I study how organizations search for strategies, represent problems, and aggregate judgment under cognitive limits. My work uses formal models, empirical tests, and essays for broader audiences. The publications below are organized by theme rather than by date. For the full reverse-chronological record, see my CV or Google Scholar profile.

Unbounding rationality and AI

This work asks what happens when AI relaxes the cognitive limits that have long shaped strategy theory and practice.

• AI is revolutionizing strategic decision-making: the Harvard Business Review version of the argument that AI expands strategic search, representation, and aggregation.
• Unbounding rationality: Why AI is a fundamental issue for strategy: the core theoretical statement of why AI matters for the foundations of strategy.
• Handbook of Artificial Intelligence and Strategy: a 2026 Edward Elgar volume co-edited with Nan Jia, including our editors’ introduction, “Artificial intelligence and strategy: Charting new frontiers.”
• Can AI do strategy?: the introduction essay to the Strategy Science special issue on AI and strategy.
• Can AI do strategy? A dialogue and debate: a set of essays from the Strategy Science debate on what AI can and cannot do in strategy.
• Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors: evidence on how AI performs when generating and evaluating business plans.
• Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory: a review of how organization theory has drawn on AI ideas, and why organizations can be understood as artificial intelligences.

Search

Search is the problem of looking for better actions when the full landscape cannot be seen. This stream studies imitation, positioning, innovation, foresight, and policy through that lens.

• The strategic foresight of LLMs: Evidence from a fully prospective venture tournament: a live tournament comparing LLMs and human evaluators on uncertain venture outcomes.
• When to innovate and when to imitate: a framework for choosing between pushing the frontier and learning from nearby competitors.
• Cognitive and structural antecedents of innovation: A large-sample study: evidence on adoption and implementation during disruptive change.
• Government as landscape designer: A behavioral view of industrial policy: a behavioral strategy view of how policy shapes firm search.
• Positioning on a multi-attribute landscape: a model of competitive positioning when firms choose among several product attributes.
• An efficient frontier in organization design: Organizational structure as a determinant of exploration and exploitation: a model of how organization design shapes exploration and exploitation.
• How much to copy? Determinants of effective imitation breadth: a theory of why copying more is not always better.

Representation

Strategy depends on how decision-makers model the world. This work studies mental models, external visuals, and distributed representations built from many partial views.

• The power and limits of distributed representations in strategic decision-making: a formal account of when aggregating many simplified models helps or hurts.
• External representations in strategic decision making: Understanding strategy’s reliance on visuals: why strategy relies so heavily on diagrams, frameworks, and other external representations.
• Learning strategic representations: Exploring the effects of taking a strategy course: evidence on what MBA students learn from a strategy course.
• A contingency theory of representational complexity in organizations: when simple models are useful and when richer representations are worth the cost.
• Individual and organizational antecedents of strategic foresight: A representational approach: an early study of what improves forward-looking judgment.
• What makes a decision strategic? Strategic representations: a concise statement of the representational view of strategy.
• Mental representation and the discovery of new strategies: how managers’ mental models shape the strategies they can find.

Aggregation

Organizations do cognitive work by combining the judgments, information, and preferences of many people. This stream studies how those aggregation structures change decisions.

• Revisiting the unitary actor assumption: Toward realistic aggregation of individual preferences in strategy research: a model of how individual preferences become organizational utility.
• When ‘less is more’: How statistical discrimination can decrease predictive accuracy: why using less information can sometimes improve prediction.
• Limits to the wisdom of the crowd in idea selection: when larger crowds stop improving idea selection.
• When consensus hurts the company: a practitioner essay on when consensus helps and when it gets in the way.
• Organizational decision making: An information aggregation view: a model of delegation, voting, averaging, and individual decision-making.
• Organizational structure as a determinant of performance: Evidence from mutual funds: large-sample evidence on how structure shapes decision errors.

Methods, Foundations, and Teaching

Some pieces are methodological or foundational. Others study what strategy education does and how AI changes the classroom.

• The effects of artificial intelligence on management education: what AI means for business schools and management education.
• Introduction to Strategy (University of Michigan, 2023): a short introductory volume I use in teaching and share with faculty and graduate students on request.
• A note on calculating the average span of control: a technical note on measuring organizational structure.
• Certum quod factum: How formal models contribute to the theoretical and empirical robustness of organization theory: why formal models matter for theory development.
• A note on how NK landscapes work: a short guide to a common method in strategy research.
• Strategic decision making: an encyclopedia entry on the field’s central concepts and tools.

Current Work and Doctoral Advising

My current agenda revolves around AI, foresight, innovation, and strategic decision-making. I advise PhD students and serve on doctoral committees in strategy and organization theory. Former students I have advised or co-advised have placed at Wharton, UT Austin McCombs, Bocconi, and Washington Foster. If these questions speak to you, the best starting point is to read a few papers above and then write.