Decisions as ill-posed problems: A scoping review of regularization methods in decision science

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Real-life decisions often require individuals to make inferences based upon a large number of possible predictive features, but only limited observations. These so called ill-posed problems can be translated into well-structured ones using regularization methods. Regularization methods have gained popularity in AI research; yet, they are quite unknown in decision science. In a scoping review, I investigate how the concept of regularization has influenced decision science in applications, methodologically, and theoretically. Within the 25 reviewed articles, most studies applied regularization methods as a tool to select the most relevant problem features and improve interpretability. Methodological advances through regularization have rendered cognitive choice models testable on high-dimensional data or allowed to map descriptive models onto normative models of human decision making. Regularization less often initiated theoretical innovations and those were confined to modelling constraints in information processing and re-integrating heuristic with rational choice models. Looking ahead, I discuss how future applications in decision may harness regularization techniques to communicate insights from decision analysis, to improve forecasting tools, and to innovate theoretical approaches in decision science.
Original languageEnglish
Publication statusAcceptance date - 20 May 2024


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