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Human Decision Making in Dynamic Resource Allocation
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AI
DOI：10.1287/mnsc.2021.01097.png)
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Abstract
En 中文
We experimentally study dynamic resource allocation decisions using product development as the context. A product manager must accept or reject a series of design improvement opportunities, given a limited budget. Human subjects perform well when the cost-to-implement is fixed throughout the project. However, in a more complex setting where the cost increases for the latter half of the project, subjects' performance worsens substantially. We use the strategy frequency estimation method to analyze subjects' decision mechanisms and find that many subjects are (a) mis-weighting future periods (underweighting in the simple case, overweighting in the complex) and (b) focusing on only the highest value opportunities. These heuristics perform poorly in the complex setting, leading to excess savings and are a counterproductive reaction to the cost increase. Top performers in the complex setting do well by decomposing the problem into two subproblems resembling the simpler setting, which they can handle nearly optimally. In a second study, we test managerial interventions based on prompting this decomposition approach to improve performance in the complex setting. Merely prompting subjects to consider problem decomposition is largely ineffective. However, additionally sharing a best practice budget plan that gives information about how and why top performers decompose the problem significantly improves performance. Our results highlight when decision makers will perform well or poorly in a dynamic resource allocation problem and show effective ways to reframe the problem and improve their performance.
Keywords:
dynamic decision making
resource allocation
decision behavior
laboratory experiments
managerial treatment
Journal
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780
Citations:
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