A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making

Dynamic Bayesian Decision - Making :

Dynamic Non-Bayesian Decision Making

The model of a non-Bayesian agent who faces a repeated game with incomplete information against Nature is an appropriate tool for modeling general agent-environment interactions. In such a model the environment state (controlled by Nature) may change arbitrarily, and the feedback/reward function is initially unknown. The agent is not Bayesian, that is he does not form a prior probability neithe...

Bayesian Toolbox for Dynamic Distributed Decision Making

The paper outlines a new challenging area of control, namely distributed decision making under uncertainty. Presence of many decision-making units, called the participants is the main distinction from the classical control theory. The problem is analysed from Bayesian point of view. Each participant has the abilities of classical adaptive controller, i.e. learning of the environment and design ...

A GA Model Development for Decision Making Under Reverse Logistics

  Managing products’ end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to s...

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence ab...