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Ben Yellin
OMGene AI Lab
E
Ehud Ezra
OMGene AI Lab
M
Mark Foreman
OMGene AI Lab
S
Shula Grinapol
OMGene AI Lab
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References (8)
[3]
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Founder's Pitch
"Large Behavioral Model predicts individual strategic decisions for applications in foresight, negotiation, and decision support."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
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3/4 signals
Series A Potential
4/4 signals
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Why It Matters
This research enhances the predictive fidelity of human decision-making models in strategic settings by linking psychological profiles with behavior, crucial for fields like negotiation, cognitive security, and strategic foresight.
Product Angle
Focus on industries that need precise behavioral predictions, offering LBM as a SaaS for businesses involved in negotiations, strategic planning, or cognitive security, integrating detailed psychometric profiles for accuracy.
Disruption
LBM could replace existing decision support systems that rely on generic LLMs or simplistic personality models, offering more precise and individualized behavior predictions.
Product Opportunity
The market for strategic foresight and decision support in high-stakes environments such as diplomacy, military planning, and high-level negotiations is substantial, with companies and governments willing to invest in technology that enhances predictive accuracy.
Use Case Idea
Develop a decision support tool for negotiators and strategic planners to simulate and forecast scenarios based on psychological profiling of stakeholders.
Science
The Large Behavioral Model (LBM) is a foundation model fine-tuned to predict individual strategic choices using structured trait profiles from comprehensive psychometric tests, improving prediction fidelity over traditional prompting techniques.
Method & Eval
Tested on a proprietary dataset linking psychological traits to strategic decisions, the model improves over baseline LLMs in prediction accuracy, particularly under Big Five trait conditioning.
Caveats
Major limitations include dependency on a comprehensive psychometric dataset, potential bias in participant sampling, and scalability of the model to different cultural contexts.
Author Intelligence
Ben Yellin
OMGene AI Lab
Ehud Ezra
OMGene AI Lab
Mark Foreman
OMGene AI Lab
Shula Grinapol
OMGene AI Lab