Search

Diagnostic errors are considered a blind spot of health care delivery and occur in up to 15% of patient cases. Cognitive failures are a leading cause of diagnostic error and often occur as a result of overreliance on system 1 thinking. This narrative review describes why diagnostic errors occur by shedding additional light on systems 1 and 2 forms of thinking, reviews literature on debiasing strategies in medicine, and provides a framework for teaching critical thinking in the intensive care unit as a strategy to promote learner development and minimize cognitive failures.

We propose a framework that outlines several predominant cognitive biases of climate change, identifies potential causes, and proposes debiasing tools, with the ultimate goal of depolarizing climate beliefs and promoting actions to mitigate climate change.

Diagnostic errors are an important cause of adverse safety incidents in intensive care units. Many of these misdiagnoses can be attributed to cognitive errors; however relatively little importance is given to this topic in medical education. In this article, we describe how cognitive processes affect decision-making and how the human mind can be susceptible to error. We describe a universal model of cognition and decision-making called Dual Process Theory, which provides a framework to understand the key human cognitive processes and the biases that affect it. We provide three clinical cases as examples to describe common cognitive biases that can lead to diagnostic failures. Finally, we review potential debiasing strategies at individual and at organisational level that can help to mitigate the risks of cognitive errors in clinical decision-making.

We document a negative relation between air pollution during corporate site visits by investment analysts and subsequent earnings forecasts. After accounting for analyst, weather, and firm characteristics, an extreme worsening of air quality from “good/excellent” to “severely polluted” is associated with a more than 1 percentage point lower profit forecast, relative to realized profits. We explore heterogeneity in the pollution-forecast relation to understand better the underlying mechanism. Pollution only affects forecasts that are announced in the weeks immediately following a visit, indicating that mood likely plays a role, and the effect of pollution is less pronounced when analysts from different brokerages visit on the same date, suggesting a debiasing effect of multiple perspectives. Finally, there is suggestive evidence of adaptability to environmental circumstances – forecasts from analysts based in high pollution cities are relatively unaffected by site visit pollution.

Cognitive biases are flaws in our decision-making processes, often leading to negative outcomes. These biases, if not addressed, can block much-needed change efforts, including those addressing moral distress, burnout, and unhealthy work environments. Resistance to change is a common problem in nursing and health care in general. We often cling tightly to our policies, precedent, and hierarchical structures, preferring the comfort of familiarity over the benefits of change. This article describes the use of cognitive debiasing strategies to effectively “change our mind” about long-held traditions, beliefs, and/or cultural norms.

This paper investigates the presence of local bias in the peer-to-peer (P2P) lending market and explores the social heterogeneous factors that may affect the formulation of the investor's local bias. We find that local biases are commonly present among investors. Investors have a 9.3% higher probability and put 105% more money in lending to local borrowers. We also find that overinvesting in local loans is correlated with higher default risk, lower recovery rate, and lower realized return, suggesting the underperformance of these locally biased investors. By taking advantage of the diverse local culture and institutional features in China, we further show that social heterogeneity, including geography, language, and social trust, affects the degree of local biases in the P2P lending market. We propose two debiasing techniques from the P2P platforms’ perspective.

Prior research suggests that disfluency leads to unfavorable product judgments and can be mitigated by debiasing techniques (i.e., explicit warnings about disfluency). We suggest a moderating role for the need for cognitive closure (NFCC), such that when information about a favorable brand is present, disfluency leads to less favorable judgments for low NFCC consumers but not for high NFCC consumers. Moreover, warnings about disfluency mitigate the negative effect for low NFCC consumers, but backfire for high NFCC ones. Across five experiments, we demonstrate these effects through individual trait differences and cognitive load manipulations of NFCC. We suggest that the backfiring effect among high NFCC consumers is due to shifts in attention away from brand reputation towards the experience of disfluency, making disfluency (vs. brand reputation) the primary judgmental cue. Implications of the findings for consumer inference are discussed.

Implicit bias has entered modern discourse as a result of our current sociopolitical climate. It is an area that has been largely explored in the social sciences, and was highlighted in the landmark 2003 IOM report, Unequal Treatment, as a contributor to racial/ethnic health disparities. Implicit bias is the process of unconscious societal attitudes affecting our individual understanding, actions and decisions, thus leading to assumptions about groups. Immigrant populations are particularly at risk in our present-day environment, and as a result experience limited healthcare access and higher levels of psychological distress. There are many measures of implicit bias, but the most highly regarded tool is the Implicit Association Test (IAT), as it is valid and reliable. Some level of pro-White/anti-Black bias has been found in most systematic reviews and studies, although there are less studies on bias towards Latinx populations. Limited evidence exists about the association between implicit bias and health outcomes. However, existing publications have demonstrated clear associations between bias and treatment recommendations, nonverbal communication, adverse birth outcomes and provider communication styles. Implicit biases can be unlearned via debiasing strategies, but these have not been examined extensively amongst health care providers. Future research must rely on more than pre- and post-IAT measurements to examine the effect of these strategies on improving patient outcomes. Additionally, healthcare system leadership must prioritize implicit bias trainings for students and medical staff and make greater tangible efforts to improve workforce diversity as a debiasing strategy.

The perception of risk affects how people behave during crises. We conduct a series of experiments to explore how people form COVID-19 mortality risk beliefs and the implications for prosocial behavior. We first document that people overestimate their own risk and that of young people, while underestimating the risk old people face. We show that the availability heuristic contributes to these biased beliefs. Using information about the actual risk to debias people’s own risk perception does not affect donations to the Centers for Disease Control but does decrease the amount of time invested in learning how to protect older people. This constitutes a debiasing social dilemma. Additionally providing information on the risk for the elderly, however, counteracts these negative effects. Importantly, debiasing seems to operate through the subjective categorization of and emotional response to new information.

In a lab experiment, we investigate whether social information can improve the accuracy of self-assessments of relative performance. In particular, we compare the effectiveness of different types of social information: subjects either learn their close peers' (i) average absolute performance, (ii) average self-assessment or (iii) average bias of selfassessments. Additionally, we explore the demand for the different types of social information. Our results suggest that social information can help debiasing subjects' selfassessments, but not all types of information are equally effective. Only learning about the average bias of peers' self-assessments improves own self-assessments. Subjects are, in general, willing to pay for social information but mostly prefer information about their peers' absolute performance, which is the least helpful type of social information. Consequently, endogenous choice of social information does not further improve self-assessments.

Many human decisions, ranging from the taking of loans with compound interest to fighting deadly pandemics, involve phenomena that entail exponential growth. Yet a wide and robust body of empirical studies demonstrates that people systematically underestimate exponential growth. This phenomenon, dubbed the exponential growth bias (EGB), has been documented in numerous contexts, across different populations, using both experimental and observational methods. Despite its centrality to human decision making, legal scholarship has thus far failed to account for the EGB. This Article presents the first comprehensive study of EGB and the law. Incorporating the EGB into legal analysis sheds new light on legal measures that are already in use, and highlights new solutions to numerous problems that the law strives to solve. More concretely, the EGB calls for the introduction of new disclosure duties that would assist people grasp the long-term implications of their choices; the imposition of new mandatory rules that would minimize the exploitation of the EGB by savvy profit-maximizing entrepreneurs; and the adoption of new debiasing techniques that could improve policymakers’ decisions.

Disputes over transactions on two-sided platforms are common and are usually arbitrated through platforms’ customer service departments or third-party service providers. In this paper, we study crowd-judging, a novel crowd-sourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. To understand this phenomenon, we use a rich dataset from the dispute resolution center at Taobao, a leading Chinese e-commerce platform. While this mechanism enhances resolution speed, there are concerns that crowd-jurors may exhibit a form of in-group bias (where buyers favor the buyer and sellers favor the seller in a dispute), and that such in-group bias may systematically sway case outcomes given the majority of users on such platforms are buyers. We find evidence consistent with this concern: on average, a seller juror is approximately 10% likelier to vote for a seller. Such bias is 70% higher among cases that are less clear-cut and decided by a thin margin. Conversely, the bias reduces dramatically as users gain crowd-judging experience: in-group bias when jurors have the sample-median level of experience is 95% lower than when jurors are completely inexperienced. This suggests learning-by-doing may mitigate biases associated with socioeconomic identification. Partly due to this learning effect, our simulation shows that in-group bias influences the outcomes of no more than 2% of cases under the current randomized case allocation process, and can be further reduced under dynamic policies that better allocate experienced jurors. Such findings offer promising evidence that crowd-sourcing can be an effective dispute resolution mechanism to govern online platforms, and that properly designed operating policies can further improve its efficacy.

This paper shows that decision-makers are less biased in the presence of a neutral colleague. We com- pile data on umpiring decisions from international cricket matches and analyze reforms which mandated the introduction of neutral umpires. Prior to the reforms, both on-field umpires are from the host country and make more discretionary calls against the foreign team. Requiring one of the two on-field umpires to be from a neutral country eliminates this bias. Host country umpires are less partial toward the home team when paired with a neutral umpire. Collectively, our results suggest that social pressure from colleagues can discipline discriminators.

Prior research suggests that averaging two guesses from the same person can improve quantitative judgments, a phenomenon known as the “wisdom of the inner crowd.” In this article, we find that this effect hinges on whether people explicitly decide in which direction their first guess had erred before making their second guess. In nine studies (N = 8,465), we found that asking people to explicitly indicate whether their first guess was too high or too low before making their second guess made people more likely to provide a second guess that was more extreme (in the same direction) than their first guess. As a consequence, the introduction of that “Too High/Too Low” question reduced (and sometimes eliminated or reversed) the wisdom-of-the-inner-crowd effect for (the majority of) questions with non-extreme correct answers and increased the wisdom-of-the-inner-crowd effect for questions with extreme correct answers. Our findings suggest that the wisdom-of-the-inner-crowd effect is not inevitable but rather that it depends on the processes people use to generate their second guesses. This paper was accepted by Yuval Rottenstreich, decision analysis.

Although risk assessment has increasingly been used as a tool to help reform the criminal justice system, some stakeholders are adamantly opposed to using algorithms. The principal concern is that any benefits achieved by safely reducing rates of incarceration will be offset by costs to racial justice claimed to be inherent in the algorithms themselves. But fairness tradeoffs are inherent to the task of predicting recidivism, whether the prediction is made by an algorithm or human. Based on a matched sample of 67,784 Black and White federal supervisees assessed with the Post Conviction Risk Assessment (PCRA), we compare how three alternative strategies for “debiasing” algorithms affect these tradeoffs, using arrest for a violent crime as the criterion. These candidate algorithms all strongly predict violent re-offending (AUCs=.71-72), but vary in their association with race (r= .00-.21) and shift tradeoffs between balance in positive predictive value and false positive rates. Providing algorithms with access to race (rather than omitting race or ‘blinding’ its effects) can maximize calibration and minimize imbalanced error rates. Implications for policymakers with value preferences for efficiency vs. equity are discussed.

We propose a continuous-time portfolio selection model that explains the active-passive continuum. Our model illuminates the pivotal role of expert opinions and factors in the asset allocation process. In the model, investors aim to outperform a benchmark. As securities and benchmark's drift depends on unobservable factors, portfolio selection becomes a partial observation risk-sensitive control problem. We find that the optimal investment policy combines Kelly, benchmark-tracking, and intertemporal hedging portfolios. The Kelly portfolio sums two alpha-generating strategies. Our simulation reveals the importance of debiasing expert opinions and shows that factor choice is crucial at every stage of the investment process.

We collect the data from Hong Kong’s major news media Facebook pages from 2019 to 2020 to examine the online ideological clashes between pro-democracy and pro-Beijing parties. We find that specific writing habits signify users’ backgrounds and elicit readers’ group segregation cues. Compared to Traditional Chinese comments, the increase of pro-police comments in Simplified Chinese induced a more polarized reaction from the opposite side with more anti-police comments and demand for regional independence. On the other hand, contents generated by the suspected bots alleviate the clashes. The results demonstrate the need for debiasing intervention and regulation in social media platforms.

Because time is a key determinant of entrepreneurial decision making, time-to-event models are ubiquitous in entrepreneurship. Widespread econometric misconception, however, may cause complicated biases in existing studies. The reason is spurious duration dependency, a complicated form of endogeneity caused by unobserved heterogeneity. This article discusses the endogeneity problem and methods to debias time-to-event models in entrepreneurship. Simulations and empirical evidence indicate that only the frailty approach yields consistently unbiased parameter estimates. An application to start-up firms’ time-to-funding shows that other methods lead to dramatic biases. Therefore, this article advocates a paradigm shift in the modeling of time variables in entrepreneurship.

The perception of risk affects how people behave during crises. We conduct a series of experiments to explore how people form COVID-19 mortality risk beliefs and the implications for prosocial behavior. We first document that people overestimate their own risk and that of young people, while underestimating the risk old people face. We show that the availability heuristic contributes to these biased beliefs. Using information about the actual risk to debias people's own risk perception does not affect donations to the Centers for Disease Control but does decrease the amount of time invested in learning how to protect older people. This constitutes a debiasing social dilemma. Additionally providing information on the risk for the elderly, however, counteracts these negative effects. Importantly, debiasing seems to operate through the subjective categorization of and emotional response to new information.

To investigate how cognitive biases inhibit value-based pricing practices among managers in business markets, this article considers five different cognitive biases—perceived lack of control, herdi ...

Although recommender systems (RSs) play a crucial role in our society, previous studies have revealed that the performance of RSs may considerably differ between groups of individuals with different characteristics or from different demographics. In this case, a RS is considered to be unfair when it does not perform equally well for different groups of users. Considering the importance of RSs in the distribution and consumption of musical content worldwide, a careful evaluation of fairness in the context of music RSs is crucial. To this end, we first introduce LFM-2b, a novel large-scale real-world dataset of music listening records, comprising a subset to investigate bias of RSs regarding users’ demographics. We then define a notion of fairness based on the performance gap of a RS between the users with different demographics, and evaluate a variety of collaborative filtering algorithms in terms of accuracy and beyond-accuracy metrics to explore the fairness in the RS results toward a specific gender group. We observe the existence of significant discrepancies (unfairness) between the performance of algorithms across male and female user groups. Based on these discrepancies, we explore to what extent recommender algorithms lead to intensifying the underlying population bias in the final results. We also study the effect of a resampling strategy, commonly used as debiasing method , which yields slight improvements in the fairness measures of various algorithms while maintaining their accuracy and beyond-accuracy performance.

Priority decisions concerning maintenance or reconstruction of roads are made with the aim of road improvements with as little traffic disturbance and time loss as possible. However, it cannot be avoided that speed will be reduced and travel time increased during the time of construction. The present study shows how intuitive judgments of travel time losses are biased in a way similar to the times saving bias (Svenson, 2008), but not perfectly corresponding to that bias. This means that when speed is decreased from a slow speed 80 km/h it is overestimated. Also, drivers, politicians and policy makers who do not make exact calculations are likely victims of the time loss bias. The time loss bias was weakened but not eliminated by a debiasing instruction including mathematical computations of travel times. When driving speed restrictions are implemented, in particular on fast motorways, it is necessary to consider and counteract the time loss bias and inform the public. This can be done, for example, in communications about travel time facts, by information in driver training and by mounting temporary road signs informing about the average travel time prolongation due to a road work.

Safety leadership is often tasked with making critical decisions in the face of complex information and external pressures. Given situations with many variables and potential courses of action to be considered, the brain has evolved to attempt to simplify these judgmental operations. Consequently, at times the result may be flawed judgments concerning our assessments of future probabilities due to certain cognitive biases. These cognitive biases are systematic distortions of decision making arising from innate heuristics used to simplify large quantities of data (Krause, 2009) [1]. By developing a better understanding of the types of cognitive biases that influence decision making, steps can be taken to more effectively manage these bias. However, research has shown that technologies associated with training in statistical and probabilistic reasoning and cognitive repair techniques do not sufficiently address elements of intuitive belief and individual affect. Therefore, this paper suggests that certain Emotional Intelligence (EI) competencies, when applied to the individual decision making process, can be useful in mitigating the effects of cognitive bias. Emotional Intelligence can be defined as an array of interrelated emotional and social competencies, skills and facilitators that impact intelligent behavior (Bar-On, 1997) [2]. For the purposes of this paper competencies associated with the EQ-i 2.0 , a revision of the original model by Reuven Bar-On, will serve as a template for discussion. The composite scales include Self-Perception, Self-Expression, Interpersonal, Decision Making and Stress Management. Certain scales are considered to be especially relevant to affect-based debiasing strategies. In addition to the potential benefits for the individual decision maker, leveraging EI competencies may positively influence team interactions in the process of safety decision making and promote an organizational culture more open to diversity of perspective.

By integrating cognitive diversity into debiasing literature, this paper contributes towards opening the black box of executive judgment. Based on information processing theory we investigate the role of cognitive diversity in strategic decision making. We apply a vignette-based experimental research design to examine the effect of cognitive diversity in teams on decision maker's illusion of control. The results of these experiments provide evidence for a positive influence of high cognitive diversity for debiasing judgment while similarly indicating no such effect for groups with low cognitive diversity. These findings suggest that group composition aspects can play an important role for improving judgment in decision making teams and open promising new avenues for studying debiasing in behavioral strategy research.

German Abstract: Die Entscheidungslehre ist die anwendungsorientierte, praskriptive Richtung der Entscheidungstheorie, die fur wichtige Entscheidungsprobleme wertvolle Hilfe liefert. Empfohlen wird ein Entscheidungsprozess in drei Schritten: (1) Strukturierung der Entscheidungssituation, (2) Entwicklung eines Wirkungsmodells und (3) Evaluation der Handlungsalternativen. In diesem Beitrag werden die wesentlichen Inhalte dieser Schritte skizziert. Von besonderer Bedeutung fur die Qualitat der Entscheidung ist eine reflektierte Analyse der Ziele im ersten Schritt auf der Basis des Value-focused Thinking und eine darauf aufbauende kreative Herangehensweise bei der Identifikation von Handlungsalternativen. Hingewiesen wird ebenfalls auf die Notwendigkeit, durch Debiasing den Einfluss typischer Schatzfehler und Verzerrungsfaktoren auf die Wirkungsprognosen zu verringern. Nach den theoretischen Ausfuhrungen schliest der Beitrag mit Hinweisen, wie sich die Vorgehensweise mit Hilfe eines Online-Tools konkret zur Losung praktischer Entscheidungsfragen umsetzen lasst. English Abstract: Decision analysis is the application-oriented, prescriptive direction of decision theory, which provides valuable help for important decision problems. A decision process in three steps is recommended: (1) Structuring the decision frame, (2) developing an impact model, and (3) evaluating the alternatives. This paper outlines the main contents of these steps. A well reflected analysis of the objectives based on value-focused thinking and a creative approach to the identification of alternatives based on this analysis are of particular importance for the quality of the decision. Furthermore, the necessity to reduce the influence of typical estimation errors and distortion factors on impact prognoses through debiasing is pointed out. Hence, the paper concludes on how the approach can be implemented with the help of an online tool to solve practical decision questions.

Purpose The purpose of this paper is to investigate if and how firms approach debiasing and what determines its success. In particular, this study examines if debiasing is effective in reducing cognitive decision biases. This paper also investigates organizational characteristics that determine the effectiveness of debiasing. Design/methodology/approach This study uses survey data from German firms to answer the research questions. Target respondents are individuals in a senior management accounting function. Findings In line with the hypotheses, this paper finds that debiasing can reduce cognitive biases. Moreover, this study finds that psychological safety not only directly influences the occurrence of cognitive biases but is also an important factor that determines the effectiveness of debiasing. Research limitations/implications This paper provides evidence that debiasing can serve as a powerful management accounting tool and discusses debiasing in the context of recent management accounting literature. This study also adds to the stream of research that investigates the role of psychological safety in organizations by highlighting its importance for successful debiasing. Practical implications This paper informs firms that use or intend to use debiasing about crucial determinants to consider when debating its implementation, i.e. psychological safety. This study also identifies risk management as a potential interface for the implementation of systematic debiasing. Originality/value While previous research primarily addresses specific cognitive biases and debiasing mechanisms using lab experiments, this is – to the best of the knowledge – the first study investigating cognitive biases and debiasing on a broad conceptual level using survey data.

There is no consensus in the literature on the effect of personal finance courses on individuals' financial literacy level. This research analyzes the influence of a personal finance course of 72 class hours per semester on the financial literacy level of Brazilian undergraduate students and also analyzes the influence of the cognitive ability on this learning process. We performed a quasi-experiment and we used an approach of difference in differences in which we compared the results obtained by the same students in two distinct groups (students who participated in the course and students who did not participate). From the results, treatment group participants significantly increased the level of financial literacy, considering the three dimensions assessed: financial knowledge, attitude, and behavior. However, the individuals with higher cognitive ability did not present higher levels of learning when compared to the others. Additionally, in order to visualize in practice the influence of financial literacy, we tested whether the personal finance course was also able to reduce the bias of a behavior called disposition effect. The results indicated that the course worked as a debiasing strategy for this bias.

Recommender systems learn from historical users' feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the underlying platform itself. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages equal probability of being recommended across items, while the second one encourages true positive rates for items to be equal. We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity. To promote a more equal treatment of items along the popularity tail, we propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity. Extensive experiments show that, with small losses in accuracy, our popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality., Accepted in Information Processing & Management, 58(1), 102387

Biased forecasts, particularly the inadequate adjustment from current values and excessive clustering, are increasingly explained as resulting from anchoring. However, experiments presented in support of this interpretation lack economic conditions, particularly monetary incentives, feedback for learning effects and an optimal strategy of unbiased predictions. In a novel forecasting experiment, we find monetary incentives to reduce anchoring for simple forecasting tasks only, while higher task complexity and risk increase the bias in spite of incentives for accuracy. Anchors ubiquitously reduce the forecasts’ variance, while individual cognitive abilities and learning effects show debiasing effects only in some conditions. Our results emphasize that biased forecasts and their specific variance can result from anchoring.

There is increasing evidence that the field of applied ethology is prone to expectation biases invalidating research outcomes. Nevertheless, outcome assessors are rarely blinded. We surveyed delegates of the International Society for Applied Ethology (ISAE) 2014 congress shortly before (n = 39 respondents) and after (n = 51 respondents) a combined congress plenary and workshop on expectation bias in applied ethology. The aims were to evaluate the effect of the plenary and workshop on the opinion of applied ethologists in order to better comprehend why blinding outcome assessors seems so rarely practiced as a debiasing technique in this field of research. The results suggest that a moderate awareness about expectancy effects among ethologists and the logistic constraints of blinded observations rather than a perceived low susceptibility of the research field is the larger part of the explanation. Although awareness about expectancy effects and debiasing techniques was higher immediately after than before the congress plenary and workshop, a more sustained and concerted effort is needed throughout all stages of the research process to avoid expectation bias invalidating research finding and to improve the scientific credibility of the field of applied ethology.

This review considers the role of overconfidence in organizational life, focusing on ways in which individual-level overconfidence manifests in organizations. The research reviewed offers a pessimistic assessment of the efficacy of either debiasing tools or organizational correctives, and identifies some important ways in which organizational dynamics are likely to exacerbate overconfidence among individuals. The organizational consequences of overconfidence can be substantial, especially when it comes from those at the top of the organization. However, there are also reasons to suspect that the research literature exaggerates the prevalence of overconfidence.

Seismic reflectivity inversion using basis pursuit de-noising algorithm (BPDN) produces sparse solutions by applying l1-norm regularization. Due to the presence of the regularization term, there is always a deviation between the solution obtained by BPDN and the true one. A debiasing step of “adding back the residual” was introduced into seismic reflectivity inversion using BPDN to reduce the deviation, so that the accuracy of seismic reflectivity inversion using BPDN is improved. The debiasing step is carried out using a moderately large trade-off factor by iteratively performing the fast iterative soft thresholding algorithm (FISTA) to solve the objective function of BPDN with the input updated by adding back the residual to the initial input. The solution obtained by using FISTA with incorporation of the debiasing step of “adding back the residual” is a better approximation of the true solution than the result obtained by using FISTA a single time. In addition, the debiasing step of “adding back the residual” is not limited to the number of nonzero components in the solution and can be used in a low signal-to-noise ratio circumstance. A multichannel inversion method was adopted to recover the acoustic impedance from the inverted reflection coefficient, and it gained good results both in numerical and field examples.

This paper investigates the presence of local bias in the peer-to-peer (P2P) lending market and explores the social heterogeneous factors that may affect the formulation of the investor’s local bias. We find that local biases are commonly present among investors. Investors have a 9.3% higher probability and put 105% more money in lending to local borrowers. We also find that overinvesting in local loans is correlated with a higher default risk, lower recovery rate, and lower realized return, suggesting the underperformance of these locally biased investors. By taking advantage of the diverse local culture and institutional features in China, we further show that social heterogeneity, including geography, language, and social trust, affects the degree of local biases in the P2P lending market. We propose two debiasing techniques from the P2P platforms’ perspective.

The consumer-protection literature can be divided into two camps: laissez-faire libertarianism and paternalism. Paternalism, as advanced by behavioral law and economics, calls for nudging consumers toward their utility-maximizing preference. Laissez-faire libertarianism, instead, calls for relying on rational-choice theory and the free market to allocate consumer goods. Although each camp presents the other as its diametric opponent, this Note shows that this dichotomy is overstated. Neither camp is incompatible with the other, nor infallible on its own. Through an original behavioral study, this Note reveals flaws in the fundamental assumptions of both camps: that no information can be conveyed neutrally (behavioral law and economics) and that consumer-oriented regulation diminishes autonomy (rational-choice theory). It does so by focusing on an understudied form of consumer-protection regulation: mixed framing. Legal scholars and regulators have largely ignored this phenomenon, yet it offers a more robust and actionable regulatory approach than the existing literature and one that is distinct from both paternalism and libertarianism. By examining the case study of food-safety regulations, this Note sketches the analytic and normative case for why regulators should embrace mixed framing. Using a process of debiasing through mixed framing, agencies can promulgate rules that minimize the risk of deceptive advertising tactics and maximize the provision of neutral and complete information—without running afoul of the First Amendment or falling into paternalistic restrictions on autonomy.

A growing body of evidence shows that politicians use motivated reasoning to fit evidence with prior beliefs. In this, they are not unlike other people. We use survey experiments to reaffirm prior work showing that politicians, like the public they represent, engage in motivated reasoning. However, we also show that politicians are more resistant to debiasing interventions than others. When required to justify their evaluations, politicians rely more on prior political attitudes and less on policy information, increasing the probability of erroneous decisions. The results raise the troubling implication that the specialized role of elected officials makes them more immune to the correction of biases, and in this way less representative of the voters they serve when they process policy information.

We propose and empirically test statistical approaches to debiasing judgmental corporate cash flow forecasts. Accuracy of cash flow forecasts plays a pivotal role in corporate planning as liquidity and foreign exchange risk management are based on such forecasts. Surprisingly, to our knowledge there is no previous empirical work on the identification, statistical correction, and interpretation of prediction biases in large enterprise financial forecast data in general, and cash flow forecasting in particular. Employing a unique set of empirical forecasts delivered by 34 legal entities of a multinational corporation over a multi-year period, we compare different forecast correction techniques such as Theil’s method and approaches employing robust regression, both with various discount factors. Our findings indicate that rectifiable mean as well as regression biases exist for all business divisions of the company and that statistical correction increases forecast accuracy significantly. We show that the parameters estimated by the models for different business divisions can also be related to the characteristics of the business environment and provide valuable insights for corporate financial controllers to better understand, quantify, and feedback the biases to the forecasters aiming to systematically improve predictive accuracy over time.

Anecdotal evidence and academic research show that judges’ subjective feelings toward litigants have undue influence on their judgments. This article suggests a simple debiasing procedure, namely, requiring judges to write their reasons before making a decision. I conduct experiments on incumbent Chinese judges to test its effectiveness. Study 1 uses a between-subjects design to explore the interaction between reason writing and a stimulus that induces a judge’s negative feeling toward a defendant. Judges who are required to write their reasons before they decide a case are significantly less affected by the stimulus than those who directly enter the decision-making stage. Study 2 provides evidence that a forced deliberation period achieves a similar debiasing effect. Study 3 studies the polar opposite of reason writing—the delegation of reason writing, which resembles the judge–law clerk delegation of opinion writing setting. I find that delegation serves to reinforce biases.

When a jurisdiction considers introducing legal casino gambling, the public debate tends to be dominated by the yes-or-no question concerning the opening of casinos. This (understandable) focus takes as a near-given, then, the manner in which casinos themselves operate. Nevertheless, the benefits and costs of casino gambling are closely tied to details of casino behavior. These details are particularly vital because much gambling behavior is suggestive of less-than-rational decision making by consumers. Regulations can be instituted that offer some protection against disordered gambling, without impinging in a serious way on the choices of ardent but content and (arguably) rational bettors. These regulations tend to fall into three categories: information provision, commitment devices, and debiasing. This paper examines the prospects that such regulations hold for reducing disordered gambling, and develops guidelines for casino regulations that are likely to improve social welfare relative to current standard practices. [The title of this paper is an homage to a pivotal book about lottery gambling by Charles T. Clotfelter and Philip Cook, Selling Hope: State Lotteries in America, Harvard University Press, 1989.]

Empirical evidence indicates that national court judges fall prey to cognitive biases and heuristics. The same may be assumed for international arbitrators. Improving third-party adjudication through behavior-ally informed rules on procedure thus seems to be an avenue of research worth being pursued. In applying behavioral law and economics to international commercial arbitration, the present analysis shows (1) that behavioral economics can help to understand arbitrators’ behavior and (2) suggests how the law may mitigate their cognitive biases and heuristics in order to design more effective, efficient, and fair arbitral proceedings under the UNCITRAL Arbitration Rules. The analysis focuses on (i) the representativeness heuristic, (ii) anchoring, (iii) the hindsight bias, (iv) framing effects, and (v) the egocentric bias. Building on their underlying dynamics and recent research on context-dependent decision-making, corresponding debiasing mechanisms may be implemented into arbitral proceedings through either behaviorally informed (model) arbitration clauses or by complementing existing frameworks such as the UNCITRAL Notes on Organizing Arbitral Proceedings in a behaviorally informed manner. Hence, in applying insights from economics and psychology to international arbitration, the present analysis adopts a prescriptive approach, examining how to actively mitigate arbitrators’ cognitive shortcomings as much as possible. Ac-curacy in fact determination – or the search for the truth – is perceived as the central motivation of this approach. As prescriptive insights from behavioral economics are able to allow for more accurate judgment, behaviorally informed rules on procedure not only benefit disputing parties by enhancing the idea of due process, but in doing so, they also empower international arbitration as a legal institution when con-fronted with national legal systems.

Reviewers play a key role in the peer-review process. Serving a dual role, reviewers provide authors feedback to improve their manuscript's quality, and assist editors in choosing acceptable manuscripts for publication. Even though reviewers fully understand their dual roles, generally are knowledgeable about the subject, and typically are motivated to write constructive comments, they can still produce biased reviews. This article addresses two questions. Why do some reviewers provide more biased evaluations than others? How can those biases be reduced to improve the quality of reviews? Drawing on the judgment and decision making literature, this article identifies the sources of reviewer biases and proposes potential bias reduction strategies.

During the last 2 decades, there has been a shift in the U.S. health care system towards improving the quality of health care provided by enhancing patient safety and reducing medical errors. Unfortunately, surgical complications, patient harm events, and malpractice claims remain common in the field of neurosurgery. Many of these events are potentially avoidable. There are an increasing number of publications in the medical literature in which authors address cognitive errors in diagnosis and treatment and strategies for reducing such errors, but these are for the most part absent in the neurosurgical literature. The purpose of this article is to highlight the complexities of medical decision making to a neurosurgical audience, with the hope of providing insight into the biases that lead us towards error and strategies to overcome our innate cognitive deficiencies. To accomplish this goal, we review the current literature on medical errors and just culture, explain the dual process theory of cognition, identify common cognitive errors affecting neurosurgeons in practice, review cognitive debiasing strategies, and finally provide simple methods that can be easily assimilated into neurosurgical practice to improve clinical decision making.

We examined how testing potentiates self-regulated learning and alleviates the foresight bias—an illusion of competence that arises from information being present during study but absent at test—and whether such benefits can transfer to non-tested material. After studying paired associates that varied in difficulty, participants either restudied or were tested on all the pairs (Experiment 1); were tested on only half of the pairs (Experiment 2); or were tested on half of the pairs and restudied the remaining pairs (Experiments 3 and 4). All items were then restudied at participants’ own pace before a final cued-recall test. In Experiment 1, interim tests enhanced the effectiveness of subsequent study time and alleviated the foresight bias, whereas interim restudying had no such benefits. Experiments 2, 3, and 4 demonstrated that such test-potentiated self-regulated learning can transfer to non-tested items if restudied intermixed with items that were tested. The results demonstrate yet another practical benefit of testing and suggest that retrieval practice can foster metacognitive sophistication among learners, serving as an experiencebased debiasing procedure.

The motivational value of confidence postulates that individual effort provision is increasing in beliefs on one’s own ability. This relation is supposed to also hold for overconfident individuals who have exaggerated ability beliefs. We present the first empirical evidence on the existence of a motivational value of absolute confidence that many microeconomic models with overconfident agents build on. Moreover, we document that negative debiasing information on individual ability diminishes effort provision – a result that is of obvious relevance for many contexts such as labor relations or learning at school. We also offer a strategy for identifying significant absolute overconfidence at the individual level.

An increasing focus on the prevention of medical errors is a direct result of a growing patient safety movement. Although the reduction of technical errors has been the focus of most interventions, cognitive errors, usually more than one error linked together, actually cause the majority of misdiagnoses. This article examines the most common types of cognitive errors in dermatology. Two methods to minimize these errors are recommended: first, cognitive debiasing techniques reduce the common initiating factor of error cascades; and secondly, the application of prospective hindsight attacks the final common pathway that leads to misdiagnosis.

We investigated, using questionnaires, different strategies for removing drivers' overoptimism (Svenson et al., 2012a) about how fast their speed could be decreased when they were speeding compared with braking at the speed limit speed. Three different learning groups and a control group made collision speed judgments. The first learning group had the distance a car travels during a driver's reaction time for each problem. The second group had this information and also feedback after each judgment (correct speed). The third group judged collision speed but also braking distance and received correct facts after each problem. The control group had no information at all about reaction time and the distance traveled during that time. The results suggested the following rank order from poor to improved performance: control, group 1, group 3 and group 2 indicating that information about distance driven during a driver's reaction time improved collision speed judgments and that adding stopping distance information did not add to this improvement.

Low numerical probabilities tend to be directionally ambiguous, meaning they can be interpreted either positively, suggesting the occurrence of the target event, or negatively, suggesting its non-occurrence. High numerical probabilities, however, are typically interpreted positively. We argue that the greater directional ambiguity of low numerical probabilities may make them more susceptible than high probabilities to contextual influences. Results from five experiments supported this premise, with perceived base rate affecting the interpretation of an event’s numerical posterior probability more when it was low than high. The effect is consistent with a confirmatory hypothesis testing process, with the relevant perceived base rate suggesting the directional hypothesis which people then test in a confirmatory manner.

Purpose The goal of this study is to investigate positive hypothesis testing among consumers of health information when they search the Web. After demonstrating the extent of positive hypothesis testing using Experiment 1, we conduct Experiment 2 to test the effectiveness of two debiasing techniques. Methods A total of 60 undergraduate students searched a tightly controlled online database developed by the authors to test the validity of a hypothesis. The database had four abstracts that confirmed the hypothesis and three abstracts that disconfirmed it. Results Findings of Experiment 1 showed that majority of participants (85%) exhibited positive hypothesis testing. In Experiment 2, we found that the recommendation technique was not effective in reducing positive hypothesis testing since none of the participants assigned to this server could retrieve disconfirming evidence. Experiment 2 also showed that the incorporation technique successfully reduced positive hypothesis testing since 75% of the participants could retrieve disconfirming evidence. Conclusion Positive hypothesis testing on the Web is an understudied topic. More studies are needed to validate the effectiveness of the debiasing techniques discussed in this study and develop new techniques. Search engine developers should consider developing new options for users so that both confirming and disconfirming evidence can be presented in search results as users test hypotheses using search engines.

There is overwhelming evidence that performance ratings and evaluations are context dependent. A special case of such context effects is the decoy effect, which implies that the inclusion of a dominated alternative can influence the preference for non-dominated alternatives. Adapting the well-known experimental setting from the area of consumer behavior to the performance evaluation context of Data Envelopment Analysis (DEA), an experiment was conducted. The results show that adding a dominated decision making unit (DMU) to the set of DMUs augments the attractiveness of certain dominating DMUs and that DEA efficiency scores discriminating between efficient and inefficient DMUs serve as an appropriate debiasing procedure. The mention of the existence of slacks for distinguishing between strong and weak efficient DMUs also contributes to reducing the decoy effect, but it is also associated to other unexpected effects.