socialization and preference-formation of individuals, including some institutions like families shaping desires, representations and, possibly, cognitive abilities. Nonexchange mechanisms of interactions appear in the explanation from the start: authority, violence and persuasion of parents upon children; obedience; schools; churches; and, generally, the adaptation to particular social roles. Here 'institutions' are the primitives, while 'preferences' and the very idea of 'rationality' are derived entities. Which of the primitive tale is chosen bears far-reaching consequences for the interpretation of socioeconomic organizational forms and their dynamics, and involves different theoretical commitments on the interactions between agencies and structures in human affairs. In this work , we argue for the need of moving away from rationality-cum-equilibrium interpretations and of focusing on the varying balances between self’orginizing dynamics and institution-shaped constraints

Learning in Evolutionary Environments

The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality