Mapping collective behavior in the big-data era

Patterns in negative emotions, sleep disorders, and temperature: Evidence from microblog big data

Existing studies have shown that temperature is related to mental illness and sleep disorders. However, few studies have explored the relationship between temperature and microblog negative emotions (MNE) and the predictive effect of MNE on sleep disorders. The present study elucidating the temperature patterns of MNE and sleep disorders, examines the predictive capability of these adverse emotions in precipitating sleep disorders, and operating within the schema of "climate-psychology-behavior". A negative binomial regression model (NBR) was formulated, amalgamating Temperature data, negative affective information procured from microblog, and sleep disorder records. Temperature and Apparent Air Temperature (AAT) were found to have a non-linear association with microblog negative emotions and sleep disorders, exhibiting a modest effect within a specified range, while extreme temperatures (both high and low) demonstrated substantial effects. In the constructed model, gender serves as a moderating factor, with females being more susceptible to temperature and AAT effects on MNE and sleep disorders than their male counterparts. Interestingly, AAT surfaced as a superior predictor compared to actual temperature. MNE were effective predictors of sleep disorders. Employing social media-centric models, as showcased in this study, augments the identification and prevention strategies targeting disease symptoms or pathologies within mental and public health domains.

Exploring Large Digital Bodies for the Study of Human Behavior

Internet access has become a fundamental component of contemporary society, with major impacts in many areas that offer opportunities for new research insights. The search and deposition of information in digital media form large sets of data known as digital corpora, which can be used to generate structured data, representing repositories of knowledge and evidence of human culture. This information offers opportunities for scientific investigations that contribute to the understanding of human behavior on a large scale, reaching human populations/individuals that would normally be difficult to access. These tools can help access social and cultural varieties worldwide. In this article, we briefly review the potential of these corpora in the study of human behavior. Therefore, we propose Culturomics of Human Behavior as an approach to understand, explain, and predict human behavior using digital corpora.

A cultural evolutionary theory that explains both gradual and punctuated change

Cumulative cultural evolution (CCE) occurs among humans who may be presented with many similar options from which to choose, as well as many social influences and diverse environments. It is unknown what general principles underlie the wide range of CCE dynamics and whether they can all be explained by the same unified paradigm. Here, we present a scalable evolutionary model of discrete choice with social learning, based on a few behavioural science assumptions. This paradigm connects the degree of transparency in social learning to the human tendency to imitate others. Computer simulations and quantitative analysis show the interaction of three primary factors-information transparency, popularity bias and population size-drives the pace of CCE. The model predicts a stable rate of evolutionary change for modest degrees of popularity bias. As popularity bias grows, the transition from gradual to punctuated change occurs, with maladaptive subpopulations arising on their own. When the popularity bias gets too severe, CCE stops. This provides a consistent framework for explaining the rich and complex adaptive dynamics taking place in the real world, such as modern digital media.

A Theory of Information Compression: When Judgments Are Costly

A Theory of Information Compression: When Judgments Are Costly

How useful to tourists are thousands of reviews of different five-star hotels in a city on a travel website when the mean rating is 4.5, and all the five-star hotels score around the mean? How insightful are reviews of physicians on a physician review website to potential patients when the ratings cluster tightly around an average for all physicians? Are there costs to the physicians, the patients, and to society as a whole? When all the students at a university score “A” grades on most courses, are there consequences for the university, the students, and potential employers? This paper calls the “clustering around a mean” phenomenon “information compression” and the systems in which it occurs (e.g., universities, students, employers) “judgment networks.” When there is extensive information compression in a system, measures such as ratings or grades have little value for decision makers. When all five-star hotels in a city score an average of 4.5 does it really matter which one a traveler chooses? The paper introduces a way of measuring information compression. It also suggests ways for organizations to overcome the negative consequences of information compression for themselves and their various stakeholders.

Monitoring event-driven dynamics on Twitter: a case study in Belarus

Analysts of social media differ in their emphasis on the effects of message content versus social network structure. The balance of these factors may change substantially across time. When a major event occurs, initial independent reactions may give way to more social diffusion of interpretations of the event among different communities, including those committed to disinformation. Here, we explore these dynamics through a case study analysis of the Russian-language Twitter content emerging from Belarus before and after its presidential election of August 9, 2020. From these Russian-language tweets, we extracted a set of topics that characterize the social media data and construct networks to represent the sharing of these topics before and after the election. The case study in Belarus reveals how misinformation can be re-invigorated in discourse through the novelty of a major event. More generally, it suggests how audience networks can shift from influentials dispensing information before an event to a de-centralized sharing of information after it.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43545-022-00330-x.

How social learning shapes the efficacy of preventative health behaviors in an outbreak

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.

Big Data and Atrial Fibrillation: Current Understanding and New Opportunities

Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathophysiological mechanism, as well as treatment of AF have made huge progress. However, many unresolved issues remain, including the core mechanisms and key intervention targets. Big data approach has produced new insights into the improvement of the situation. A large amount of data have been accumulated in the field of AF research, thus using the big data to achieve prevention and precise treatment of AF may be the direction of future development. In this review, we will discuss the current understanding of big data and explore the potential applications of big data in AF research, including predictive models of disease processes, disease heterogeneity, drug safety and development, precision medicine, and the potential source for big data acquisition. Grapical abstract.

Young people and gambling content in social media: An experimental insight

INTRODUCTION AND AIMS

Online gambling advertising and user-generated gambling content have increased. This study used a social psychological online experiment to analyse young people's reactions towards and self-reported interests in social media gambling messages.

DESIGN AND METHODS

A vignette experiment with a two-level between-subjects factor (group condition or control condition) and three two-level within-subjects factors (expressed stance on gambling, narrative perspective and majority opinion) was conducted with two samples of young Finnish people aged 15 to 25 years (N = 1200, 50% female, mean age 21.29 years) and 15 to 30 years (N = 230, 53% female, mean age 24.35 years). Participants were asked to indicate how they would react to presented gambling messages (i.e. like or dislike the content) and how interesting would the content appear to them. In addition to experimental factors, the Attitudes Towards Gambling Scale and a global self-esteem measure were used as the independent variables. A statistical analysis included multilevel linear and logistic regressions.

RESULTS

Young people preferred anti-gambling messages instead of pro-gambling messages. This effect was moderated by personal gambling attitudes as participants with highly positive gambling attitudes preferred pro-gambling content. Fact-driven messages were favoured over experience-driven messages. Positive majority opinions predicted more favourable reactions and positive interest.

DISCUSSION AND CONCLUSIONS

Young people prefer anti-gambling content and factual argumentation but their online behaviour is also influenced by perceived group norms. The potential risks of online gambling promotion mainly concern young people already interested in gambling.

Affective responses to uncertain real-world outcomes: Sentiment change on Twitter

We use data from Twitter.com to study the interplay between affect and expectations about uncertain outcomes. In two studies, we obtained tweets about candidates in the 2014 US Senate elections and tweets about National Football League (NFL) teams in the 2014/2015 NFL season. We chose these events because a) their outcomes are highly uncertain and b) they attract a lot of attention and feature heavily in the communication on social media. We also obtained a priori expectations for the events from political forecasting and sport betting websites. Using this quasi-experimental design, we found that unexpected events are associated with more intense affect than expected events. Moreover, the effect of expectations is larger for outcomes that fall below expectations than outcomes that exceed expectations. Our results are consistent with fundamental principles in psychological science, such as reference-dependence in experienced affect. We discuss how naturally occurring online data can be used to test psychological predictions and develop novel psychological insights.

Positioning Locality Using Cognitive Directions Based on Indoor Landmark Reference System

Locality descriptions are generally communicated using reference objects and spatial relations that reflect human spatial cognition. However, uncertainty is inevitable in locality descriptions. Positioning locality with locality description, with a mapping mechanism between the qualitative and quantitative data, is one of the important research issues in next-generation geographic information sciences. Spatial relations play an important role in the uncertainty of positioning locality. In indoor landmark reference systems, the nearest landmarks can be selected when describing localities by using direction relations indoors. By using probability operation, we combine a set of uncertainties, that is, near and direction relations to positioning locality. Some definitions are proposed from cognitive and computational perspectives. We evaluate the performance of our method through indoor cognitive experiments. Test results demonstrate that a positioning accuracy of 3.55 m can be achieved with the semantically derived direction relationships in indoor landmark reference systems.

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment

Cyber attacks have been increasingly detrimental to networks, systems, and users, and are increasing in number and severity globally. To better predict system vulnerabilities, cybersecurity researchers are developing new and more holistic approaches to characterizing cybersecurity system risk. The process must include characterizing the human factors that contribute to cyber security vulnerabilities and risk. Rationality, expertise, and maliciousness are key human characteristics influencing cyber risk within this context, yet maliciousness is poorly characterized in the literature. There is a clear absence of literature pertaining to human factor maliciousness as it relates to cybersecurity and only limited literature relating to aspects of maliciousness in other disciplinary literatures, such as psychology, sociology, and law. In an attempt to characterize human factors as a contribution to cybersecurity risk, the Cybersecurity Collaborative Research Alliance (CSec-CRA) has developed a Human Factors risk framework. This framework identifies the characteristics of an attacker, user, or defender, all of whom may be adding to or mitigating against cyber risk. The maliciousness literature and the proposed maliciousness assessment metrics are discussed within the context of the Human Factors Framework and Ontology. Maliciousness is defined as the intent to harm. Most maliciousness cyber research to date has focused on detecting malicious software but fails to analyze an individual's intent to do harm to others by deploying malware or performing malicious attacks. Recent efforts to identify malicious human behavior as it relates to cybersecurity, include analyzing motives driving insider threats as well as user profiling analyses. However, cyber-related maliciousness is neither well-studied nor is it well understood because individuals are not forced to expose their true selves to others while performing malicious attacks. Given the difficulty of interviewing malicious-behaving individuals and the potential untrustworthy nature of their responses, we aim to explore the maliciousness as a human factor through the observable behaviors and attributes of an individual from their actions and interactions with society and networks, but to do so we will need to develop a set of analyzable metrics. The purpose of this paper is twofold: (1) to review human maliciousness-related literature in diverse disciplines (sociology, economics, law, psychology, philosophy, informatics, terrorism, and cybersecurity); and (2) to identify an initial set of proposed assessment metrics and instruments that might be culled from in a future effort to characterize human maliciousness within the cyber realm. The future goal is to integrate these assessment metrics into holistic cybersecurity risk analyses to determine the risk an individual poses to themselves as well as other networks, systems, and/or users.

Mapping multiple drivers of human obesity

The insurance hypothesis is a reasonable explanation for the current obesity epidemic. One alternative explanation is that the marketing of high-sugar foods, especially sugar-sweetened beverages, drives the rise in obesity. Another prominent hypothesis is that obesity spreads through social influence. We offer a framework for estimating the extent to which these different models explain the rise in obesity.

Cultural learning and the Clovis colonization of North America

The timing of the earliest colonization of North America is debatable, but what is not at issue is the point of origin of the early colonists: Humans entered the continent from Beringia and then made their way south along or near the Pacific Coast and/or through a corridor that ran between the Cordilleran and Laurentide ice sheets in western North America. At some point, they abandoned their Arctic-based tool complex for one more adapted to an entirely different environment. That new techno-complex is termed "Clovis"; its dispersal allows us to examine, at a fine scale, how colonization processes played out across a vast continent that at the time had, at best, a very small resident population. Clovis has figured prominently in American archeology since the first Clovis points were identified in eastern New Mexico in the 1930s. However, the successful marriage of learning models grounded in evolutionary theory and modern analytical methods that began roughly a decade ago has begun to pay significant dividends in terms of what we know about the rapid spread of human groups across the last sizable landmass to witness human occupation.

Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.

Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data

Background

Substance use–related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use–related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems.

Objective

The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction.

Methods

First, we provided a narrative summary of the literature on drug use–related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of “infodemiology and infoveillance” in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use–related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology.

Results

We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use–related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use–related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied.

Conclusions

We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

BACKGROUND

Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems.

METHODOLOGY / PRINCIPAL FINDINGS

In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access.

CONCLUSIONS

Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.

Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process.

Online Activity Levels Are Related to Caffeine Dependency

Online activity could serve in the future as behavioral markers of emotional states for computer systems (i.e., affective computing). Hence, this study considered relationships between self-reported stimulant use and online study patterns. Sixty-two undergraduate psychology students estimated their daily caffeine use, and this was related to study patterns as tracked by their use of a Learning Management System (Blackboard). Caffeine dependency was associated with less time spent online, lower rates of file access, and fewer online activities completed. Reduced breadth or depth of processing during work/study could be used as a behavioral marker of stimulant use.

Evaluating reproductive decisions as discrete choices under social influence

Discrete choice, coupled with social influence, plays a significant role in evolutionary studies of human fertility, as investigators explore how and why reproductive decisions are made. We have previously proposed that the relative magnitude of social influence can be compared against the transparency of pay-off, also known as the transparency of a decision, through a heuristic diagram that maps decision-making along two axes. The horizontal axis represents the degree to which an agent makes a decision individually versus one that is socially influenced, and the vertical axis represents the degree to which there is transparency in the pay-offs and risks associated with the decision the agent makes. Having previously parametrized the functions that underlie the diagram, we detail here how our estimation methods can be applied to real-world datasets concerning sexual health and contraception.

Fitness landscapes among many options under social influence

Cultural learning represents a novel problem in that an optimal decision depends not only on intrinsic utility of the decision/behavior but also on transparency of costs and benefits, the degree of social versus individual learning, and the relative popularity of each possible choice in a population. In terms of a fitness-landscape function, this recursive relationship means that multiple equilibria can exist. Here we use discrete-choice theory to construct a fitness-landscape function for a bi-axial decision-making map that plots the magnitude of social influence in the learning process against the costs and payoffs of decisions. Specifically, we use econometric and statistical methods to estimate not only the fitness function but also movements along the map axes. To search for these equilibria, we employ a hill-climbing algorithm that leads to the expected values of optimal decisions, which we define as peaks on the fitness landscape. We illustrate how estimation of a measure of transparency, a measure of social influence, and the associated fitness landscape can be accomplished using panel data sets.

Collective behaviour, uncertainty and environmental change

A central aspect of cultural evolutionary theory concerns how human groups respond to environmental change. Although we are painting with a broad brush, it is fair to say that prior to the twenty-first century, adaptation often happened gradually over multiple human generations, through a combination of individual and social learning, cumulative cultural evolution and demographic shifts. The result was a generally resilient and sustainable population. In the twenty-first century, however, considerable change happens within small portions of a human generation, on a vastly larger range of geographical and population scales and involving a greater degree of horizontal learning. As a way of gauging the complexity of societal response to environmental change in a globalized future, we discuss several theoretical tools for understanding how human groups adapt to uncertainty. We use our analysis to estimate the limits of predictability of future societal change, in the belief that knowing when to hedge bets is better than relying on a false sense of predictability.

I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?

Due to the increasing amount of scientific work and the typical delays in publication, promptly assessing the impact of scholarly work is a huge challenge. To meet this challenge, one solution may be to create and discover innovative indicators. The goal of this paper is to investigate whether Facebook likes for unpublished manuscripts that are uploaded to the Internet could be used as an early indicator of the future impact of the scientific work. To address our research question, we compared Facebook likes for manuscripts uploaded to the Harvard Business School website (Study 1) and the bioRxiv website (Study 2) with traditional impact indicators (journal article citations, Impact Factor, Immediacy Index) for those manuscripts that have been published as a journal article. Although based on our full sample of Study 1 (N = 170), Facebook likes do not predict traditional impact indicators, for manuscripts with one or more Facebook likes (n = 95), our results indicate that the more Facebook likes a manuscript receives, the more journal article citations the manuscript receives. In additional analyses (for which we categorized the manuscripts as psychological and non-psychological manuscripts), we found that the significant prediction of citations stems from the psychological and not the non-psychological manuscripts. In Study 2, we observed that Facebook likes (N = 270) and non-zero Facebook likes (n = 84) do not predict traditional impact indicators. Taken together, our findings indicate an interdisciplinary difference in the predictive value of Facebook likes, according to which Facebook likes only predict citations in the psychological area but not in the non-psychological area of business or in the field of life sciences. Our paper contributes to understanding the possibilities and limits of the use of social media indicators as potential early indicators of the impact of scientific work.

Self-reported gambling problems and digital traces

The Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition (DSM-5), lists concealment as one of the symptoms of a gambling disorder. However, some transactions are more likely to leave permanent records of gambling transactions (credit, consumer loyalty schemes) than others (cash, Internet cash, Internet cafes, prepaid phones). An online survey of 815 participants recruited through newspaper and online sites elicited consumer preferences for a variety of transactions and communication media. Hierarchical multiple regression accounted for age, gender, housing status, and involvement in gambling before considering relationships between consumer preferences and scores on the Problem Gambling Severity Index. Even after statistically allowing for the contributions of other variables, a greater risk of developing a gambling problem was associated with a preference for cash transactions, prepaid mobile phones, and Internet cafes. Problem gamblers may seek to reduce their digital trace.

Evidence-informed person-centered healthcare part I: do 'cognitive biases plus' at organizational levels influence quality of evidence?

INTRODUCTION

There is increasing concern about the unreliability of much of health care evidence, especially in its application to individuals.

HYPOTHESIS

Cognitive biases, financial and non-financial conflicts of interest, and ethical violations (which, together with fallacies, we collectively refer to as 'cognitive biases plus') at the levels of individuals and organizations involved in health care undermine the evidence that informs person-centred care.

METHODS

This study used qualitative review of the pertinent literature from basic, medical and social sciences, ethics, philosophy, law etc.

RESULTS

Financial conflicts of interest (primarily industry related) have become systemic in several organizations that influence health care evidence. There is also plausible evidence for non-financial conflicts of interest, especially in academic organizations. Financial and non-financial conflicts of interest frequently result in self-serving bias. Self-serving bias can lead to self-deception and rationalization of actions that entrench self-serving behaviour, both potentially resulting in unethical acts. Individuals and organizations are also susceptible to other cognitive biases. Qualitative evidence suggests that 'cognitive biases plus' can erode the quality of evidence.

CONCLUSIONS

'Cognitive biases plus' are hard wired, primarily at the unconscious level, and the resulting behaviours are not easily corrected. Social behavioural researchers advocate multi-pronged measures in similar situations: (i) abolish incentives that spawn self-serving bias; (ii) enforce severe deterrents for breaches of conduct; (iii) value integrity; (iv) strengthen self-awareness; and (v) design curricula especially at the trainee level to promote awareness of consequences to society. Virtuous professionals and organizations are essential to fulfil the vision for high-quality individualized health care globally.

Estimating a path through a map of decision making

Studies of the evolution of collective behavior consider the payoffs of individual versus social learning. We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the “transparency” of the decision, through a heuristic, two-dimensional map. Moving from west to east, the estimated strength of social influence increases. As the decision maker proceeds from south to north, transparency of choice increases, and it becomes easier to identify the best choice itself and/or the best social role model from whom to learn (depending on position on east–west axis). Here we show how to parameterize the functions that underlie the map, how to estimate these functions, and thus how to describe estimated paths through the map. We develop estimation methods on artificial data sets and discuss real-world applications such as modeling changes in health decisions.