Overconfidence among young decision-makers: assessing the effectiveness of a video intervention and the role of gender, age, feedback, and repetition

Child development research on overconfidence suggests that the bias is present and persistent in preschoolers and kindergartners. However, little is known about what drives overconfidence among young decision-makers, how it changes over a large number of repetitions, and whether such changes differ by gender or age. The current experimental study analyzes data from 60 children, aged 4 years 0 months to 6 years 10 months, who played 60 turns of the Children's Gambling Task and provided regular estimates on their performance. A video intervention, designed to demonstrate the consequences of disadvantageous choices, was tested in a double-blind randomized controlled trial to assess its impact on overconfidence. The results show that every third participant remained overconfident even after 60 trials and constant feedback. Unlike previously reported, gender seems to be a determining factor in this process. Lastly, providing additional information through a video intervention appears to have no impact on participants' overconfidence levels

Bot detection in online studies and experiments

Most experimental and online studies in the empirical social sciences rely on online panels from crowdsourcing platforms, such as Amazon Mechanical Turk (MTurk), Prolific, Qualtrics Online Panel, and their lesser known competitors. The key benefit of all of these services is an easy and affordable access to a large pool of diverse participants, a privilege that was previously reserved for globally leading and financially independent universities. However, this newly achieved leveled playing field comes at a cost. Semi- or fully automated response tools, also called bots, decrease data quality and reliability. This case describes how two online studies were conducted on a crowdsourcing platform in anticipation of bot responses. Specifically, the case offers insights into the study design process, the selection of appropriate survey questions and bot traps, as well as the ex-post analysis and filtering of bot responses. Best practices are identified, and potential pitfalls explained. The description should aid readers in designing anticipatory online studies and experiments to increase their data quality, validity, and reliability

Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice

Adaptive online platforms, powered by artificial intelligence, commonly referred to as robo-advice, steadily increase their market share. Yet these comparably new financial services are critically understudied. Little is known about why some investors adopt robo-advice for something as essential as asset allocation. The current paper tries to close this gap by shedding light on the causal effect of investor overconfidence on the propensity of using robo-advice. The study proposes a theoretical framework that combines the divergence of opinion hypothesis with consumer behavior insights and information technology diffusion research. The framework is empirically tested on the Investor Sample of the 2015 National Financial Capability Study, a subsample of 2000 US investors. The results from a series of generalized linear, structural, and semiparametric models show that in a pre-chasm market, overconfident investors have a significantly higher propensity of adopting robo-advice. While higher financial literacy seems to decrease robo-advice uptake, unjustified confidence in one’s knowledge causally increases it. Willingness to take financial risk cannot account for the significantly increased adoption of robo-advice among overconfident investors. The findings help managers to better position robo-advice by offering behavioral insights into their user base. In addition, the results outline a managerial tool to take demand-side actions to increase the likelihood of an end-user innovation crossing the chasm

The one man show: experimental evidence for the impact of joint decision-making on overconfidence

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Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform

Background: The COVID-19 pandemic disrupted healthcare and may have impacted ethnic inequalities in healthcare. We aimed to describe the impact of pandemic-related disruption on ethnic differences in clinical monitoring and hospital admissions for non-COVID conditions in England. // Methods: In this population-based, observational cohort study we used primary care electronic health record data with linkage to hospital episode statistics data and mortality data within OpenSAFELY, a data analytics platform created, with approval of NHS England, to address urgent COVID-19 research questions. We included adults aged 18 years and over registered with a TPP practice between March 1, 2018, and April 30, 2022. We excluded those with missing age, sex, geographic region, or Index of Multiple Deprivation. We grouped ethnicity (exposure), into five categories: White, Asian, Black, Other, and Mixed. We used interrupted time-series regression to estimate ethnic differences in clinical monitoring frequency (blood pressure and Hba1c measurements, chronic obstructive pulmonary disease and asthma annual reviews) before and after March 23, 2020. We used multivariable Cox regression to quantify ethnic differences in hospitalisations related to diabetes, cardiovascular disease, respiratory disease, and mental health before and after March 23, 2020. // Findings: Of 33,510,937 registered with a GP as of 1st January 2020, 19,064,019 were adults, alive and registered for at least 3 months, 3,010,751 met the exclusion criteria and 1,122,912 were missing ethnicity. This resulted in 14,930,356 adults with known ethnicity (92% of sample): 86.6% were White, 7.3% Asian, 2.6% Black, 1.4% Mixed ethnicity, and 2.2% Other ethnicities. Clinical monitoring did not return to pre-pandemic levels for any ethnic group. Ethnic differences were apparent pre-pandemic, except for diabetes monitoring, and remained unchanged, except for blood pressure monitoring in those with mental health conditions where differences narrowed during the pandemic. For those of Black ethnicity, there were seven additional admissions for diabetic ketoacidosis per month during the pandemic, and relative ethnic differences narrowed during the pandemic compared to the White ethnic group (Pre-pandemic hazard ratio (HR): 0.50, 95% confidence interval (CI) 0.41, 0.60, Pandemic HR: 0.75, 95% CI: 0.65, 0.87). There was increased admissions for heart failure during the pandemic for all ethnic groups, though highest in those of White ethnicity (heart failure risk difference: 5.4). Relatively, ethnic differences narrowed for heart failure admission in those of Asian (Pre-pandemic HR 1.56, 95% CI 1.49, 1.64, Pandemic HR 1.24, 95% CI 1.19, 1.29) and Black ethnicity (Pre-pandemic HR 1.41, 95% CI: 1.30, 1.53, Pandemic HR: 1.16, 95% CI 1.09, 1.25) compared with White ethnicity. For other outcomes the pandemic had minimal impact on ethnic differences. // Interpretation: Our study suggests that ethnic differences in clinical monitoring and hospitalisations remained largely unchanged during the pandemic for most conditions. Key exceptions were hospitalisations for diabetic ketoacidosis and heart failure, which warrant further investigation to understand the causes

Living alone and mental health: parallel analyses in UK longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic

BACKGROUND: People who live alone experience greater levels of mental illness; however, it is unclear whether the COVID-19 pandemic had a disproportionately negative impact on this demographic. OBJECTIVE: To describe the mental health gap between those who live alone and with others in the UK prior to and during the COVID-19 pandemic. METHODS: Self-reported psychological distress and life satisfaction in 10 prospective longitudinal population surveys (LPSs) assessed in the nearest pre-pandemic sweep and three periods during the pandemic. Recorded diagnosis of common and severe mental illnesses between March 2018 and January 2022 in electronic healthcare records (EHRs) within the OpenSAFELY-TPP. FINDINGS: In 37 544 LPS participants, pooled models showed greater psychological distress (standardised mean difference (SMD): 0.09 (95% CI: 0.04; 0.14); relative risk: 1.25 (95% CI: 1.12; 1.39)) and lower life satisfaction (SMD: −0.22 (95% CI: −0.30; −0.15)) for those living alone pre-pandemic. This gap did not change during the pandemic. In the EHR analysis of c.16 million records, mental health conditions were more common in those who lived alone (eg, depression 26 (95% CI: 18 to 33) and severe mental illness 58 (95% CI: 54 to 62) more cases more per 100 000). For common mental health disorders, the gap in recorded cases in EHRs narrowed during the pandemic. CONCLUSIONS: People living alone have poorer mental health and lower life satisfaction. During the pandemic, this gap in self-reported distress remained; however, there was a narrowing of the gap in service use. CLINICAL IMPLICATIONS: Greater mental health need and potentially greater barriers to mental healthcare access for those who live alone need to be considered in healthcare planning

Living alone and mental health: parallel analyses in UK longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic

Background: People who live alone experience greater levels of mental illness; however, it is unclear whether the COVID-19 pandemic had a disproportionately negative impact on this demographic. Objective: To describe the mental health gap between those who live alone and with others in the UK prior to and during the COVID-19 pandemic. Methods: Self-reported psychological distress and life satisfaction in 10 prospective longitudinal population surveys (LPSs) assessed in the nearest pre-pandemic sweep and three periods during the pandemic. Recorded diagnosis of common and severe mental illnesses between March 2018 and January 2022 in electronic healthcare records (EHRs) within the OpenSAFELY-TPP. Findings: In 37 544 LPS participants, pooled models showed greater psychological distress (standardised mean difference (SMD): 0.09 (95% CI: 0.04; 0.14); relative risk: 1.25 (95% CI: 1.12; 1.39)) and lower life satisfaction (SMD: −0.22 (95% CI: −0.30; −0.15)) for those living alone pre-pandemic. This gap did not change during the pandemic. In the EHR analysis of c.16 million records, mental health conditions were more common in those who lived alone (eg, depression 26 (95% CI: 18 to 33) and severe mental illness 58 (95% CI: 54 to 62) more cases more per 100 000). For common mental health disorders, the gap in recorded cases in EHRs narrowed during the pandemic. Conclusions: People living alone have poorer mental health and lower life satisfaction. During the pandemic, this gap in self-reported distress remained; however, there was a narrowing of the gap in service use. Clinical implications: Greater mental health need and potentially greater barriers to mental healthcare access for those who live alone need to be considered in healthcare planning

The one-man show: the effect of joint decision-making on investor overconfidence

This study examines the impact of shared decision-making on investor overconfidence. Data from 2,000 investors, 6,394 consumers, and 657 experimental participants shed light on whether consumers who engage in joint financial decision-making are less affected by investor overconfidence than those who decide on their own. The findings show that investors who jointly decide are substantially less overconfident. However, family- or friend-inclined interactions are more effective in reducing overconfidence than relying on a financial advisor. The current research theoretically argues and empirically shows that shared metaknowledge drives this diminishing effect by highlighting unknown aspects of a financial decision. Compared to providing investors with solutions, problem reformulation, validation, or legitimation, only metaknowledge consistently decreases overconfidence in joint financial decision-making. It is argued that the process of highlighting unknowns can explain why interactions with family and friends have a more pronounced impact on investor overconfidence than consulting a professional advisor. The study provides a feasible debiasing tool to consumers, financial institutions, and other financial service providers to decrease overconfidence by emphasizing unknown aspects of an investment toward improving the quality of a consumer’s financial decisions under uncertainty

Irrational and overrated: is Our unrealistic self-perception connected to educational achievements?

This work examines the relationship between education and excessive confidence in situations of uncertainty. For this purpose, a questionnaire with 10 pseudo general knowledge questions was designed, whereby their degree of difficulty exceeds the knowledge of an average student by far. It was investigated whether subjects (N = 535) would acknowledge this condition and its associated nescience. If that is the case, they will answer the 10 questions within an extremely wide confidence interval in order to meet the predefined 90% accuracy requirement. The focus of investigation was in Southern Germany, as the school system regularly receives top marks in national educational rankings. The data analysis resulted in the stochastic proof that there are significant differences between the various educational institutions in accuracy and overconfidence. In addition to the empirical study the paper defines the distortion of judgment and identifies its relevant factors. It gives a detailed explanation of the German education system and states the criticism of the concept of overconfidence. The paper concludes with a recommendation for action and ventures a look ahead

Predictive approaches to customer loyalty: the impact of missing data on the predictability of customer loyalty models

Large quantities of observations are utilised to predict and capitalise on customer loyalty. Yet virtually all gathered datasets contain some form of missingness. Incomplete cases may arise for a host of reasons, such as dropouts during an online survey for store card holders, inactive devices or partial WiFi coverage for in-store analytics, missing date of birth or other demographics on loyalty program signup forms, and so forth. The status quo in predictive approaches to customer loyalty is to ignore or discard such incomplete cases. However, this would diminish the external validity of the predictions and may substantially bias the derived results. Data are missing for a reason. Clients who skip certain sections of a survey do so for a reason and might share common traits. Similarly, clusters of incomplete observations in a company’s internal loyalty database could be caused by underlying technical or operational issues. This chapter explores missing data in customer loyalty research in order to proactively assess and handle incomplete observations. Three types of missingness are defined and differentiated. Ad hoc, likelihood, and chained equation approaches are discussed and theoretically as well as empirically compared. Lastly, the chapter provides hands-on techniques to solve missing data problems in customer loyalty research