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Shopbots and Pricebots
, 1999
"... 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 ..."
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Cited by 96 (12 self)
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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 economicallymotivated 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.
Shopbot Economics
 JAAMAS
, 1999
"... . Shopbots are agents that search the Internet for information pertaining to the price and quality of goods or services. With the advent of shopbots, a dramatic reduction in search costs is imminent, which promises (or threatens) to radically alter market behavior. This research includes the proposa ..."
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Cited by 54 (6 self)
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. Shopbots are agents that search the Internet for information pertaining to the price and quality of goods or services. With the advent of shopbots, a dramatic reduction in search costs is imminent, which promises (or threatens) to radically alter market behavior. This research includes the proposal and theoretical analysis of a simple economic model which is intended to capture some of the essence of shopbots, and attempts to shed light on their potential impact on markets. Moreover, experimental simulations of an economy of software agents are described, which are designed to model the dynamic interaction of electronic buyers, sellers, and shopbots. This study forms part of a larger research program that aims to provide new insights on the impact of agent and information technology on the nascent information economy. 1 Introduction Shopbots, agents that automatically search the Internet for goods and services on behalf of consumers, herald a future in which autonomous agents become...
Strategic Pricebot Dynamics
"... Shopbots are software agents that automatically query multiple sellers on the Internet to gather information about prices and other attributes of consumer goods and services. Rapidly increasing in number and sophistication, shopbots are helping more and more buyers minimize expenditure and maximize ..."
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Cited by 38 (7 self)
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Shopbots are software agents that automatically query multiple sellers on the Internet to gather information about prices and other attributes of consumer goods and services. Rapidly increasing in number and sophistication, shopbots are helping more and more buyers minimize expenditure and maximize satisfaction. In response at least partly to this trend, it is anticipated that sellers will come to rely on pricebots, automated agents that employ pricesetting algorithms in an attempt to maximize profits. This paper reaches toward an understanding of strategic pricebot dynamics. More specifically, this paper is a comparative study of four candidate pricesetting strategies that differ in informational and computational requirements: gametheoretic pricing (GT), myoptimal pricing (MY), derivative following (DF), and Qlearning (Q). In an effort to gain insights into the tradeoffs between practicality and pro tability of pricebot algorithms, the dynamic behavior that arises among homogeneous and heterogeneous collections of pricebots and shopbotassisted buyers is analyzed and simulated. In homogeneous settings  when all pricebots use the same pricing algorithm  DFs outperform MYs and GTs. Investigation of heterogeneous collections of pricebots, however, reveals an incentive for individual DFs to deviate to MY or GT. The Q strategy exhibits superior performance to all the others since it learns to predict and account for the longterm consequences of its actions. Although the current implementation of Q is impractically expensive, techniques for achieving similar performance at greatly reduced computational cost are under investigation.
Learning and implementation on the internet
 Rutgers University, Department of Economics
, 1997
"... We address the problem of learning and implementation in the Internet. When agents play repeated games in distributed environments like the Internet, they have very limited a priori information about the other players and the payo matrix. Consequently, standard solution concepts like Nash equilibria ..."
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Cited by 20 (3 self)
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We address the problem of learning and implementation in the Internet. When agents play repeated games in distributed environments like the Internet, they have very limited a priori information about the other players and the payo matrix. Consequently, standard solution concepts like Nash equilibria, or even the serially undominated set, do not apply in such a setting. To construct more appropriate solution concepts, we rst describe the essential properties that constitute \reasonable &quot; learning behavior in distributed environments. We then study the convergence behavior of such algorithms; these results lead us to propose rather non traditional solutions concepts for this context. Finally, we discuss implementation of social choice functions with these solution concepts, and nd that only strictly coalitionally strategyproof social choice functions are implementable. 1 1
A General Class of NoRegret Learning Algorithms and GameTheoretic Equilibria
, 2003
"... A general class of noregret learning algorithms, called  noregret learning algorithms is defined, which spans the spectrum from nointernalregret learning to noexternalregret learning, and beyond. describes the set of strategies to which the play of a learning algorithm is compared: a lea ..."
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Cited by 17 (0 self)
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A general class of noregret learning algorithms, called  noregret learning algorithms is defined, which spans the spectrum from nointernalregret learning to noexternalregret learning, and beyond. describes the set of strategies to which the play of a learning algorithm is compared: a learning algorithm satisfies noregret iff no regret is experienced for playing as the algorithm prescribes, rather than playing according to any of the transformations of the algorithm's play prescribed by elements of . Analogously, a class of gametheoretic equilibria, called equilibria, is defined, and it is shown that the empirical distribution of play of noregret algorithms converges to the set of equilibria.
Synchronous and Asynchronous Learning by Responsive Learning Automata
 Learning and Implementation on the Internet.&quot; Manuscript
, 1996
"... We consider the ability of economic agents to learn in a decentralized environment in which agents do not know the (stochastic) payoff matrix and can not observe their opponents' actions; they merely know, at each stage of the game, their own action and the resulting payoff. We discuss the r ..."
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Cited by 12 (5 self)
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We consider the ability of economic agents to learn in a decentralized environment in which agents do not know the (stochastic) payoff matrix and can not observe their opponents' actions; they merely know, at each stage of the game, their own action and the resulting payoff. We discuss the requirements for learning in such an environment, and show that a simple probabilistic learning algorithm satisfies two important optimizing properties: i) When placed in an unknown but eventually stationary random environment, they converge in bounded time, in a sense we make precise, to strategies that maximize average payoff. ii) They satisfy a monotonicity property (related to the "law of the effect") in which increasing the payoffs for a given strategy increases the probability of that strategy being played in the future. We then study how groups of such learners interact in a general game. We show that synchronous groups of these learners converge to the serially undominated set. ...
The Santa Fe Bar problem revisited: Theoretical and practical implications
 Festival on Game Theory: Interactive Dynamics and Learning, SUNY Stony
, 1998
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Probabilistic Pricebots
 Proc. 3rd. Workshop on Game Theoretic and Decision Theoretic Agents
, 2000
"... Past research has been concerned with the potential of embedding deterministic pricing algorithms into pricebots: software agents used by online sellers to automatically price Internet goods. In this work, probabilistic pricing algorithms based on noregret learning are explored, in both highinfor ..."
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Cited by 10 (2 self)
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Past research has been concerned with the potential of embedding deterministic pricing algorithms into pricebots: software agents used by online sellers to automatically price Internet goods. In this work, probabilistic pricing algorithms based on noregret learning are explored, in both highinformation and lowinformation settings. It is shown via simulations that the longrun empirical frequencies of prices in a market of noregret pricebots can converge to equilibria arbitrarily close to an asymmetric Nash equilibrium; however, instantaneous price distributions need not converge. Keywords Shopbots, Pricebots, Economic software agents 1. INTRODUCTION Pricebots, agents that employ automated pricing algorithms, are beginning to appear on the Internet. An early example resides at buy.com. This agent monitors its primary competitors' prices and then automatically undercuts the lowest. Driven by the evergrowing use of shopbots, which enhance buyer price sensitivity, we anticipate a...
An algorith for computing stochastically stable distributions with applications to multiagent learning in replicated games
 In Uncertainty in Artificial Intelligence: Proceedings of the TwentyFirst Conference
, 2005
"... One of the proposed solutions to the equilibrium selection problem for agents learning in repeated games is obtained via the notion of stochastic stability. Learning algorithms are perturbed so that the Markov chain underlying the learning dynamics is necessarily irreducible and yields a unique stab ..."
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Cited by 5 (2 self)
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One of the proposed solutions to the equilibrium selection problem for agents learning in repeated games is obtained via the notion of stochastic stability. Learning algorithms are perturbed so that the Markov chain underlying the learning dynamics is necessarily irreducible and yields a unique stable distribution. The stochastically stable distribution is the limit of these stable distributions as the perturbation rate tends to zero. We present the first exact algorithm for computing the stochastically stable distribution of a Markov chain. We use our algorithm to predict the longterm dynamics of simple learning algorithms in sample repeated games. 1
NoΦRegret: A Connection between Computational Learning Theory and Game Theory
"... This paper explores a fundamental connection between computational learning theory and game theory through a property we call noΦregret. Given a set of transformations Φ (i.e., mappings from actions to actions), a learning algorithm is said to exhibit no Φregret if an agent experiences no regret ..."
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This paper explores a fundamental connection between computational learning theory and game theory through a property we call noΦregret. Given a set of transformations Φ (i.e., mappings from actions to actions), a learning algorithm is said to exhibit no Φregret if an agent experiences no regret for playing the actions the algorithm prescribes, rather than playing the transformed actions prescribed by any of the elements of Φ. The existence of noΦregret learning algorithms is established, for all finite Φ. Analogously, a class of gametheoretic equilibria, called � Φequilibria, for � Φ = (Φi)1≤i≤n, is defined (here n is the number of agents/players). The main contribution of this paper is to show that that the empirical distribution of play of noΦiregret algorithms converges to the set of � Φequilibria. The wellknown result that the empirical distribution of play of nointernalregret learning converges to the set of correlated equilibria follows as an immediate corollary of this general theorem. In addition to providing a sufficient condition, a necessary condition for convergence to the set of � Φequilibria is also derived. This work was originally motivated by an attempt to design a noregret learning scheme that would converge to a tighter solution concept than the set of correlated equilibria. However, it is argued that the strongest form of noΦregret learning is nointernalregret learning. Hence, the tightest gametheoretic solution concept to which any noΦregret algorithm converges is correlated equilibrium. In particular, Nash equilibrium is not a necessary outcome of learning via any noΦregret algorithms.