Personalization as a Game: Equilibrium-Guided Generative Modeling for Physician Behavior in Pharmaceutical Engagement
New framework unifies Bayesian games, category theory, and LLMs to model physician behavior with 28% better content relevance.
Researcher Suyash Mishra has introduced the Equilibrium-Guided Personalization Framework (EGPF), a groundbreaking AI architecture that mathematically models the complex relationship between pharmaceutical companies and physicians. The system treats their interaction as an incomplete-information Bayesian game, where physician behavioral types are inferred through category-theoretic functors. This allows for the creation of modular, composable physician archetypes that can adapt to domain shifts. The framework then uses these models to guide large language models (LLMs) in generating hyper-personalized engagement content, all while maintaining structural invariants.
EGPF incorporates several novel theoretical components to ensure robustness and privacy. It introduces a Rate-Distortion Equilibrium (RDE) criterion to formally bound the trade-off between personalization effectiveness and data privacy. An Evolutionary Game Dynamics layer models population-level behavior shifts, while a Mechanism Design module ensures engagement strategies are incentive-compatible. The paper proves the system's iterative belief-update mechanism converges at a rate of O(K log K / (t * C_min)) and establishes finite-sample regret bounds. In practical tests on synthetic datasets and a real-world healthcare professional (HCP) engagement pilot, EGPF demonstrated a 34% improvement in engagement prediction (measured by AUC) and a 28% lift in content relevance scores over existing state-of-the-art methods.
- EGPF models pharma-physician interaction as a Bayesian game, using category theory to create adaptive physician archetypes.
- The framework's novel Rate-Distortion Equilibrium (RDE) criterion mathematically bounds the personalization-privacy tradeoff.
- Real-world pilot tests showed a 34% boost in engagement prediction and 28% higher content relevance versus current methods.
Why It Matters
This provides a rigorous, ethical AI blueprint for hyper-personalized marketing that respects privacy, moving beyond simple LLM prompts.