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Felipe A. Csaszar

Publications and source records attributed to Felipe A. Csaszar.

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Revisiting the Unitary Actor Assumption: Toward Realistic Aggregation of Individual Preferences in Strategy Research

The long-standing unitary-actor assumption in strategy research -- treating firms as monolithic entities with coherent preferences -- misses that organizations are coalitions of individuals with diverse and often conflicting goals. Although behavioral perspectives have challenged this assumption, the field lacks an operational method for deriving an organizational utility function from the disparate preferences of its members and the specific structures used to aggregate them. We develop a mathematical framework that (i) maps individual utility functions into choice probabilities via a random-utility model, (ii) combines those probabilities using an explicit aggregation structure (e.g., unanimity or polyarchy), and (iii) recovers an organizational utility function that rationalizes the collective behavior. This establishes organizational utility functions as operationally meaningful: they summarize and predict organizational choice, yet are generally not simple averages of members' utilities. Instead, aggregation structures systematically reshape preferences -- unanimity approximates the pointwise minima of underlying utility functions, amplifying risk aversion; polyarchy approximates the pointwise maxima, promoting risk-seeking. We illustrate strategic implications in Cournot competition and principal-agent settings, showing how internal aggregation structures shift competitive and collaborative outcomes. Overall, the framework provides a parsimonious way to retrofit unitary-actor models with behaviorally grounded organizational preferences, reconciling the coalition view of the firm with rigorous and tractable strategic analysis.

econ.TH

The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament

Can artificial intelligence outperform humans at strategic foresight -- the capacity to form accurate judgments about uncertain, high-stakes outcomes before they unfold? We address this question through a fully prospective prediction tournament using live Kickstarter crowdfunding projects. Thirty U.S.-based technology ventures, launched after the training cutoffs of all models studied, were evaluated while fundraising remained in progress and outcomes were unknown. A diverse suite of frontier and open-weight large language models (LLMs) completed 870 pairwise comparisons, producing complete rankings of predicted fundraising success. We benchmarked these forecasts against 346 experienced managers recruited via Prolific and three MBA-trained investors working under monitored conditions. The results are striking: human evaluators achieved rank correlations with actual outcomes between 0.04 and 0.45, while several frontier LLMs exceeded 0.60, with the best (Gemini 2.5 Pro) reaching 0.74 -- correctly ordering nearly four of every five venture pairs. These differences persist across multiple performance metrics and robustness checks. Neither wisdom-of-the-crowd ensembles nor human-AI hybrid teams outperformed the best standalone model.

econ.GN

Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors

This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a startup competition that current Large Language Models (LLMs) can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for key cognitive processes underlying SDM -- search, representation, and aggregation. Our analysis suggests AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy.

econ.GN