arXiv (All)
open access
Prospect Theory in Large Language Models: A Meta-Analysis of Loss Aversion, Diminishing Sensitivity, and Probability Weighting
Prospect theory splits risky choice into three parts: loss aversion, measured by the relative weight of losses to gains, $λ$ (e.g., $λ= 2$ implies that losses weigh twice as much as gains), diminishing sensitivity to the size of outcomes (i.e., the curvature and linearity of the value function), and
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Finite-Capacity Prospect Theory: State-Dependent Risk Preferences under Resource, Attention, and Boundary-Risk Constraints#203517
- Dual-process theory and decision-making in large language models#1078447
- Stochastic Potential Choices and Outcomes#615110
- Stochastic Potential Choices and Outcomes#614143
- Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty#1128512
Record · ID 1455584
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive