Decision Biases Research Articles

Biased expectations about future choice options predict sequential economic decisions

Considerable research has shown that people make biased decisions in “optimal stopping problems”, where options are encountered sequentially, and there is no opportunity to recall rejected options or to know upcoming options in advance (e.g. when flat hunting or choosing a spouse). Here, we used computational modelling to identify the mechanisms that best explain decision bias in the context of an especially realistic version of this problem: the full-information problem. We eliminated a number of factors as potential instigators of bias. Then, we examined sequence length and payoff scheme: two manipulations where an optimality model recommends adjusting the sampling rate. Here, participants were more reluctant to increase their sampling rates when it was optimal to do so, leading to increased undersampling bias. Our comparison of several computational models of bias demonstrates that many participants maintain these relatively low sampling rates because of suboptimally pessimistic expectations about the quality of future options (i.e. a mis-specified prior distribution). These results support a new theory about how humans solve full information problems. Understanding the causes of decision error could enhance how we conduct real world sequential searches for options, for example how online shopping or dating applications present options to users.

The Role of Culture in Shaping Managerial Overconfidence and Its Impact on Financing Decisions

Traditional economic models that assume rational decision-making may not accurately capture the complexities of real-world financial decision-making. Behavioral Decision Theory can explain the gap between classical capital structure theories and inconsistent empirical evidence. Under the assumption of bounded rationality Behavioral Decision Theory linking cultural values, overconfidence and financial decision making. Culture is an important aspect in organization which need close attention. Culture play a vital role in shaping behavioral biases. Overconfidence is one of the well-known cognitive biases in financial decision making that often seen in managers. Cultural differences and managerial overconfidence have impact on financing decisions.

Bias and Discrimination Against Women and Parents in Semi‐Automated Hiring Systems

ABSTRACTToday, organizations are increasingly relying on automated hiring. The mechanization of the hiring process is assumed to render it more neutral, but a growing literature shows algorithmic decisions are as likely to be biased (Dickson, 2018). In this study, we test two types of biases: (1) gender bias; and (2) parenting bias, (i.e., whether mothers and fathers with an extended gap to care for children are penalized vis‐à‐vis those with uninterrupted employment net of equivalent high‐impact qualifications). We apply a classic counterfactual study sending gender and parenthood manipulated CVs to 211 job advertisements across three occupations (men‐dominated, women‐dominated, and gender‐balanced, to mitigate confounding variables associated with gender composition) in the United States and measure penalty‐premium bias in response rates. Our results identify semi‐automated hiring bias against parents who took leave to care for children relative to those with uninterrupted employment. Importantly, we find fathers who have an extended parental leave were the most severely penalized, followed by mothers with an extended parental leave and women and men without parental leave respectively. Ultimately, we identify gender and parenting bias in algorithmic and human hiring decisions.

Leadership Traits and Investment Behavior: Examining the Influence of Leadership Characteristics on Behavioral Biases in Financial Decision-Making

This study investigates the relationship between leadership traits and behavioral biases in investment decisionmaking. Through a quantitative survey of 172 investors, the research examines how leadership characteristics influence susceptibility to overconfidence bias, anchoring bias, herding behavior, and loss aversion in investment contexts. The findings reveal a moderate negative correlation (-0.256) between leadership traits and behavioral biases, suggesting that stronger leadership qualities may help mitigate investment biases. However, regression analysis indicates this relationship is complex, with leadership traits explaining only a small portion of bias variance. The study finds that strategic decision-making capacity has the strongest negative correlation (-0.32) with overconfidence bias, while proactive management skills show consistent negative correlations across all behavioral biases. These findings contribute to both leadership and behavioral finance literature by establishing a novel connection between leadership traits and investment behavior, while highlighting the multifaceted nature of this relationship. The research has practical implications for investment advisors and leadership development programs, suggesting the need for integrated approaches that consider both leadership qualities and behavioral aspects in improving investment decision-making.

Cognitive biases in Mohs micrographic surgery.

Mohs surgeons' fallibility is an important topic to explore. There are no studies on the role of cognitive biases in heuristic decision-making within Mohs micrographic surgery (MMS) as a contributing factor in mistakes or errors of clinical judgement. We reviewed established cognitive biases applied to heuristics during the pre-operative, intraoperative and reconstruction stages of MMS, and recommend conscious cognitive bias mitigating strategies for Mohs surgeons. Clinical errors/incidents may be reframed as opportunities to learn and develop, rather than evidence of competency or status. These experiences offer the opportunity to sharpen skills within metacognition and allow for more cognizant Mohs surgeons. We highly recommend all Mohs surgeons develop in-depth knowledge and maintain awareness of cognitive biases throughout clinical practice, to enhance clinical performance.

Minimizing Waste, Maximizing Sustainability: Analyzing Collaborative Practices Implementation Barriers in the Agri-Fresh Produce Industry

The agri-fresh produce industry confronts problems in minimizing food waste and achieving supply chain sustainability due to its complicated structure and lack of stakeholder participation. Significant food waste at numerous supply chain stages undermines the triple bottom line (Society, economy and environment) and wastes resources. Stakeholder collaboration, research, and information exchange can all help to minimize waste and improve sustainability. This study identifies barriers to implementing collaborative practices through literature analysis and expert insights and categorizes them into seven dimensions: relationship management practice, contractual and economic practice, joint practice, technological and information sharing practice, relationships management practice, and supply chain design practice. Furthermore, the grey-Decision Making Trial and Evaluation Laboratory (grey-DEMATEL) and Analytic Network Process (ANP) were employed to develop an integrated framework that identifies interrelationships and prioritizes dimensions and attributes. The influence relationship diagraph helps decision-makers make policy recommendations by understanding the cause-effect interrelationships among the dimensions and their corresponding barriers. Finally, sensitivity analysis was performed to ensure that the results are robust and reduce decision-making biases.

Age and Surgical Volume Impact Likelihood and Technique of Lateral Extraarticular Augmentation During Primary Anterior Cruciate Ligament Reconstruction.

The aim of this study was to evaluate the current utilization trends of practicing surgeons performing and lateral extraarticular augmentation (LEA) at the time of primary anterior cruciate ligament reconstruction (ACLR). The survey was distributed via email in August 2023 to members of the Arthroscopy Association of North America (AANA) who identified as knee surgeons and was available online on the AANA website from January to September 2023. The eighteen-question survey was designed regarding surgeons' surgical utilization patterns of LEA during ACLR. Survey questions were created based on prior published research and recommendations regarding indications for LEA, as well as surgeon factors that have been shown to influence operative decision making. Data were analyzed by surgeon geographics, procedure preferences, patient-based decision factors, surgeon-based decision factors, and surgeon age. The survey was completed by 165 sports medicine surgeons who identified as arthroscopic knee surgeons. Majority practice types included private practice (42.1%), academic centers (26.8%), and hospital systems (20.7%). Surgeon age was 50.36 years (range 33-77). 6.8% perform < 20 ACLR per year, 30.2% perform 20-40, 26.5% perform 40-60, 10.5% perform 60-80, and 25.9% perform >80 per year. 79.4% of surgeons conduct LEA. The modified Lemaire being the most common technique (43.5%), followed by other lateral extraarticular tenodesis (LET) techniques (42.0%), and anterolateral ligament reconstruction (ALL) (27.5%), some surgeons (14.5%) use more than one technique. High volume (>60 ACLR/year) surgeons were more likely to perform LEA (23.1% vs 10.0%, p=0.061), and more likely to perform ALL (32.2% vs 16.5%, p=0.034). Younger surgeons (Age <50) were more likely to use the modified Lemaire (44.4% vs 24.3%, p=0.014). Decision-making to perform LEA weighted highly on patient hyperlaxity, pivot shift severity, knee hyperextension, sport-type, and age, respectively. The most reported surgeon-related factor influencing LEA utilization was training bias (38.9%). A large majority of orthopedic surgeons with diverse geographics, demographics, practice setting, and ACLR volume perform LEA. Younger surgeons performed more frequent LEA, and surgeons admit to training bias in decision making. Patient factors highly impacting utilization of LEA were hyperlaxity, pivot shift severity, knee hyperextension, sport, and age. LEA procedures for ACL reconstruction have become increasingly utilized in populations at high risk for re-rupture. However, there is not currently a clear standard of care with regards to LEA procedure type or indications for augmentation.

Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs

The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection. Computed tomography (CT) was performed for correlation. Ground truth (detectability) was established through a human consensus reading. Overall sensitivity and specificity of 0.978 and 0.812, respectively, were achieved for nodule detection. The false-positive rate was low with an overall rate of 0.19. The overall accuracy was calculated as 0.84 for all nodules. While most studies evaluating AI performance in the detection of pulmonary nodules have evaluated a mix of varying nodules, these are the first results of a controlled phantom-based study using a balanced number of nodules of all sizes and densities. To increase the radiologist’s diagnostic performance and minimize the risk of decision bias, such algorithms have an obvious benefit in a clinical scenario.

Effect of age on susceptibility to the attraction effect in sequential risky decision-making

Abstract Our study aims to contribute to the existing body of research on age-related changes in decision-making by investigating susceptibility to the attraction effect across adulthood. Prior studies have produced inconsistent conclusions regarding the decision-making abilities of older individuals, with some portraying them as easily manipulated and risk-averse, while others suggest the opposite. To address this issue, we conducted two experiments using a novel paradigm of the roulette task: (1) in an online environment with 357 participants and (2) in a laboratory setting with 173 participants. The results were consistent and demonstrated the robustness of the attraction effect. However, no age differences in susceptibility to the attraction effect as a common decision bias were found. As predicted, older adults were more likely to commit simple decision-making mistakes, especially in the preliminary trials, which could have serious financial or societal consequences. Additionally, older adults exhibited more risk-seeking behaviours. Furthermore, we observed that the dynamics of decision competence (as indicated by a decrease in the selection of erroneous decoy options and an increase in decision fluency) were similar for both younger and older adults, suggesting preservation of the ability to optimise decision-making while becoming familiar with new tasks. These findings provide insight into the cognitive functioning of older adults and indicate that decision-making abilities in late adulthood may be more complex than commonly assumed.

Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study.

Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. This study aimed to explore the role of large language models (LLMs) in mitigating these biases through the use of the multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy compared with humans. A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 (OpenAI) to facilitate interactions among different simulated agents to replicate clinical team dynamics. Each agent was assigned a distinct role: (1) making the final diagnosis after considering the discussions, (2) acting as a devil's advocate to correct confirmation and anchoring biases, (3) serving as a field expert in the required medical subspecialty, (4) facilitating discussions to mitigate premature closure bias, and (5) recording and summarizing findings. We tested varying combinations of these agents within the framework to determine which configuration yielded the highest rate of correct final diagnoses. Each scenario was repeated 5 times for consistency. The accuracy of the initial diagnoses and the final differential diagnoses were evaluated, and comparisons with human-generated answers were made using the Fisher exact test. A total of 240 responses were evaluated (3 different multi-agent frameworks). The initial diagnosis had an accuracy of 0% (0/80). However, following multi-agent discussions, the accuracy for the top 2 differential diagnoses increased to 76% (61/80) for the best-performing multi-agent framework (Framework 4-C). This was significantly higher compared with the accuracy achieved by human evaluators (odds ratio 3.49; P=.002). The multi-agent framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. In addition, the LLM-driven, multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.

From the brain’s encoding of input dynamics to its behavior: neural dynamics shape bias in decision making

The human brain is tightly connected to the individual’s environment and its input dynamics. How the dynamics of periodic environmental stimuli influence neural activity and subsequent behavior via neural entrainment or alignment is not fully clear yet, though. This study explores how periodic environmental stimuli influence neural activity and behavior. EEG data was collected during a Go-NoGo task with a periodic intertrial interval (ITI) of 1.3 s (0.769 Hz). Results showed that the task’s temporal structure increased power spectrum activity at 0.769 Hz, which showed high intersubject variability. Higher task-periodicity effects were linked to stronger phase-based intertrial coherence (ITC) and reduced neural complexity, as measured by lower Lempel-Ziv Complexity (LZC). Additionally, higher periodicity in the power spectrum correlated with faster reaction times and stronger response bias. We conclude that the encoding of the inputs’ dynamics into the brains power spectrum shapes subsequent behavior, e.g., RT and response bias, through reducing neural complexity.

Behavioural Biases in Investment Decision-Making and Their Impact on Investment Performance

This paper provides an in-depth discussion of behavioural bias in investment decision-making and its impact on investment performance. Current research mostly focuses on theories and lacks specific coping strategies, while behavioural biases are prevalent in investment decision-making, affecting investment performance and the stable development of financial markets. By analysing cognitive bias, emotional bias and volitional bias, their manifestations and negative impacts on investment performance are elaborated, such as overconfidence leading to frequent trading and loss aversion making investors short-sighted. In order to reduce the behavioural biases, coping strategies such as searching for counter evidence, timely detection of errors and stop loss, broadening horizons and reducing noise are proposed. Investors can make rational decisions by collecting information through multiple channels, verifying the reasonableness of viewpoints, establishing a stop-loss mechanism, enriching thinking paths, and avoiding noise interference. In conclusion, investors should recognise the existence of behavioural biases, adopt effective strategies to improve the accuracy and rationality of investment decisions and achieve better investment performance, and financial institutions and regulators should also strengthen investor education and guidance.

The Text-Belief Consistency Effect Among Recent Upper Secondary Graduates: An Eye Tracking Study

Readers tend to allocate more cognitive resources to processing belief-consistent than belief-inconsistent information when reading multiple texts displaying discrepant views. This phenomenon, known as the text-belief consistency effect, results in individuals being more prone to making biased decisions and falling victim to manipulation and misinformation. This issue is gaining relevance due to the undeniably vast amount of information surrounding us. Hence, schools must ensure that students complete their education prepared to face this challenge. However, international surveys and research indicate a generalized literacy deficiency among students. In the present study, recent upper secondary graduates read four texts discussing a controversial topic to explore whether they effectively overcome the text-belief consistency effect or not. Eye tracking was used to explore immediate (or passive) and delayed (or strategic) text processing, and an essay task was used to measure their resulting mental representation of the text content. Results revealed no significant differences in immediate and delayed processing depending on whether the arguments were belief-consistent or belief-inconsistent. Moreover, essays displayed a balanced and unbiased approach to the debate. Despite these results suggesting this population may be capable of overcoming the text-belief consistency effect, limitations in the study and alternative explanations must be explored before drawing definite conclusions.

Emergency Response Inference Mapping (ERIMap): A Bayesian network-based method for dynamic observation processing

In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches for information processing and situation assessment which meet the particular demands of emergency situations. To address this gap, we present a Bayesian network-based method called ERIMap that is tailored to the complex information-scape during emergencies. The method enables the systematic and rapid processing of heterogeneous and potentially uncertain observations and draws inferences about key variables of an emergency. It thereby reduces complexity and cognitive load for decision makers. The output of the ERIMap method is a dynamically evolving and spatially resolved map of beliefs about key variables of an emergency that is updated each time a new observation becomes available. The method is illustrated in a case study in which an emergency response is triggered by an accident causing a gas leakage on a chemical plant site.

Emotions in Robo-Advisory: Understanding Biases, Designing Sensitive Platforms, and Enhancing User Experience through Emotional Intelligence

Introduction: Robo-advisory services revolutionize investment management by providing automated, data-centric solutions, utilizing vast data sets to tailor investment plans to individual client needs. However, the importance of emotions in investor behavior is still a crucial but sometimes disregarded feature of these systems. By using automated procedures and computational calculations to provide customized investment options, robo-advisors have completely transformed the investment management industry. Purpose: The article explores how robo-advisory services can improve investor outcomes by addressing emotional biases in investment decisions. It proposes emotionally intelligent robo- advisors that understand investor sentiment and addresses challenges, empowering investors to achieve long-term financial goals. Methodology: The article explores the challenges of integrating emotional intelligence into robo- advisory services through a comprehensive review of the existing literature, focusing on behavioral finance and empirical evidence to understand the practical implications of irrational behavior through reviewing case studies and empirical evidence. Result: The study suggests a multi-faceted approach for designing emotionally intelligent robo-advisors, considering technological innovation, regulatory compliance, ethical considerations, and user engagement strategies, addressing challenges like algorithmic biases, privacy concerns, and regulatory compliance.

A contest study to reduce attractiveness-based discrimination in social judgment.

Discrimination in the evaluation of others is a key cause of social inequality around the world. However, relatively little is known about psychological interventions that can be used to prevent biased evaluations. The limited evidence that exists on these strategies is spread across many methods and populations, making it difficult to generate reliable best practices that can be effective across contexts. In the present work, we held a research contest to solicit interventions with the goal of reducing discrimination based on physical attractiveness using a hypothetical admissions task. Thirty interventions were tested across four rounds of data collection (total N > 20,000). Using a signal detection theory approach to evaluate interventions, we identified two interventions that reduced discrimination by lessening both decision noise and decision bias, while two other interventions reduced overall discrimination by only lessening noise or bias. The most effective interventions largely provided concrete strategies that directed participants' attention toward decision-relevant criteria and away from socially biasing information, though the fact that very similar interventions produced differing effects on discrimination suggests certain key characteristics that are needed for manipulations to reliably impact judgment. The effects of these four interventions on decision bias, noise, or both also replicated in a different discrimination domain, political affiliation, and generalized to populations with self-reported hiring experience. Results of the contest for decreasing attractiveness-based favoritism suggest that identifying effective routes for changing discriminatory behavior is a challenge and that greater investment is needed to develop impactful, flexible, and scalable strategies for reducing discrimination. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Ethics of AI in the Administration of Higher Education

The rising presence of artificial intelligence (AI) in higher education carries great promise and peril. This paper will examine the ethics of using AI for the operation of these institutions. Key areas of focus include data privacy, bias in decision making, accountability, and the impact on academic integrity. So is the importance of transparency, stakeholder involvement, and the need for ethical guidelines. The paper closes with suggestions for the responsible adoption of AI systems in higher education.

Designing a machine learning-based lending model to enhance access to capital for small and medium enterprises

This paper presents the design of a machine learning-based lending model aimed at enhancing access to capital for Small and Medium Enterprises (SMEs). SMEs play a crucial role in driving economic growth, yet they often face significant barriers in securing funding due to traditional credit assessment methods that rely heavily on financial history and collateral. By leveraging machine learning (ML) algorithms, this proposed model incorporates diverse data sources, including financial statements, market trends, customer behavior, and non-traditional data such as social media activity and payment histories, to better assess creditworthiness. The model utilizes supervised learning techniques, such as decision trees, random forests, and neural networks, to analyze this diverse data and identify patterns that traditional models may overlook. Additionally, it integrates real-time data processing to continuously update credit profiles, allowing for more dynamic and responsive lending decisions. The primary aim is to improve the accuracy of risk assessment, reduce default rates, and ultimately facilitate more inclusive lending practices for SMEs. One of the core advantages of the ML-based model is its ability to offer personalized lending terms and products by identifying the unique characteristics and risk factors of each SME. This approach not only improves access to capital for underbanked businesses but also ensures that lenders can manage risk more effectively. The paper also discusses the potential ethical considerations, such as ensuring data privacy and avoiding biases in algorithmic decision-making, which are critical for the responsible implementation of such a system. In conclusion, this machine learning-based lending model presents an innovative solution to the long-standing challenges SMEs face in accessing capital. By harnessing the power of data analytics and advanced algorithms, it paves the way for more equitable and efficient lending processes, supporting the growth and sustainability of SMEs. Keywords: Machine Learning, Lending Model, Access To Capital, Small And Medium Enterprises, Risk Assessment, Creditworthiness, Financial Inclusion, Data Privacy, Algorithmic Decision-Making, Supervised Learning.

Zeal Spillovers: The Adversarial Bias in Simulated Pre-Trial Decision Making

The adversarial legal procedure is one of the defining characteristics of Anglo-American law. Adversarialism is lauded for its capacity to discover the factual truth, which is said to be revealed by the clash between zealous advocates. This study further explores the proposition that adversarial contests trigger a bias that pervades adversarial actors’ judgments, beliefs, motivations, and emotions, and thus skews how they perceive and approach their cases. Specifically, we set out to extend prior research by testing whether the adversarial bias could spill over to skew simulated pre-trail prosecutorial tasks: the decision to seek an indictment and forming judgments in advance of the plea negotiation. In a simulation with lay people, we find that the adversarial bias does indeed skew pre-trial decisions. This result is troubling because the epistemic justification of adversarialism has little to contribute to these decisions, as indictment decisions are made unilaterally by prosecutors, and plea negotiations take place under heavily lopsided power and access to information. It follows that while adversarial zeal has little to contribute to pre-trial decisions, it can readily infest them with zeal. While ecological validity considerations abound, this study demonstrates the psychological plausibility that the adversarial bias results in heightened levels of indictment and stiffer punishments, thus highlighting prosecutors’ potential contribution to the exceptionally high rate of people sent to prison in the US.

The Role of Cognitive Bias in Principal Decision Making: A Narrative Analysis of the Literature

This study aims to critically examine the role of cognitive biases in principal decision-making processes by synthesizing and analysing existing literature. It seeks to identify key cognitive biases affecting principal decisions, their impact on organizational outcomes, and potential mitigation strategies. A comprehensive narrative analysis of the literature was conducted. Relevant peer-reviewed articles, books, and book chapters published between 1970 and 2023 were systematically identified and reviewed using major academic databases such as Web of Science, JSTOR, and Google Scholar. The analysis revealed that cognitive biases, particularly confirmation bias, overconfidence, anchoring, and availability bias, significantly influence principal decision-making. These biases can lead to suboptimal decisions, affecting strategic planning, risk assessment, and overall organizational performance. The study also identified several debiasing techniques, including awareness training and structured decision-making frameworks, which show promise in mitigating the impact of cognitive biases. This study provides a comprehensive and up-to-date synthesis of research on cognitive biases in principal decision-making, offering a holistic view of the field’s current state. It uniquely combines insights from cognitive psychology, behavioural economics, and management science to provide a multidisciplinary perspective on the topic. The study contributes to both theory and practice by offering a structured understanding of how cognitive biases influence principal decision-making. It provides valuable insights for principals and organizations to improve their decision-making processes and highlights areas for future research in this critical field. The findings can inform the development of more effective principal training programs and decision support systems.