Knowledge Content Library
A Scientific Approach to Entrepreneurial Decision Making
Arnaldo Camuffo, Bocconi University, Milan, Italy
July 17, 2024 (SDP Invited Talk)
Abstract: A scientific approach to decision-making encourages entrepreneurs to formulate their decision problems as theories of value. Theories are future state spaces, comprising selected attributes and the causal relationships among them, to which they assign a belief. Theories are then used as a guide to determine whether and when to collect evidence that can validate or update beliefs as well as determine the design of the experiments to be conducted. Beliefs on a theory vary in their strength and can be thought of as subjective probabilities. Belief formation through theorizing involves engaging in deliberate cognitive efforts to frame a problem and carefully formulate it as a conceptual causal structure. Theories are then translated into testable hypotheses, to be tested through experiments. Experiments elicit signals that provide evidence about the hypotheses and their assumptions. This information allows to update beliefs in the form of either changes in subjective probabilities or changes on priors on the state space. Entrepreneurs can therefore make more informed decisions about whether to continue with the current plan, pivot to a different business model, or terminate the venture. The positive effects of the adoption of the scientific approach to entrepreneurial decision-making stem from: a) increased efficiency in problem formulation and solution; and b) methodic doubt about the problems they formulate and the solutions they evaluate. The scientific approach fosters critical thinking, contrasting decision-makers' tendencies to unawareness, overconfidence and the neglect of opportunity costs. The evidence coming from a dozen randomized control trials involving approximately 3,000 startups around the world shows that a scientific approach to entrepreneurial decision-making increases the probability of terminating projects, of efficient pivoting and, conditional on survival, leads to higher performance.
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Key Words: Analysis & Modeling (anamod), Decision-Making, Framing & Structuring (framestruc), Risk and Uncertainty (riskunc).
