COOPERATIVE DECISION MAKING WITHOUT FACILITATOR

Estimation, learning, pattern recognition, diagnostics, fault detection and adaptive control are prominent examples of dynamic decision making under uncertainty. Under rather general conditions, they can be cast into a common theoretical framework labelled as Bayesian decision making. Richness of the practically developed variants stems from: (i) domain-specific models used; (ii) adopted approximations fighting with limited perceiving and evaluation abilities of the involved decision-making units, called here participants. While modelling is a well-developed art, the item (ii) still lacks a systematic theoretical framework. This paper provides a promising direction that could become a basis of such framework. It can be characterized as multiple-participant decision making exploiting Bayesian participants equipped with tools for sharing their knowledge and harmonizing their aims and restrictions with their neighbors. Intentional avoiding of the negotiation facilitator makes the solution fully scalable.