Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman Martin Weiss Manuel Wüthrich Yoshua Bengio Li Erran Li Chris Pal Bernhard Schölkopf
This work addresses the buyer's inspection paradox for information markets.
The paradox is that buyers need to access information to determine its value,
while sellers need to limit access to prevent theft. To study this, we
introduce an open-source simulated digital marketplace where intelligent
agents, powered by language models, buy and sell information on behalf of
external participants. The central mechanism enabling this marketplace is the
agents' dual capabilities: they not only have the capacity to assess the
quality of privileged information but also come equipped with the ability to
forget. This ability to induce amnesia allows vendors to grant temporary access
to proprietary information, significantly reducing the risk of unauthorized
retention while enabling agents to accurately gauge the information's relevance
to specific queries or tasks. To perform well, agents must make rational
decisions, strategically explore the marketplace through generated sub-queries,
and synthesize answers from purchased information. Concretely, our experiments
(a) uncover biases in language models leading to irrational behavior and
evaluate techniques to mitigate these biases, (b) investigate how price affects
demand in the context of informational goods, and (c) show that inspection and
higher budgets both lead to higher quality outcomes.