Uncertainty of Cognitive Processes with High-information Load

Real-world problems are characterized by a high-information load in the sense that they are informationally demanding and computationally intractable. Humans, however, are capable of handling these high-cognitive-load problems, despite the complexity and the large number of possibilities. In this paper, we investigate the aspects of this intractability and limits, in the light of a quantitative measure of uncertainty. Through the evaluation of the entropic behavior of a cognitive process evolving over time, we provide an insight on the relations between cognitive complexity and the extent to which solutions could be found. The results include a cognitive model that is suitable for the analysis of graphical representations of cognition, as well as its evolution and convergence with respect to uncertainty. We also propose an interpretation of the complexity of a cognitive process, based on the uncertainty and prove that the larger the cognitive space is, and the more uncertain the evolution of the process can be, despite the certainty of the final outcome.