Over and under commitment to a course of action in decisions from experience.

Cohen, Doron; Erev, Ido

doi:10.1037/xge0001066

Cited by 8 publications

(4 citation statements)

References 51 publications

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“…A careful analysis revealed that this sunk cost effect was greatly reduced, although still present, when accounting for latent variables, such as on-court performance or injuries. In a recent study, model-based analysis demonstrated that in situations with stochastic outcomes (e.g., gambling) apparent sunk cost sensitivity can emerge from a selection bias, because longer investments made extreme outcomes more likely ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

Apparent sunk cost effect in rational agents

et al. 2022

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Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats were reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments to the detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost–dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.

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Section: Discussionmentioning

confidence: 99%

Apparent sunk cost effect in rational agents

et al. 2022

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“…In a series of multiple reinvestments, each investment can also be understood as costly information search to reduce uncertainty and the sampled information can inform search decisions ( Cohen and Erev, 2021 ). The more prior investments have been made, the more shared cards are revealed in the VIP-Task.…”

Section: Discussionmentioning

confidence: 99%

Exploring the determinants of reinvestment decisions: Sense of personal responsibility, preferences, and loss framing

2023

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Two potentially costly errors are common in sequential investment decisions: sticking too long to a failing course of action (escalation of commitment), and abandoning a successful course of action prematurely. Past research has mostly focused on escalation of commitment, and identified three critical determinants: personal responsibility, preferences for prior decisions, and decision framing. We demonstrate in three studies using an incentivized poker inspired task that these determinants of escalation reliably lead decision makers to keep investing even when real money is on the line. We observed in Experiments 1, 2 and 3 that reinvestments were more likely when decision makers were personally responsible for prior decisions. This likelihood was also increased when the decision makers had indicated a preference for initial investments (Experiments 2 and 3), and when outcomes were framed in terms of losses as compared to gains (Experiment 3). Both types of decision errors – escalation of commitment and prematurely abandoning a course of action – could be traced to the same set of determinants. Being personally responsible for prior decisions, having a preference for the initial investment, and loss framing did increase escalation, whereas lacking personal responsibility, having no preference for the initial investment, and gain framing increased the likelihood of prematurely opting out. Finally, personal responsibility had a negative effect on decision quality, as decision-makers were still more likely to reinvest when they were personally responsible for prior decisions, than when prior decisions were assigned optimally by an algorithm (Experiments 2 and 3).

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“…While this set of situations has clear boundaries, it contains many important members. Examples include settings in which safety devices increase accidents (Cohen & Erev, 2018), taxation backfires (YCNE), people over and under-commit to a course of action (Cohen & Erev, 2021), experience reduces the tendency to trust well-calibrated experts (Erev et al, 2022) and it is necessary to enforce rules (Plonsky et al, 2021). Yet, more insight into how people respond to different dimensions of similarity, and how these similarities interact, is necessary to predict behavior when dynamic regularities are easily detectable.…”

Section: Discussionmentioning

confidence: 99%

On the descriptive value of the reliance on small-samples assumption

Erev¹,

Cohen

Yakobi

2022

Judgm. decis. mak.

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Experience is the best teacher. Yet, in the context of repeated decisions, experience was found to trigger deviations from maximization in the direction of underweighting of rare events. Evaluations of alternative explanations for this bias led to contradicting conclusions. Studies that focused on the aggregate choice rates, including a series of choice prediction competitions, favored the assumption that this bias reflects reliance on small samples. In contrast, studies that focused on individual decisions suggest that the bias reflects a strong myopic tendency by a significant minority of participants. The current analysis clarifies the apparent inconsistency by reanalyzing a data set that previously led to contradicting conclusions. Our analysis suggests that the apparent inconsistency reflects the differing focus of the cognitive models. Specifically, sequential adjustment models (that assume sensitivity to the payoffs’ weighted averages) tend to find support for the hypothesis that the deviations from maximization are a product of strong positive recency (a form of myopia). Conversely, models assuming random sampling of past experiences tend to find support to the hypothesis that the deviations reflect reliance on small samples. We propose that the debate should be resolved by using the assumptions that provide better predictions. Applying this solution to the data set we analyzed shows that the random sampling assumption outperforms the weighted average assumption both when predicting the aggregate choice rates and when predicting the individual decisions.

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Over and under commitment to a course of action in decisions from experience.

Cited by 8 publications

References 51 publications

Apparent sunk cost effect in rational agents

Apparent sunk cost effect in rational agents

Exploring the determinants of reinvestment decisions: Sense of personal responsibility, preferences, and loss framing

On the descriptive value of the reliance on small-samples assumption

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