Signposts for Problemistic Search: Reference Points and Adaptation in Rugged Landscapes

Axel Zeijen (), Manuel Romagnoli () and Luigi Marengo ()
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Axel Zeijen: Department of Management, Technology, and Economics, ETH Zurich, 8092 Zurich, Switzerland
Manuel Romagnoli: Faculty of Economics and Business Administration, Friedrich Schiller University Jena, 07743 Jena, Germany
Luigi Marengo: Department of Business and Management, LUISS University, 00197 Rome, Italy

Strategy Science, 2025, vol. 10, issue 3, 263-279

Abstract: Reference points form an essential element of organizations’ problemistic search and adaptation behavior. Yet, if search is triggered by shortfalls compared with peers but alternatives are discovered on the fly, it is not clear whether and when peer comparison leads to better search outcomes. We contribute to the literature by studying how reference points guide search and which outcomes they allow organizations to achieve. Specifically, we develop a model of search in complex landscapes in which agents’ search behavior is guided by an upper (aspiration-level) social reference point and a lower (survival-point) social reference point. In our model, agents move across a subjective “terraced” landscape that is a simplified transformation of the “real” one. The vertical positions and shapes of these terraces are determined by the agents’ reference points and change over time as a result of their own and their peers’ performance evolution. In turn, these terraces define the search space that is navigated and the outcomes that can be reached. We show that the upper and lower bounds play fundamentally different roles in the search process, with the upper bound being more important in the short run and the lower bound more important in the long run. Studying heterogeneous populations, we find that reference points drive dynamic trade-offs between how easily decision makers can reach their aspiration level and how much they benefit from doing so. We highlight the importance of both internal fit between reference points and external fit with environmental factors.

Keywords: complex adaptive systems; simulations; organizational evolution (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orstsc:v:10:y:2025:i:3:p:263-279

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