#behavioral-economics News & Analysis

4 articles tagged with #behavioral-economics. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBearisharXiv – CS AI · Apr 157/10

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Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models

Researchers tested whether large language models exhibit the Identifiable Victim Effect (IVE)—a well-documented cognitive bias where people prioritize helping a specific individual over a larger group facing equal hardship. Across 51,955 API trials spanning 16 frontier models, instruction-tuned LLMs showed amplified IVE compared to humans, while reasoning-specialized models inverted the effect, raising critical concerns about AI deployment in humanitarian decision-making.

🏢 OpenAI🏢 Anthropic🏢 xAI

AIBearisharXiv – CS AI · Apr 137/10

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Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty

Researchers challenge the applicability of Prospect Theory to Large Language Models, finding that PT parameters are unstable when models encounter epistemic uncertainty markers like "likely" or "probably." The study warns against deploying PT-based frameworks in real-world applications where linguistic ambiguity is common, raising critical questions about LLM decision-making reliability.

AINeutralarXiv – CS AI · Mar 167/10

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Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach

Researchers developed a supervised fine-tuning approach to align large language model agents with specific economic preferences, addressing systematic deviations from rational behavior in strategic environments. The study demonstrates how LLM agents can be trained to follow either self-interested or morally-guided strategies, producing distinct outcomes in economic games and pricing scenarios.

AINeutralarXiv – CS AI · Apr 76/10

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Incentives shape how humans co-create with generative AI

A randomized control trial reveals that incentive structures significantly influence how humans use generative AI in creative tasks. When participants were rewarded for originality rather than just quality, they produced more diverse collective output by using AI more selectively for brainstorming and editing rather than copying suggestions verbatim.