Optimal nudging

People often face decisions where errors are costly but computing the optimal choice is intractable or prohibitively difficult. To address this, researchers have developed nudge theory as a way to lead people to better options without imposing restrictions on their freedom of choice. While heuristics and case-by-case evaluations are usually used to predict and explain nudges’ effects on choice, another way of interpreting these effects is that nudges can change the costs of attaining certain pieces of information. These changes in costs then bias people towards or away from making particular choices. In this paper, we propose a method for predicting the effects of choice architecture on option selection by modeling deliberation as a metalevel Markov decision process and nudging as the reduction of certain computational costs. This allows us to con- struct optimal nudges by choosing cost modifications to maximize some objective function. This approach is flexible and can be adapted to arbitrary decision making problems. Furthermore, by making the objectives of nudging explicit, the approach can address ethical concerns regarding the effects of nudging and the role people should have in choosing how, when, and why they are nudged. We demonstrate the strength of this framework by applying it to the Mouselab paradigm, where deliberation costs are made explicit. We find that a version of our approach leads to significantly higher participant reward, both increasing the quality of their choices and lowering the cost of making these choices.

Authors

• Mathew Hardy
• Frederick Callaway

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