The Impact of Cognitive Biases on Professionals' Decision-Making: A Review of Four Occupational Areas

Cognitive Biases in High-Stakes Decision-Making: Implications for Joint Pediatric Cardiology and Cardiothoracic Surgery Conference

Extensive research has consistently demonstrated that humans frequently diverge from rational decision-making processes due to the pervasive influence of cognitive biases. This paper conducts an examination of the impact of cognitive biases on high-stakes decision-making within the context of the joint pediatric cardiology and cardiothoracic surgery conference, offering practical recommendations for mitigating their effects. Recognized biases such as confirmation bias, availability bias, outcome bias, overconfidence bias, sunk cost fallacy, loss aversion, planning fallacy, authority bias, and illusion of agreement are analyzed concerning their specific implications within this conference setting. To counteract these biases and enhance the quality of decision-making, practical strategies are proposed, including the implementation of a no-interruption policy until all data is reviewed, leaders refraining from immediate input, requiring participants to formulate independent judgments prior to sharing recommendations, explicit probability estimations grounded in base rates, seeking external opinions, and promoting an environment that encourages dissenting perspectives.

The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior

More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden-but serious-problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them "inversion problems": The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science.

Sociocrinology: Impact of Social Media on Endocrine Health - A Review

Social media (SM) refers to social networking sites (SNSs), which are defined as online services that enable individuals to build a public or semi-public profile and give them the opportunity to create a network of contacts and interact. SM affects all aspects of life and may offer new opportunities to explore new experiences and perspectives of life because of its feasibility. But several times, because of feasibility, misinformation is generated intentionally or unintentionally, which spreads rapidly, and such misinformation can affect all aspects of life. However, health-related misinformation can be life-threatening to individuals. Endocrinology is the branch of medicine that deals with endocrine glands and hormones, which regulates mood, growth, development, metabolism and the way our organ works to maintain internal homeostasis. SM usage and endocrine health impact each other in both positive and negative ways. So, in this review, we will discuss about the effect of SM on Endocrine health.

Humans inherit artificial intelligence biases

Artificial intelligence recommendations are sometimes erroneous and biased. In our research, we hypothesized that people who perform a (simulated) medical diagnostic task assisted by a biased AI system will reproduce the model's bias in their own decisions, even when they move to a context without AI support. In three experiments, participants completed a medical-themed classification task with or without the help of a biased AI system. The biased recommendations by the AI influenced participants' decisions. Moreover, when those participants, assisted by the AI, moved on to perform the task without assistance, they made the same errors as the AI had made during the previous phase. Thus, participants' responses mimicked AI bias even when the AI was no longer making suggestions. These results provide evidence of human inheritance of AI bias.

Differences in decisions affected by cognitive biases: examining human values, need for cognition, and numeracy

A better understanding of factors that can affect preferences and choices may contribute to more accurate decision-making. Several studies have investigated the effects of cognitive biases on decision-making and their relationship with cognitive abilities and thinking dispositions. While studies on behaviour, attitude, personality, and health worries have examined their relationship with human values, research on cognitive bias has not investigated its relationship to individual differences in human values. The purpose of this study was to explore individual differences in biased choices, examining the relationships of the human values self-direction, conformity, power, and universalism with the anchoring effect, the framing effect, the certainty effect, and the outcome bias, as well as the mediation of need for cognition and the moderation of numeracy in these relationships. We measured individual differences and within-participant effects with an online questionnaire completed by 409 Brazilian participants, with an age range from 18 to 80 years, 56.7% female, and 43.3% male. The cognitive biases studied consistently influenced choices and preferences. However, the biases showed distinct relationships with the individual differences investigated, indicating the involvement of diverse psychological mechanisms. For example, people who value more self-direction were less affected only by anchoring. Hence, people more susceptible to one bias were not similarly susceptible to another. This can help in research on how to weaken or strengthen cognitive biases and heuristics.

Errors in clinical diagnosis: a narrative review

Diagnostic errors are often caused by cognitive biases and sometimes by other cognitive errors, which are driven by factors specific to clinicians, patients, diseases, and health care systems. An experienced clinician diagnoses routine cases intuitively, effortlessly, and automatically through non-analytic reasoning and uses deliberate, cognitively effortful analytic reasoning to diagnose atypical or complicated clinical cases. However, diagnostic errors can never be completely avoided. To minimize the frequency of diagnostic errors, it is advisable to rely on multiple sources of information including the clinician's personal experience, expert opinion, principals of statistics, evidence-based data, and well-designed algorithms and guidelines, if available. It is also important to frequently engage in thoughtful, reflective, and metacognitive practices that can serve to strengthen the clinician's diagnostic skills, with a consequent reduction in the risk of diagnostic error. The purpose of this narrative review was to highlight certain factors that influence the genesis of diagnostic errors. Understanding the dynamic, adaptive, and complex interactions among these factors may assist clinicians, managers of health care systems, and public health policy makers in formulating strategies and guidelines aimed at reducing the incidence and prevalence of the phenomenon of clinical diagnostic error, which poses a public health hazard.

Eristic reasoning: Adaptation to extreme uncertainty

Heuristics (shortcut solution rules) can help adaptation to uncertainty by leading to sufficiently accurate decisions with little information. However, heuristics would fail under extreme uncertainty where information is so scarce that any heuristic would be highly misleading for accuracy-seeking. Thus, under very high levels of uncertainty, decision-makers rely on heuristics to no avail. We posit that eristic reasoning (i.e., self-serving inferences for hedonic pursuits), rather than heuristic reasoning, is adaptive when uncertainty is extreme, as eristic reasoning produces instant hedonic gratifications helpful for coping. Eristic reasoning aims at hedonic gains (e.g., relief from the anxiety of uncertainty) that can be pursued by self-serving inferences. As such, eristic reasoning does not require any information about the environment as it instead gets cues introspectively from bodily signals informing what the organism hedonically needs as shaped by individual differences. We explain how decision-makers can benefit from heuristic vs. eristic reasoning under different levels of uncertainty. As a result, by integrating the outputs of formerly published empirical research and our conceptual discussions pertaining to eristic reasoning, we conceptually criticize the fast-and-frugal heuristics approach, which implies that heuristics are the only means of adapting to uncertainty.

Computational meaningfulness as the source of beneficial cognitive biases

The human brain has evolved to solve the problems it encounters in multiple environments. In solving these challenges, it forms mental simulations about multidimensional information about the world. These processes produce context-dependent behaviors. The brain as overparameterized modeling organ is an evolutionary solution for producing behavior in a complex world. One of the most essential characteristics of living creatures is that they compute the values of information they receive from external and internal contexts. As a result of this computation, the creature can behave in optimal ways in each environment. Whereas most other living creatures compute almost exclusively biological values (e.g., how to get food), the human as a cultural creature computes meaningfulness from the perspective of one's activity. The computational meaningfulness means the process of the human brain, with the help of which an individual tries to make the respective situation comprehensible to herself to know how to behave optimally. This paper challenges the bias-centric approach of behavioral economics by exploring different possibilities opened up by computational meaningfulness with insight into wider perspectives. We concentrate on confirmation bias and framing effect as behavioral economics examples of cognitive biases. We conclude that from the computational meaningfulness perspective of the brain, the use of these biases are indispensable property of an optimally designed computational system of what the human brain is like. From this perspective, cognitive biases can be rational under some conditions. Whereas the bias-centric approach relies on small-scale interpretable models which include only a few explanatory variables, the computational meaningfulness perspective emphasizes the behavioral models, which allow multiple variables in these models. People are used to working in multidimensional and varying environments. The human brain is at its best in such an environment and scientific study should increasingly take place in such situations simulating the real environment. By using naturalistic stimuli (e.g., videos and VR) we can create more realistic, life-like contexts for research purposes and analyze resulting data using machine learning algorithms. In this manner, we can better explain, understand and predict human behavior and choice in different contexts.

Executives' ESG cognition and enterprise green innovation: Evidence based on executives' personal microblogs

Based on cognitive theory, we investigated the influence of executives' ESG cognition on corporate green innovation using data from Chinese manufacturing listed companies from 2010 to 2019. The paper first constructs a metric of ESG cognition of company executives by presenting a quantitative analysis of data from their personal microblogs using textual analysis. The findings show that executive ESG perceptions significantly improve corporate green innovation. After addressing the endogeneity issue through a series of robustness tests, the findings of this paper still held true. Further research found that the enhancement effect of executive ESG perceptions on firms' green innovation level was mainly found in the sample without heavy pollution and with lower financing constraints and a higher marketization process. This study makes an important contribution to the research on corporate green innovation based on the perspective of executive ESG cognition while also providing a theoretical basis and practical reference for corporate green innovation practices.

An empirical assessment of financial literacy and behavioral biases on investment decision: Fresh evidence from small investor perception

To have enough financial literacy, an investor must be able to make intelligent investment choices, and on the other hand, the heuristic bias, the framing effect, cognitive illusions, and herd mentality are all variables that contribute to the formation of behavioral biases, also known as illogical conduct, in the decision-making process. The current research looks specifically at behavioral biases and financial literacy influence investment choices, particularly on stock market investment. For the research, a representative sample of 450 individual investors was evaluated. A structured questionnaire was designed using the Likert’s scale method to elicit the research variables, and the data acquired were analyzed using the SEM method. According to the findings, there was a statistically significant link between heuristic bias and the development of behavioral bias in decision-making. Nevertheless, cognitive illusions, the herd mentality, and the framing effect all have a deleterious impact on behavioral biases. In addition, investors often adhere to heuristic biases rather than other irrational strategies when making investment judgments. Therefore, individual investors’ financial literacy level greatly influences the choices made about investments in the stock market.