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[2602.17222] Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight
Summary
The paper presents the Large Behavioral Model (LBM), a novel AI framework designed to enhance the prediction of human decision-making in complex environments by utilizing detailed psychological profiles.
Why It Matters
Understanding human behavior is crucial for applications in strategic foresight and decision support. This research addresses the limitations of existing models by integrating psychological traits into AI predictions, potentially improving outcomes in high-stakes scenarios.
Key Takeaways
- The Large Behavioral Model (LBM) improves behavioral prediction by using detailed psychological profiles.
- LBM outperforms traditional prompting-based approaches, which struggle with identity drift.
- The model shows scalability and enhanced performance with more complex trait profiles.
- Applications include strategic foresight, negotiation analysis, and cognitive security.
- LBM's training on a proprietary dataset links psychological traits to decision-making.
Computer Science > Artificial Intelligence arXiv:2602.17222 (cs) [Submitted on 19 Feb 2026] Title:Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight Authors:Ben Yellin, Ehud Ezra, Mark Foreman, Shula Grinapol View a PDF of the paper titled Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight, by Ben Yellin and 3 other authors View PDF HTML (experimental) Abstract:Predicting human decision-making in high-stakes environments remains a central challenge for artificial intelligence. While large language models (LLMs) demonstrate strong general reasoning, they often struggle to generate consistent, individual-specific behavior, particularly when accurate prediction depends on complex interactions between psychological traits and situational constraints. Prompting-based approaches can be brittle in this setting, exhibiting identity drift and limited ability to leverage increasingly detailed persona descriptions. To address these limitations, we introduce the Large Behavioral Model (LBM), a behavioral foundation model fine-tuned to predict individual strategic choices with high fidelity. LBM shifts from transient persona prompting to behavioral embedding by conditioning on a structured, high-dimensional trait profile derived from a comprehensive psychometric battery. Trained on a proprietary dataset linking stable dispositions, motivational states, and situational constraints to observed choices, LBM learns to ...
