Extrapolating Volition with Recursive Information Markets
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Abstract (arXiv)
One of the impediments to the efficiency of information markets is the inherent information asymmetry present in them, exacerbated by the "buyer's inspection paradox" (the buyer cannot mitigate the asymmetry by "inspecting" the information, because in doing so the buyer obtains the information without paying for it). Previous work has suggested that using Large Language Model (LLM) buyers to inspect and purchase information could overcome this information asymmetry, as an LLM buyer can simply "forget" the information it inspects. In this work, we analyze this mechanism formally through a "value-of-information" paradigm, i.e. whether it incentivizes information to be priced and provided in accordance with its "true value". We focus in particular on our new recursive version of the mechanism, which we believe has a range of applications including in AI alignment research, where it is related to Extrapolated Volition and Scalable Oversight.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 38 references that are in the map. First posted on arXiv 8 Apr 2026.
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References · 7
- 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
- 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
- 2018AI safety via debatecited by 384
- 2023EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activitiescited by 154
- 2018Supervising strong learners by amplifying weak expertscited by 139
- 2024Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracycited by 66
- 2024Imperfect-Recall Games: Equilibrium Concepts and Their Complexitycited by 4
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