We present a formalization of the Recursive Modeling Method, which we have previously, somewhat informally, proposed as a method that autonomous artificial agents can use for intelligent coordination and communication with other agents. Our formalism is closely related to models proposed in the area of game theory, but contains new elements that lead to a different solution concept. The advantage of our solution method is that always yields the optimal solution, which is the rational action of the agent in a multi-agent environment, given the agent's state of knowledge and its preferences, and that it works in realistic cases when agents have only a finite amount of information about the agents they interact with. Introduction Since its initial conceptual development several years ago (Gmytrasiewicz, Durfee, & Wehe 1991a; 1991b), the Recursive Modeling Method (RMM) has provided a powerful decision-theoretic underpinning for coordination and communication decisionmaking, including dec...

‘‘Human experience, which is constantly contradicting theory, is the great test of truth.’’ �Dr. Johnson, quoted in James Boswell, The Life of Samuel Johnson L.L.D. This paper reports experiments designed to study strategic sophistication, the extent to which behavior in games reflects attempts to predict others ’ decisions, taking their incentives into account. We study subjects ’ initial responses to normal-form games with various patterns of iterated dominance and unique pure-strategy equilibria without dominance, using a computer interface that allowed them to search for hidden payoff information, while recording their searches. Monitoring subjects ’ information searches along with their decisions allows us to better understand how their decisions are determined, and subjects ’ deviations from the search patterns suggested by equilibrium analysis help to predict their deviations from equilibrium decisions.

A rational agent in a multiagent world must decide on its actions based on the decisions it expects others to make, but it might believe that they in turn might be basing decisions on what they believe the initial agent will decide. Such reciprocal rationality leads to a nesting of models that can potentially become intractable. To solve such problems, game theory has developed techniques for discovering rational, equilibrium solutions, and AI has developed computational, recursive methods. These different approaches can involve different solution concepts. For example, the Recursive Modeling Method (RMM) finds different solutions than game-theoretic methods when solving problems that require mixed-strategy equilibrium solutions. In this paper, we show that a crucial difference between the approaches is that RMM employs a solution concept that is overeager. This eagerness can be reduced by introducing into RMM second-order knowledge about what it knows, in the form of a flexible functi...

. Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment effectively changes. Moreover, other agents' actions are often not directly observable, and the actions taken by the learning agent can strongly bias which range of behaviors are encountered. We define the concept of a conjectural equilibrium, where all agents' expectations are realized, and each agent responds optimally to its expectations. We present a generic multiagent exchange situation, in which competitive behavior constitutes a conjectural equilibrium. We then introduce an agent that executes a more sophisticated strategic learning strategy, building a model of the response of other agents. We find that the system reliably converges to a conjectural equilibrium, but that the final result achieved is highly sensitive to initial belief. In essence, the strategic learner's actions tend to fulfill its expectations. Depending on the starting point, the agent may be better or...

We address the issue of rational communicative behavior among autonomous intelligent agents that have to make decisions as to what, to whom, and how to communicate. We treat communicative actions as aimed at increasing the efficiency of interaction among agents. We postulate that a rational speaker design a speech act so as to maximally increase the benefit obtained as the result of the interaction. We quantify the gain in the quality of interaction as the expected utility, and we present a framework that allows an agent to compute the expected utility of various communicative actions. Our framework uses the Recursive Modeling Method as the representation of the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents ' beliefs, and so on. A decision-theoretic pragmatics of a communicative act can be then defined as the transformation it induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of the interaction, expressed in terms of the benefit to the agent. We analyze decision-theoretic pragmatics of a number of important communicative acts, and investigate their expected utility using examples.

We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm to determine the choice of coordinated action. We endow an agent with a specialized representation that captures the agent's knowledge about the environment and about the other agents, including its knowledge about their states of knowledge, which can include what they know about the other agents, and so on. This reciprocity leads to a recursive nesting of models. Our framework puts forth a representation for the recursive models and, under the assumption that the nesting of models is finite, uses dynamic programming to solve this representation for the agent's rational choice of action. Using a decision-theoretic approach, our work addresses concerns of agent decision-making about coordinated action in unpredictable situations, without imposing upon agents pre-designed prescriptions, or protocols, ...

. Drawing on our work in the area of distributed artificial intelligence, we put forth a framework for modeling a human user interacting with a knowledge-based system. We assume that the human user is situated in some decision making setting, and view the computer system as taking an active role in supporting the user's decision making and problem solving activities. The model the system has of the decision making situation and of the user can be applied to determine what the system should do, both in terms of the system's physical action, if such is possible, as well as in terms of the information that should be transmitted to the user. An important part of the user's model is the model that the user may have of the system itself, and, further, how the user may think it is being modeled by the system. Our framework, the Recursive Modeling Method (RMM), explicitly represents this nesting of models, and lets the system to coordinate with the expected actions of the human user, and to r...

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In 1952, Thompson defined four transformations on extensive games, and proved that they leave the reduced normal form intact (a so-- called "adequacy" theorem). Kohlberg&Mertens, in 1986, proposed two additional game transformations. In this paper, I will state and prove adequacy results for all six transformations in a uniform manner. Then, a number of well--known solution concepts will be surveyed. Finally, I will examine the (in)variance of the solution concepts under the game transformations, so as to determine whether they induced a plausible notion of game equivalence. 1 Introduction Game theory is often defined as the analysis of conflict---a description, however broad and misleading, that sets the stage for many of its applications in fields as diverse as the political sciences and evolutionary biology. More specifically, game theory is regarded as the study of solutions to conflicts. That is, a game theorist is expected to come up with a solution to particular games---this is...

Abstract Models with multiple equilibria are a popular way to explain currency attacks. Morris and Shin (1998) have shown that, in the context of those models, unique equilibria may prevail once noisy private information is introduced. In this paper, we generalize the results of Morris and Shin to a broader class of probability distributions and show | using the technique of iterated elimination of dominated strategies | that uniqueness will hold, even if we allow for sunspots and individual uncertainty about strategic behavior of other agents. We provide a clear exposition of the logic of this model and we analyse the impact of transparency on the probability of a speculative attack. For the case of uniform distribution of noisy signals, we show that increased transparency of government policy reduces the likelihood of attacks.