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Staying the course: Decision makers who escalate commitment are trusted and trustworthy.

ABSTRACT: Escalation of commitment-the tendency to remain committed to a course of action, often despite negative prospects-is common. Why does it persist? Across three preregistered experiments (N = 3,888), we tested the hypothesis that escalating commitment signals trustworthiness. Experiments 1-2, respectively, revealed that decision makers who escalated commitment were perceived as more trustworthy and entrusted with 29% more money by third-party observers. Experiment 3 revealed that decision makers who escalated commitment subsequently made more trustworthy choices, returning 15% more money than those who de-escalated. Decision makers were equally likely to escalate commitment in public versus in private, possibly because they previously internalized how others would evaluate them. Complementing research examining cognitive factors driving escalation of commitment, the present work reveals that accounting for the reputational causes and consequences of decisions to escalate enhances understanding of why escalation is so common and suggests how organizations might reduce it. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

SUBMITTER: Dorison CA

PROVIDER: S-EPMC9354500 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Staying the course: Decision makers who escalate commitment are trusted and trustworthy.

Dorison Charles A CA Umphres Christopher K CK Lerner Jennifer S JS

Journal of experimental psychology. General 20210902 4

Escalation of commitment-the tendency to remain committed to a course of action, often despite negative prospects-is common. Why does it persist? Across three preregistered experiments (<i>N</i> = 3,888), we tested the hypothesis that escalating commitment signals trustworthiness. Experiments 1-2, respectively, revealed that decision makers who escalated commitment were perceived as more trustworthy and entrusted with 29% more money by third-party observers. Experiment 3 revealed that decision m ...[more]

PMID: 34472961

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Project description:BackgroundMigraine is the world's second most common disabling disorder, affecting 15% of UK adults and costing the UK over £1.5 billion per year. Several costly new drugs have been approved by National Institute for Health and Care Excellence.AimTo assess the cost-effectiveness of drugs used to treat adults with chronic migraine.MethodsWe did a systematic review of placebo-controlled trials of preventive drugs for chronic migraine. We then assessed the cost-effectiveness of the currently prescribable drugs included in the review: Onabotulinum toxin A (BTA), Eptinezumab (100mg or 300mg), Fremanezumab (monthly or quarterly dose), Galcanezumab or Topiramate, each compared to placebo, and we evaluated them jointly. We developed a Markov (state-transition) model with a three-month cycle length to estimate the costs and quality-adjusted life years (QALYs) for the different medications from a UK NHS and Personal Social Services perspective. We used a two-year time horizon with a starting age of 30 years for the patient cohort. We estimated transition probabilities based on monthly headache days using a network meta-analysis (NMA) developed by us, and from published literature. We obtained costs from published sources and applied discount rates of 3.5% to both costs and outcomes.ResultsDeterministic results suggest Topiramate was the least costly option and generated slightly more QALYs than the placebo, whereas Eptinezumab 300mg was the more costly option and generated the most QALYs. After excluding dominated options, the incremental cost-effectiveness ratio (ICER) between BTA and Topiramate was £68,000 per QALY gained and the ICER between Eptinezumab 300mg and BTA was not within plausible cost-effectiveness thresholds. The cost-effectiveness acceptability frontier showed that Topiramate is the most cost-effective medication for any amount the decision maker is willing-to-pay per QALY.ConclusionsAmong the various prophylactic medications for managing chronic migraine, only Topiramate was within typical cost-effectiveness threshold ranges. Further research is needed, ideally an economic evaluation alongside a randomised trial, to compare these newer, expensive CGRP MAbs with the cheaper oral medications.

Project description:Introduction: Widespread adoption of AI for medical decision-making is still hindered due to ethical and safety-related concerns. For AI-based decision support systems in healthcare settings, it is paramount to be reliable and trustworthy. Common deep learning approaches, however, have the tendency towards overconfidence when faced with unfamiliar or changing conditions. Inappropriate extrapolation beyond well-supported scenarios may have dire consequences highlighting the importance of the reliable estimation of local knowledge uncertainty and its communication to the end user. Materials and Methods: While neural network ensembles (ENNs) have been heralded as a potential solution to these issues for many years, deep learning methods, specifically modeling the amount of knowledge, promise more principled and reliable behavior. This study compares their reliability in clinical applications. We centered our analysis on experiments with low-dimensional toy datasets and the exemplary case study of mortality prediction for intensive care unit hospitalizations using Electronic Health Records (EHRs) from the MIMIC3 study. For predictions on the EHR time series, Encoder-Only Transformer models were employed. Knowledge uncertainty estimation is achieved with both ensemble and Spectral Normalized Neural Gaussian Process (SNGP) variants of the common Transformer model. We designed two datasets to test their reliability in detecting token level and more subtle discrepancies both for toy datasets and an EHR dataset. Results: While both SNGP and ENN model variants achieve similar prediction performance (AUROC: ≈0.85, AUPRC: ≈0.52 for in-hospital mortality prediction from a selected MIMIC3 benchmark), the former demonstrates improved capabilities to quantify knowledge uncertainty for individual samples/patients. Discussion/Conclusions: Methods including a knowledge model, such as SNGP, offer superior uncertainty estimation compared to traditional stochastic deep learning, leading to more trustworthy and safe clinical decision support.

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Staying the course: Decision makers who escalate commitment are trusted and trustworthy.

Publications

Staying the course: Decision makers who escalate commitment are trusted and trustworthy.

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