Krationality

Krationality is a theoretical framework in decision theory and epistemology that evaluates rational action under explicit knowledge constraints. It treats knowledge as a resource that can be scarce, imperfect, or evolving, rather than assuming complete information. Agents are described by a knowledge state K that captures what is known, what remains uncertain, and how new information would change beliefs and opportunities. The framework aims to maximize expected utility while accounting for the costs of acquiring and processing knowledge.

The term emerged in discussions connecting bounded rationality and computational rationality, with K commonly read as knowledge or context. Proponents contend that Krationality generalizes standard rational choice by incorporating epistemic costs and learning dynamics, rather than treating information as free. Critics note that the concept can be underspecified and its empirical measurement hinges on how K is defined and quantified.

Core ideas include the knowledge state K_t, information costs, belief updates, and decision policies that trade accuracy against cognitive and computational effort. Models often use information-theoretic measures to quantify costs and Bayesian updating to reflect changes in K. Policy optimization seeks a balance between better predictions and the resources required to obtain information.

Methodologically, Krationality is formalized in dynamic settings via variants of partially observable decision processes with additional information-cost terms. Related approaches include resource-rational analysis and computational rationality, which integrate cognitive costs into optimal or satisficing strategies using techniques from Bayesian decision theory and reinforcement learning.

Applications appear in artificial intelligence, behavioral economics, and policy design where information costs and knowledge gaps matter. Critics warn that Krationality risks vagueness if K is ill-defined and that operationalizing the costs of knowledge is challenging. Empirical work requires clear specifications of knowledge, its evolution, and processing costs.