14 June 2012
University of Western Sydney, Australia
Dongmo Zhang

As one of the most fundamental models in modern economic theory, the Nash bargaining solution (Nash 1950) has been developed in the past six decades into a high sophisticated theory with varieties of models, extensions, and applications. Despite mathematical simplicity and elegance, it has been found that such a purely numerical theory of bargaining is difficult to model bargaining among autonomous software agents.
From Artificial Intelligence (AI) point of view, bargaining is a rivalry between intelligent agents. Logical reasoning must play an essential role in a bargaining process. A bargaining theory should be able to model both logical reasoning behind bargaining. This talk gives a brief introduction to a logical theory of bargaining. Instead of abstracting a bargaining problem as a numerical game, we describe bargaining terms and their constraints in logical language. A bargaining problem is then modelled as a vector of sets of ordered bargaining terms in terms of bargainers’ preferences. A solution to a bargaining problem is defined as a logically consistent collection of bargaining terms that maximize individual demand and minimize the imbalance of individual satisfaction. Depending on the representation of bargainers’ preferences, the characterization of a bargaining solution can be in pure logical axioms or consists of a combination of logical and game-theoretic axioms. The logical theory of bargaining seemingly has offered a new methodology for bargaining analysis therefore could be also applied to the disciplines other than computer science, such as economics, social science and political science.

About the speaker: Dongmo Zhang is an associate professor at the University of Western Sydney. His research interests include belief revision, reasoning about action, bargaining theory, e-trading and multiagent systems. He is the leader of jackaroo trading agent team.