I am a strategy scholar who studies how organizations make decisions when attention, information, and judgment are limited. For most of my career, that has meant studying how managers search for strategies, how organizations combine judgment, and how the models they use shape what they can see. In recent years, that research program has led directly to a newer question: what changes when artificial intelligence begins to do some of the cognitive work that strategy once assumed was exclusively human?
I am the Alexander M. Nick Professor and Chair of the Strategy Area at the Ross School of Business, University of Michigan. Before joining Michigan, I taught strategy at INSEAD.
The Path Here
I grew up in Chile, where I studied computer science, graduating as valedictorian in 1996, and went on to earn an MBA. I then completed a PhD in strategy at Wharton, where my committee included Dan Levinthal, Nicolaj Siggelkow, Jitendra Singh, and Sid Winter.
Before academia, I was head of research at an asset management firm and founder and CEO of an internet startup. Those experiences still shape how I think. They are why I care about decision processes that can survive uncertainty, feedback, disagreement, and real consequences rather than only elegant theory.
What Connects the Work
Everything I study is a version of one question: how are strategic decisions made, and how can they be made better? Strategic decisions are the ones that matter most to a firm—which market to enter, what to build, whom to imitate, when to change course. Sometimes a single manager decides: a CEO, an entrepreneur. Sometimes it takes a team, a board, or a whole organization. Either way, the decision is produced by a process: the problem is framed, alternatives are searched, judgments form and get combined into a commitment. What fascinates me is how much of the deciding the process itself does—the same decision maker, working through a different process, will often decide differently. That makes the quality of strategic decisions something managers and organizations can design, and my research is ultimately about how to design it.
Three cognitive processes underlie strategic decision-making, and they organize the work: search (how decision makers generate and explore alternatives), representation (the models through which they see their situation), and aggregation (how many partial judgments become one choice). A weakness in any of the three caps the quality of the whole.
The search thread asks how organizations look for better strategies when they cannot see the whole landscape. It includes How much to copy?, Positioning on a multi-attribute landscape, and Government as landscape designer: A behavioral view of industrial policy.
The representation thread starts from the observation that an organization can only choose among options it can see, and what it sees depends on its models: the internal mental models managers carry, the external visuals and frameworks they rely on, and the distributed representations that organizations build by combining many partial views. Papers such as Mental representation and the discovery of new strategies, External representations in strategic decision making, and The power and limits of distributed representations sit in that stream.
The aggregation thread is where the machinery is most visible: how organizations combine judgments, how structure shapes performance, and why firms should not be treated as unitary actors with clean, stable preferences—the way judgments are combined changes not just what an organization does but what it appears to want. That work runs from Organizational decision making, Organizational structure as a determinant of performance, and Individual and organizational antecedents of strategic foresight to Revisiting the unitary actor assumption.
The newest thread is what I call unbounding rationality. All three processes matter because human cognition is limited—bounded rationality is the assumption on which strategy was built. AI relaxes some of those limits: it searches more broadly, builds richer representations, and adds a new kind of judgment to the room. My current work asks what that does to strategy, its processes, and the role of the strategist. That idea appears in Unbounding rationality, in the forthcoming Harvard Business Review piece AI is revolutionizing strategic decision-making, in the Handbook of Artificial Intelligence and Strategy, and in the Strategy Science special issue Can AI do strategy?.
Research, Teaching, and Service
I serve as Senior Editor for Strategy Science. Earlier, I was an associate editor for Management Science and a senior editor for Organization Science. In 2025, Ross named me Researcher of the Year.
Teaching is not separate from the research. At Ross I teach strategy and AI & Strategy in the MBA, Weekend MBA, and Executive MBA programs, along with a doctoral seminar, and many of the questions I work on are sharpened by the classroom. The paper Learning strategic representations grew directly out of that belief, as does my continuing work on what AI means for management education. More on that is on the Teaching page.
Beyond the CV
I do not run a consulting practice, license proprietary frameworks, or try to turn every idea into a product. When I write or speak for practitioners, I try to do so in the same spirit as the academic work: evidence first, clear argument, and no hype. That is also how I approach the talks collected on the Talks page.
For the full professional record, see my CV. For academic, media, or institutional inquiries, use the Contact page.
And since people often ask: this is how my name is pronounced.
Last updated 2026-07-09