Hierarchical Team Learning using Fuzzy Perceptron Algorithms

Document Type

Presentation Abstract

Presentation Date

1-26-2006

Abstract

An approach to the problems of learning in team theory and perceptrons is presented. Economic theory of teams was initiated by Marschak and developed by Radner. The Marschak-Radner models of static teams are groups of individuals with a common goal but with individual information structures and decision rules. In the previous works, we studied fuzzy information structures and fuzzy decision rules and presented various models of team decision-making under fuzziness. Team theory enables one to decide analytically the optimal decision rules only in few cases. This drawback and the need for a computational distributed algorithm lead us to approximate the functional optimal team decision problem to a parametric one. Here, we discuss the formal relationship between perceptrons and team models, and introduce the various learning concepts of perceptrons and fuzzy sets to the extended team models because there is yet no fully developed theory of team learning. Hierarchical learning team models using fuzzy perceptron algorithms are also proposed. These models use the learning rules to adjust a weight matrix interpreted as the intensity of the team member's informal human relations expressed by the ideas of fuzzy relations.

Additional Details

Thursday, 26 January 2006
4:10 p.m. in Math 109

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