In real-life situations involving risk and uncertainty, optimal policy hinges on selecting a course of action characterized by the highest expected value (i.e., future outcomes weighted by their probabilities). Nevertheless, a vast body of findings from economic and psychological studies indicate that people rarely follow this principle1–3. The attempts to make optimal decisions are impeded by the complexity of a task and the computational capabilities of decision makers, often leading to suboptimal choices4. Recent research has demonstrated that decision makers are systematically biased toward suboptimal options5. However, little is known about the nature of this bias. Here we show that recurring suboptimal choices result in superior decision making. In one simulation study and three well-powered (N = 1,046) fully-incentivized empirical studies employing a task to mimic decision making under uncertainty, we demonstrated that people who traded off their decision accuracy for the number of possible choices performed better (i.e., they earned more money) than those who made optimal decisions in terms of maximizing the expected value. Our results demonstrate that decision makers can adapt to the requirements of a decision task. They are inclined to make more suboptimal decisions, resulting in better overall performance than normatively better choices.
A vast body of research has indicated that individuals with higher statistical numeracy, in comparison to individuals with lower statistical numeracy, make superior decisions by employing more deliberative processes leading to selecting options with the highest expected value (EV). However, it is not feasible to deliberate every time we make a choice due to cognitive and environmental constraints. In one simulation study and three well-powered, fully-incentivized empirical studies using the decision-from-experience task, we identified conditions where recurring suboptimal choices were more rewarding than a normatively superior strategy. That is, even if individual choices in isolation are considered suboptimal in light of the EV maximization principle, individuals with higher numeracy can adapt their decision strategy in accordance with changes in the task structure, and make faster suboptimal (or random in terms of EV maximization) decisions that result in overall superior performance (e.g., earning more money). We found that individuals who maximized EV without time constraints accumulated higher total gain. However, the trend reversed in the following two studies. Participants who made more suboptimal choices, under time constraints, earned more money than those who spent more time maximizing EV. Importantly, we found that more numerate individuals made significant adjustments to their meta-cognitive decision processes and made more quick suboptimal choices resulting in better overall earnings than less numerate individuals. Finally, our results also indicate that more numerate individuals are better at identifying the changes in the task structure and are more rational in their use of cognitive and environmental resources.
In real-life situations involving risk and uncertainty, optimal policy hinges on selecting a course of action characterized by the highest expected value (i.e., future outcomes weighted by their probabilities). Nevertheless, a vast body of findings from economic and psychological studies indicate that people rarely follow this principle and make suboptimal choices. In the current research, we tested a hypothesis that recurring suboptimal choices result in superior decision making. In one simulation study and three well-powered (N = 1,046) fully-incentivized empirical studies, we demonstrated that people who traded off their decision accuracy for the number of possible choices performed better (i.e., they earned more money) than those who made optimal decisions in terms of maximizing the expected value. Our results demonstrate that decision makers can adapt to the requirements of a decision task. They are inclined to make more suboptimal decisions, resulting in better overall performance than normatively better choices.
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