- Proceedings. Biological sciences / The Royal Society
- Published almost 8 years ago
Environmental problems have contributed to numerous collapses of civilizations in the past. Now, for the first time, a global collapse appears likely. Overpopulation, overconsumption by the rich and poor choices of technologies are major drivers; dramatic cultural change provides the main hope of averting calamity.
Nature within cities will have a central role in helping address key global public health challenges associated with urbanization. However, there is almost no guidance on how much or how frequently people need to engage with nature, and what types or characteristics of nature need to be incorporated in cities for the best health outcomes. Here we use a nature dose framework to examine the associations between the duration, frequency and intensity of exposure to nature and health in an urban population. We show that people who made long visits to green spaces had lower rates of depression and high blood pressure, and those who visited more frequently had greater social cohesion. Higher levels of physical activity were linked to both duration and frequency of green space visits. A dose-response analysis for depression and high blood pressure suggest that visits to outdoor green spaces of 30 minutes or more during the course of a week could reduce the population prevalence of these illnesses by up to 7% and 9% respectively. Given that the societal costs of depression alone in Australia are estimated at AUD$12.6 billion per annum, savings to public health budgets across all health outcomes could be immense.
According to a “parasite stress” hypothesis, authoritarian governments are more likely to emerge in regions characterized by a high prevalence of disease-causing pathogens. Recent cross-national evidence is consistent with this hypothesis, but there are inferential limitations associated with that evidence. We report two studies that address some of these limitations, and provide further tests of the hypothesis. Study 1 revealed that parasite prevalence strongly predicted cross-national differences on measures assessing individuals' authoritarian personalities, and this effect statistically mediated the relationship between parasite prevalence and authoritarian governance. The mediation result is inconsistent with an alternative explanation for previous findings. To address further limitations associated with cross-national comparisons, Study 2 tested the parasite stress hypothesis on a sample of traditional small-scale societies (the Standard Cross-Cultural Sample). Results revealed that parasite prevalence predicted measures of authoritarian governance, and did so even when statistically controlling for other threats to human welfare. (One additional threat-famine-also uniquely predicted authoritarianism.) Together, these results further substantiate the parasite stress hypothesis of authoritarianism, and suggest that societal differences in authoritarian governance result, in part, from cultural differences in individuals' authoritarian personalities.
Though religion has been shown to have generally positive effects on normative ‘prosocial’ behavior, recent laboratory research suggests that these effects may be driven primarily by supernatural punishment. Supernatural benevolence, on the other hand, may actually be associated with less prosocial behavior. Here, we investigate these effects at the societal level, showing that the proportion of people who believe in hell negatively predicts national crime rates whereas belief in heaven predicts higher crime rates. These effects remain after accounting for a host of covariates, and ultimately prove stronger predictors of national crime rates than economic variables such as GDP and income inequality. Expanding on laboratory research on religious prosociality, this is the first study to tie religious beliefs to large-scale cross-national trends in pro- and anti-social behavior.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 3 years ago
First impressions based on facial appearance predict many important social outcomes. We investigated whether such impressions also influence the communication of scientific findings to lay audiences, a process that shapes public beliefs, opinion, and policy. First, we investigated the traits that engender interest in a scientist’s work, and those that create the impression of a “good scientist” who does high-quality research. Apparent competence and morality were positively related to both interest and quality judgments, whereas attractiveness boosted interest but decreased perceived quality. Next, we had members of the public choose real science news stories to read or watch and found that people were more likely to choose items that were paired with “interesting-looking” scientists, especially when selecting video-based communications. Finally, we had people read real science news items and found that the research was judged to be of higher quality when paired with researchers who look like “good scientists.” Our findings offer insights into the social psychology of science, and indicate a source of bias in the dissemination of scientific findings to broader society.
Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society’s reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.
Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks. Here we study gender-specific differences of a multiplex network from a complete behavioral dataset of an online-game society of about 300,000 players. On the individual level females perform better economically and are less risk-taking than males. Males reciprocate friendship requests from females faster than vice versa and hesitate to reciprocate hostile actions of females. On the network level females have more communication partners, who are less connected than partners of males. We find a strong homophily effect for females and higher clustering coefficients of females in trade and attack networks. Cooperative links between males are under-represented, reflecting competition for resources among males. These results confirm quantitatively that females and males manage their social networks in substantially different ways.
As social scientists have investigated the political and social factors influencing public opinion in science-related policy debates, there has been growing interest in the implications of this research for public communication and outreach. Given the level of political polarization in the United States, much of the focus has been on partisan differences in public opinion, the strategies employed by political leaders and advocates that promote those differences, and the counter-strategies for overcoming them. Yet this focus on partisan differences tends to overlook the processes by which core beliefs about science and society impact public opinion and how these schema are often activated by specific frames of reference embedded in media coverage and popular discourse. In this study, analyzing cross-sectional, nationally representative survey data collected between 2002 and 2010, we investigate the relative influence of political partisanship and science-related schema on Americans' support for embryonic stem cell research. In comparison to the influence of partisan identity, our findings suggest that generalized beliefs about science and society were more chronically accessible, less volatile in relation to media attention and focusing events, and an overall stronger influence on public opinion. Classifying respondents into four unique audience groups based on their beliefs about science and society, we additionally find that individuals within each of these groups split relatively evenly by partisanship but differ on other important dimensions. The implications for public engagement and future research on controversies related to biomedical science are discussed.
There is currently widespread public misunderstanding about the degree of scientific consensus on human-caused climate change, both in the US as well as internationally. Moreover, previous research has identified important associations between public perceptions of the scientific consensus, belief in climate change and support for climate policy. This paper extends this line of research by advancing and providing experimental evidence for a “gateway belief model” (GBM). Using national data (N = 1104) from a consensus-message experiment, we find that increasing public perceptions of the scientific consensus is significantly and causally associated with an increase in the belief that climate change is happening, human-caused and a worrisome threat. In turn, changes in these key beliefs are predictive of increased support for public action. In short, we find that perceived scientific agreement is an important gateway belief, ultimately influencing public responses to climate change.
The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.