Shopbots are agents that automatically search the Internet to obtain information about prices and other attributes of goods and services. They herald a future in which autonomous agents profoundly influence electronic markets. In this study, a simple economic model is proposed and analyzed, which is intended to quantify some of the likely impacts of a proliferation of shopbots and other economically-motivated software agents. In addition, this paper reports on simulations of pricebots - adaptive, pricesetting agents which firms may well implement to combat, or even take advantage of, the growing community of shopbots. This study forms part of a larger research program that aims to provide insights into the impact of agent technology on the nascent information economy.

This paper addresses the question what is the outcome of multi-agent learning via no-regret algorithms in repeated games? Specically, can the outcome of no-regret learning be characterized by traditional game-theoretic solution concepts, such as Nash equilibrium ? The conclusion of this study is that no-regret learning is reminiscent of ctitious play: play converges to Nash equilibrium in dominancesolvable, constant-sum, and generalsum 2 2 games, but cycles exponentially in the Shapley game. Notably, however, the information required of ctitious play far exceeds that of noregret learning. 1.

We construct an uncoupled randomized strategy of repeated play such that, if every player plays according to it, mixed action profiles converge almost surely to a Nash equilibrium of the stage game. The strategy requires very little in terms of information about the game, as players ’ actions are based only on their own past payoffs. Moreover, in a variant of the procedure, players need not know that there are other players in the game and that payoffs are determined through other players ’ actions. The procedure works for finite generic games and is based on appropriate modifications of a simple stochastic learning rule introduced by Foster and Young [12]. Keywords Regret testing; Regret-based learning; Random search; Stochastic dynamics; Uncoupled dynamics; Global convergence to

This paper investigates the Santa Fe (i.e., El Farol) bar problem from both a theoretical and a practical perspective. Theoretically, it is shown that belief-based learning (e.g., Bayesian updating) yields unstable behavior in this repeated game. In particular, rationality and predictivity, two conditions sufficient for convergence to Nash equilibrium, are inherently incompatible. On the practical side, it is demonstrated via simulations that computational learning algorithms in which agents occasionally act irrationally do indeed give rise to near-equilibrium behavior.

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Sequential decision making under uncertainty is always a challenge for autonomous agents populating a multiagent environment, since their behaviour is inevitably influenced by the be-haviour of others. Further, agents have to constantly struggle to find the right balance between exploiting current information regarding the environment and the rest of its inhabitants, and ex-ploring so that they acquire additional information. Moreover, they need to profitably trade off short-term rewards with anticipated long-term ones, while learning through interaction about the environment and others—employing techniques from reinforcement learning (RL), a fun-damental area of study within artificial intelligence (AI). Coalition formation is a problem of great interest within game theory and AI, allowing autonomous individually rational agents to form stable or transient teams (or coalitions) to tackle an underlying task. Agents participating in realistic scenarios of repeated coalition formation under uncertainty face the issues identified above, and need to bargain to succesfully negotiate the terms of their participation in coalitions—often having to compromise individual with team welfare effectively. In this thesis, we provide theoretical and algorithmic tools to accommodate sequential de-

Young's regret testing

Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configuration implies that there is no motivation for one player to change its strategy if the other does not. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond optimally to the other player. By developing mutual trust, agents can avoid iterated best responses that will lead to a lesser payoff Nash Equilibrium. In this paper we work with agents who select actions based on expected utility calculations that incorporates the observed frequencies of the actions of the opponent(s). We augment this stochasticallygreedy agents with an interesting action revelation strategy that involves strategic revealing of one’s action to avoid worst-case, pessimistic moves. We argue that in certain situations, such apparently risky revealing can indeed produce better payoff than a non-revealing approach. In particular, it is possible to obtain Pareto-optimal solutions that dominate Nash Equilibrium. We present results over a large number of randomly generated payoff matrices of varying sizes and compare the payoffs of strategically revealing learners to payoffs at Nash equilibrium.

The market selection hypothesis states that, among expected utility maximizers, competitive markets select for agents with correct beliefs. In some economies this holds, while in others it fails. It holds in complete market economies with a common discount factor and bounded aggregate consumption. It can fail when markets are incomplete, when consumption grows too quickly, or when discount factors and beliefs are correlated. These insights have implication for the analysis of the heterogeneous agent stochastic dynamic general equilibrium models common in finance and macroeconomics. 1 “The trading floor is a jungle, ” he went on, “and the guy you end up working for is your jungle leader. Whether you succeed here or not depends on knowing how to survive in the jungle.” Lewis (1989, pp. 39–40.) 1

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