kriteresh

Kriteresh is a theoretical construct used in decision theory and related fields to represent a single numeric score that summarizes how well an option meets a set of criteria. It is defined as the value produced by an aggregation function applied to a vector of criterion scores, usually written as K = F(w1*c1, w2*c2, ..., wn*cn), where ci are normalized scores in [0,1] and wi are nonnegative weights representing the importance of each criterion. The kriteresh score is intended to capture both the strength of individual criteria and their interactions in a compact measure of overall desirability.

The term is a neologism, often employed in theoretical discussions or speculative fiction to illuminate evaluation procedures. It can be instantiated with linear aggregation (a weighted sum) or with nonlinear monotone functions that permit interactions between criteria.

Typical properties include monotonicity (raising any criterion cannot decrease K when others are fixed), normalization invariance (scales can be normalized to a common [0,1] range without changing comparisons), and sensitivity to weights and the chosen aggregation function. Some models introduce interaction terms or threshold behaviors to reflect minimum acceptable levels or synergy among criteria.

Applications of kriteresh appear in discussions of multi-criteria decision analysis, policy evaluation, ethics and value-based design, and artificial intelligence systems that must balance competing objectives. As a formal device, it provides a point of comparison across alternatives and supports exploration of trade-offs between different criteria.

Related concepts include multi-criteria decision analysis, aggregation functions, weighted sum models, thresholds, and preference modeling.