Abstract not found

Focal points refer to prominent solutions of an interaction, solutions to which agents are drawn. This paper considers how automated agents could use focal points for coordination in communication-impoverished situations. Coordination is a central theme of Distributed Artificial Intelligence. Much work in this field can be seen as a search for mechanisms that allow agents with differing knowledge and goals to coordinate their actions for mutual benefit. Additionally, one of the main assumptions of the field is that communication is expensive relative to computation. Thus, coordination techniques that minimize communication are of particular importance. Our purpose in this paper is to consider how to model the process of finding focal points from domain-independent criteria, under the assumption that agents cannot communicate with one another. We consider two alternative approaches for finding focal points, one based on decision theory, the second on step-logic. The first provides for ...

This paper investigates optimal play in coordination games in which cognition plays an important role. In our game logically omniscient players would be able to identify a distinct coordination opportunity from other obvious facts. Real players may be unable to make the required inference. Our main experimental results are that in a coordination task with a cognitive component (1) players play differently when playing against themselves rather than against another player, and (2) given the opportunity, players signal cognition by choosing the coordination task over an outside option, a phenomenon which we refer to as cognitive forward induction.

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tactic coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tactic coordination domains. Learning to classify general human choices, however, is very difficult. Nevertheless, humans are often able to coordinate with one another in communication-free games, by using focal points, “prominent ” solutions to coordination problems. We integrate focal points into the machine learning process, by transforming raw domain data into a new hypothesis space. This results in classifiers with an improved classification rate and shorter training time. Integration of focal points into learning algorithms also results in agents that are more robust to changes in the environment. 1

This paper presents an experimental investigation of optimal learning in repeated coordination games. We find evidence for such learning when we limit both the cognitive demands on players and the information available to them. We also find that uniqueness of the optimal strategy is no guarantee that it will be used. Optimal learning can be impeded by both irrelevant information and the complexity of the

Abstract. Understanding more about how the brain functions should help us understand economic behaviour. But some would have us believe that it has done this already, and that insights from neuroscience have already provided insights in economics that we would not otherwise have. Much of this is just academic marketing hype, and to get down to substantive issues we need to identify that fluff for what it is. After we clear away the distractions, what is left? The answer is that a lot is left, but it is still all potential. That is not a bad thing, or a reason to stop the effort, but it does point to the need for a serious reconsideration of what neuroeconomics is and what passes for explanation in this literature. I argue that neuroeconomics can be a valuable field, but not the way it is being developed and “sold ” now. The same is true more generally of behavioural economics, which shares

Abstract not found

Abstract not found

Abstract not found

Abstract 5