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Abstract: Dynamic decision problems are common in operations applications. However, solving them optimally is likely to challenge the cognitive abilities of most human decision makers. Many human decision makers are likely to simplify the problem in some way, however there are a variety of plausible simplifying heuristics, including both simple static decision rules, and more sophisticated forward-looking heuristics. This study considers a range of dynamic decision problems (including technology adoption, capacity allocation and search problems), and experimentally measures which approaches to solving dynamic decision problems are the most common. Specifically, we consider three questions: Do more people use static or forward-looking approaches? Which heuristics are most common, and does it vary by task? Are there individual traits that predict decision strategy? We find that sophisticated forward-looking strategies are commonly used in the technology adoption and capacity allocation problems, while by contrast simpler static decision rules are prevalent in search and stopping problems. There is very little relationship in the approach an individual takes across tasks, however score on the Cognitive Reflection Test has some predictive power (particularly for the simplest tasks).
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