How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases

This review summarizes results of field experiments examining individual behaviors across several market settings—from open-air markets to rideshare markets to tax-compliance markets—where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.

Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts

Background Decision aid developers have to convey complex task-specific numeric information in a way that minimizes bias and promotes understanding of the options available within a particular decision. Whereas our companion paper summarizes fundamental issues, this article focuses on more complex, task-specific aspects of presenting numeric information in patient decision aids. Methods As part of the International Patient Decision Aids Standards third evidence update, we gathered an expert panel of 9 international experts who revised and expanded the topics covered in the 2013 review working in groups of 2 to 3 to update the evidence, based on their expertise and targeted searches of the literature. The full panel then reviewed and provided additional revisions, reaching consensus on the final version. Results Five of the 10 topics addressed more complex task-specific issues. We found strong evidence for using independent event rates and/or incremental absolute risk differences for the effect size of test and screening outcomes. Simple visual formats can help to reduce common judgment biases and enhance comprehension but can be misleading if not well designed. Graph literacy can moderate the effectiveness of visual formats and hence should be considered in tool design. There is less evidence supporting the inclusion of personalized and interactive risk estimates. Discussion More complex numeric information. such as the size of the benefits and harms for decision options, can be better understood by using incremental absolute risk differences alongside well-designed visual formats that consider the graph literacy of the intended audience. More research is needed into when and how to use personalized and/or interactive risk estimates because their complexity and accessibility may affect their feasibility in clinical practice.

Cancer blues? A validated judgment bias task suggests pessimism in nude mice with tumors

In humans, affective states can bias responses to ambiguous information: a phenomenon termed judgment bias (JB). Judgment biases have great potential for assessing affective states in animals, in both animal welfare and biomedical research. New animal JB tasks require construct validation, but for laboratory mice (Mus musculus), the most common research vertebrate, a valid JB task has proved elusive. Here (Experiment 1), we demonstrate construct validity for a novel mouse JB test: an olfactory Go/Go task in which subjects dig for high- or low-value food rewards. In C57BL/6 and Balb/c mice faced with ambiguous cues, latencies to dig were sensitive to high/low welfare housing, environmentally-enriched animals responding with relative ‘optimism’ through shorter latencies. Illustrating the versatility of a validated JB task across fields of research, it further allowed us to test hypotheses about the mood-altering effects of cancer (Experiment 2). Male nude mice bearing subcutaneous lung adenocarcinomas responded more pessimistically than healthy controls to ambiguous cues. Similar effects were not seen in females, however. To our knowledge, this is the first validation of a mouse JB task and the first demonstration of pessimism in tumor-bearing animals. This task, especially if refined to improve its sensitivity, thus has great potential for investigating mouse welfare, the links between affective state and disease, depression-like states in animals, and hypotheses regarding the neurobiological mechanisms that underlie affect-mediated biases in judgment.

Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics

A person is labelled as having COVID-19 infection either from a positive PCR-based diagnostic test, or by a health professional’s assessment of the clinical picture in a process described by some as symptom screening. There is considerable fragility in the resulting data as both of these methods are susceptible to human biases in judgment and decision-making. In this article we show the value of a casual representation that maps out the relations between observed and inferred evidence of contamination, in order to expose what is lacking and what is needed to reduce the uncertainty in classifying an individual as infected with COVID-19.

Elaborating on Internet addiction and cyberchondria – relationships, direct and mediated effects

The present paper focuses on the effects of personality traits on the levels of Internet addiction and cyberchondria and their consequent effects on well-being. For the purposes of the research, mediation analyses were carried out, clarifying the infl uence of personality traits on well-being mediated by Internet addiction, cyberchondria and anxiety aroused by online health information seeking. It was established that Neuroticism leads to decreased well-being through the mediating infl uence of health anxiety, escalation and persistence of health concerns. Health anxiety is a mediator in the relationship between Openness to Experience and well-being. It was established that low levels of Agreeableness and Conscientiousness, and high levels of Openness, and Neuroticism, infl uence Internet addiction, which in turn leads to decreased well-being. Furthermore, we established the mediated infl uence of Internet addiction on the tendency for cyberchondria and health anxiety through using the Internet for health information and judgment biases.

Analytical Procedures: Are More Good Ideas Always Better for Audit Quality?

ABSTRACT Analytical procedures are critical to the financial statement auditing process and involve the auditor generating and considering multiple explanations for account fluctuations. We examine whether generating more or fewer explanations during analytical procedures improves audit quality. Research from fields outside of accounting suggests that generating many explanations can exacerbate judgment biases. We conduct an experiment with 92 senior auditors from two Big 4 firms to investigate whether the generation of more plausible explanations about potential misstatements hinders professional skepticism by increasing auditors' tendencies to anchor on client-provided explanations. We find that the generation of more plausible explanations increases the perceived difficulty of the task, which leads to anchoring on client explanations. Increased anchoring results in reduced assessments of fraud risk, an important component of the risk assessment process. These findings suggest that generating more explanations during analytics procedures can be counterproductive.

Reducing Social Judgment Biases May Require Identifying the Potential Source of Bias

Social judgment is shaped by multiple biases operating simultaneously, but most bias-reduction interventions target only a single social category. In seven preregistered studies (total N > 7,000), we investigated whether asking participants to avoid one social bias affected that and other social biases. Participants selected honor society applicants based on academic credentials. Applicants also differed on social categories irrelevant for selection: attractiveness and ingroup status. Participants asked to avoid potential bias in one social category showed small but reliable reductions in bias for that category ( r = .095), but showed near-zero bias reduction on the unmentioned social category ( r = .006). Asking participants to avoid many possible social biases or alerting them to bias without specifically identifying a category did not consistently reduce bias. The effectiveness of interventions for reducing social biases may be highly specific, perhaps even contingent on explicitly and narrowly identifying the potential source of bias.