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(eng): 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 modeling 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.

(cze): kooperující rozhodování bez usnadňujícího prostředku