Information Games: Strategic Crowding and Firm Repositioning in Language-Model Space
Abstract
Firms’ capacity adjustments to cash-flow shocks are modeled as costly competitive moves and tested through simulation and news/analyst data using language-model-based relevance measures.
When firms respond to new market opportunities, their capacity choices alter the future discounted free cash flows available to both competitors and investors. In competitive markets, these investment decisions publicized in news articles and analyst reports, measuring the changes in capital structure, product mix, firm leadership, risk exposure and operating procedure made by the firm. Modern search engines rely on Large Language Models to measure the relevancy of source documents to given search query. These measurements let researchers represent these news articles as clouds of dense vectors whose information geometry reflects the dissimilarity of firms over time. Building on Xu et al. (2022), we develop a model of costly competitive adjustment in which firms allocate capacity in which firms make firm capacity adjustment towards free cash flow shocks. Using a novel sandwich estimator, we analyse the sign and magnitude of this predicted adjustment using multi-agent simulation, and validate our findings on a sample of news and analyst reports from Nasdaq-listed companies between 2018-2022.
Community
The recent work by Jan LeCunn and Randall Balestriero "Proving and Improving the Platonic Representation Hypothesis with JEPAs", begs some deep question about the role of representation learning in the study of body natural and social phenomenon - might measures of similarity be an intrinsic economic property, and if so in efficient markets might movements in information space reveal something fundamental about competition and incentive? To economists this is a deep and fascinating question, but to machine learning researchers, I think this begs a question as to whether evals can be market-driven: distributed and leakage proof? Might this be the future of measurement or reward.
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