Provave

Provave is a theoretical framework in cognitive science and artificial intelligence that describes how agents proactively evaluate potential actions by estimating the valence and uncertainty of expected outcomes. It integrates predictive processing with probabilistic valuation to guide decision making in uncertain environments.

Provave stands for Proactive Valence Estimation. In literature, the term is sometimes used informally to refer to similar concepts of anticipatory valuation, though it is not tied to a formal standardized theory across disciplines.

The model comprises three core components: an anticipatory module that predicts possible futures; a valuation module that assigns valence or desirability to predicted outcomes using Bayesian or reinforcement-learning-based estimates; and a control module that selects actions to maximize expected utility while minimizing surprise. It emphasizes proactive rather than reactive decision making and aims to account for how agents prepare for likely events rather than simply reacting to them.

Implementation and scope: In computational studies, Provave can be realized within model-based reinforcement learning or hierarchical Bayesian frameworks. It has been discussed in the context of robotic planning and human–computer interaction to explain rapid adaptation, goal-directed behavior, and the management of uncertainty in dynamic environments.

Criticism: Critics note that Provave relies on subjective valence definitions and requires careful calibration of priors and reward structures. The approach can be computationally intensive, and translating theoretical formulations into robust real-world systems remains a challenge.

See also: predictive processing, reinforcement learning, Bayesian inference, value estimation, decision theory.