Decision Biases Research Articles

New technologies in HR: bridging efficiency and ethical considerations

Purpose This systematic literature review examines the impact of Web-based human resource management (HRM) systems on organizational practices, exploring both the benefits and challenges associated with their implementation. This study aims to provide a comprehensive understanding of how these technologies transform HR processes while addressing ethical concerns and implications for employee well-being and engagement. Design/methodology/approach This study uses a systematic literature review methodology, analysing peer-reviewed academic journals, industry reports and relevant books published between 2000 and 2023. The research uses a tripartite framework to explore the positive aspects (“The Good”), challenges and limitations (“The Bad”) and ethical considerations (“The Ugly”) of Web-based HRM systems, with particular attention to employee engagement and organizational effectiveness. Findings This study reveals that Web-based HR systems significantly enhance efficiency in recruitment, compensation and payroll management. However, they also present substantial challenges related to data security, user competence and system integration. Ethical concerns emerge regarding privacy, potential biases in artificial intelligence-driven decision-making and the risk of over-dependency on technology. The research underscores the need for a balanced approach in implementing these systems, considering both their transformative potential and impact on employee well-being. Originality/value This study contributes to the existing literature by providing a nuanced, holistic examination of Web-based HRM systems. It extends the theoretical landscape by addressing the darker sides of HR technology adoption, including ethical dilemmas and potential negative impacts on employee engagement. The research offers valuable insights for both academics and practitioners, providing a foundation for future research and practical recommendations for effective implementation.

A Systematic and Objective Framework for Evaluating Subcontractor Performance Using Monte Carlo Simulation Coupled with the Analytic Hierarchy Process and a Linear Additive Utility Model

The general contractor (GC)–subcontractor (SC) relationship is a crucial aspect of construction supply chain management, heavily influencing project outcomes. This study investigates a method for assessing SC performance and underscores its essential role in construction projects. Traditionally, SC assessments are based on subjective evaluations, which can lead to biased decision-making. To counter this, this study introduces a comprehensive framework that employs objective indices and a systematic evaluation method. The study begins with a comprehensive literature review and expert consultations to identify key indices for SC evaluation: time, cost, quality, safety, resources, satisfaction, and leadership. A hybrid method combining Monte Carlo simulation and the Analytic Hierarchy Process (AHP) is employed to assign weights to these indices through the development of probability distributions, thereby reducing judgment uncertainty. The developed evaluation model incorporates normalization and a linear additive utility model (LAUM) to calculate a performance index (PI) that quantifies SC performance across various levels, from outstanding to poor. The normalization process is applied with three tolerance levels (high, medium, and low). A real case study with a three-scenario sensitivity analysis demonstrates the model’s effectiveness. This approach provides general contractors with a more objective and transparent assessment process, minimizing bias in evaluations.

Construction and validation of an overconfidence scale in investment decisions

Existing studies that directly measure the three types of overconfidence—overprecision, overplacement, and overestimation—are largely exploratory, highlighting the need for further confirmatory research to establish robust overconfidence measures. Moreover, there is no comprehensive instrument for analyzing these types of overconfidence in the context of investment decisions. To address these gaps, the present study aims to develop and validate a scale for assessing overconfidence in investment decisions, considering its three forms. The research involved 380 Brazilian participants and employed confirmatory factor analysis and standard multivariate validation methods. The scale, based on a theoretical framework of the three types of overconfidence, consisted of 30 questions (10 for each type) designed to measure the degree of overconfidence in investment decision-making. The scale demonstrated acceptable factor loadings and internal and external validity, indicating its suitability for evaluating overconfidence in investment decisions. The standardized factor loadings showed p values < 0.01 for all factors. The average variance extracted was above 0.5, and the composite reliability exceeded 0.7 for all three constructs analyzed. These results support the convergent validity of the proposed measurement model. In this way, the developed scale fills a gap in the literature by providing a valid tool to assess overconfidence in investment decision-making, which can be practically applied by financial market participants, such as investors, fund managers, and financial professionals. This instrument enables more informed and personalized investment decisions, mitigates the risks of bias in decision-making, and contributes to a more efficient and resilient financial market.

Approaches to Incorporation of Preferences into Health Economic Models of Genomic Medicine: A Critical Interpretive Synthesis and Conceptual Framework.

Genomic medicine has features that make it preference sensitive and amenable to model-based health economic evaluation. Preferences of patients, caregivers, and clinicians related to the uptake and delivery of genomic medicine technologies and services that are not captured in health state utility weights can affect the intervention's cost-effectiveness and budget impact. However, there is currently no established or agreed-on approach for integrating preference information into economic evaluations. The objective of this study was to explore approaches for incorporating preferences into model-based economic evaluations of genomic medicine and to develop a conceptual framework to consider preferences in health economic models. We conducted a critical interpretive synthesis of published literature guided by the following question: how have preferences been incorporated into model-based economic evaluations of genomic medicine interventions? We integrated findings from the literature and expert opinion to develop a conceptual framework of ways in which preferences influence economic value in the context of genomic medicine. Our synthesis included 14 articles. Revealed and stated preference data were used to estimate choice probabilities and to value outcomes. Our conceptual framework situates preference data in the context of health system, patient, clinician, and family characteristics. Preference data were sourced from clinicians, patients and families impacted by a condition or intervention, and the general public. Evaluations employed various types of models, including discrete event simulation, microsimulation, Markov, and decision tree models. When evaluating the broad benefits and costs of implementing new interventions, sufficiently accounting for preferences in the form of model inputs and valuation of outcomes in economic evaluations is important to avoid biased implementation decisions. Incorporation of preference data may improve alignment between predicted and real-world uptake and more accurately estimate welfare impacts, and this study provides critical insights to support researchers who seek to incorporate preference information into model-based health economic evaluations.

Navigating Ethics and Regulation: The Role of AI in Modern Financial Services

In this paper, I delve into the ethical and regulatory aspects of using artificial intelligence (AI) in the finance sector. As AI technologies increasingly influence financial decision-making, addressing issues of fairness, transparency, and regulation becomes crucial. The research will involve reviewing relevant academic literature, industry reports, and regulatory documents to gather information and insights on the ethical and regulatory dimensions of AI in finance. My research focuses on three main areas: the presence of bias in AI-driven financial decisions, the regulatory challenges hindering ethical AI deployment, and the need for transparency and explainability in AI processes. By examining these aspects, I argue that mitigating bias, enhancing regulatory frameworks, and promoting clarity in AI applications are essential for building trust and ensuring ethical practices in financial services. Key findings include the presence of bias in AI-driven financial decisions, the need for updated regulatory frameworks to address AI complexities, and the importance of transparency and explainability in AI processes. These elements are crucial for building trust and ensuring ethical practices in financial services. Ultimately, this work advocates for a collaborative approach among regulators, financial institutions, and AI developers to create a more equitable financial ecosystem.

Data Governance for Emerging Technologies: A Conceptual Framework for Managing Blockchain, IoT, and AI

As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.

The Impact of Status Quo Bias on Monetary Policy Decision-Making: A Case Study of the 2008 Financial Crisis

This research examines the role of status quo bias in shaping monetary policy decision-making during the 2008 financial crisis, focusing on the responses of the Federal Reserve and the European Central Bank (ECB). Status quo bias, a cognitive bias that leads individuals and institutions to resist change, often results in delayed decisions and sub-optimal outcomes. This study employs a comparative case study methodology, analyzing the timing and nature of central bank interventions, such as interest rate cuts and quantitative easing during the crisis. By systematically reviewing policy actions and utilizing qualitative content analysis, the research identifies how status quo bias contributed to delayed responses and inadequate adjustments. The findings indicate that while the Federal Reserve, despite initial hesitation, quickly implemented unconventional measures that helped stabilize the U.S. economy, the ECB's adherence to traditional frameworks led to critical delays, resulting in a slower recovery in the Eurozone. These insights underscore the significant influence of cognitive biases on monetary policy, with implications for future crisis management strategies. Addressing the effects of status quo bias could lead to more agile and adaptive central banking policies, ultimately enhancing economic resilience during times of crisis.

Gender Differences in the Association Between Anxiety and Interoceptive Insight.

Anxiety is one of the most common and debilitating mental health disorders, and is related to changes in interoception (perception of bodily states). While anxiety is more prevalent in women than men, gender differences in interoception-anxiety associations are often overlooked. Here, we examined gender-specific relationships between anxiety and interoception in the breathing domain, utilising multicentre data pooled from four study sites (N = 175; 51% women). State anxiety scores were quantified via the Spielberger State-Trait Anxiety Inventory, and breathing-related interoceptive dimensions via an inspiratory load task to quantify sensitivity, decision bias, metacognitive bias (confidence in interoceptive decisions), and metacognitive insight (congruency between performance and confidence). Regression analyses revealed a significant negative relationship between state anxiety and metacognitive bias (β = -0.28; p = 0.01) and insight (β = -0.09; 95% highest density interval [HDI] in a hierarchical Bayesian regression = [-0.18, -0.004]) across the whole sample, while state anxiety did not relate to interoceptive sensitivity nor decision bias. While no mean interoceptive effects relating to gender were observed, the relationship between anxiety and metacognitive insight towards breathing was driven by women (women: β = -0.18; HDI = [-0.31, -0.05]; men: β = 0.02; HDI = [-0.12, 0.15]) with a significant interaction effect (β difference = -0.20; HDI = [-0.37, -0.01]), which did not hold for trait anxiety nor depression measures. In summary, state anxiety was associated with decreased metacognitive bias across all participants, while decreased interoceptive insight was only associated with anxiety in women but not men. Therefore, treatment programmes focusing on interoceptive metacognitive bias may be useful for all anxiety patients, while interoceptive insight might represent a specific treatment target for women with anxiety.

Bias toward escape responding during reinforcement learning among those with suicidal ideation.

Self-injury and suicide can be characterized by reward system dysfunction and self-reports of active efforts to escape unpleasant emotional states. Therefore, individuals with histories of suicidal ideation (SI) should exhibit a preference for active escape from unpleasant states, which exceed the effects of impulsive behavior under distress and lack of premeditation. Participants made active (Go) or passive (No-Go) choices in response to stimuli to escape or avoid an unpleasant state in a behavioral task. A drift-diffusion reinforcement learning model was used to estimate latent biases for active escape and avoidance in people with and without SI history. Bayesian logistic regression was used to examine the relationship between escape and avoid bias with SI. Escape bias predicted SI history, whereas avoidance bias did not. Escape bias remained a significant predictor of SI when controlling for negative urgency and lack of premeditation. Those with histories of SI demonstrate a decision-making bias favoring escape from aversive states. This bias remains significant after adjusting for facets of impulsivity linked to hasty decisions under distress and lack of premeditation. A heightened escape response may help clinicians to identify SI risk and develop targeted treatments to attenuate the escape bias.

A meta-learning imbalanced classification framework via boundary enhancement strategy with Bayes imbalance impact index.

For imbalanced classification problem, algorithm-level methods can effectively avoid the information loss and noise introduction of data-level methods. However, the differences in the characteristics of the datasets, such as imbalance ratio, data dimension, and sample distribution, make it difficult to determine the optimal parameters of the algorithm-level methods, which leads to low universality. This paper proposes a meta-learning imbalanced classification framework via boundary enhancement strategy with Bayes imbalance impact index. First, the meta-learning idea is introduced to build multiple comprehensive training tasks. Each task contains a support set and a query set, which are randomly sampled from the original dataset. The support set is used to train the meta-classifier, and the meta-classifier's performance on the query set serves as an optimization object. This approach enables the meta-classifier to enhance itself based on the experiences gained from multiple training tasks, which makes it quickly adapt in the fine-tuning stage and exhibit excellent performance on the test set. Second, a boundary enhancement strategy with Bayes imbalance impact index (IBI3) is designed. The IBI3 value is used to identify boundary samples. On this basis, the feature interpolation among the boundary samples and their neighbor samples is applied to enhance the classification boundary, effectively alleviating the meta-classifier decision-making bias caused by data imbalance in the fine-tuning stage. Experimental results on 38 imbalanced public datasets with diverse characteristics show that the proposed method outperforms 24 typical imbalanced classification methods in F-measure and G-mean without parameters adjustment, which proves that the proposed method has greater universality.

Heterogeneity in Category Recognition across the Visual Field.

Visual information emerging from the extrafoveal locations is important for visual search, saccadic eye movement control, and spatial attention allocation. Our everyday sensory experience with visual object categories varies across different parts of the visual field which may result in location-contingent variations in visual object recognition. We used a body, animal body, and chair two-forced choice object category recognition task to investigate this possibility. Animal body and chair images with various levels of visual ambiguity were presented at the fovea and different extrafoveal locations across the vertical and horizontal meridians. We found heterogeneous body and chair category recognition across the visual field. Specifically, while the recognition performance of the body and chair presented at the fovea were similar, it varied across different extrafoveal locations. The largest difference was observed when the body and chair images were presented at the lower-left and upper-right visual fields, respectively. The lower/upper visual field bias of the body/chair recognition was particularly observed in low/high stimulus visual signals. Finally, when subjects' performances were adjusted for a potential location-contingent decision bias in category recognition by subtracting the category detection in full noise condition, location-dependent category recognition was observed only for the body category. These results suggest heterogeneous body recognition bias across the visual field potentially due to more frequent exposure of the lower visual field to body stimuli.

Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction

The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health records (EHRs) frequently suffer from class imbalances, leading to prediction biases and differences between specificity and sensitivity, which hinder reliable model development despite the valuable insights contained in these datasets. To address this, we have introduced a novel approach to enhance MI risk prediction using self-reported attributes from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) dataset. Our approach incorporates three innovative techniques: the Dual-Path Artificial Neural Network (DP-ANN) to mitigate biased decision making across imbalanced datasets, the Triple Criteria Selection (TCS) for unbiased feature selection, and Minority Weighted Sampling (MWS) to tackle challenges of uncontrolled minority class sampling. These methods collectively enhance MI prediction and feature relevance. The DP-ANN model has achieved balanced performance, with an average specificity of 80%, sensitivity of 82%, and AUC–ROC of 89.5%, improving imbalance variance by approximately 14.96% compared to prior studies. By outperforming other models across four heavily imbalanced datasets, our approach demonstrates robustness and generalizability. Additionally, SHapley Additive exPlanations (SHAP) analysis has revealed key predictors and risk factors for MI, such as coronary heart disease and bronchitis in females, and stroke among individuals aged 35–54. In conclusion, our study provides a robust model for healthcare professionals to assess MI risk through targeted factors, promoting early detection and potentially improving patient outcomes.

Do FinTech algorithms reduce gender inequality in banks loans? A quantitative study from the USA

ABSTRACT The potential of FinTech algorithms to decrease gender bias in credit decisions is limited by the impartiality of the data used to train them. If the data is partial or biased, the algorithmic decision-making process may also be discriminatory, exacerbating existing inequalities. In this study, the effect of FinTech Firms on reducing gender inequality in bank loans in the USA is examined using a loan application from 60 U.S. banks from 2012 to 2022. We use a two-step system GMM approach to estimate the effect of FinTech algorithms on gender bias in credit decisions, focusing on female loan approval rates. Our results show that by controlling the other factor, banks with credit algorithms significantly increased the loan approval rates and thus reduced gender inequality in bank loans. Specifically, the female loan approval rates increased by 8% after banks adopted FinTech algorithms. We also find that the effect is more substantial for banks with higher baseline gender bias in credit decisions. We also performed the Difference in Difference analysis to analyse the policy shocks and FinTech adoption on bank loans’ gender inequality. Results of the study show that FinTech adoption and policy implications have significantly increased the loans for female borrowers. Our findings suggest that FinTech algorithms can potentially mitigate gender bias in credit decisions and promote gender equality in financial services.

Study of the Artificial Intelligence Role in Achieving Cybersecurity for Critical Information Infrastructure

This research aims to examine the role of Artificial Intelligence (AI) in strengthening cybersecurity for critical information infrastructure (CII) in Indonesia. The research methodology used is a systematic literature review (SLR) involving identification, review, and evaluation, data collected through literature studies relevant to the research topic. Data were analyzed using critical synthesis method, gap analysis and SOAR (Strengths, Opportunities, Aspirations, Results) analysis, compared with the NIST Cybersecurity Framework to determine suitable strategic alternatives. The research results indicate that AI can strengthen cybersecurity in the CII sector by enhancing threat detection and response capabilities and optimizing risk management. The study also identified potential opportunities, such as cross-sector collaboration and the development of innovative technologies, as well as challenges, including the need to maintain transparency and ethical AI. In addition, the proposed strategy to realize AI as a tool in cyber security includes strengthening investment in technology, increasing human resource skills, and supporting development policies while taking into account potential risks that may arise, such as privacy violations, data leaks, and potential bias in decision making by AI systems. It is hoped that the results of this research will provide insight for stakeholders in efforts to strengthen cyber security in the IIV sector in Indonesia.

Artificial Intelligence Based Behavioural Finance in Shaping Investment Strategies to Analysis of Key Biases and Heuristics

This paper explores the job of behavioural finance in forming investment strategies zeroing in on key predispositions and heuristics. behavioural finance looks at what mental elements and inclinations mean for monetary choices, frequently wandering from level-headed assumptions. Conventional money expects objective way of behaving pointed toward augmenting returns while limiting dangers; in any case, genuine financial backer way of behaving habitually strays from this ideal because of mental easy routes and predispositions. This study examines critical predispositions like overconfidence, loss aversion, mental accounting, and herd behaviour, exploring their effect on market elements and individual financial backer results. By understanding these predispositions, financial backers and monetary consultants can foster techniques that adjust better to financial backer brain science, possibly prompting more predictable investment results and further developed portfolio the executives. This study explores the influence of behavioral biases on investment decision-making using the PMBAC (Probabilistic Modeling and Behavioral Analysis for Cognitive biases) framework. Key biases, such as overconfidence, loss aversion, and herding behavior, are examined to understand their impact on market performance and investor returns. The analysis reveals that overconfidence occurred in 78% of the cases, resulting in an average return impact of -25%, while loss aversion occurred in 85% of instances, leading to a -5% return impact. Herding behavior, observed in 65% of the cases, was associated with a 15% return impact, showing that following the crowd can lead to short-term gains but greater long-term risks. The study also identifies the role of other biases such as mental accounting (55% occurrence, -3% return impact), anchoring (72% occurrence, -10% return impact), and status quo bias (63% occurrence, -2% return impact). In terms of market performance, herding behavior was linked to a 40% overvaluation, while loss aversion contributed to a 10% market overvaluation. The findings highlight the pervasive nature of these biases in financial decision-making and their significant consequences on risk-adjusted returns, with some biases leading to a negative impact on returns and others fueling market bubbles.

Mitigating AI bias in financial decision-making: A DEI perspective

The growing reliance on Artificial Intelligence (AI) in financial decision-making offers significant potential for increased efficiency and innovation but also raises critical concerns about perpetuating existing inequities. As financial services adopt AI for tasks like credit scoring, loan approvals, and investment analysis, the risk of algorithmic bias impacting marginalized groups has garnered significant attention. Biases in training data, model design, and deployment processes can lead to discriminatory outcomes, undermining efforts to promote Diversity, Equity, and Inclusion (DEI). Addressing these challenges requires a comprehensive framework for aligning AI systems with DEI principles. This study analyses the risks of AI reinforcing systemic inequities in financial decision-making and identifies key strategies for mitigating these biases. Transparent algorithmic processes are essential to enable stakeholders to understand decision-making logic and detect potential biases. Accountability mechanisms, such as regular audits and independent evaluations, ensure that AI systems comply with ethical standards and DEI objectives. Inclusivity must be prioritized in both data collection and model design, ensuring diverse representation to reduce inherent biases. By implementing these strategies, financial institutions can leverage AI to drive equitable decision-making, fostering trust among stakeholders while addressing regulatory and societal demands. This paper proposes a roadmap for developing and deploying bias-mitigating AI systems in financial services, with a focus on creating fair, inclusive, and accountable models. Through case studies and best practices, it highlights actionable solutions to bridge the gap between technological innovation and ethical imperatives, ensuring AI serves as a tool for equity and inclusion rather than a perpetuator of inequality.

Behavioral Biases in Investment Decisions: A Mixed-Methods Study on Retail Investors in Emerging Markets

Retail investment in emerging markets has experienced rapid growth, driven by technological advancements and increasing public awareness of financial management. However, behavioral biases often influence retail investment decision-making, such as overconfidence and herding, which can undermine portfolio performance. This study aims to identify the key behavioral biases affecting retail investors' decision-making in emerging markets and analyze their impact on investment performance. A mixed-methods approach was employed, combining a quantitative survey of 200 retail investors with in-depth interviews to gain qualitative insights. The results reveal that overconfidence bias dominates, with a prevalence of 70%, followed by herding bias at 50%, anchoring at 40%, and loss aversion at 60%. Overconfidence bias showed a significant positive correlation with investment returns (r = 0.65, p &lt; 0.01), while herding (r = -0.48, p &lt; 0.03), anchoring (r = -0.35, p &lt; 0.05), and loss aversion (r = -0.60, p &lt; 0.02) had negative impacts on portfolio performance. This research contributes to the behavioral finance literature by highlighting the unique conditions of emerging markets, such as low financial literacy and limited access to information, which exacerbate the effects of behavioral biases. As a practical implication, the development of behavioral finance-based educational programs is recommended to help investors understand and manage these biases. Furthermore, future research is encouraged to explore the use of alternative data, such as social media, to monitor behavioral trends in real-time

Confirmation Bias in Decision-making: Implications in Finance, Business, and Digital Media

Confirmation bias is the tendency to look for supporting evidence for an established belief. Many applications of confirmation bias occur in people's lives unconsciously, resulting in poor decision-making as they become unable to evaluate the situation objectively. It can also perpetuate false beliefs, increase group polarization, and make people more vulnerable to manipulation. Understanding and mitigating this bias is crucial for improving rational decision-making across various fields. This study examines three applications of confirmation bias in the real world by extrapolating from case studies and experiments. In finance, investors and traders are influenced by confirmation bias to make suboptimal investments and trades. In marketing, consumers are influenced by advertisements and brand loyalty to have a product fit their expectations. In digital media, confirmation bias reinforces echo chambers, promoting group polarization. Finally, the study provides policy recommendations to reduce the effect of confirmation bias, such as improving transparency in marketing and promoting partisan dialogue. It is not enough that society is aware of the problems confirmation bias poses, people should act to reduce the impact of the bias in the economy and online.

An insight of financial literacy and artificial intelligence to mitigate behavioral biases: a bibliometric and systematic review analysis using SPAR-4-SLR

PurposeThe study explores new aspects of financial investment management with technological involvement, providing detailed knowledge for future research. It identifies gaps in the literature and summarizes key research topics, utilizing a precise data collection framework.Design/methodology/approachThe study is structured using systematic and bibliometric analysis with the antecedents, decisions, outcome-theories, context, and methods (ADO-TCM) framework. Data from Scopus and Web of Science were filtered based on Q1, Q2, social sciences citation index (SSCI) and Australian Business Deans Council (ABDC) criteria, resulting in 128 articles majorly emphasizing the last ten years. The “R” package facilitated bibliometric analysis, starting with data cleaning and import into Biblioshiny for effective results interpretation.FindingsThe study found that artificial intelligence detects and mitigates biases in investment decisions through rigorous pattern analysis, including social and ethical biases. The ADO-TCM framework revealed emerging theories, such as robo-advisory theory, offering new directions in behavioral finance for researchers and practitioners. The top authors and articles highlighted existing work in financial management.Originality/valueThe study’s originality is highlighted by its use of unique frameworks for data collection (SPAR-4-SLR) and interpretation (ADO-TCM).

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.