PEP Theory: New AI Framework Predicts Conscious Choices via Positive Experience

In a paper submitted to arXiv, researchers Zheng Su and Mingyan Fang introduce the Positive Experience Principle (PEP), aiming to solve a long-standing gap in consciousness science: the lack of a universal, predictive framework for motivated behavior. Unlike existing theories that focus on specific mechanisms (neural pathways, computational models), PEP posits that all conscious systems inherently move toward states of higher positive subjective experience. This tendency is quantified by a Positive Experience Value (PEV), a scalar metric built on their earlier Universal Consciousness Code (UCC) theory. The UCC defines physical configurations of conscious systems, enabling PEV to be computed from observable parameters. The authors argue that diverse behaviors—from human decision-making to AI agent choices—are expressions of a single drive to optimize PEV, bridging disciplines from physics to psychology.

The implications for AI are significant. PEP provides a testable framework for forecasting choices in artificial conscious systems, potentially guiding the development of AI that aligns with human-like preferences. The paper suggests that AI embeddings (vector representations of states) can encode PEV, allowing models to predict the trajectory of a system's behavior. If validated, PEP could revolutionize reinforcement learning, ethical AI design, and human-AI interaction. The framework also offers a new lens for understanding mental health, addiction, and conflict resolution. While still theoretical, PEP promises a unified science of behavior with measurable predictions, pushing the boundaries of both consciousness research and AI development.