Cooperative decision making without facilitator
Miroslav Karny, Jan Kracik
Estimation, learning, pattern recognition, diagnostics, fault detection and feedback 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.