Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation.

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.

Communicating Uncertainties About the Effects of Medical Interventions Using Different Display Formats.

Communicating uncertainties in scientific evidence is important to accurately reflect scientific knowledge , increase public understanding of uncertainty, and to signal transparency and honesty in reporting. While techniques have been developed to facilitate the communication of uncertainty, many have not been empirically tested, compared for communicating different types of uncertainty, or their effects on different cognitive, trust, and behavioral outcomes have not been evaluated. The present study examined how a point estimate, imprecise estimate, conflicting estimates, or a statement about the lack of evidence about treatment effects, influenced participant's responses to communications about medical evidence. For each type of uncertainty, we adapted three display formats to communicate the information: tables, bar graphs, and icon arrays. We compared participant's best estimates of treatment effects, as well as effects on recall, subjective evaluations (understandability and usefuleness), certainty perceptions, perceptions of trustworthiness of the information, and behavioral intentions. We did not find any detrimental effects from communicating imprecision or conflicting estimates relative to a point estimate across any outcome. Furthermore, there were more favorable responses to communicating imprecision or conflicting estimates relative to lack of evidence, where participants estimated the treatment would improve outcomes by 30-50% relative to a placebo. There were no differences across display formats, suggesting that, if well-designed, it may not matter which format is used. Future research on specific display formats or uncertainty types and with larger sample sizes would be needed to detect small effects. Implications for the communication of uncertainty are discussed.

How selfish is a thirsty man? A pilot study on comparing sharing behavior with primary and secondary rewards.

Human social interactions in daily life involve sharing various types of rewards. Previous research evolving around issues of selfish versus altruistic behavior indicates that when individuals share rewards like money with powerless others, some are purely selfish while a substantial number shares evenly. It is, however, mostly unknown how they share primary rewards like water, compared to secondary rewards like money. We adopt the widely studied Dictator Game for comparing water to be divided among study participants with a monetary reward. We show that thirsty participants share water more often equally with powerless, anonymous others than they do money. This is the case even when they earned both types of rewards in a preceding task. Results indicate that altruistic behavior is more likely to occur when it comes to sharing primary rewards. The ecologically more valid scenario employed in this study provides initial evidence that the concept of a self-interested homo economicus might not apply to everyday social interactions involving rewards other than money.

A Sampling Framework for Uncertainty in Individual Environmental Decisions.

Decisions in the environmental and in particular the climate domain are burdened with uncertainty. Here, we focus on uncertainties faced by individuals when making decisions about environmental behavior, and we use the statistical sampling framework to develop a classification of different sources of uncertainty they encounter. We then map these sources to different public policy strategies aiming to help individuals cope with uncertainty when making environmental decisions.