Heterogeneity of rules in Bayesian reasoning: A toolbox analysis.

How do people infer the Bayesian posterior probability from stated base rate, hit rate, and false alarm rate? This question is not only of theoretical relevance but also of practical relevance in medical and legal settings. We test two competing theoretical views: single-process theories versus toolbox theories. Single-process theories assume that a single process explains people's inferences and have indeed been observed to fit people's inferences well. Examples are Bayes's rule, the representativeness heuristic, and a weighing-and-adding model. Their assumed process homogeneity implies unimodal response distributions. Toolbox theories, in contrast, assume process heterogeneity, implying multimodal response distributions. After analyzing response distributions in studies with laypeople and professionals, we find little support for the single-process theories tested. Using simulations, we find that a single process, the weighing-and-adding model, nevertheless can best fit the aggregate data and, surprisingly, also achieve the best out-of-sample prediction even though it fails to predict any single respondent's inferences. To identify the potential toolbox of rules, we test how well candidate rules predict a set of over 10,000 inferences (culled from the literature) from 4,188 participants and 106 different Bayesian tasks. A toolbox of five non-Bayesian rules plus Bayes's rule captures 64% of inferences. Finally, we validate the Five-Plus toolbox in three experiments that measure response times, self-reports, and strategy use. The most important conclusion from these analyses is that the fitting of single-process theories to aggregate data risks misidentifying the cognitive process. Antidotes to that risk are careful analyses of process and rule heterogeneity across people.

How do narratives relate to heuristics?

Narratives and heuristics are both tools for dealing with uncertainty, intractability, and incommensurability, that is, for all real-world situations outside the domain of Bayesian decision theory. But how do narratives and heuristics relate? I suggest two links: Heuristics select narratives to explain events, and "big" narratives select the heuristics that people live by, to execute their values and moral principles.

We need to think more about how we conduct research.

Research practice is too often shaped by routines rather than reflection. The routine of sampling subjects, but not stimuli, is a case in point, leading to unwarranted generalizations. It likely originated out of administrative rather than scientific concerns. The routine of sampling subjects and testing their averages for significance is reinforced by delusions about its meaningfulness, including the replicability delusion.

Mismatch of corneal specialists' expectations and keratoconus knowledge in general ophthalmologists - a prospective observational study in Switzerland.

BACKGROUND: To assess whether Swiss general ophthalmologists have the minimal keratoconus knowledge that corneal specialists would expect them to have. METHODS: Corneal specialists defined "minimal keratoconus knowledge" (MKK) with respect to definition, risk factors, symptoms and possible treatment options of keratoconus. A telephone interview survey was conducted among one hundred ophthalmologists (mean age 51.9 years (SD 9.5), 60 % male) from the German-speaking part of Switzerland. For each participant, years of work experience, number of keratoconus patients seen per year and access to a topography device were obtained. We calculated the proportion of MKK and examined in multivariate analyses whether ophthalmologists with access to topography and with greater work experience performed better than other groups. RESULTS: No single ophthalmologist had MKK. The mean MKK was 52.0 %, and the range was 28.6-81.0 %. Per 10 years of working in private practice, the MKK decreased by 8.1 % points (95 % CI: -14.2, -2.00; p = 0.01). Only 24 % of participants correctly recalled the definition of keratoconus, 9 % all risk factors, 5 % all symptoms and 20 % all treatment modalities. The MKK values were not associated with the number of keratoconus patients seen per year and the availability of topography to diagnose keratoconus. CONCLUSIONS: There is a substantial mismatch between corneal specialist' expectations and general ophthalmologists' knowledge about keratoconus. The low recall of symptoms and risk factors may explain why ophthalmologists diagnose relatively few cases of keratoconus, resulting in inefficient care delivery and delayed intervention.

[Assessing the quality of digital health services: How can informed decisions be promoted?] / Einschätzung der Qualität digitaler Gesundheitsangebote: Wie können informierte Entscheidungen gefördert werden?

An important prerequisite for the success of the digitisation of the healthcare system are risk-literate users. Risk literacy means the ability to weigh potential benefits and harms of digital technologies and information, to use digital services critically, and to understand statistical evidence. How do people find reliable and comprehensible health information on the Internet? How can they better assess the quality of algorithmic decision systems? This narrative contribution describes two approaches that show how the competence to make informed decisions can be promoted.Evidence-based and reliable health information exists on the Internet but must be distinguished from a large amount of unreliable information. Various institutions in the German-speaking world have therefore provided guidance to help laypersons make informed decisions. The Harding Center for Risk Literacy in Potsdam, for example, has developed a decision tree ("fast-and-frugal tree"). When dealing with algorithms, natural frequency trees (NFTs) can help to assess the quality and fairness of an algorithmic decision system.Independent of reliable and comprehensible digital health services, further tools for laypersons to assess information and algorithms should be developed and provided. These tools can also be included in institutional training programmes for the promotion of digital literacy. This would be an important step towards the success of digitisation in prevention and health promotion.

The Barrier to Informed Choice in Cancer Screening: Statistical Illiteracy in Physicians and Patients.

An efficient health care requires both informed doctors and patients. Our current healthcare system falls short on both counts. Most doctors and patients do not understand the available medical evidence. To illustrate the extent of the problem in the setting of cancer screening: In a representative sample of some 5000 women in nine European countries, 92% overestimated the reduction of breast cancer mortality by mammography by a factor of 10-200, or did not know. For a similar sample of about 5000 men with respect to PSA screening, this number was 89%. Of more than 300 US citizens who regularly attended one or more cancer screening test, more than 90% had never been informed about the biggest harms of screening-overdiagnosis and overtreatment-by their physicians. Among 160 German gynecologists, some 80% did not understand the positive predictive value of a positive mammogram, with estimates varying between 1 and 90%. In a national sample of 412 US primary care physicians, 47% mistakenly believed that if more cancers are detected by a screening test, this proves that the test saves lives, and 76% wrongly thought that if screen-detected cancers have better 5-year survival rates than cancers detected by symptoms, this would prove that the screening test saves lives. And of 20 German gynecologists, not a single one provided a woman with all information on the benefits and harms of cancer screening required in order to make an informed choice. Why is risk literacy so scarce in health care? One frequently discussed explanation assumes that people suffer from cognitive deficits that make them predictably irrational and basically hopeless at dealing with risks, so that they need to be "nudged" into healthy behavior. Yet research has demonstrated that the problem lies less in stable cognitive deficits than in how information is presented to physicians and patients. This includes biased reporting in medical journals, brochures, and the media that uses relative risks and other misleading statistics, motivated by conflicts of interest and defensive medicine that do not promote informed physicians and patients. What can be done? Every medical school should teach its students how to understand evidence in general and health statistics in particular. To cultivate informed patients, elementary and high schools should start teaching the mathematics of uncertainty-statistical thinking. Guidelines about complete and transparent reporting in journals, brochures, and the media need to be better enforced, and laws need to be changed in order to protect patients and doctors alike against the practice of defensive medicine instead of encouraging it. A critical mass of informed citizens will not resolve all healthcare problems, but it can constitute a major triggering factor for better care.

Less can be more: How to make operations more flexible and robust with fewer resources.

We review empirical evidence from practice and general theoretical conditions, under which simple rules of thumb can help to make operations flexible and robust. An operation is flexible when it responds adaptively to adverse events such as natural disasters; an operation is robust when it is less affected by adverse events in the first place. We illustrate the relationship between flexibility and robustness in the context of supply chain risk. In addition to increasing flexibility and robustness, simple rules simultaneously reduce the need for resources such as time, money, information, and computation. We illustrate the simple-rules approach with an easy-to-use graphical aid for diagnosing and managing supply chain risk. More generally, we recommend a four-step process for determining the amount of resources that decision makers should invest in so as to increase flexibility and robustness.

Symptom recognition of heart attack and stroke in nine European countries: a representative survey.

BACKGROUND: Cardiovascular diseases are the number one cause of death and a source of chronic disability. OBJECTIVES: To assess recognition of and reaction to symptoms of heart attack and stroke, and how recognition is related to the frequency of consulting physicians and other information sources. DESIGN: Face-to-face computer-assisted personal interviews. PARTICIPANTS: Representative sample of 10,228 persons in Austria, France, Germany, Italy, the Netherlands, Poland, Russia, Spain and UK, aged 14-98. MAIN OUTCOME VARIABLES: Recognition of heart attack and stroke symptoms and proper reaction to symptoms. RESULTS: Chest pain was the only heart attack symptom recognized by more than 50% of participants. Eight percent knew no symptoms. Of 14 stroke symptoms, none was recognized by more than 50% of participants; 19% could not identify any symptom. For both heart attack and stroke, Germans and Austrians recognized the largest number of symptoms. Persons in Italy, Poland, Russia and Spain knew only about half as many symptoms as in Germany or Austria. Only 51% of Europeans would call an ambulance when someone suffers a stroke, the fewest (33 and 34%) in Germany and Austria. In most countries, people who consulted their physician more frequently had no better recognition of heart attack or stroke symptoms. CONCLUSIONS: The majority of persons in nine European countries recognize few heart attack and stroke symptoms; many do not know how to react. This low level of knowledge constitutes a major health risk and likely leads to delay in treatment, contributing to the high mortality and morbidity from these diseases.

"Big data" needs an analysis of decision processes.

We demonstrate by means of a simulation that the conceptual map presented by Bentley et al. is incomplete without taking into account people's decision processes. Within the same environment, two decision processes can generate strikingly different collective behavior; in two environments that fundamentally differ in transparency, a single process gives rise to virtually identical behavior.

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States.

BACKGROUND: Unlike reduced mortality rates, improved survival rates and increased early detection do not prove that cancer screening tests save lives. Nevertheless, these 2 statistics are often used to promote screening. OBJECTIVE: To learn whether primary care physicians understand which statistics provide evidence about whether screening saves lives. DESIGN: Parallel-group, randomized trial (randomization controlled for order effect only), conducted by Internet survey. (ClinicalTrials.gov registration number: NCT00981019) SETTING: National sample of U.S. primary care physicians from a research panel maintained by Harris Interactive (79% cooperation rate). PARTICIPANTS: 297 physicians who practiced both inpatient and outpatient medicine were surveyed in 2010, and 115 physicians who practiced exclusively outpatient medicine were surveyed in 2011. INTERVENTION: Physicians received scenarios about the effect of 2 hypothetical screening tests: The effect was described as improved 5-year survival and increased early detection in one scenario and as decreased cancer mortality and increased incidence in the other. MEASUREMENTS: Physicians' recommendation of screening and perception of its benefit in the scenarios and general knowledge of screening statistics. RESULTS: Primary care physicians were more enthusiastic about the screening test supported by irrelevant evidence (5-year survival increased from 68% to 99%) than about the test supported by relevant evidence (cancer mortality reduced from 2 to 1.6 in 1000 persons). When presented with irrelevant evidence, 69% of physicians recommended the test, compared with 23% when presented with relevant evidence (P < 0.001). When asked general knowledge questions about screening statistics, many physicians did not distinguish between irrelevant and relevant screening evidence; 76% versus 81%, respectively, stated that each of these statistics proves that screening saves lives (P = 0.39). About one half (47%) of the physicians incorrectly said that finding more cases of cancer in screened as opposed to unscreened populations "proves that screening saves lives." LIMITATION: Physicians' recommendations for screening were based on hypothetical scenarios, not actual practice. CONCLUSION: Most primary care physicians mistakenly interpreted improved survival and increased detection with screening as evidence that screening saves lives. Few correctly recognized that only reduced mortality in a randomized trial constitutes evidence of the benefit of screening. PRIMARY FUNDING SOURCE: Harding Center for Risk Literacy, Max Planck Institute for Human Development.

Psychological AI: Designing Algorithms Informed by Human Psychology.

Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency-the human tendency to rely on the most recent information and ignore base rates-can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends's big-data algorithms. The second uses a result from memory research-the paradoxical effect that making numbers less precise increases recall-in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms.

9/11, Act II: a fine-grained analysis of regional variations in traffic fatalities in the aftermath of the terrorist attacks.

Terrorists can strike twice--first, by directly killing people, and second, through dangerous behaviors induced by fear in people's minds. Previous research identified a substantial increase in U.S. traffic fatalities subsequent to the September 11 terrorist attacks, which were accounted for as due to a substitution of driving for flying, induced by fear of dread risks. Here, we show that this increase in fatalities varied widely by region, a fact that was best explained by regional variations in increased driving. Two factors, in turn, explained these variations in increased driving. The weaker factor was proximity to New York City, where stress reactions to the attacks were previously shown to be greatest. The stronger factor was driving opportunity, which was operationalized both as number of highway miles and as number of car registrations per inhabitant. Thus, terrorists' second strike exploited both fear of dread risks and, paradoxically, an environmental structure conducive to generating increased driving, which ultimately increased fatalities.

Decision making in a human population living sustainably.

The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.

Heuristic decision making.

As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.

Homo Heuristicus: Less-is-More Effects in Adaptive Cognition.

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We discuss some of the major progress made so far, focusing on the discovery of less-is-more effects and the study of the ecological rationality of heuristics which examines in which environments a given strategy succeeds or fails, and why. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.

Simple heuristics to run a research group.

Collaboration between researchers has become increasingly common, enabling a level of discovery and innovation that is difficult if not impossible to achieve by a single person. But how can one establish and maintain an environment that fosters successful collaboration within a research group? In this case study, I use my own experience when directing the ABC Research Group at the Max Planck Institute for Human Development in Berlin. I first describe the heuristic principles for setting up a research group, including (i) common topic and multiple disciplines, (ii) open culture, (iii) spatial proximity, and (iv) temporal proximity. Then I describe heuristics for maintaining the open culture, such as setting collective goals, including contrarians, distributing responsibility, making bets, the cake rule, and side-by-side writing. These heuristics form an "adaptive toolbox" that shapes the intellectual and social climate. They create a culture of friendly but rigorous discussion, embedded in a family-like climate of trust where everyone is willing to expose their ignorance and learn from the other members. Feeling accepted and trusted encourages taking the necessary risks to achieve progress in science.

Modeling fast-and-frugal heuristics.

Heuristics are simple rules that experts and laypeople rely on to make decisions under uncertainty as opposed to situations with calculable risk. The research program on fast-and-frugal heuristics studies formal models of heuristics and is motivated by Herbert Simon's seminal work on bounded rationality and satisficing. In this article, we first introduce the major theoretical principles (e.g., ecological rationality) and research approaches (e.g., competitive testing) that have been adopted in this research program, and then illustrate these principles and approaches with two heuristics: take-the-best and fast-and-frugal trees. We describe conditions under which simple heuristics predict as accurately as or better than more complex models, despite requiring less effort. We close by pointing out several issues that need to be further studied and better understood in the research on fast-and-frugal heuristics.