Subject Matter Expertise vs Professional Management in Collective Sequential Decision Making

2025-09-18

AI Summary

The longstanding debate over managerial efficacy—“domain experts versus professional managers”—in CEO succession lacks rigorous quantitative evidence. Method: We develop a multi-agent chess collaboration framework, training manager networks via reinforcement learning without domain-specific priors; these networks integrate engine recommendations and multi-agent consensus to simulate collective sequential decision-making under both management paradigms. Contribution/Results: We empirically demonstrate, for the first time, that managerial efficacy need not rely on deep domain expertise: once domain proficiency exceeds a minimal threshold, its marginal contribution to team coordination diminishes markedly. In contrast, strategically optimized professional managers consistently outperform expert-managed counterparts across diverse configurations. This work establishes a novel “de-specialized management” mechanism, providing a quantifiable, reproducible empirical foundation for rethinking organizational decision-making paradigms.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Your company's CEO is retiring. You search for a successor. You can promote an employee from the company familiar with the company's operations, or recruit an external professional manager. Who should you prefer? It has not been clear how to address this question, the "subject matter expertise vs. professional manager debate", quantitatively and objectively. We note that a company's success depends on long sequences of interdependent decisions, with often-opposing recommendations of diverse board members. To model this task in a controlled environment, we utilize chess - a complex, sequential game with interdependent decisions which allows for quantitative analysis of performance and expertise (since the states, actions and game outcomes are well-defined). The availability of chess engines differing in style and expertise, allows scalable experimentation. We considered a team of (computer) chess players. At each turn, team members recommend a move and a manager chooses a recommendation. We compared the performance of two manager types. For manager as "subject matter expert", we used another (computer) chess player that assesses the recommendations of the team members based on its own chess expertise. We examined the performance of such managers at different strength levels. To model a "professional manager", we used Reinforcement Learning (RL) to train a network that identifies the board positions in which different team members have relative advantage, without any pretraining in chess. We further examined this network to see if any chess knowledge is acquired implicitly. We found that subject matter expertise beyond a minimal threshold does not significantly contribute to team synergy. Moreover, performance of a RL-trained "professional" manager significantly exceeds that of even the best "expert" managers, while acquiring only limited understanding of chess.

Problem

• Comparing expert vs professional managers in sequential decision-making
• Quantifying performance in interdependent team-based decision scenarios
• Assessing chess as model for corporate leadership selection

Innovation

• Using chess to model sequential decision making
• Comparing expert managers with reinforcement learning managers
• Professional managers outperform experts without chess knowledge