We favor formal models of heuristics rather than lists of loose dichotomies: a reply to Evans and Over

In their comment on Marewski et al. (good judgments do not require complex cognition, 2009) Evans and Over (heuristic thinking and human intelligence: a commentary on Marewski, Gaissmaier and Gigerenzer, 2009) conjectured that heuristics can often lead to biases and are not error free. This is a most surprising critique. The computational models of heuristics we have tested allow for quantitative predictions of how many errors a given heuristic will make, and we and others have measured the amount of error by analysis, computer simulation, and experiment. This is clear progress over simply giving heuristics labels, such as availability, that do not allow for quantitative comparisons of errors. Evans and Over argue that the reason people rely on heuristics is the accuracy-effort trade-off. However, the comparison between heuristics and more effortful strategies, such as multiple regression, has shown that there are many situations in which a heuristic is more accurate with less effort. Finally, we do not see how the fast and frugal heuristics program could benefit from a dual-process framework unless the dual-process framework is made more precise. Instead, the dual-process framework could benefit if its two “black boxes” (Type 1 and Type 2 processes) were substituted by computational models of both heuristics and other processes

The Illogicality of Stock-Brokers: Psychological Experiments on the Effects of Prior Knowledge and Belief Biases on Logical Reasoning in Stock Trading

BACKGROUND: Explanations for the current worldwide financial crisis are primarily provided by economists and politicians. However, in the present work we focus on the psychological-cognitive factors that most likely affect the thinking of people on the economic stage and thus might also have had an effect on the progression of the crises. One of these factors might be the effect of prior beliefs on reasoning and decision-making. So far, this question has been explored only to a limited extent. METHODS: We report two experiments on logical reasoning competences of nineteen stock-brokers with long-lasting vocational experiences at the stock market. The premises of reasoning problems concerned stock trading and the experiments varied whether or not their conclusions--a proposition which is reached after considering the premises--agreed with the brokers' prior beliefs. Half of the problems had a conclusion that was highly plausible for stock-brokers while the other half had a highly implausible conclusion. RESULTS: The data show a strong belief bias. Stock-brokers were strongly biased by their prior knowledge. Lowest performance was found for inferences in which the problems caused a conflict between logical validity and the experts' belief. In these cases, the stock-brokers tended to make logically invalid inferences rather than give up their existing beliefs. CONCLUSIONS: Our findings support the thesis that cognitive factors have an effect on the decision-making on the financial market. In the present study, stock-brokers were guided more by past experience and existing beliefs than by logical thinking and rational decision-making. They had difficulties to disengage themselves from vastly anchored thinking patterns. However, we believe, that it is wrong to accuse the brokers for their "malfunctions", because such hard-wired cognitive principles are difficult to suppress even if the person is aware of them

Interpretation of evidence in data by untrained medical students: a scenario-based study

<p>Abstract</p> <p>Background</p> <p>To determine which approach to assessment of evidence in data - statistical tests or likelihood ratios - comes closest to the interpretation of evidence by untrained medical students.</p> <p>Methods</p> <p>Empirical study of medical students (N = 842), untrained in statistical inference or in the interpretation of diagnostic tests. They were asked to interpret a hypothetical diagnostic test, presented in four versions that differed in the distributions of test scores in diseased and non-diseased populations. Each student received only one version. The intuitive application of the statistical test approach would lead to rejecting the null hypothesis of no disease in version A, and to accepting the null in version B. Application of the likelihood ratio approach led to opposite conclusions - against the disease in A, and in favour of disease in B. Version C tested the importance of the p-value (A: 0.04 versus C: 0.08) and version D the importance of the likelihood ratio (C: 1/4 versus D: 1/8).</p> <p>Results</p> <p>In version A, 7.5% concluded that the result was in favour of disease (compatible with p value), 43.6% ruled against the disease (compatible with likelihood ratio), and 48.9% were undecided. In version B, 69.0% were in favour of disease (compatible with likelihood ratio), 4.5% against (compatible with p value), and 26.5% undecided. Increasing the p value from 0.04 to 0.08 did not change the results. The change in the likelihood ratio from 1/4 to 1/8 increased the proportion of non-committed responses.</p> <p>Conclusions</p> <p>Most untrained medical students appear to interpret evidence from data in a manner that is compatible with the use of likelihood ratios.</p

Heuristic Theorizing in Software Development: Deriving Design Principles for Smart Glasses-based Systems

Design knowledge on smart glasses-based systems is scarce. Utilizing literature analysis on software development publications, insights from the design and implementation of four smart glasses-based systems and expert interviews, we elicited 16 design principles to provide guidance in the development of future service support systems. Heuristic Theorizing is an abductive Design Science Research method, hitherto far too little known or little noticed, which was applied to conduct the research. We contribute to theory and practice with applicable design principles to support the development of smart glasses-based systems. Phenomena known to have an impact on the adoption of smart glasses are addressed by these design principles

What automaton model captures decision making? A call for finding a behavioral taxonomy of complexity

When investigating bounded rationality, economists favor finite-state automatons - for example the Mealy machine - and state complexity as a model for human decision making over other concepts. Finite-state automatons are a machine model, which are especially suited for (repetitions of) decision problems with limited strategy sets. In this paper, we argue that finite-state automatons do not suffice to capture human decision making when it comes to problems with infinite strategy sets, such as choice rules. To proof our arguments, we apply the concept of Turing machines to choice rules and show that rational choice has minimal complexity if choices are rationalizable, while complexity of rational choice dramatically increases if choices are no longer rationalizable. We conclude that modeling human behavior using space and time complexity best captures human behavior and suggest to introduce a behavioral taxonomy of complexity describing adequate boundaries for human capabilities

On getting inside the judge’s mind

According to the scales of justice, the judge, in an unbiased way and directed by law, attends to all of the available information in a case, weighs it according to its significance, and integrates it to make a decision. By contrast, research suggests that judicial decision-making departs from the cognitive balancing act depicted by the scales of justice. Nevertheless, the research is often dismissed as irrelevant, and the judiciary, legal policy-makers and the public remain largely unconvinced that the status quo needs improving. One potential rebuttal to the scientific findings is that they lack validity because researchers did not study judges making decisions on real cases. Another potential argument is that researchers have not pinpointed the psychological processes of any specific judge because they analyzed data over judges and/or used statistical models lacking in psychological plausibility. We review these two grounds for appeal against the scientific research on judicial decision-making, and note that it appears researchers’ choices of data collection methods and analytic techniques may, indeed, be inappropriate for understanding the phenomena. We offer two remedies from the sphere of decision-making research: collecting data on judicial decision-making using representative design, and analyzing judicial decision data using more psychologically plausible models. Used together, we believe these solutions can help researchers better understand and improve legal decision-making

Recognizing decision-making using eye movement: A case study with children

[EN] The use of visual attention for evaluating consumer behavior has become a relevant field in recent years, allowing researchers to understand the decision-making processes beyond classical self-reports. In our research, we focused on using eye-tracking as a method to understand consumer preferences in children. Twenty-eight subjects with ages between 7 and 12 years participated in the experiment. Participants were involved in two consecutive phases. The initial phase consisted of the visualization of a set of stimuli for decision-making in an eight-position layout called Alternative Forced-choice. Then the subjects were asked to freely analyze the set of stimuli, they needed to choose the best in terms of preference. The sample was randomly divided into two groups balanced by gender. One group visualized a set of icons and the other a set of toys. The final phase was an independent assessment of each stimulus viewed in the initial phase in terms of liking/disliking using a 7-point Likert scale. Sixty-four stimuli were designed for each of the groups. The visual attention was measured using a non-obstructive eye-tracking device. The results revealed two novel insights. Firstly, the time of fixation during the last four visits to each stimulus before the decision-making instant allows us to recognize the icon or toy chosen from the eight alternatives with a 71.2 and 67.2% of accuracy, respectively. The result supports the use of visual attention measurements as an implicit tool to analyze decision-making and preferences in children. Secondly, eye movement and the choice of liking/disliking choice are influenced by stimuli design dimensions. The icon observation results revealed how gender samples have different fixation and different visit times which depend on stimuli design dimension. The toy observations results revealed how the materials determinate the largest amount fixations, also, the visit times were differentiated by gender. This research presents a relevant empirical data to understand the decision-making phenomenon by analyzing eye movement behavior. The presented method can be applied to recognize the choice likelihood between several alternatives. Finally, children's opinions represent an extra difficulty judgment to be determined, and the eye-tracking technique seen as an implicit measure to tackle it.The authors thank Design Deparment of Tecnologico de Monterrey and I3B - Universitat Politecnica de Valencia for their support in the development of this work.Rojas, J.; Marín-Morales, J.; Ausin Azofra, JM.; Contero, M. (2020). Recognizing decision-making using eye movement: A case study with children. Frontiers in Psychology. 11:1-11. https://doi.org/10.3389/fpsyg.2020.570470S11111Arkes, H. R., Gigerenzer, G., & Hertwig, R. (2016). 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On getting inside the judge’s mind

According to the scales of justice, the judge, in an unbiased way and directed by law, attends to all of the available information in a case, weighs it according to its significance, and integrates it to make a decision. By contrast, research suggests that judicial decision-making departs from the cognitive balancing act depicted by the scales of justice. Nevertheless, the research is often dismissed as irrelevant, and the judiciary, legal policy-makers and the public remain largely unconvinced that the status quo needs improving. One potential rebuttal to the scientific findings is that they lack validity because researchers did not study judges making decisions on real cases. Another potential argument is that researchers have not pinpointed the psychological processes of any specific judge because they analyzed data over judges and/or used statistical models lacking in psychological plausibility. We review these two grounds for appeal against the scientific research on judicial decision-making, and note that it appears researchers’ choices of data collection methods and analytic techniques may, indeed, be inappropriate for understanding the phenomena. We offer two remedies from the sphere of decision-making research: collecting data on judicial decision-making using representative design, and analyzing judicial decision data using more psychologically plausible models. Used together, we believe these solutions can help researchers better understand and improve legal decision-making