Heuristic Bias Research Articles

Yland et al. Respond to "Heuristics and Wish Bias".

Yland et al. Respond to "Heuristics and Wish Bias".

Probing the impact of recognition-based heuristic biases on investment decision-making and performance

PurposeThis study aims to explore and clarify the mechanism by which recognition-based heuristic biases influence the investment decision-making and performance of individual investors, with the mediating role of fundamental and technical anomalies.Design/methodology/approachThe deductive approach was used, as the research is based on behavioral finance's theoretical framework. A questionnaire and cross-sectional design were employed for data collection from the sample of 323 individual investors trading on the Pakistan Stock Exchange (PSX). Hypotheses were tested through the structural equation modeling (SEM) technique.FindingsThe article provides further insights into the relationship between recognition-based heuristic-driven biases and investment management activities. The results suggest that recognition-based heuristic-driven biases have a markedly positive influence on investment decision-making and negatively influence the investment performance of individual investors. The results also suggest that fundamental and technical anomalies mediate the relationships between the recognition-based heuristic-driven biases on the one hand and investment management activities on the other.Practical implicationsThe results of the study suggested that investment management activities that rely on recognition-based heuristics would not result in better returns to investors. The article encourages investors to base decisions on investors' financial capability and experience levels and to avoid relying on recognition-based heuristics when making decisions related to investment management activities. The results provides awareness and understanding of recognition-based heuristic-driven biases in investment management activities, which could be very useful for decision-makers and professionals in financial institutions, such as portfolio managers and traders in commercial banks, investment banks and mutual funds. This paper helps investors to select better investment tools and avoid repeating the expensive errors that occur due to recognition-based heuristic-driven biases.Originality/valueThe current study is the first to focus on links recognition-based heuristic-driven biases, fundamental and technical anomalies, investment decision-making and performance of individual investors. This article enhanced the understanding of the role that recognition-based heuristic-driven biases plays in investment management. More importantly, the study went some way toward enhancing understanding of behavioral aspects and the aspects' influence on investment decision-making and performance in an emerging market.

Poor Statistical Intuitions and Heuristic Biases: a Call for an Audit

Poor Statistical Intuitions and Heuristic Biases: a Call for an Audit

The role of behavioral bias on financial decision making: a systematic literature review and future research agenda

Purpose — This paper aims to analyze current research trends, identify theoretical perspectives, and identify research topics of behavioral bias in financial decision-making in the future.Research method — To perform bibliometric analysis, this article uses a systematic literature review, as well as content analysis. This article uses a total of 51 publications between 2018 and 2022 as the sample for the literature review, directed by PRISMA. The tool used in analyzing bibliometrics is VOSviewer. Meanwhile, content analysis is conducted to build theoretical perspectives and proposed future research agendas.Result — This systematic review explains the number of articles per year, most influential articles, leading journals, leading countries, leading authors, important keywords, and research cluster networks. Besides, this article also discovers seven behavioral biases that can be analyzed to gain a theoretical perspective on behavioral bias. The seven behavioral biases are Heuristic Bias, Self-Attribution Bias, Framing Bias, Herding Bias, Aversion Bias, Disposition Effect, and Overconfidence Bias,. In the scientific mapping analysis, important keywords are obtained, and the author's research cluster network is to discover topics that rarely researched to be offered in future research.Recommendation/significance/contribution — In contrast to previous studies of behavioral bias, which were dominated by survey-based research, this paper provides a different reference by using a systematic literature review method that provides coverage of the main research issues and theoretical arguments about behavioral bias in financial decisions. In addition, this paper offers new ideas about potential research fields by identifying studies in developing countries that are still rarely carried out compared to developed countries.

Influence of Heuristic Techniques and Biases in Investment Decision-Making: A Conceptual Analysis and Directions for Future Research

Influence of Heuristic Techniques and Biases in Investment Decision-Making: A Conceptual Analysis and Directions for Future Research

Representing Socio‐Economic Uncertainty in Human System Models

AbstractSocio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

A Fast Path Planing Solution For Multiple Obstacle Spaces

Among the many path planning algorithms, the RRT* algorithm and the Informed RRT* algorithm are the most popular. Both of them can complete the single path planning effectively. However, because the planning effect of these algorithms is limited by the number of iterations, the optimal solution cannot be planned in a limited time. Therefore, a new algorithm-Self-Optimizing Growing Tree (SOGT) is proposed in this paper. On the premise of ensuring complete probability, heuristic bias sampling is used to improve the efficiency of the algorithm to explore free space. Then, the initial path is optimized by the maximum distance and the inflection point optimization using prior information to obtain an optimal path. In the simulation experiments, we give the comparison results of numerical simulations of SOGT algorithm, RRT* algorithm and Informed RRT* algorithm. The experiment proves that the SOGT algorithm can get an optimal path in less time, which fully reflects the characteristics of the algorithm with good real-time performance and strong adaptability.

Habits of Mathematical Thinking and Development of Heuristics

Psychological theory indicates that human use heuristics, the psychology of mental shortcuts, to make decisions, solve problems, or learn new knowledge. In this article, I will discuss how some common mathematical mistakes generate from the heuristics process while students learn mathematics or solve mathematics problems, and how we may help students avoid the hurdle of heuristic bias. Constructing heuristics ability of non-linear thinking will be discussed to shed light on teaching students high order of mathematical thinking.

The Impact of Heuristic Biases on Investors’ Investment Decision Making; Evidence from Colombo Stock Exchange

Journal of Business & Technology (JBT) is an open-access journal published by the Department of the Commerce and Financial Management of the University of Kelaniya, Sri Lanka. The journal follows a double-blind peer-review process and is published bi-annually (January, July). JBT aims to publish scholarly research, critical reviews, case studies, practitioner reviews and technology reviews in the fields of business and technology.

Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism.

The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.

Choice architecture for young adult blood donor recruitment – a feasibility study

ABSTRACT Blood shortages remain an ongoing challenge, ameliorable by increasing blood donations. Choice architecture is an emerging concept in psychology dealing with the targeted presentation of options to encourage a desired decision. A pilot study was designed to test the feasibility of implementing six choice architecture strategies on a Midwest high-school blood drive. These include framing, conformity, mere measurement, availability heuristic, loss aversion and status quo bias. A pre-post interrupted time-series evaluation was performed to evaluate for an immediate impact on blood donations. All six of the intended choice architecture strategies were successfully implemented. The pre-intervention 5-year average number of blood donations per blood drive was 15 (4.3% of age eligible donors) whereas post intervention, the number of blood donors rose to 25 (7.1% of age eligible donors; p-value = .0013). The application of choice architecture to blood donor recruitment strategies is feasible and has the potential to reduce the burden of blood shortages.

Heuristic biases and investment decisions: multiple mediation mechanisms of risk tolerance and financial literacy—a survey at the Tanzania stock market

PurposeThe purpose of this research is to examine the effect of heuristic biases on investment decisions through multiple mediation mechanisms of risk tolerance and financial literacy in the Tanzanian stock market.Design/methodology/approachA sample of 316 individual investors in the Tanzanian stock market was obtained through questionnaires. The data were analyzed using structural equation modeling (SEM).FindingsThe findings show that financial literacy mediates insignificantly the effects of overconfidence, availability, anchoring and representativeness heuristics on investment decisions. Further, financial literacy does not influence the effect of risk tolerance and investment decisions. Risk tolerance is confirmed as a positive mediator of overconfidence, availability, anchoring and representativeness heuristics in investment decisions. Also, the study shows that overconfidence exerts a stronger influence on investment decisions, followed by availability, representativeness, risk tolerance, anchoring and financial literacy.Research limitations/implicationsThe study deals with real investors. Therefore, it uses fewer items to measure the constructs in order to avoid respondent bias. Further research could examine the effects of heuristic biases on investment decisions by adding or modifying the items of particular constructs and studying institutional investors.Practical implicationsThe findings can help individual investors to analyze and evaluate their behavior toward stock selection. Securities institutions can use this research to understand investors' behavior, evaluate future market trends and provide advice to the investors.Originality/valuePrevious studies have examined the impact of heuristics on the investment decisions of individual investors. The unique empirical analysis developed in this paper is that it examines the multiple mediation mechanisms of risk tolerance and financial literacy with respect to heuristic biases and investment decisions in the Tanzanian stock market.

Covid-19 and heuristic biases: evidence from India

Stock markets worldwide have witnessed high volatility during the year 2020 owing to the eruption of Covid-19. Due to the world’s unprecedented economic challenges, this study could potentially guide financial advisors and individual investors in dealing with pandemics. An association between investors’ perception toward the intensity of Covid-19 and heuristic biases is analyzed using the responses of 290 stock investors of National Capital Region (NCR), India. The data are validated through Cronbach’s alpha, and the model fit is analyzed using EFA. Confirmatory factor analysis (CFA) is employed to investigate the relationship between Covid-19 and heuristic biases. Covid-19 does not have any influence on the overconfidence of investors. A significant positive relationship is found between Covid-19 and the remaining three heuristics, i.e., availability, anchoring, and representativeness. The present study analyzes the association between Covid-19 and specific investors’ bias only and should not be interpreted for causality. The study has the potential to guide investors in understanding the errors they are making while investing during the pandemic and the ways to deal with them. The study could provide insights to the financial advisors in understanding their customers. The implications of the study may include inputs of the errors committed by them during the pandemic. Despite the fact that an enormous amount of literature exists in the field of investors’ sentiment, a scarcity of literature is available that measures the relationship between heuristic biases and the perceived impact of the pandemic. The current study attempts to fill this gap in the literature.

Prosecution or Persecution? Extraneous Events and Prosecutorial Decisions

In 2004, a Dutch‐Moroccan Islamic extremist in Amsterdam brutally murdered Theo van Gogh, a Dutch filmmaker known for his critique of Islam. Using a difference‐in‐differences approach, we show that, in Amsterdam, the assassination resulted in more than a 19‐percentage‐point increase in the likelihood of prosecution for unrelated violent crimes of male suspects born in Muslim‐majority countries. The effect is detectable during the first month following the murder but dissipates thereafter. We find no evidence of the murder's effect for nonviolent crimes and for violent crimes processed by other Dutch prosecution offices. Our findings are thus not consistent with a purely emotions‐based explanation of the murder's effect. Instead, our results are congruent with both a signaling explanation, whereby career‐motivated prosecutors chose to showcase their toughness, and a behavioral explanation entailing prosecutors' susceptibility to availability heuristic bias. On institutional grounds, we view the latter as more plausible than the former. Our article adds to an emerging literature demonstrating that extraneous events can critically shape criminal justice outcomes.

Expert’s Heuristic Biases in Airport Predictive Risk Assessments

Expert perceptions have been increasingly used to perform risk assessments in airport predictive risk assessments in recent years. Although it is known that biases are less influential in groups of experts when compared to laypeople, they still can be residually present in such tasks with this specific group. Therefore, this article aims to propose (1) the pragmatic organization of knowledge about the biases that may affect airport risk assessments by groups of experts and (2) which of them most often arise in this type of analysis and at what intensity. For the development of the work, we carried out a dense bibliographic review of the theme. Later, we performed a predictive risk assessment and a survey, with the support of an experienced group of 30 experts from Brazilian regulatory agency and airport operators. After 1224 risk judgments, experts were able to clearly indicate regulations and their sections that are disproportionately more and less important in terms of risk, leading States to a better regulatory quality only by changing their logic of actions to a risk-based approach. Later the group answered a survey on a list of 12 heuristic biases created from the bibliographic review. Results showed that, in fact experts have a resistance to biases influence, mainly based in their academic and professional background, but also showed this influence is not exactly negligible. It was also possible to rank the heuristic biases in terms of importance and indicate that experts tend to concern with three hierarchical information characteristics when judging risk: at first, would be the form in which risk problems are presented; second, how they interpret information presented; and third, the amount of information presented.

Inductive Processes, Heuristics, and Biases Modulated by High-Reliability Organizing (HRO) for COVID-19 and Disasters

We view and understand the world through our internal logic, both public and private internal logic. The logic of practice differs by the frame of reference – a fixed point or within the flux of events, which can have “cosmology episodes” that collapse sensemaking. We have different perceptions and capabilities from the different reference frames, Eulerian and Lagrangian specificities, Euclidean, and topological spaces. When approaching a situation, all we have is observation, induction, and the capability to learn through action. Because people have limited time and knowledge, they must make inferences from the information they have available. We almost universally use heuristic, subjective approaches for better decision-making for complex, interactive problems and processes. Heuristics work through the nearness of information between the old problem-solution and the new problem, a topological space. In routine operations, we are susceptible to heuristic bias, yet error corrects this heuristic bias counterintuitively. We have found four predominant heuristics that cause consequential bias and interfere with effective decision-making: availability, representativeness, confirmation bias, and over-conservative revision. Motivated reasoning, a fifth bias but not from a heuristic, overly scrutinizes information that conflicts with closely held beliefs. Unless we assume that every word and behavior could instantly be wrong, we can too easily begin treating our treatments.

Impact of Financial Literacy and Behavioural Biases on Investment Decision-making

Investors’ financial literacy entails making sound investment decisions and the behavioural biases or irrational behaviour in decision-making that are collectively formed by heuristic bias, framing effect, cognitive illusions and herd mentality factors. The present study examines the combined impact of financial literacy and behavioural biases on investment decisions. A questionnaire was developed using Likert scaling technique to elicit study variables and collected data was analysed using SEM technique. The results showed that heuristic bias had a significant positive association with the creation of behavioural bias in decision-making. However, the framing effect, cognitive illusions and herd mentality have negative associations in the formation of behavioural biases. Further, investors often practice and follow heuristic biases rather than other irrational techniques for making investment decisions. Therefore, the financial literacy of individual investors has a significant impact on affecting stock market investment decisions.

Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis.

BackgroundSemantic textual similarity (STS) is a natural language processing (NLP) task that involves assigning a similarity score to 2 snippets of text based on their meaning. This task is particularly difficult in the domain of clinical text, which often features specialized language and the frequent use of abbreviations.ObjectiveWe created an NLP system to predict similarity scores for sentence pairs as part of the Clinical Semantic Textual Similarity track in the 2019 n2c2/OHNLP Shared Task on Challenges in Natural Language Processing for Clinical Data. We subsequently sought to analyze the intermediary token vectors extracted from our models while processing a pair of clinical sentences to identify where and how representations of semantic similarity are built in transformer models.MethodsGiven a clinical sentence pair, we take the average predicted similarity score across several independently fine-tuned transformers. In our model analysis we investigated the relationship between the final model’s loss and surface features of the sentence pairs and assessed the decodability and representational similarity of the token vectors generated by each model.ResultsOur model achieved a correlation of 0.87 with the ground-truth similarity score, reaching 6th place out of 33 teams (with a first-place score of 0.90). In detailed qualitative and quantitative analyses of the model’s loss, we identified the system’s failure to correctly model semantic similarity when both sentence pairs contain details of medical prescriptions, as well as its general tendency to overpredict semantic similarity given significant token overlap. The token vector analysis revealed divergent representational strategies for predicting textual similarity between bidirectional encoder representations from transformers (BERT)–style models and XLNet. We also found that a large amount information relevant to predicting STS can be captured using a combination of a classification token and the cosine distance between sentence-pair representations in the first layer of a transformer model that did not produce the best predictions on the test set.ConclusionsWe designed and trained a system that uses state-of-the-art NLP models to achieve very competitive results on a new clinical STS data set. As our approach uses no hand-crafted rules, it serves as a strong deep learning baseline for this task. Our key contribution is a detailed analysis of the model’s outputs and an investigation of the heuristic biases learned by transformer models. We suggest future improvements based on these findings. In our representational analysis we explore how different transformer models converge or diverge in their representation of semantic signals as the tokens of the sentences are augmented by successive layers. This analysis sheds light on how these “black box” models integrate semantic similarity information in intermediate layers, and points to new research directions in model distillation and sentence embedding extraction for applications in clinical NLP.

Institutional injustice: Implications for system transformation emerging from the mental health recovery narratives of people experiencing marginalisation.

BackgroundInstitutional injustice refers to structures that create disparities in resources, opportunities and representation. Marginalised people experience institutional injustice, inequalities and discrimination through intersecting personal characteristics and social circumstances. This study aimed to investigate sources of institutional injustice and their effects on marginalised people with experience of mental health problems.MethodsSemi-structured interviews were conducted with 77 individuals from marginalised groups with experience of mental health problems, including psychosis, Black, Asian and minority ethnic (BAME) populations, complex needs and lived experience as a work requirement. These were analysed inductively enabling sensitising concepts to emerge.FindingsThree processes of institutional injustice were identified: not being believed because of social status and personal backgrounds; not being heard where narratives did not align with dominant discourses, and not being acknowledged where aspects of identity were disregarded. Harmful outcomes included disengagement from formal institutions through fear and mistrust, tensions and reduced affiliation with informal institutions when trying to consolidate new ways of being, and damaging impacts on mental health and wellbeing through multiple oppression.ConclusionsInstitutional injustice perpetuates health inequalities and marginalised status. Master status, arising from dominant discourses and heuristic bias, overshadow the narratives and experiences of marginalised people. Cultural competency has the potential to improve heuristic availability through social understandings of narrative and experience, whilst coproduction and narrative development through approaches such as communities of practice might offer meaningful avenues for authentic expression.

The impact of heuristic biases on investors’ investment decision in Pakistan stock market: moderating role of long term orientation

PurposeThe purpose of this paper is to investigate the effect of heuristic biases, namely, availability bias and representativeness bias on investors’ investment decisions in the Pakistan stock exchange, as well as the moderating role of long-term orientation.Design/methodology/approachUsing a structured questionnaire, a total of 374 responses have been collected from individual investors trading in PSX. The relationship was tested by applying the partial least square structural equation model using SmartPLS 3.2.2. Further, Henseler and Chin’s (2010) product indicator approach for moderation analysis was applied to the data set.FindingsThe results revealed that availability bias and representativeness bias have a significant and positive influence on the investment decisions of investors. Furthermore, a significant moderating effect of long term orientation on the effect of representativeness bias on investment decision is observed. This suggests that investors’ long term orientation weaken the effect of representativeness bias on investment decision. However, no significant moderating effect was observed for availability bias.Originality/valueThe paper provides novel insights on the role of heuristic-driven biases on the investment decisions of individual investors in the stock market. Particularly, it enhanced the understanding of behavioral aspects of investment decision-making in an emerging market.