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

SSRN Electronic Journal · 2026-01-01

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

arXiv (Cornell University) · 2026-02-24

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.

Can AI Do Strategy? A Dialogue and Debate

Strategy Science · 2026-01-01 · 1 citations

Editors’ introduction: artificial intelligence and strategy—charting new frontiers

Edward Elgar Publishing eBooks · 2026-03-17

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

ArXiv.org · 2026-02-02

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.

Can AI Do Strategy?

Strategy Science · 2026-01-01 · 1 citations

Can artificial intelligence (AI) do strategy? This question is both urgent and foundational: urgent because AI is already reshaping strategic practice and foundational because answering it forces us to articulate what strategy actually is. In this introductory essay to the Strategy Science Special Issue on AI and Strategy, we propose a dual-ladder framework: a causal ladder that maps the cognitive hierarchy of strategic tasks and a delegation ladder that specifies when organizations will grant AI autonomy over those tasks. A core insight emerges: AI will not enter strategy where required reasoning is deepest but where its performance is most measurable. We organize the Special Issue contributions around what AI can do today, could do as capabilities develop, and should do given the imperatives of accountability and human judgment. We close with a challenge and an invitation: if strategy scholars do not define good strategizing precisely enough to be encoded, tested, and refined, other disciplines will, embedding thinner conceptions of strategy into the tools managers use. Teaching machines to strategize and support strategizing is ultimately a method for rediscovering what strategy is. History: Accepted for the Special Issue: Can AI Do Strategy?

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

Open MIND · 2026-02-02

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.

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

arXiv (Cornell University) · 2026-02-24

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.

Editors' Introduction: Artificial Intelligence and Strategy—Charting New Frontiers

SSRN Electronic Journal · 2025-01-01

Counterfactual Histories and Counterfactual Futures: A New Direction for Cognition

Academy of Management Proceedings · 2025-07-01

This symposium advances the exploration of imagination as a structured cognitive process that shapes strategic thinking, innovation, and entrepreneurship. While imagination is often perceived as spontaneous, free-associative, or akin to daydreaming, research in cognitive science and psychology reveals that counterfactual imagination—focused on reconstructing pasts and envisioning futures—follows an identifiable structure. This symposium highlights the role of counterfactual imagination in generating novel strategies, reassessing historical contingencies, and envisioning desirable futures. Panelists will discuss the epistemic value of counterfactual thinking, its function in sensemaking, the role of AI in counterfactual imagination, the construction of environments through the use of counterfactual imagination, and its potential to be cultivated as a skill for strategic management and entrepreneurship.