Matteo Tranchero

• Matteo Tranchero, Cecil-Francis Brenninkmeijer, Arul Murugan, Abhishek Nagaraj (2026), Simulating strategic interactions with AI agents, Strategic Management Journal. Abstract
  We explore how Large Language Models (LLMs) can serve as synthetic subjects to inform strategy research. We introduce a framework for designing and running simulated experiments with LLM-powered agents. We argue that this approach is useful for rapid, low-cost prototyping of human experiments and for generating novel hypotheses. We apply the framework to the exploration–exploitation dilemma and show that LLM-based experiments reproduce patterns observed among human participants. We then vary parameters and boundary conditions to illustrate how the same setup can support design iteration and surface hypotheses about when and why established results change. In the conclusion, we discuss the promise and limitations of artificial intelligence agents as “model organisms” for strategy.
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• Matteo Tranchero, Enrico Berkes, Matthew Lee Che (2026), 300 years of British patents, Research Policy, 105347 (). Abstract
  The study of innovation depends heavily on high-quality patent data. Yet, datasets containing complete patent documents focus only on recent decades, while historical patent datasets with broader temporal coverage typically lack detailed information. Therefore, our ability to leverage advances in textual analyses to study long-run innovation dynamics remains limited. To this end, we introduce a large-scale dataset of the universe of technical specifications of British patents granted between 1617–1899. Our data consists of the full specification texts alongside linked information about inventors, including their disambiguated names, occupations, and addresses. We use our data to document changes over time in total inventive activity, the geography of innovation, inventor occupations, and patent novelty and impact. Finally, we discuss use cases and avenues for subsequent research.
• Matteo Tranchero (Working), Finding Diamonds in the Rough: Data-Driven Opportunities and Pharmaceutical Innovation. Abstract

  Big data are increasingly used to make predictions about uncertain investments, thereby helping firms identify innovation opportunities without the need for domain knowledge. This trend has led to questions about which firms will primarily benefit from the availability of these data-driven predictions. Contrary to existing research suggesting that data-driven predictions level the playing field for firms lacking domain knowledge, I argue—using a simple theoretical framework—that these predictions actually reinforce the competitive advantage of firms with domain knowledge. In innovation contexts, where returns are skewed and not all leads can be pursued, domain knowledge helps evaluate predictions and avoid false positives. I test this idea in the context of pharmaceutical innovation, exploiting the features of genome-wide association studies (GWASs) that provide data-driven predictions about new drug targets. The results show that GWASs stimulate corporate investments, but around one-third of these resources are misallocated toward false positive predictions. Companies lacking domain knowledge react more strongly but are disproportionally likely to fall into the trap of false positives. Instead, domain knowledge helps firms make fewer investments that target only the best opportunities. Together, the results show that even if data-driven predictions hold value when searching for innovations, domain knowledge remains the crucial source of competitive advantage in the age of big data.

• Abhishek Nagaraj and Matteo Tranchero (Working), How Does Data Access Shape Science? The Impact of Federal Statistical Research Data Centers on Economics Research. Abstract

  This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau’s Federal Statistical Research Data Centers (FSRDCs). FSRDCs boost data diffusion, help empirical researchers publish more articles in top outlets, and increase citation-weighted publications. Besides direct data usage, spillovers to non-adopters also drive this effect. Further, citations to exposed researchers in policy documents increase significantly. Our findings underscore the importance of data access for scientific progress and evidence-based policy formulation.

• Johannes Hoelzemann, Gustavo Manso, Abhishek Nagaraj, Matteo Tranchero (Working), The Streetlight Effect in Data-Driven Exploration. Abstract

  We examine innovative contexts like scientific research or technical R\&D where agents must search across many potential projects of varying and uncertain returns. Is it better to possess incomplete but accurate data on the value of some projects, or might there be cases where it is better to explore on a blank slate? While more data usually improves welfare, we present a theoretical framework to understand how it can unexpectedly decrease it. In our model of the streetlight effect, we predict that when data shines a light on attractive but not optimal projects, it can severely narrow the breadth of exploration and lower individual and group payoffs. We test our predictions in an online lab experiment and show that the availability of data on the true value of one project can lower individual payoffs by 17% and reduce the likelihood of discovering the optimal outcome by 54% compared to cases where no data is provided. Suggestive empirical evidence from genetics research illustrates our framework in a real-world setting: data on moderately promising genetic targets delays valuable discoveries by 1.6 years on average. Our paper provides the first systematic examination of the streetlight effect, outlining the conditions under which data leads agents to look under the lamppost rather than engage in socially beneficial exploration.

Current Courses

• MGMT6110 - Managing Established Enterprises

  This course is about managing large enterprises that face the strategic challenge of being the incumbent in the market and the organizational challenge of needing to balance the forces of inertia and change. The firms of interest in this course tend to operate in a wide range of markets and segments, frequently on a global basis, and need to constantly deploy their resources to fend off challenges from new entrants and technologies that threaten their established positions. The class is organized around three distinct but related topics that managers of established firms must consider: strategy, human and social capital, and global strategy.

  MGMT6110001 ( Syllabus )

  MGMT6110002 ( Syllabus )

  MGMT6110003 ( Syllabus )

Past Courses

• MGMT6110 - Managing Est Enterprise

  This course is about managing large enterprises that face the strategic challenge of being the incumbent in the market and the organizational challenge of needing to balance the forces of inertia and change. The firms of interest in this course tend to operate in a wide range of markets and segments, frequently on a global basis, and need to constantly deploy their resources to fend off challenges from new entrants and technologies that threaten their established positions. The class is organized around three distinct but related topics that managers of established firms must consider: strategy, human and social capital, and global strategy.

• Outstanding Dissertation Award, Strategic Management Division, AOM, 2026
• Best Dissertation Award, Technology and Innovation Management, AOM, 2026
• Ronald H. Coase Dissertation Award, SIOE, 2026
• Giarrantani Rising Star Award (runner-up), 2025
• The Oliver E. Williamson Best Conference Paper Award, SIOE, 2025
• William H. Newman Award, Academy of Management, 2024
• TIM Division Best Paper Award, Academy of Management, 2024
• Winner, INFORMS/Organization Science Dissertation Proposal Competition, 2023
• Emergent Thought Award, Panmure House, 2021
• Alessandro Pansa Research Prize, Feltrinelli Foundation, 2019
• Summer School Program Award, Unicredit and Universities Foundation, 2017
• 5-years merit-based full scholarship, Scuola Superiore Sant’Anna, 2013