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AbstractThe claim that a person can use different strategies or processes to solve the same task is pervasive in decision making, categorization, estimation, reasoning, and other research fields. Yet suchmulti‐strategyapproaches differ widely in how they envision that the different strategies are coordinated and therefore do not represent one unitary approach.Toolboxmodels, for example, assume that people shift from one strategy to another as they adapt to specific task environments based on past experience. Unlike such multi‐strategyselectionapproaches, multi‐strategyblendingapproaches assume that the outputs of different strategies are blended into a joint, hybrid response (i.e., "wisdom of strategies" in one mind). The goal of this article is twofold. First, we discussstrategy blendingas a conceptual alternative to strategy selection for modeling human judgment. Second, we investigate the predictive performance of the different approaches in synthetic and real‐world environments. Taking anormative perspective, we study the coordination of rule‐based and exemplar‐based processes in estimation tasks. Our simulations using synthetic and real‐world environments indicate that, for medium‐sized samples, multi‐strategy blending approaches lead to more accurate estimates than relying on a single strategy or selecting a strategy based on past experience—possibly because neither rule‐ nor exemplar‐based processes in isolation are sufficient to capture statistical regularities that enable accurate estimates. This suggests that multi‐strategy blending approaches can be advantageous to the degree that they rely on qualitatively different strategies. Copyright © 2016 John Wiley & Sons, Ltd.

AbstractAs climate change unfolds, extreme weather events are on the rise worldwide. According to experts, extreme weather risks already outrank those of terrorism and migration in likelihood and impact. But how well does the public understand weather risks and forecast uncertainty and thus grasp the amplified weather risks that climate change poses for the future? In a nationally representative survey (N = 1004; Germany), we tested the public's weather literacy and awareness of climate change using 62 factual questions. Many respondents misjudged important weather risks (e.g., they were unaware that UV radiation can be higher under patchy cloud cover than on a cloudless day) and struggled to connect weather conditions to their impacts (e.g., they overestimated the distance to a thunderstorm). Most misinterpreted a probabilistic forecast deterministically, yet they strongly underestimated the uncertainty of deterministic forecasts. Respondents with higher weather literacy obtained weather information more often and spent more time outside but were not more educated. Those better informed about climate change were only slightly more weather literate. Overall, the public does not seem well equipped to anticipate weather risks in the here and now and may thus also fail to fully grasp what climate change implies for the future. These deficits in weather literacy highlight the need for impact forecasts that translate what the weather may be into what the weather may do and for transparent communication of uncertainty to the public. Boosting weather literacy may help to improve the public's understanding of weather and climate change risks, thereby fostering informed decisions and mitigation support.

Individual judgments, feelings, and behaviors can spread from person to person in social networks, similarly to the propagation of infectious diseases. Despite major implications for many social phenomena, the underlying social-contagion processes are poorly understood. We examined how participants' perceptual judgments spread from one person to another and across diffusion chains. We gauged the speed, reach, and scale of social contagion. Judgment propagation tended to slow down with increasing social distance from the source. Crucially, it vanished beyond a social horizon of three to four people. These results advance the understanding of some of the mechanisms underlying social-contagion phenomena as well as their scope across domains as diverse as political mobilization, health practices, and emotions.

We introduce a framework aimed at the information science (IS), information retrieval (IR) and data science communities as well as behavioral and cognitive scientists and policy makers inside government and at corporations operating Web search platforms. The goal of this framework is to instigate collaborative discussion and research across these communities to address potential dangers searchers and society face in modern Web search. We provide an overview of the harms, such as poor health outcomes, and their possible causes, including searcher and system biases being tuned for maximum profit. Modifications to policy, additional evaluation metrics, a mixture of cognitive decision making tools and improvements to the IR system are the suggested pathways. Examples are provided of how the framework can be put into practice.

Online platforms' data give advertisers the ability to "microtarget" recipients' personal vulnerabilities by tailoring different messages for the same thing, such as a product or political candidate. One possible response is to raise awareness for and resilience against such manipulative strategies through psychological inoculation. Two online experiments (total [Formula: see text] ) demonstrated that a short, simple intervention prompting participants to reflect on an attribute of their own personality—by completing a short personality questionnaire—boosted their ability to accurately identify ads that were targeted at them by up to 26 percentage points. Accuracy increased even without personalized feedback, but merely providing a description of the targeted personality dimension did not improve accuracy. We argue that such a "boosting approach," which here aims to improve people's competence to detect manipulative strategies themselves, should be part of a policy mix aiming to increase platforms' transparency and user autonomy.

Online platforms' data give advertisers the ability to "microtarget" recipients' personal vulnerabilities by tailoring different messages for the same thing, such as a product or political candidate. One possible response is to raise awareness for and resilience against such manipulative strategies through psychological inoculation. Two online experiments (total N=828) demonstrated that a short, simple intervention prompting participants to reflect on an attribute of their own personality—by completing a short personality questionnaire—boosted their ability to accurately identify ads that were targeted at them by up to 26 percentage points. Accuracy increased even without personalized feedback, but merely providing a description of the targeted personality dimension did not improve accuracy. We argue that such a "boosting approach," which here aims to improve people's competence to detect manipulative strategies themselves, should be part of a policy mix aiming to increase platforms' transparency and user autonomy.

The COVID-19 pandemic has seen one of the first large-scale uses of digital contact tracing to track a chain of infection and contain the spread of a virus. The new technology has posed challenges both for governments aiming at high and effective uptake and for citizens weighing its benefits (e.g., protecting others' health) against the potential risks (e.g., loss of data privacy). Our cross-sectional survey with repeated measures across four samples in Germany ([Formula: see text] ) focused on psychological factors contributing to the public adoption of digital contact tracing. We found that public acceptance of privacy-encroaching measures (e.g., granting the government emergency access to people's medical records or location tracking data) decreased over the course of the pandemic. Intentions to use contact tracing apps—hypothetical ones or the Corona-Warn-App launched in Germany in June 2020—were high. Users and non-users of the Corona-Warn-App differed in their assessment of its risks and benefits, in their knowledge of the underlying technology, and in their reasons to download or not to download the app. Trust in the app's perceived security and belief in its effectiveness emerged as psychological factors playing a key role in its adoption. We incorporate our findings into a behavioral framework for digital contact tracing and provide policy recommendations.

Entscheidungsträger*innen berücksichtigen zunehmend Erkenntnisse aus den Verhaltenswissenschaften - insbesondere "Nudges": Interventionen, welche Menschen in eine bestimmte Richtung lenken. Dieser Beitrag stellt einen anderen, verhaltenswissenschaftlich gestützten Ansatz vor ("Boosting"), der sogenannte "Boosts" anbietet: evidenzbasierte Interventionen, die Entscheidungs- und Selbstkontrollkompetenzen steigern und damit die Entscheidungsautonomie
von Bürger*innen bewahren.

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.