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Judgment Under Uncertainty: Heuristics and Biases
Abstract
Many decisions are based on beliefs concerning the likelihood of uncertain events such as the outcome of an election, the guilt of a defendant, or the future value of the dollar. Occasionally, beliefs concerning uncertain events are expressed in numerical form as odds or subjective probabilities. In general, the heuristics are quite useful, but sometimes they lead to severe and systematic errors. The subjective assessment of probability resembles the subjective assessment of physical quantities such as distance or size. These judgments are all based on data of limited validity, which are processed according to heuristic rules. However, the reliance on this rule leads to systematic errors in the estimation of distance. This chapter describes three heuristics that are employed in making judgments under uncertainty. The first is representativeness, which is usually employed when people are asked to judge the probability that an object or event belongs to a class or event. The second is the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development, and the third is adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.
... Some of these anomalies-especially on the demand side-can be explained by people's misperception of risk. Tversky and Kahneman (1974) describe this risk misperception in terms of four biases: representativeness, availability, adjustment, and anchoring. In addition, myopic loss aversion, as discussed in Thaler (1999), is related to loss aversion and mental accounting. ...
... 1. Representativeness bias As discussed in Tversky and Kahneman (1974), representativeness bias includes sample size insensitivity. Subject to the base-rate fallacy in Kahneman and Tversky (1973), people tend to ignore the difference in sample size when calculating the posterior probability (i.e., the conditional probability assigned within a given state). ...
... 2. Availability: As another bias mentioned by Tversky and Kahneman (1974), the availability bias indicates that the ease of recalling a certain event leads to an overestimation of the probability of the event. Salience is one of the factors that influences this ease. ...
On-demand insurance products cover risks for short periods of time via a smartphone or other electronic device. Such insurance contracts give policyholders the freedom to choose when to be insured in a flexible way. On-demand contracts may change the way risk is perceived. Therefore, we conduct an experiment and show that individuals can become exceptionally risk-averse when offered short-term insurance. We show two main reasons for this result: first, the underlying risk is often overestimated by the subjects due to a miscalculation of the loss probabilities. Second, the shorter valuation horizon of on-demand insurance contracts leads to myopic loss aversion.
... It is a common phenomenon in everyday life, yet highly impactful on risky decision-making, even without awareness. [Tve74] It is essential, nevertheless, to differentiate between risk and ambiguity, the two most studied sources of uncertainty [Kah79]. Risk refers to situations where individuals have precise probabilistic information about possible outcomes, while ambiguity arises when individuals lack sufficient information to assign probabilities to outcomes [Cam92]. ...
... Decision-making is not rational. In fact, Tversky and Kahneman (1974) [Tve74] describe that people use mental shortcuts or heuristics as cognitive tools to cope with uncertainty. These heuristics can lead to systematic biases and errors in judgment. ...
... Decision-making is not rational. In fact, Tversky and Kahneman (1974) [Tve74] describe that people use mental shortcuts or heuristics as cognitive tools to cope with uncertainty. These heuristics can lead to systematic biases and errors in judgment. ...
... According to psychological literature, heuristic reasoning is used to make decisions in situations of uncertainty 28 . Heuristic processes, however, may be prone to bias 28,29 . ...
... According to psychological literature, heuristic reasoning is used to make decisions in situations of uncertainty 28 . Heuristic processes, however, may be prone to bias 28,29 . Confirmation bias is "a tendency to look for confirmatory evidence for our hypotheses" (p. ...
... Science was occasionally invoked both to prove the existence of climate change through data collection and to justify scepticism if there was only anecdotal evidence at hand. Confirmation biases [28][29][30][31] were also displayed both to support and to question evidence of climate change. ...
... Integrating behavioral finance insights with big data analytics presents a promising approach to addressing the complexities of supply chain optimization. Behavioral finance offers a framework for understanding how psychological factors and cognitive biases influence decision-making processes, including those related to consumer behavior and market dynamics (Tversky & Kahneman, 1974). By incorporating these insights, companies can better anticipate and respond to consumer preferences, price sensitivity, and other market variables (Kahneman, 2003). ...
... Behavioral finance, a field that blends psychological theories with financial decision-making, provides valuable insights into how psychological factors influence consumer and investor behavior . It challenges the traditional notion of rational decision-making by incorporating the effects of cognitive biases, emotions, and social influences on financial decisions (Tversky & Kahneman, 1974). Key concepts in behavioral finance include prospect theory, which posits that individuals value gains and losses differently, leading to irrational decisionmaking when faced with uncertainty (Kahneman & Tversky, 1979). ...
... One effective strategy is to implement structured decision-making processes that incorporate data-driven analysis and reduce reliance on intuition (Adeniran, et al., 2024, Bello & Uzu-Okoh, 2024). By combining quantitative models with behavioral insights, companies can counteract biases like overconfidence and make more informed decisions (Tversky & Kahneman, 1974). For instance, using predictive analytics to support demand forecasting can provide a more objective basis for decision-making, helping to balance confidence with empirical data (Chen et al., 2012). ...
Optimizing food and Fast-Moving Consumer Goods (FMCG) supply chains is crucial for enhancing efficiency and responsiveness in today's dynamic market. This paper presents a dual approach integrating behavioral finance insights with big data analytics to refine strategic decision-making processes. Behavioral finance provides valuable understanding of how psychological factors and biases influence decision-making among supply chain stakeholders. By analyzing patterns such as overreaction to market trends or herd behavior, companies can anticipate and mitigate irrational decision-making that may lead to inefficiencies and supply chain disruptions. Big data analytics, on the other hand, enables organizations to process and analyze vast amounts of data from various sources, including sales figures, inventory levels, and consumer behavior. Advanced analytics techniques, such as predictive modeling and machine learning, offer actionable insights into demand forecasting, inventory management, and logistics optimization. Integrating these insights with behavioral finance principles allows for a more comprehensive approach to managing supply chain risks and opportunities. This dual approach supports strategic decision-making by addressing both the human and data-driven aspects of supply chain management. For instance, understanding cognitive biases can help in designing better forecasting models and inventory policies, while big data analytics can provide real-time insights to correct course deviations and align supply with actual demand patterns. The synergy between these methodologies enhances overall supply chain resilience, reduces costs, and improves service levels. The paper discusses practical applications of this integrated approach, including case studies where companies have successfully employed behavioral finance principles alongside big data analytics to optimize their supply chains. It also highlights the challenges and considerations in implementing this dual strategy, offering recommendations for best practices and future research directions.
... Others also find evidence that there is a nonlinear causality relationship between both volume and stock returns over several years (Hiemstra & Jones, 1994). However, while the firm size of the technology industry sample companies is totally different, the availability of information may increase the familiarity of viewers with their activities, thereby benefiting large companies (Tversky & Kahneman, 1974). Yet, it seems the trading volume is much larger in larger firms. ...
... nonlinear causality relationship between both volume and stock returns over several years (Hiemstra & Jones, 1994). However, while the firm size of the technology industry sample companies is totally different, the availability of information may increase the familiarity of viewers with their activities, thereby benefiting large companies (Tversky & Kahneman, 1974). Yet, it seems the trading volume is much larger in larger firms. ...
... A high beta coefficient means that macroeconomic changes are able to greatly nonlinear causality relationship between both volume and stock returns over several years (Hiemstra & Jones, 1994). However, while the firm size of the technology industry sample companies is totally different, the availability of information may increase the familiarity of viewers with their activities, thereby benefiting large companies (Tversky & Kahneman, 1974). Yet, it seems the trading volume is much larger in larger firms. ...
This study delves into the intra-industry effects following a firm-specific scandal, with a particular focus on the Facebook data leakage scandal and its associated events within the U.S. tech industry and two additional relevant groups. We employ various metrics including daily spread, volatility, volume-weighted return, and CAPM-beta for the pre-analysis clustering, and subsequently utilize CAR (Cumulative Abnormal Return) to evaluate the impact on firms grouped within these clusters. From a broader industry viewpoint, significant positive CAARs are observed across U.S. sample firms over the three days post-scandal announcement, indicating no adverse impact on the tech sector overall. Conversely, after Facebook's initial quarterly earnings report, it showed a notable negative effect despite reported positive performance. The clustering principle should aid in identifying directly related companies and thus reducing the influence of randomness. This was indeed achieved for the effect of the key event, namely "The Effect of Congressional Hearing on Certain Clusters across U.S. Tech Stock Market," which was identified as delayed and significantly negative. Therefore, we recommend applying the clustering method when conducting such or similar event studies.
... Так, эвристика репрезентативности может привести к ошибочному предположению о достаточности небольшой выборки для отражения всей отрасли. Как отмечают авторы, "сходство, или репрезентативность, не зависит от многих факторов, которые должны влиять на вероятностные оценки" [Tversky & Kahneman, 1974]. Это приводит к игнорированию изменчивости более значительных выборок. ...
... Эвристика доступности может исказить оценку состояния отрасли, если исследователи полагаются на легко доступные, но ограниченные данные. По мнению Тверски и Канемана, "опора на доступность приводит к предсказуемым предвзятостям" [Tversky & Kahneman, 1974], поскольку имеющиеся данные не всегда отражают полную картину. ...
... Эвристика якорения может воздействовать на исследователей, склоняя их использовать начальные значения из ограниченных данных в качестве отправной точки, часто приводя к необоснованным корректировкам и смещениям к первоначальным значениям [Tversky & Kahneman, 1974]. ...
В статье показана фундаментальная роль отраслевых ставок роялти в лицензионных договорах и аналитического метода их расчета. Аналитический метод расчета ставок роялти (RoS - Royalty on Sales price) на основе рентабельности продаж (ROS) и прибыли до вычета процентов и налогов (EBIT - Earnings Before Interest and Taxes) отличается высокой точностью и учитывает специфику отраслей, в которых используется объект интеллектуальной собственности. Этот метод широко применяется как в судебных экспертизах, так и в коммерческих сделках, охватывая расчеты убытков, оценку рыночной стоимости исключительных прав, а также определение размеров компенсации и вознаграждения авторам в различных юрисдикциях. Показано, что ставка роялти является ключевым элементом двухкомпонентного ценообразования в лицензионных сделках, обеспечивая гибкость и эффективность в управлении интеллектуальной собственностью.
The article demonstrates the fundamental role of industry royalty rates in licensing agreements and the analytical method for their calculation. The analytical method for calculating royalty rates (RoS - Royalty on Sales price) based on Return on Sales (ROS) and Earnings Before Interest and Taxes (EBIT) is highly accurate and takes into account the specifics of the industries where the intellectual property is used. This method is widely applied both in forensic examinations and in commercial transactions, covering loss calculations, market value assessments of exclusive rights, as well as determining compensation and remuneration for authors in various jurisdictions. It is shown that the royalty rate is a key element of two-component pricing in licensing deals, providing flexibility and efficiency in intellectual property management.
... Cette dimension est ici plutôt solide mais essentiellement axée sur les pratiques à l'échelle publique. Comme souvent à l'égard de risques côtiers (Navarro, 2016) (Botzen et al., 2009 ;Lin et al., 2008 1. Processus psychologiques impliqués dans la perception du risque Les premiers travaux sur la perception du risque ont tenté de la modéliser par des raisonnements rationnels (Fleury-Bahi, 2010), mais la psychologie a permis d'apprécier le rôles de processus aussi bien cognitifs (Tversky & Kahneman, 1982) (Slovic, 1987), qu'affectifs (Finucane et al., 2000;Slovic et al., 1991Slovic et al., , 2004. D'un point de vue cognitif, des heuristiques permettent d'élaborer un jugement sur le risque sans démarche analytique, le rendant simple et rapide à effectuer (Tversky & Kahneman, 1982), mais aussi parfois source de biais (ex : ...
... Comme souvent à l'égard de risques côtiers (Navarro, 2016) (Botzen et al., 2009 ;Lin et al., 2008 1. Processus psychologiques impliqués dans la perception du risque Les premiers travaux sur la perception du risque ont tenté de la modéliser par des raisonnements rationnels (Fleury-Bahi, 2010), mais la psychologie a permis d'apprécier le rôles de processus aussi bien cognitifs (Tversky & Kahneman, 1982) (Slovic, 1987), qu'affectifs (Finucane et al., 2000;Slovic et al., 1991Slovic et al., , 2004. D'un point de vue cognitif, des heuristiques permettent d'élaborer un jugement sur le risque sans démarche analytique, le rendant simple et rapide à effectuer (Tversky & Kahneman, 1982), mais aussi parfois source de biais (ex : ...
This thesis explores the relationship between risk perception and adaptation, taking into account the temporal dimension of risk (long-term or sudden) and adaptation (before and during exposure). Four studies have applied this approach to coastal hazards.
The first, a qualitative study, provides an overview of the social representations of coastal risks and their prescriptive dimension. The second study determined how risk map communication presenting future projections influences the perception of a long-term risk such as coastal erosion, by measuring risk map observation using eye-tracking.
The following two studies then explores the interaction between risk perception and individual and public adaptations according to different temporalities of risk and exposure. The first identified individual reactions to exposure to a sudden risk (tsunami) using a virtual reality simulation, and in particular reconsidered the "myth of panicked victim" in the face of a natural disaster. The second looked at public adaptation to a long-term risk (coastal erosion). It showed that the generally hotly contested strategy of relocating the stakes is ultimately the most accepted, and identified factors in the acceptability of different strategies, with the time step (short or long term) of their application not being one of them.
... Anchoring effect is a robust phenomenon in various judgments (for a review, see Furnham & Boo, 2011). According to the traditional explanation for anchoring effect (Tversky & Kahneman, 1974), when individuals make various judgments, the initial value is set by accessible information (i.e., anchoring information) and they attempt to adjust from this value. However, this adjustment process is often insufficient, and the bias persists in the judgment. ...
... This study aimed to clarify the effects of informative and uninformative anchoring information on JOLs. In each experiment, we focused on the optimistic/pessimistic and differential-scaling hypotheses using the standard paradigm of the anchoring effect (Tversky & Kahneman, 1974). Participants were presented with an anchor value and asked to make comparative judgments (e.g., whether their recall performance on a future memory test would be higher or lower than the anchor value). ...
This study examined informative and uninformative anchoring effects on judgments of learning (JOLs), focusing on two hypotheses: the optimistic/pessimistic and differential-scaling hypotheses. The optimistic/pessimistic hypothesis states that anchoring information changes subjective confidence in memory, whereas the differential-scaling hypothesis states that anchoring information elicits a scaling bias in the conversion process of subjective internal confidence into scale JOLs (i.e., 0–100% responses). Experiment 1 focused on binary JOLs (i.e., Yes/No predictions). The results confirmed that the informative anchoring effect occurred (i.e., binary JOLs in the high anchor condition were higher than those in the low anchor condition), whereas the uninformative anchoring effect did not. Experiment 2 evaluated whether the difference in response scales between anchoring information and JOLs elicited the anchoring effect, demonstrating that the informative anchoring effect occurred when different response scales were used for the anchoring information (i.e., the number of words correctly recalled) and JOLs (i.e., 0–100% scale), and the uninformative anchoring effect did not. Experiment 3 examined whether the uninformative anchoring effect can be explained by numeric priming rather than scaling bias, demonstrating that anchoring information unrelated to test performance using a 0–100% scale did not elicit the uninformative anchoring effect. These findings suggest that the informative anchoring effect supports the optimistic/pessimistic hypothesis, whereas the uninformative anchoring effect supports the differential-scaling hypothesis. Thus, the nature of anchoring information affects the process of forming JOLs. Specifically, the uninformative anchor elicits only scaling bias, whereas the informative anchor changes subjective confidence in memory.
... This effect arises from the increased availability of the imagined event in memory and can occur for both positive events, such as winning the jackpot, and negative events, like contracting a disease (Carroll, 1978;Gregory et al., 1982;Sherman et al., 1985). Similarly, the availability heuristic suggests that events more easily imagined or recalled are perceived to be more likely (Tversky & Kahneman, 1974). This process is driven by the attention allocated to mentally simulating these events, which increases their accessibility and hence likelihood judgment (see Schwarz & Vaughn, 2002). ...
... As a form of representativeness heuristics (Tversky & Kahneman, 1974), Guryan and Kearney (2008) show that a store that sells a winning ticket experiences a rapid Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
This research investigates the phenomenon of intertemporal purchase acceleration in the context of lottery purchases. Using actual sales data, we demonstrate that individuals’ willingness to purchase lottery tickets increases as the draw day approaches, despite the constant prize amount and near-zero winning odds. We explore the role of temporal proximity, visualization, and likelihood judgment in influencing purchase intent. The study replicates the “lucky store effect,” showing that stores with previous winning tickets experience a sharper acceleration in sales. In two follow-up experiments, we identify that visualization and perceived likelihood mediate the relationship between temporal proximity and purchase intent. Additionally, we find that habit moderates this effect, with habitual buyers being less influenced by temporal proximity. These insights into consumer behavior in high-uncertainty contexts have implications for marketing strategies and public policy interventions.
... Biases occur when a heuristic is used in the 'wrong' context and can '. . . lead to severe and systematic errors' (Tversky & Kahneman, 1974, p. 1124). There are two distinct schools of thought regarding heuristics-the heuristics and bias (H&B) school led by Kahneman (2011) and Tversky (1981) and the fast and frugal (F&F) school led by Gigerenzer (2008). ...
... The H&B school argue that in most cases, heuristic-based decision-making is inferior to elaborate thinking (Kahneman & Tversky, 1986). Despite this, heuristics are seen as sometimes useful by the H&B school (Tversky & Kahneman, 1974), particularly in contexts where there is high cue validity (consistent, reliable behavioural signals with little feedback delay) and the opportunity to develop skilled intuition through pattern recognition (Kahneman & Klein, 2009). ...
The political elite make policy decisions in noisy environments and under time pressure, and so are prone to using heuristics. There are conflicting schools of thought as to whether it is appropriate for them to do so. Experienced decision‐makers are thought to be more effective at using heuristics, so it is possible that for the political elite with experience in a particular context, heuristic decision‐making is appropriate. Yet, many politicians are asked to make decisions on matters about which they are not experts. To add to the debate, we facilitated a discussion with a highly experienced cohort of 21 current and former senior politicians, former advisers, and current and former senior bureaucrats. When presented with a carefully considered and innovative new transport network pricing policy, we sought to identify whether and, if so, how they used heuristics to make a decision. We found that they used heuristics (1) to decide whether to engage with the issue at all and (2) how to act, having made the decision to engage. We describe how these heuristics were used and discuss the implications for theory and public administration practice.
Points for practitioners
There is a growing body of evidence that the political elite use heuristics for decision‐making and that the use of heuristics is influenced by seven factors.
We gained rare access to the political elite deciding on a politically risky issue and observed not only which heuristics they used, but how they used them.
We observed a three‐step decision tree, incorporating the ‘wait‐and‐see’ heuristic being used to decide whether to act, and political empathy, or intuiting voter heuristics to help decide how to act.
We outline five options for public administrators who think that the political elite are using heuristics inappropriately for decision‐making.
... System 1 refers to an automatic, intuitive, unconscious, fast, and effortless or routine mechanism to make most common decisions ( Figure 1). Conversely, system 2 makes deliberate decisions, which are non-programmed, conscious, usually slow and effortful [47]. It has been suggested that most cognitive biases are likely due to the overuse of system 1 or when system 1 overrides system 2 [48][49][50]. ...
... However, physician-level factors were largely ignored as reflected by reports from scientific organizations [53][54][55]. It was not until the 1970s that cognitive biases were initially recognized to affect individual physicians' performance in daily medical decisions [9,10,47,56,57]. Despite these efforts, little is known about the influence of cognitive biases and personality traits on physicians' decisions that lead to diagnostic inaccuracies, medical errors or impact on patient outcomes. ...
... They are defined by Shah and Oppenheimer (2008) as "rules of thumb or mental shortcuts aimed to reduce the effort required to complete a job, primarily by taking into account less information." Heuristics are useful but can sometimes lead to major and systematic errors (Tversky & Kahneman, 1974). They refer to them as "prediction or estimation errors," which they call bias. ...
... Anchoring bias leads to the tendency to consider illogical price levels as a base in decisions making process. Kahneman and Tversky (1974) demonstrate the existence of bias, i.e., anchoring, in which individuals construct values by beginning with a base value and adjusting it to arrive at a final value. ...
This paper examines the relationship between investors' demographic variables and behavioral biases. This study includes demographic factors like age, gender, marital status, income level, level of education, occupation, and investment experience of individual investors. The study uses t-test and ANOVA to examine the survey data of 184 retail investors in Delhi-NCR. The research findings show the existence of behavioral biases like anchoring, mental accounting, overconfidence, herding, and loss aversion. The findings support the existence of biases among individuals and show that their behavior is not always rational. This study is to find an association of variables, not to find out the causality. The results of this research should not be taken as implying causation. The findings could have consequences for financial educators who want to promote personal financial awareness. Understanding the decision-making processes of their clients can help financial advisors become more effective. Despite detailed literature on behavioral finance, only a small amount of academic research has been done to analyze the relationship between biases and demographic factors in the context of retail investors in Delhi-NCR. By attempting to fill this gap in the literature, this study contributes to it.
... They are defined by Shah and Oppenheimer (2008) as "rules of thumb or mental shortcuts aimed to reduce the effort required to complete a job, primarily by taking into account less information." Heuristics are useful but can sometimes lead to major and systematic errors (Tversky & Kahneman, 1974). They refer to them as "prediction or estimation errors," which they call bias. ...
... Anchoring bias leads to the tendency to consider illogical price levels as a base in decisions making process. Kahneman and Tversky (1974) demonstrate the existence of bias, i.e., anchoring, in which individuals construct values by beginning with a base value and adjusting it to arrive at a final value. ...
This paper examines the relationship between investors' demographic variables and behavioral biases. This study includes demographic factors like age, gender, marital status, income level, level of education, occupation, and investment experience of individual investors. The study uses t-test and ANOVA to examine the survey data of 184 retail investors in Delhi-NCR. The research findings show the existence of behavioral biases like anchoring, mental accounting, overconfidence, herding, and loss aversion. The findings support the existence of biases among individuals and show that their behavior is not always rational. This study is to find an association of variables, not to find out the causality. The results of this research should not be taken as implying causation. The findings could have consequences for financial educators who want to promote personal financial awareness. Understanding the decision-making processes of their clients can help financial advisors become more effective. Despite detailed literature on behavioral finance, only a small amount of academic research has been done to analyze the relationship between biases and demographic factors in the context of retail investors in Delhi-NCR. By attempting to fill this gap in the literature, this study contributes to it.
... This is possible through a simplification mechanism called heuristics, where we opt to answer a question that requires cognitive effort and elaborate thinking, with a simpler alternative. This gives way to systemic biases of the human mind that can impact judgement and decision making (Ehrlinger et al., 2016;Murata et al., 2015;Tversky and Kahneman, 1974) ...
... Each GPT-4o response was run against 9 lists, containing keywords associated with each cognitive bias. The keywords were extracted from prominent literature resources that either introduced, or made significant contribution to our understanding of these cognitive heuristics (McDermott et al., 2008;Moro, 2009;Schade et al., 2004;Sun et al., 2020;Tversky and Kahneman, 1974;Yechiam, 2019). This was done in order to examine whether GPT-4o recognized or addressed any of the prompts in its responses. ...
We investigate whether biases inherent in human cognition, such as loss aversion, framing effects, and conjunction fallacy, manifest in how GPT-4o judges and makes decisions in probabilistic scenarios. By conducting 1350 experiments across nine cognitive biases and analyzing the responses for statistical versus heuristic reasoning, we demonstrate GPT-4o's contradicting approach while responding to prompts with similar underlying probability notations. Our findings also reveal mixed performances with the AI demonstrating both human-like heuristic errors and statistically sound decisions, even as it goes through identical iterations of the same prompt.
... El término creencia se entiende como "entendimientos, premisas o proposiciones psicológicas sobre el mundo, que se cree que son verdaderas" (Philipp, 2007, p. 259). Estas creencias generalmente se expresan en afirmaciones como "Creo que...", "es probable que...", "es poco probable que..." (Tversky y Kahneman, 1974, p. 1124. De acuerdo con Thurm y Barzel (2022) los estudios sobre creencias suelen ser muy generales, requiriéndose análisis detallados que den cuenta de cómo las creencias pueden estar relacionadas entre sí en sus diferentes dimensiones. ...
Introducción: En esta investigación se exploran y comparan las creencias inferidas por los investigadores y las auto percibidas en un grupo de profesores de matemáticas en relación con la matemática, su enseñanza y el uso de tecnología digital. Metodología: Es un estudio cualitativo, de caso, que utiliza narrativas y la comparación. Resultados: En la comparación entre las creencias inferidas por los investigadores respecto a las autopercibidas por los docentes se encontró que la creencia más consistente fue la creencia post-dominio, relativa a que los alumnos primero deben dominar los conceptos matemáticos antes de utilizar tecnología digital al estudiar matemáticas en clase. Discusión: Se argumenta que las caraterísticas de los docentes participantes podría explicar los resultados al haberse formado en el modelo tradicional a lápiz y papel. Conclusiones: La utilización de narrativas y la comparación permitió identificar creencias fuertemente arraigadas en los docentes y el conocer otras creencias que no se detectaron en las narrativas.
... Despite their strengths, disaggregation methods have been subject to critique regarding their consistency and robustness, as they may sometimes struggle to fully address the inconsistencies and biases inherent in human judgment. Behavioral decision theory suggests that decision-makers often exhibit inconsistencies in their judgment and are influenced by various cognitive biases (Tversky and Kahneman, 1974). Disaggregation methods, which typically involve only a single round of comparisons, are particularly vulnerable to these inconsistencies. ...
Preference disaggregation methods in Multi-Criteria Decision-Making (MCDM) often encounter challenges related to inconsistency and cognitive biases when deriving a value function from experts' holistic preferences. This paper introduces the Best-Worst Disaggregation (BWD) method, a novel approach that integrates the principles of the Best-Worst Method (BWM) into the disaggregation framework to enhance the consistency and reliability of derived preference models. BWD employs the "consider-the-opposite" strategy from BWM, allowing experts to provide two opposite pairwise comparison vectors of alternatives. This approach reduces cognitive load and mitigates anchoring bias, possibly leading to more reliable criteria weights and attribute value functions. An optimization model is formulated to determine the most suitable additive value function to the preferences expressed by an expert. The method also incorporates a consistency analysis to quantify and improve the reliability of the judgments. Additionally, BWD is extended to handle interval-valued preferences, enhancing its applicability in situations with uncertainty or imprecise information. We also developed an approach to identify a reference set, which is used for pairwise comparisons to elicit the value functions and weights. A case study in logistics performance evaluation demonstrates the practicality and effectiveness of BWD, showing that it produces reliable rankings aligned closely with experts' preferences.
... According to Krislov (1974), bureaucrats like to advocate for the needs and rights of people with similar backgrounds because they understand them better due to shared norms and values. Since public policy decisions are frequently made under uncertain conditions, some irrational heuristic decisions are made (Tversky and Kahneman, 1973;Fiske and Taylor, 1991;Glaser, Spencer, and Charbonnea, 2014). According to Fiske and Taylor (1991) and Glaser et al. (2014), stereotypebased judgments are a common heuristic that infl uences policy outcomes. ...
This paper uses empirical studies and case studies to examine the role of ethnicity and tribalism in the Nigerian public bureaucracy. The paper is anchored on social dominance theory and bureaucratic theory. The paper, based on a triangulation approach (content analysis combined with a survey), reveals that tribal affiliation, followed by religion, is the topmost factor influencing the composition and management of the Nigerian federal bureaucracy. The study reveals that ethnic and tribal ties between different ethnic groups have a significant impact on bureaucratic processes and administrative functions such as recruitment and appointments. The study reveals that bureaucrats in Nigeria use administrative discretion to alter public policies and influence public values in favor of their kinship. This causes public bureaucracy to become irrational, unethical, divisive, parochial, and unmeritorious as ethnic and racial prejudices and biases take precedence in the composition and management of the public sector. The paper recommends inclusive governance and must redesign the institutional structure by creating an overarching level of board management to oversee the affairs of the Federal Character Commission (FCC) to reduce its apparent politicization or skewness that contradicts the original tenets upon which it was founded.
... For example, according to the Abstract-Specific Hypothesis (Cummins et al., 2002;Davern et al., 2007;Schwarz & Strack, 1999), individuals do not systematically consider all facets of their lives before responding to abstract or global questions about their lives (e.g., "How content are you with your life as a whole?"). Rather, they are constrained by cognitive shortcuts (Tversky & Kahneman, 1974) and current moods (Schwarz & Strack, 1991) in making rapid evaluations. However, when questions are more specific, people typically become more sensible and attentive to the actual domains in focus and depend much less on cognitive shortcuts and heuristics. ...
This study explores how being satisfied with one’s life as an entrepreneur is a crucial ethical and psychological outcome of early volition and, subsequently, a vital resource in the development of a richer eudaimonic experience from entrepreneurship. We develop and test our predictions based on two independent datasets: American and Swedish business owners and early stage entrepreneurs. We argue and demonstrate that satisfaction with life as an entrepreneur conveys a distinct state of entrepreneurial well-being and constitutes a crucial self-evaluation which mediates the effects of early volition in entrepreneurship on long-term eudaimonia. We contribute to the emerging conversations on entrepreneurship, business ethics, and well-being.
... The relative strengths of humans and algorithms. Our work is motivated by large body of literature which studies the relative strengths of human judgment and algorithmic decision making (Cowgill, 2018;Dawes et al., 1989;Grove et al., 2000;Kuncel et al., 2013b) or identifies behavioral biases in decision making (Tversky and Kahneman, 1974;Camerer and Johnson, 1991;Arnold et al., 2020;Rambachan, 2022). More recent work also studies whether predictive algorithms can improve expert decision making (Kleinberg et al., 2017;Mullainathan and Obermeyer, 2019;Bastani et al., 2021;Agarwal et al., 2023). ...
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically indistinguishable, or "look the same" to any feasible predictive algorithm. We argue that this framing clarifies the problem of human-AI collaboration in prediction and decision tasks, as experts often form judgments by drawing on information which is not encoded in an algorithm's training data. Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side information", and further provides a simple but principled method for selectively incorporating human feedback into algorithmic predictions. We show that this method provably improves the performance of any feasible algorithmic predictor and precisely quantify this improvement. We demonstrate the utility of our framework in a case study of emergency room triage decisions, where we find that although algorithmic risk scores are highly competitive with physicians, there is strong evidence that physician judgments provide signal which could not be replicated by any predictive algorithm. This insight yields a range of natural decision rules which leverage the complementary strengths of human experts and predictive algorithms.
... For example, during a financial downturn, banks that adopt an ERM approach aligned with stakeholder interests may prioritize long-term sustainability over short-term gains, thereby fostering trust and resilience (Kaplan & Mikes, 2012). In addition to these theories, Behavioural Risk Theory provides insights into how cognitive biases influence risk perception and decision-making within organizations (Tversky & Kahneman, 1974). Recognizing these biases is crucial for developing risk-adjusted decisionmaking frameworks that enhance the effectiveness of ERM strategies. ...
This study explores the Enterprise Risk Management (ERM) strategies employed by U.S. commercial banks during times of financial stress, with a focus on the 2008 financial crisis and the COVID-19 pandemic. It investigates how ERM frameworks enable banks to make risk-adjusted decisions and effectively manage credit risk, ensuring both operational resilience and long-term value preservation. The research highlights the critical role of ERM in guiding banks through periods of market volatility, emphasizing the need for a structured approach to risk identification, assessment, and mitigation. Through a detailed analysis, the study examines how commercial banks have adjusted their risk appetite and governance structures in response to economic disruptions, balancing short-term stability with long-term growth objectives. Key ERM components such as capital adequacy, liquidity management, and stress testing are reviewed to demonstrate their effectiveness in safeguarding financial institutions. Additionally, the research discusses the importance of leadership and governance in enhancing risk oversight and fostering a culture of risk awareness across banking operations. The findings offer valuable lessons for financial institutions on how to navigate future financial stresses by leveraging robust ERM frameworks. By examining these strategies, the paper provides insights into the adaptability and resilience of U.S. commercial banks in maintaining financial stability and shareholder value in the face of uncertainty.. Keywords: Enterprise Risk Management (ERM), Financial Stress, Credit Risk Management, Operational Resilience, U.S. Commercial Banks, Governance Structures.
... This leaves room for inconsistency in model outputs, especially when human judgments are nuanced and subjective. Ambiguity in decision-making occurs when outcomes are equally favorable or unfavorable, making individuals rely on heuristics and bias to make final decisions [Tversky and Kahneman, 1974].LLMs leverage large datasets to learn patterns and handle tasks involving ambiguous inputs. Brown et al. [2020] highlight that while these models generate relevant text, they often rely on surface-level patterns rather than comprehending deeper semantics. ...
Language models often misinterpret human intentions due to their handling of ambiguity, a limitation well-recognized in NLP research. While morally clear scenarios are more discernible to LLMs, greater difficulty is encountered in morally ambiguous contexts. In this investigation, we explored LLM calibration to show that human and LLM judgments are poorly aligned in such scenarios. We used two curated datasets from the Scruples project for evaluation: DILEMMAS, which involves pairs of distinct moral scenarios to assess the model's ability to compare and contrast ethical situations, and ANECDOTES, which presents individual narratives to evaluate the model's skill in drawing out details, interpreting, and analyzing distinct moral scenarios. Model answer probabilities were extracted for all possible choices and compared with human annotations to benchmark the alignment of three models: Llama-3.1-8b, Zephyr-7b-beta, and Mistral-7b. Significant improvements were observed after fine-tuning, with notable enhancements in both cross-entropy and Dirichlet scores, particularly in the latter. Notably, after fine-tuning, the performance of Mistral-7B-Instruct-v0.3 was on par with GPT-4o. However, the experimental models that were examined were all still outperformed by the BERT and RoBERTa models in terms of cross-entropy scores. Our fine-tuning approach, which improves the model's understanding of text distributions in a text-to-text format, effectively enhances performance and alignment in complex decision-making contexts, underscoring the need for further research to refine ethical reasoning techniques and capture human judgment nuances.
... A marvel of Danny Kahneman and Amos Tversky's epic research programs, heuristics and biases (Tversky & Kahneman, 1974) and prospect theory (Kahneman & Tversky, 1979), is that their landmark demonstration studies rarely tested their auxiliary assumptions. Rather, those assumptions were accepted on the basis of their face validity. ...
Interpreting any decision requires making auxiliary assumptions regarding how the decision makers viewed their options and related them to their lives. Theories constrain those assumptions. The more general the theory, the fewer constraints it imposes and the more assumptions must be made in any application. Like the rational actor models that they challenged, Daniel Kahneman and Amos Tversky’s heuristics-and-biases and prospect theory research programs were general theories, with broad application. One of the many marvels of their landmark studies is that they rarely test their auxiliary assumptions. Rather, readers were trusted to agree about how people interpret the tasks (e.g., select anchors in studies of that heuristic). Subsequent studies have often accepted those interpretations in order to examine boundary conditions (e.g., extreme anchors). Applying the theories to naturally occurring tasks requires making additional auxiliary assumptions. This article illustrates three ways to evaluate those assumptions: direct assessment, systematic manipulation, and archival analysis. It concludes with proposals for loosely coordinated evaluation of shared and contested assumptions.
... Research in human-computer interaction and cognitive psychology has shown that factors such as individual differences, task framing, and cognitive biases can significantly influence human judgments of AI systems [20,14,6]. For example, the anchoring effect [42] and the halo effect [33] can lead to over-or under-estimation of LLM performance based on initial impressions or salient features, while the confirmation bias [32] can cause evaluators to seek out information that supports their preconceptions. ...
This paper explores optimal architectures for evaluating the outputs of large language models (LLMs) using LLMs themselves. We propose a novel framework that interprets LLMs as advocates within an ensemble of interacting agents, allowing them to defend their answers and reach conclusions through a judge and jury system. This approach offers a more dynamic and comprehensive evaluation process compared to traditional human-based assessments or automated metrics. We discuss the motivation behind this framework, its key components, and comparative advantages. We also present a probabilistic model to evaluate the error reduction achieved by iterative advocate systems. Finally, we outline experiments to validate the effectiveness of multi-advocate architectures and discuss future research directions.
... Par ailleurs,[?] l'importance de la qualité de l'information dans la prise de décision est essentielle. Les campagnes de sensibilisation peuvent parfois échouer à surmonter les biais cognitifs ou à atteindre des publics cibles spécifiques, surtout si les informations ne sont pas perçues comme pertinentes ou urgentes [40]. ...
Ce manuel propose une vue d'ensemble des principales théories économiques appliquées aux questions environnementales. Il aborde les visions classiques de la nature comme res-source, la théorie des externalités, ainsi que les critiques de la "tragédie des communs" par Hardin et Ostrom. Les concepts d'économie comportementale, tels que le biais du statu quo et le nudging, sont examinés dans le cadre de la prise de décision environnementale. Le manuel traite également de la justice environnementale, en se concentrant sur la fracture Nord-Sud et ses causes historiques et structurelles. Enfin, il explore la transition vers une économie durable, en abordant la croissance verte, la post-croissance et les défis liés au changement climatique.
... Rationales play a crucial role in human reasoning and its accuracy (Rips, 1994;Mercier and Sperber, 2011). In reasoning problems, having accurate rationales often correlates with accurate outcomes (Tversky et al., 1982;Davis, 1984). This importance of rationales extends to Large Language Models (LLMs) as well. ...
The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are frequently left implicit (unstated). To address this challenge, we introduce RATIONALYST, a model for process-supervision of reasoning based on pre-training on a vast collection of rationale annotations extracted from unlabeled data. We extract 79k rationales from web-scale unlabelled dataset (the Pile) and a combination of reasoning datasets with minimal human intervention. This web-scale pre-training for reasoning allows RATIONALYST to consistently generalize across diverse reasoning tasks, including mathematical, commonsense, scientific, and logical reasoning. Fine-tuned from LLaMa-3-8B, RATIONALYST improves the accuracy of reasoning by an average of 3.9% on 7 representative reasoning benchmarks. It also demonstrates superior performance compared to significantly larger verifiers like GPT-4 and similarly sized models fine-tuned on matching training sets.
... • Cognitive biases in decision-making might unintentionally cause weaknesses in cybersecurity. Heuristics and biases may lead people to deviate from rational decision-making, and Tversky and Kahneman's research [16] provided a framework for analyzing these biases. Egelman et al. [17] stated that it is essential to be knowledgeable of these intellectual errors to design security techniques that realize and mitigate specific biases. ...
This chapter investigates the enigmatic relationship between human factors and cybersecurity (CSec), focusing on the importance of actions, perception, and psychology in information security measures. The human-centered computer security technique focuses on understanding user actions, cognitive preferences, and the exploitability of social engineering. We research user-centered security system design to improve security while maintaining user-friendly interfaces. This chapter devotes significant effort to exploring human errors in computer security, including classification and mitigation techniques. A comparison of purposeful and incident risks is presented, and recommendations for recognizing and avoiding insider threats are provided. This essay goes into the complex topic of insider threats. Artificial intelligence, automation, and trusting automated security solutions are being studied in the ever-changing context of human-machine collaboration in cyber defense. Involving the company culture, three main attributes contribute to computer security: leadership, employee involvement, and responsibility. The chapter asserts that a human-centric methodology to computer security ought to link security objectives to business objectives and incorporate security in all business operations. In this chapter, we elaborate on the main human elements in cybersecurity to assist in the formation of powerful and adaptable CSec techniques.
... To achieve this, humans have developed a range of mechanisms, including satisficing, cognitive biases, habit formation, and simplification strategies [60,61]. In behavioural economics, high cognitive load is often mitigated by "heuristics" or mental shortcuts [62]. ChatGPT can be viewed as an external heuristic, providing quick solutions that save time and effort. ...
[PLEASE NOTE, Old title of this paper was: ChatGPT Addiction: From Support to Dependence in AI Large Language Models]
The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models (LLMs). This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.
... Deriving from experimental findings, these 'anomalies' and 'supposedly irrelevant factors' (Thaler, 2016a(Thaler, , p. 1594 showed consistent and systematic deviations from the homo economicus' rationality (Tversky & Kahneman, 1974;Kahneman & Tversky, 1979;Tversky & Kahneman, 1981;Thaler, 2000; see also Thaler & Sunstein, 2008;Kahneman, 2011 Although it arguably reformed the economics' representative actor, the analytical framework of BE is not original if compared to the NE' conceptual apparatus (Gigerenzer, 2015a). First, it is still founded upon standard utility theory (see Moscati, 2019, pp. ...
Economics is typically a quantitative science, which exclusively relies on mathematical techniques, statistical analysis, experimental work, and neglects qualitative evidence, data, and research methods. Although economic methodology scholars have outlined this unbalance, and a few studies pursued qualitative economic research in the past, these are rather the exception to the rule. However, most social sciences and adjacent disciplines do adopt qualitative methodologies when tackling economic phenomena, issues, and topics. Drawing upon the history of economic thought and the philosophy of the social sciences, this dissertation asks why economists do not rely on qualitative inquiry, how they could implement qualitative research, and in what subject domains. In doing so, it indeed (1) unveils the potential contribution of qualitative methods to both economic theory and policy, (2) highlights the role of sociocultural factors over behavioural elements in economic analysis, and (3) suggests the need for an ontological, epistemological, and axiological shift towards ‘qualitative economics’.
... This well-documented behavioral pattern (Rosch, 1975) reflects a natural preference for simple, easy-to-remember figures in human decision-making, leading genuine traders to favor round execution prices and trade sizes such as multiples of 10. Such behavior is driven by psychological factors and cognitive biases, such as anchoring to a reference point (Tversky and Kahneman, 1974), as well as by market standards and conventions. For instance, institutional investors often trade in standardized sizes and round lots to simplify trade execution, align with portfolio structures, and minimize transaction costs (Grossman et al., 1997). ...
This article investigates wash trading as a crypto-market-wide phenomenon that affects exchange integrity and the accuracy of liquidity claims. We examine four main cryptocurrencies using a dataset spanning November 15, 2020, to January 31, 2022. We employ two detection approaches to assess the extent of wash trading: the roundness of trade sizes and Benford's Law. We examine over 40 different explanatory variables, including blockchain and crypto measures and financial and macroeconomic factors. Variable selection is conducted using a robust combination of Variance Inflation Factor and Bayesian Model Averaging. Our findings show that market volatility, exchange flows, and public attention all have a major influence on wash trading, as exchanges may use volatile conditions to engage in manipulative behaviors. Models in our study offer insights helpful for regulators and market participants to detect and mitigate such practices, thereby enhancing market integrity and investor confidence.
... Apart from the first day of listing, these stocks have lower long-term abnormal returns and larger long-term declines [4]. On the other hand, the preference or aversion to specific numbers may lead to biases in decision-making, ignoring other more important factors [5]. In some cases, due to the preference for certain numbers, decision-makers may miss better options, increasing opportunity costs [6]. ...
This study demonstrates the impact of special anniversaries and numerical worship on the risk-taking behavior of listed companies in China's traditional industries. We focus on the widespread beliefs in multiples of 5 anniversaries (such as the 5th, 10th, and 20th anniversaries) and the numbers 6 and 8 in Chinese society. Analyzing data from real estate companies listed on the Shanghai Stock Exchange from 2007 to 2019, we find that stock price volatility slightly decreases during special anniversaries but not significantly, suggesting limited influence of superstitious beliefs on corporate decision-making. Further analysis shows that corporate risk-taking behavior does not significantly change during special anniversaries, highlighting the role of market discipline and quantitative analysis in curbing irrational behavior. While some studies suggest that numerical superstition may affect financial decisions of individuals and small businesses, our findings indicate that such influences are mitigated in large and complex organizations. This study provides insights for risk management of listed companies during special anniversaries and recommends that investors focus more on fundamental data and real-time information, avoiding superstition and irrational numerical worship.
... Academic researchers have shown considerable interest in decision making under uncertainty for decades, roughly starting with Tversky and Kahneman [29]. Meyer [16] found that temporal variability increases the cost of information gathering, which suggests that variability comes with a premium. ...
Enabled by modern interaction-logging technologies, managers increasingly have access to outcome data from customer interactions. We consider the direct marketing targeting problem in situations where 1) the customer’s outcomes vary randomly and independently from occasion to occasion, 2) the firm has measures of the outcomes experienced by each customer on each occasion, and 3) the firm can customize marketing according to these measures and the customer’s behaviors. A primary contribution of this paper is a framework and methodology to use data on customer outcome data to model a customer’s evolving beliefs related to the firm and how these beliefs combine with marketing to influence purchase behavior. Thereby, this paper allows the manager to assess the marketing response of a customer with any specific outcome and behavior history, which in turn can be used to decide which customers to target for marketing. This research develops a novel, tractable way to estimate and introduce flexible heterogeneity distributions into Bayesian dynamic discrete choice learning models on large datasets. The model is estimated using data from the casino industry, an industry which generates more than $60 billion in U.S. revenues but has surprisingly little academic, econometric research. The counterfactuals suggest that casino profitability can increase substantially when marketing incorporates gamblers’ beliefs and past outcome sequences into the targeting decision.
... Executive functions and decision-making are cognitive processes that enable individuals to plan, focus attention, remember instructions, and successfully manage multiple tasks. Heuristics, biases, and risk assessments also influence them (Tversky & Kahneman, 1974;Beck, 1976;Baddeley,1992;Diamond, 2013). ...
Psychology, the study of human thought, emotion, and behavior, plays a crucial role in education. It includes subfields such as developmental, social, clinical, and cognitive psychology, each offering unique insights into the human psyche. This science primarily focuses on mental processes such as perception, attention, memory, language, and problem-solving, providing an understanding of the mechanisms that drive human cognition and behavior. Psychology is intricately linked to education, as it helps optimize learning environments and instructional strategies. By delving into psychological principles that underpin effective teaching and learning, education can improve itself, addressing topics such as motivation, assessment, and instructional design. These sciences also explore how culture, environment, and social norms influence individual and collective behavior. Interdisciplinary associations between psychology and education facilitate the translation of research findings into practical applications, informing educational practices, policy decisions, and interventions to promote psychological well-being and cognitive development. These interventions can benefit the educational system, teachers, managers, and students, improving educational outcomes. The study, conducted using a qualitative method between April and August 2024, was based on sixty-five references and evaluated the role of cognition as a mediator between psychological patterns and individual behaviors. It primarily discussed the impact on the educational system and the success that educational institutions and their administrators should ideally consider.
... Such heuristics, or cognitive biases, are seen as a result of the limitations in human information processing (Shafir & Tversky, 1992). In the context of choice overload, these heuristics become even more prominent as coping mechanisms against information overload (Kahneman & Tversky, 1974). Recommender systems can be viewed as a type of heuristic that simplifies the decision-making process. ...
In the digital era, where choices saturate daily life, the phenomenon of choice overload becomes a significant concern in consumer behavior and psychology. Recommender systems, exemplified by Netflix's sophisticated model, play a transformative role in navigating the vast landscape of digital entertainment. This qualitative study examines the impact of recommender systems on choice overload through 12 semi-structured interviews with Netflix users, revealing the intricate dynamics between personalization algorithms and user decision-making processes. The study is guided by the following research questions: (1) How does the Netflix recommendation system influence users' experiences of choice overload and ease of decision-making? (2) To what extent do users perceive Netflix's recommended content as appealing and diverse, and how reliant are they on these recommendations for content selection? (3) How do user interactions with Netflix's recommendation system, including user feedback, impact variables such as search time, choice effort, and choice satisfaction? The findings reveal a notable absence of explicit user feedback and the presence of choice overload in Netflix users. This is evident in prolonged search times, heightened choice effort, and moderate satisfaction levels, coupled with perceptions of unattractiveness and limited diversity in the recommended content. Negative emotional responses during content selection further underscore the challenges users face on the platform. Paradoxically, this gives rise to a potential “user's dilemma,” as the study exposes a high reliance and trust in recommendation lists. However, this reliance also results in users frequently experiencing frustration and disappointment when recommendations fail to meet expectations. The study provides valuable insights into the nuanced interactions between users and the Netflix platform and offers a foundational framework for ongoing refinement of recommender systems in the ever-evolving landscape of streaming services and emphasizes the need for recommendation lists to strike a delicate balance between effective guidance and user exploration.
... Активное развитие данное научное направление получило после выхода в свет работ Д. Канемана и А. Тверски, которые доказали, что люди демонстрируют отклонения от классического представления о рациональности [11]. Д. Канеман получил в 2002 году Нобелевскую премию 2002 года за «включение данных психологических исследований в экономическую науку, в особенности тех, что касаются суждений человека и принятия решения в ситуации неопределенности». ...
В условиях высочайшей конкуренции на рынке телекоммуникационных услуг быстрое и качественное их предложение потребителям, определение сегментов, а также их оценка являются важнейшими показателями эффективности и устойчивого развития компании. При этом качество донесения предложений является ключевым фактором. В последние годы все больше компаний стали использовать лид-скоринг как наиболее эффективный метод «взращивания потребностей». Данный метод позволяет развивать и управлять поведением потребителей. В телекоммуникационной отрасли модель лид-скоринга используется наиболее масштабно. В статье анализируется эффективность применения рассматриваемой модель для развития поведения потребителей с дальнейшей апробацией на практических кейсах. По итогам использования предложенной модели лид-менеджмента, проанализированы основные подходы практического применения и взращивания сущности лидов, их видов, сформированы алгоритмы последовательных действий по управлению и движению клиентского пути, управлению поведением потребителя. Данные технологии приводят к развитию и глубокой и детальной проработки базы клиентов в телекоммуникационных компаниях.
Despite the importance of reliable, valid, and equitable identification of students with specific learning disabilities (SLD), research has highlighted the potential for school psychologists’ personal characteristics to influence the identification decision. No studies to date have examined the broad range of individual characteristics and their potential to affect the SLD identification decision or individuals’ confidence in their decision. We conducted a study with 264 full-time practicing school psychologists to isolate the role of individual characteristics, including beliefs about SLD and decision-making style, on the SLD identification decision and confidence in the decision. Results indicated that, all else equal, a variety of individual characteristics emerged as significant predictors of the identification decision and confidence. Implications for research and practice are discussed, as well as suggestions for future research.
Eyewitness confidence is typically communicated verbally (e.g., certain). Interpretations for verbal confidence statements are variable which could affect decision-making. I explored the extent to which confidence format (i.e., verbal vs. numeric) influences juror decision-making. Participants (N = 468) read a trial vignette in which an expert witness draws attention to the eyewitness's confidence in their suspect identification (uncertain, or certain, or 28%, or 88%). Participants rated the expert's recommendation, rendered a conviction decision, and provided confidence in their decision on a scale of 0 = Not at all confident to 10 = Completely confident. Mock-jurors were more likely to convict when confidence was high. However, mock-jurors were also more likely to convict when confidence was presented verbally. This effect may be attributable to the ambiguity verbal confidence affords, allowing for interpretation in congruence with the eyewitness's identification decision.
Nonpregnant and pregnant women who present with acute pelvic pain can pose a diagnostic challenge in the emergency setting. The clinical presentation is often nonspecific, and the differential diagnosis may be very broad. These symptoms are often indications for pelvic US, which is the primary imaging modality when an obstetric or gynecologic cause is suspected. Interpretation of pelvic US may be challenging and a source of confusion and misinterpretation for radiologists. Additionally, cognitive biases in imaging interpretation can contribute to diagnostic errors. Cognitive biases represent systematic errors due to failure of the mental shortcuts that the brain subconsciously uses to produce quicker judgments. There are multiple different types of cognitive biases, all of which may lead to perceptual and interpretive errors. Familiarity with common and uncommon pelvic US findings in the setting of pelvic pain is imperative to assist with prompt and accurate diagnosis. Awareness of potential biases when interpreting pelvic US findings further helps hone the interpretation. The authors illustrate the imaging findings in several peer learning cases of nonpregnant and first-trimester pregnant patients who presented with acute pelvic pain in the emergency setting. Several nonobstetric and nongynecologic causes of acute pelvic pain are included for which pelvic US was the first imaging modality used in diagnosis. Diagnostic errors and cognitive biases in interpretation related to these cases are highlighted. The radiologist's awareness of potential cognitive biases in image interpretation may help to refine the differential diagnosis and mitigate errors. ©RSNA, 2025 Supplemental material is available for this article.
Метою статті є розробка методики побудови ймовірнісної графічної моделі навігаційної оцінки безпеки плавання під час керування суднами в обмежених умовах як на морських районах, так і на внутрішніх водних шляхах. Поставлена мета досягається шляхом аналізу особливостей математичного апарату байєсівської мережі та нотації схеми функціональної цілісності; визначення основних подій для побудови графу байєсівської мережі для навігаційного оцінювання безпеки плавання та відповідних математичних виразів. Встановлено, що, незважаючи на значні зусилля удосконалення організаційно-технічних заходів для забезпечення безпеки судноплавства, рівень навігаційних аварій залишається високим. Існує протиріччя між ростом вимог до безпеки судноплавства та реальним станом речей. Одним з головних факторів, що перешкоджає вирішенню питань, пов'язаних з навігаційною безпекою плавання, є відсутність формальних методів для опису процесу судноплавства. Це пояснюється труднощами у моделюванні поведінки складних організаційно-технічних систем судноплавства в імовірнісному просторі станів, оскільки вплив на процес судноплавства є стохастичним, та обмеженнями теоретичного розуміння у цій області. У результаті виникають складнощі при створенні моделей навігаційних ризиків, що можуть бути використані для комплексної оцінки навігаційної безпеки у рамках складних організаційно-технічних систем судноплавства. Розв'язання цих проблем дозволило б застосовувати формальні методи для управління навігаційними ризиками, оцінки значущості компонентів системи та їхнього впливу на загальну величину ризиків. Найбільш суттєвим результатом є методика побудови ймовірнісної графічної моделі навігаційної оцінки безпеки плавання під час керування суднами в обмежених умовах як на морських районах, так і на внутрішніх водних шляхах з використанням математичного апарату теорії байєсівських мереж і нотації схеми функціональної цілісності. Дослідження показали, що можна використовувати основні риси байєсівських мереж та логіко-імовірнісного методу для математичного опису організаційно-технічних систем управління суднами. Для опису складних організаційно-технічних систем судноводіння можна застосовувати простір імовірнісних подій та аксіоматику Колмогорова разом з правилами булевої алгебри логіки. Цей підхід дозволяє враховувати особливості та цілісність системи «судно-судноводій-середовище» при оцінюванні навігаційних ризиків. Ключові слова: судноплавство, навігаційна оцінка, безпека плавання, ймовірнісна графічна модель, байєсівська мережа, схема функціональної цілісності.
Background
Low-value care is unnecessary care that contributes to inefficient use of health resources and constitutes a considerable proportion of healthcare expenditures worldwide. Factors contributing to patients’ demand for low-value care have often been overlooked and are dispersed in the literature. Therefore, the current study aimed to systematically summarize factors associated with patients’ demand for low-value care.
Methods
In this scoping review, scientific articles were identified based on a search query conducted in Embase and Scopus. We identified articles using search terms related to low-value care and demand-related factors, published in peer-reviewed journals, and written in English or Dutch. The titles, abstracts, results, and conclusions were inspected to only include articles that were deemed relevant for this topic. From these articles we extracted text fragments that contained factors associated with patients’ demand for low-value care. Hereafter, a thematic analysis was applied to openly, axially, and selectively code textual fragments to identify themes within the data.
Results
Forty-seven articles were included in this review. We identified eight core themes associated with patients’ demand for low-value care: cognitive biases, emotions, preferences and expectations, knowledge-related factors, socio-cultural factors, biomedical and care-related factors, economic factors, and factors related to the interaction with the healthcare provider. Within these core themes, thirty-three subthemes were identified. For example, risk aversion and anticipated regret aversion are sub-themes of cognitive biases, while consumerism and present and future income effects are sub-themes of economic factors.
Conclusions
Through this review we provide a systematic overview of factors associated with the demand for low-value care. We found that patients’ demand for low-value care could relate to a multitude of factors that were clustered into eight core themes and thirty-three subthemes. To understand the demand for low-value care from the patient’s perspective in greater detail, future research should focus on the interaction between and importance of these factors in different care contexts.
Bu çalışma, uluslararası ilişkilerde yaygın olarak kullanılan Rasyonel Aktör Modeli'nin, karar vericilerin bilişsel ve psikolojik sınırlamalarını hesaba katmaması nedeniyle uluslararası ilişkileri açıklamakta sınırlı kaldığını ortaya koymayı amaçlamaktadır. Yöntem olarak bilişsel ve psikolojik literatür üzerinden Rasyonel Aktör Modeli'nin temel varsayımları eleştirilmiş; bilişsel önyargılar, motivasyonel önyargılar, algılar, duygular ve inanç sistemlerinin karar verme sürecini nasıl etkilediği incelenmiştir. Bulgular, bireysel özelliklerin karar alma süreçlerinde önemli bir rol oynadığını ve tam rasyonelliğin mümkün olmadığını göstermektedir. Sonuç olarak, uluslararası ilişkilerin analizinde bireysel ve psikolojik faktörlerin dikkate alınmasının, Rasyonel Aktör Modeli'nin dayattığı rasyonel taslaktan daha gerçekçi ve faydalı açıklamalar sunacağı önerilmektedir. Bu bağlamda, bireye odaklanan Operasyonel Kod Analizi ve Verbs In Context System gibi yöntemlerin kullanılması tavsiye edilmekte ve bu yöntemler çalışmada tanıtılmaktadır.
The use of artificial intelligence (AI) in operational decision‐making is growing, but individuals can display algorithm aversion, preventing adherence to AI system recommendations—even when the system outperforms human decision‐makers. Understanding why such algorithm aversion occurs and how to reduce it is important to ensure AI is fully leveraged. While the ability to seek an explanation from an AI may be a promising approach to mitigate this aversion, there is conflicting evidence on their benefits. Based on several behavioral theories, including Bayesian choice, loss aversion, and sunk cost avoidance, we hypothesize that if a recommendation is perceived as an anomalous loss, it will decrease recommendation adherence; however, the effect will be mediated by explanations and differ depending on whether the advisor providing the recommendation and explanation is a human or an AI. We conducted a survey‐based lab experiment set in the online rental market space and found that presenting a recommendation as a loss anomaly significantly reduces adherence compared to presenting it as a gain, however, this negative effect can be dampened if the advisor is an AI. We find explanation‐seeking has a limited impact on adherence, even after considering the influence of the advisor; we discuss the managerial and theoretical implications of these findings.
One of the first social categories learned from early childhood is gender stereotypes and the traditional perception of masculine–feminine traits encircling strong cultural and societal norms gets internalized around what real men and women should be like. Even though there is no single definition for all men and all women, the social roles, behaviors, and meanings prescribed for men and women based on sex in any society at any time, across cultures diverge, however, universally they have been institutionally organized, structured, elaborated, and experienced through interactions and expressed on the axis of identity including class, race, ethnicity, age, and sexuality. If so, then should DSD be considered an abnormality in anatomy needing correction or a social acceptance of human diversity, with the idea that differences are not equal to disease? Empirical data, mainly retrospective observations and case studies, highlighted DSDs as rare (exotic), focusing on diagnostic improvement and corrective measures (phenotypic sex), however, this congenital condition is not suggestive of a disorder or abnormality and does not imply a person’s identity and therefore this chapter explores the psychological management and normalized inclusion of adolescents with DSD on the juncture of decision-making with regard to stabilizing their identity, positive acceptance of body variance, a better quality of life and sexual health.
This paper takes a closer look at Niccolo Machiavelli's The Prince and seeks to offer a fresh perspective by focusing on specific facets, particularly those related to decision-making. The analysis will connect Machiavelli's ideas with those of contemporary scholars in this field, specifically from the psychological framework of prospect theory (and its applied form, nudging). The aim is to identify elements of The Prince that possess universal qualities and which, in turn, may help explain the enduring relevance and usefulness of Machiavelli's work.
This study has the purpose to analyze the implications of institutional cognitive, ethos, beliefs, tradition, and emotional dimensions. It is assumed that cognition, values, beliefs, and tradition are the background of institutional paradigms development that facilitates and constraints operations, actions, and activities. The method used is supported by the meta-analysis, descriptive and reflective based on the conceptual, theoretical, and empirical review of the literature. It is concluded that institutions are shaped by paradigms formed by cognitive, ethos, beliefs, tradition, and emotional dimensions.
The central argument of this chapter is that society can be shaped to lift individuals to fulfill their potential, if those who constitute Society (Us) aspire to that objective and act accordingly. Technology can help us to make that happen. It cannot do it for us. To serve the Common Good technology must be designed, delivered and used with this goal in (the human) mind. We must be aware of that feedback-loop of human aspirations and artificial intelligence in order to influence it systematically. The aim of the chapter is to show certain patterns of technology affecting society, not to draw an inventory or provide a detailed technical discussion about computers, or artificial intelligence.
Two convergent and mutually influencing dynamics are briefly analyzed: 1) the influence of values and aspirations on the impact of technology; and 2) the influence of technology on the attitude and action of users. Both venues assess how hardware and software could serve everyone and the communities they evolve in, to live a meaningful happy life; if they are designed and delivered with that intention.
We develop a theory of trust in lending that distinguishes between reputation and trust. Banks emerge as more trusted lenders than non-banks. We show that trust severs the link between performance and the cost and availability of financing for lenders, but trust can be lost and is difficult to regain. Banks survive an erosion of trust better than non-banks. Banks' trust advantage arises from the lower cost of funding due to insured deposits and an endogenous belief revision channel that complements the effect of the funding cost advantage. The results have novel policy relevance for deposit insurance scope.
People have erroneous intuitions about the laws of chance. In particular, they regard a sample randomly drawn from a population as highly representative, that is, similar to the population in all essential characteristics.
Subjects were requested to choose between gambles, where the outcome of one gamble depended on a single elementary event, and the other depended on an event compounded of a series of such elementary events. The data supported the hypothesis that the subjective probability of a compound event is systematically biased in the direction of the probability of its components resulting in overestimation of conjunctive events and underestimation of disjunctive events. Studies pertaining to this topic are discussed.
Considers that intuitive predictions follow a judgmental heuristic-representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness was supported in a series of studies with both naive and sophisticated university students (N = 871). The ranking of outcomes by likelihood coincided with the ranking by representativeness, and Ss erroneously predicted rare events and extreme values if these happened to be representative. The experience of unjustified confidence in predictions and the prevalence of fallacious intuitions concerning statistical regression are traced to the representativeness heuristic.
REPORTS 6 STUDIES USING LABORATORY REPLICAS OF THE SITUATION IN WHICH A BEGINNING CLINICIAN OBSERVES THE DIAGNOSTIC TEST PROTOCOLS OF PATIENTS WITH VARIOUS SYMPTOMS IN ORDER TO DISCOVER THE CHARACTERISTICS OF TEST PERFORMANCE THAT DISTINGUISH PATIENTS WITH EACH SYMPTOM. NAIVE UNDERGRADUATES VIEWED A SERIES OF 45 DRAW-A-PERSON TEST DRAWINGS RANDOMLY PAIRED WITH CONTRIVED SYMPTOM STATEMENTS ABOUT THE PATIENTS WHO DREW THEM. SS "REDISCOVERED" THE SAME RELATIONSHIPS BETWEEN DRAWING CHARACTERISTICS AND SYMPTOMS AS CLINICIANS REPORT OBSERVING IN CLINICAL PRACTICE, ALTHOUGH THESE RELATIONSHIPS WERE ABSENT IN THE EXPERIMENTAL MATERIALS. THE REPORTED RELATIONSHIPS CORRESPONDED TO RATED ASSOCIATIVE STRENGTH BETWEEN SYMPTOM AND DRAWING CHARACTERISTIC.
Practicing psychodiagnosticians (N = 32), when surveyed, failed to report observing Wheeler-Rorschach Signs 7 and 8 as accompanying male homosexuality although research evidence indicates that these are valid. They instead reported observing Wheeler Signs 4, 5, 16, 19, and 20, which research literature indicates are invalid. These signs were found to have much stronger rated, verbal associative connections to male homosexuality than the unpopular valid signs. 693 undergraduates (divided among 13 conditions) viewed 30 Rorschach cards on each of which was arbitrarily designated a patient's response and his 2 symptoms. The Ss "rediscovered" the same invalid Rorschach content signs of homosexuality as the clinicians reported, although these relationships were absent in the experimental materials. They did so regardless of the degree to which the clinically valid signs were valid in the contrived task materials.
Studies are reported which show that concrete and abstract words of equal objective frequency (based on available , word counts) are not perceived as being equal. The abstract word has greater perceived frequency than the concrete word. The judged variety of contexts in which a word appears correlates very highly with perceived frequency. The results have relevance to the design of learning studies in which concrete and abstract words are used. and also to the interpretation of such experiments.
This paper explores a heuristic-representativeness-according to which the subjective probability of an event, or a sample, is determined by the degree to which it: (i) is similar in essential characteristics to its parent population; and (ii) reflects the salient features of the process by which it is generated. This heuristic is explicated in a series of empirical examples demonstrating predictable and systematic errors in the evaluation of un- certain events. In particular, since sample size does not represent any property of the population, it is expected to have little or no effect on judgment of likelihood. This prediction is confirmed in studies showing that subjective sampling distributions and posterior probability judgments are determined by the most salient characteristic of the sample (e.g., proportion, mean) without regard to the size of the sample. The present heuristic approach is contrasted with the normative (Bayesian) approach to the analysis of the judgment of uncertainty.
In recent years there have been several hundred studies within the rather narrowly-defined topic of information utilization in judgment and decision making. Much of this work has been accomplished within two basic schools of research, which we have labeled the “regression” and the “Bayesian” approaches. Each has its characteristic tasks and characteristic information that must be processed to accomplish these tasks. For the most part, researchers have tended to work strictly within a single approach and there has been minimal communication between the resultant subgroups of workers. Our objective here is to present a review and comparative analysis of these two approaches. Within each, we examine (a) the models that have been developed for describing and prescribing the use of information in decision making; (b) the major experimental paradigms, including the types of judgment, prediction, and decision tasks and the kinds of information that have been available to the decision maker in these tasks; (c) the key independent variables that have been manipulated in experimental studies; and (d) the major empirical results and conclusions. In comparing these approaches, we seek the answers to two basic questions. First, do the specific models and methods characteristic of different paradigms direct the researcher's attention to certain problems and cause him to neglect others that may be equally important? Second, can a researcher studying a particular substantive problem increase his understanding by employing diverse models and diverse experimental methods?
This paper explores a judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind. In general, availability is correlated with ecological frequency, but it is also affected by other factors. Consequently, the reliance on the availability heuristic leads to systematic biases. Such biases are demonstrated in the judged frequency of classes of words, of combinatorial outcomes, and of repeated events. The phenomenon of illusory correlation is explained as an availability bias. The effects of the availability of incidents and scenarios on subjective probability are discussed.