Decision Fatigue and Heuristic Analyst Forecasts

Decision fatigue and heuristic analyst forecasts

Demands on healthcare workers are high: services are stretched, shifts are long and healthcare professionals (HCPs) regularly work lengthy periods without a break. Spending time continuously 'on task' changes decision-making in predictable ways, as described by the 'decision fatigue' phenomenon where decision-makers progressively shift towards making less cognitively effortful decisions as the time worked without a break increases. This phenomenon has been observed repeatedly in large quantitative observational studies, however, individual healthcare workers' experiences have not been explored. This qualitative study aimed to explore general practitioners' (GPs) and advanced nurse practitioners' (ANPs) experiences of working for lengthy periods in an out-of-hours primary care service in the UK. This included exploration of self-perceived changes in decision-making throughout a work shift, and mitigating strategies used to avoid changes in decision-making over time. Semi-structured interviews were conducted online. An inductive thematic analysis was carried out to identify salient issues articulated by participants. The interview sample (n = 10) comprised ANPs (n = 5) and GPs (n = 5) who regularly worked within the out-of-hours primary care service across a regional National Health Service (NHS) health board. HCPs (GPs and ANPs) provided insights into their experiences during lengthy shifts and the impact of prolonged periods of work on clinical decision-making. Four main themes were identified and developed: (1) HCPs are aware of decision fatigue effects over the course of a shift; (2) Multiple factors help and hinder stable decision-making quality; (3) HCPs deliberately use strategies to help keep the quality of their decision-making stable; and (4) HCPs are aware of contextual changes, likely related to the decision fatigue phenomenon. The findings of this study underscore the intricate interplay of personal, social and systemic factors in decision-making quality and highlight HCPs' deliberate efforts to mitigate decision fatigue's effects in practice.

The purpose of this study was to explore the experience of treatment-related decision fatigue in patients with recurrent papillary thyroid cancer. Patients with recurrent papillary thyroid carcinoma who were followed up in thyroid surgery wards and outpatient clinics of three tertiary hospitals in Jiangsu Province were included in this study. Semi-structured interviews were conducted from January to April 2024. Inductive content analysis was used to identify themes. A total of 21 participants, aged 24-58 years, were interviewed. Two themes and six sub-themes were identified: manifestations of decision fatigue including impulsive decision making, hesitation, aggravation of negative emotions, and decision making is a burden; and consequences of decision fatigue including decision regret and delay in decision making. Our study identified the manifestations and adverse consequences of decision fatigue in patients with recurrent papillary thyroid cancer, and helped medical staff to identify and develop personalized interventions as early as possible to help patients make appropriate treatment decisions and improve the quality of decision-making and patient treatment outcomes.

Contemporary general surgery is practiced in environments characterized by high cognitive complexity, time pressure, and uncertainty, where surgeons must make rapid decisions with significant clinical consequences. While technical proficiency remains essential, increasing evidence highlights the central role of cognitive processes, non-technical skills, and system-level factors in shaping surgical performance and patient safety. This study aimed to synthesize current evidence and expert convergence regarding the cognitive and decision-making demands of contemporary general surgery and to organize these demands into a coherent framework relevant to surgical education and clinical practice. A narrative review was conducted integrating foundational and contemporary literature on cognitive psychology, human factors, and surgical safety, supported by a structured Delphi-based framework to consolidate and refine key domains through iterative thematic convergence. Findings identified six core domains: cognitive load and multitasking, fatigue and decision fatigue, cognitive bias and heuristics, non-technical skills, team communication and coordination, and system and environmental stressors. Cognitive load emerged as the central domain, closely interacting with fatigue and bias, while progressive convergence across Delphi rounds supported the robustness of the final framework. Alignment with Bloom’s taxonomy highlighted the educational relevance of these domains. Overall, surgical decision-making is a multidimensional process shaped by individual cognition, team dynamics, and system design, underscoring the need to address cognitive demands alongside technical training to enhance decision quality, surgical performance, and patient safety in contemporary general surgery.

Meeting Individual Analyst Expectations

ABSTRACTThis study updates and expands upon the existing work on the accuracy of the IPF’s Consensus Forecasts. The paper evaluates the extent to which the consensus forecasts were able to predict the relative performance. It also assesses the accuracy of implied yield forecasts and concludes that failure in yield forecasting is the main source of failure in forecasts of capital growth and total returns. A high level of agreement between the actual and forecasted sector rankings was found. Evidence of a pessimism bias was identified. Yield forecasts are consistently found to perform worst using a range of forecast performance metrics.

In November 2007, the Federal Open Market Committee (FOMC) announced a change in the way it communicates its view of the economic outlook: It increased the frequency of its forecasts from two to four times per year, and it increased the length of the forecasting horizon from two to three years. The FOMC does not release the individual members' forecasts or standard measures of consensus such as the mean or median. Rather, it continues to release the forecast information as a range of forecasts, both the full range between the high and the low and a central tendency that omits the extreme values. This paper uses individual forecaster data from the Survey of Professional Forecasters (SPF) to mimic the FOMC's method for creating their central tendency. The authors show that the midpoint of the central tendency of the SPF is a reliable measure of the consensus, suggesting that the FOMC reporting method is also a reliable measure of consensus. For the dates when both are available, the authors also compare the relative forecast accuracy of the FOMC and SPF consensus forecasts for output growth and inflation. Overall, the differences in forecast accuracy are too small to be statistically significant.

The relative accuracy of profit forecasts was analysed for six firms of brokers. ANOVA was used to look first at performance across industrial sectors, then between industries. Individual broker's forecasts were significantly different in accuracy on the first measure and in general levels of accuracy between industries in the second. When a consensus forecast was calculated this always performed worse than the best individual.

Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.

Survey evidence on forecast accuracy of U.S. term spreads

This paper evaluates the performance of Consensus Forecasts of real GDP growth for a large number of industrialized and developing countries for the time period October 1989 to December 1999. The questions addressed are the following: How accurate are private sector forecasts? How does their accuracy compare with that of the IMF's World Economic Outlook? How well do forecasters predict rare events such as recessions or crises? Is discord among forecasters associated with lower forecast accuracy? Copyright 2002, International Monetary Fund

Several theories of reputation and herd behavior (e.g., Scharfstein and Stein (1990) and Zweibel (1995)) suggest that herding among agents should vary with career concerns. Our goal is to document whether such a link exists in the labor market for security analysts. We find that inexperienced analysts are more likely to be terminated for inaccurate earnings forecasts than are their more experienced counterparts. Controlling for forecast accuracy, they are also more likely to be terminated for bold forecasts that deviate from the consensus. Consistent with these implicit incentives, we find that inexperienced analysts deviate less from consensus forecasts. Additionally, inexperienced analysts are less likely to issue timely forecasts, and they revise their forecasts more frequently. These findings are broadly consistent with existing career concern motivated herding theories.

Comments and Discussion Steven N. Durlauf and Jeffrey C. Fuhrer Steven N. Durlauf: This ambitious paper tackles an extraordinarily difficult question: what is the role of formal statistical models in evaluating economic policies? In particular, the paper studies the use of such models by central banks in various capacities. Although the paper addresses a wide range of issues and provides a fair amount of qualitative description of how central banks use large-scale models, its main contributions are twofold. First, it provides an evaluation of the forecasting performance of the Federal Reserve. Second, it addresses several broad questions concerning the appropriate ways of using statistical models in the policy process. I will deal with each of these components in turn. Sims' evaluation of the forecasting accuracy of the Federal Reserve provides some useful additions to a long-standing literature, in particular a recent paper by Christina Romer and David Romer.1 Sims compares both the judgmental and the model-based forecasts of the Federal Reserve with two alternatives: naïve forecasts and a consensus forecast from the private sector. What is new in Sims' comparison relative to that of Romer and Romer is the attention to the relative virtues of the judgmental and the model-based forecasts. The main claims Sims makes are, first, that the Federal Reserve forecasts well, especially when forecasting inflation; second, that the informational contents of different forecasts are highly correlated, so that strong claims of superiority of one forecast over another should be treated as suspect; and third, that there does not appear to be strong evidence that the judgmental forecasts of the Federal Reserve are [End Page 41] superior (as measured by the root mean square forecast error) to its model-based forecasts. Although these points are well taken, the analysis succeeds less well in giving a clear understanding of the differences between the model-based forecasts and the forecasts that embody subjective judgments. One limitation is that the procedures used for forecast comparison are not well chosen if one's objective is to go beyond crude summary measures of relative forecast accuracy to an understanding of why forecasts differ. Root mean square error is certainly a sensible single summary statistic for comparing forecasts, but like all such summaries it is limited. In my view, an additional useful way of comparing two forecasts is to attempt to identify periods when the two forecasts diverge relatively sharply and compare their behavior at those times. I suspect that, during shifts across business cycle regimes, differences are larger between the Federal Reserve forecasts and the private sector forecasts than in other periods. Put differently, the fact that two forecasts are approximately equally accurate in periods when they are close to each other is not informative about their relative performance when they are far apart. Presumably what one is interested in is whether, when the differences are relatively large, one forecast performs better than the other. Further, it would seem that a deeper evaluation of the forecast differences should address in greater detail the relationship between forecasts and their use, especially if one is interested in how subjective judgment affects forecasts. In a 1997 paper (which, curiously, Sims does not reference), David Reifschneider, David Stockton, and David Wilcox give three justifications for the use of judgmental over model-based forecasts: first, the ability of the former to use "potentially valuable information contained in monthly and weekly data" not incorporated into the model; second, the integration of "extramodel information and anecdotal evidence into the forecast"; and third, the ability to address model uncertainty: "the judgmental approach . . . enables the staff to examine a range of econometric specifications—both structural and reduced form—in producing the forecast rather than relying on a single specification enshrined in the 'staff model.'"2 These are all plausible reasons for using judgment, and all would seem relevant to evaluating the effectiveness and value of subjective judgment in Federal Reserve forecasting. Although Sims' paper gives [End Page 42] some attention to the question of information asymmetries, virtually none is given to these other explanations for why judgmental forecasts deviate from model-based ones. Now, some of these reasons may not be identifiable from available data, but...

Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

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