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How to strategize under uncertainty

My most recent research stream focuses on the impact of adopting different strategies to cope with uncertainty. Although the debate is fierce and vibrant, there is little empirical evidence of how adopting a more predictive strategy impacts decision outcomes and monetary performance. Predictive strategies are based on the assumption that the environment is exogenous and given. What happens if we relax this assumption and decision-makers conveice the environment as endogenous to their actions, adopting therefore a "non--predictive" strategy?

Between 2016 and 2022 we conducted six large field experiments with more than1,700 entrepreneurs in different Countries (Italy, UK, India) where we treated them with predictive and non-predictive strategies. The studies show different outcomes and mechanisms.



with Alfonso Gambardella

(included as “best paper", AOM 2022, TIM Division)*

Decision-makers can reduce uncertainty by acquiring information via a predictive or a non-predictive strategy. Predictive strategies focus on estimating unknown states of the world through probabilistic tools, while non-predictive strategies focus on transforming unknown states of the world through design tools. This paper reconciles these two - groups of - strategies with a model of information acquisition. We test the model with data from a field experiment designed to train a group of entrepreneurs to collect information to predict unknowns, and a second group to gather information to shape the environment. The experiment included 308 entrepreneurs and 3,388 observations.
Consistent with the predictions of the model, the paper finds that the two groups acquire less information to make a decision with respect to an untrained ”pure” control group. However, only predictive entrepreneurs follow an optimal policy to acquire information because this policy requires a precise estimation of the unknown value of the idea. Conversely, non-predictive entrepreneurs pursue actions that they believe can be better or that depend on their preferences, eschewing prediction. The paper provides evidence of this mechanism, and shows that predictive decision-makers perform better in monetary terms.

*with the title "Exploring with Predictive and Control Strategies under Uncertainty"


with Arnaldo Camuffo Alfonso Gambardella Elena Novelli Emilio Paolucci and Chiara Spina

pivot cepr.jpg

This paper studies the implications of an approach in which managers and entrepreneurs make decisions under uncertainty by formulating and testing theories such as scientists do. By combining the results of four Randomized Control Trials (RCTs) involving 754 start-ups and small-medium enterprises and 10,730 data points over time, we find that managers and entrepreneurs who adopt this approach terminate more projects, do not experiment with many new ideas, and perform better. We develop a model that explains these results.

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