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"Christakis, Nicholas A"
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Blueprint : the evolutionary origins of a good society
An exploration of the biological roots of positive social behavior reveals how human genes have countered violence and self-interest with equally inherent, society-building tendencies toward friendship, cooperation, and learning.
Book
Locally noisy autonomous agents improve global human coordination in network experiments
A networked colour coordination game, with humans interacting with autonomous software bots, shows that bots acting with small levels of random noise and being placed centrally in the network improves not only human–bot interactions but also human–human interactions at distant nodes.
Erratic AI helps humans to cooperate
Collective action towards a common goal, even if everyone's interests are aligned, faces a 'coordination' problem: an individual's attempts to reach a personal, locally optimized solution may not be optimal for the group as a whole. Now Nicholas Christakis and colleagues have introduced autonomous software ('bots') in small networks of humans engaged in solving a standard colour coordination game in which the collective goal is for every node to have a colour different from all of its neighbour nodes, so as to study the potential benefits of introducing noise in the decision making. They find that noisy bots work best when displaying moderate (10%) randomness and placed centrally in the network. Such bots not only improve human–bot but also human–human interactions at distant nodes, thus helping humans to help themselves.
Coordination in groups faces a sub-optimization problem
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,
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,
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,
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,
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,
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and theory suggests that some randomness may help to achieve global optima
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,
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,
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. Here we performed experiments involving a networked colour coordination game
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in which groups of humans interacted with autonomous software agents (known as bots). Subjects (
n
= 4,000) were embedded in networks (
n
= 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.
Journal Article
Social Network Sensors for Early Detection of Contagious Outbreaks
Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9-16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.
Journal Article
Population flow drives spatio-temporal distribution of COVID-19 in China
Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics
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–
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. Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal ‘risk source’ model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.
Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage.
Journal Article
Inequality and visibility of wealth in experimental social networks
Wealth inequality and wealth visibility can potentially affect overall levels of cooperation and economic success, and an online experiment was used to test how these factors interact; wealth inequality by itself did not substantially damage overall cooperation or overall wealth, but making wealth levels visible had a detrimental effect on social welfare.
Conspicuous wealth undermines cooperation
Wealth inequality and wealth visibility can both potentially affect levels of cooperation in a society and overall levels of economic success. Akihiro Nishi
et al
. use an online game to test how the two factors interact. Surprisingly, wealth inequality by itself did not damage cooperation or overall wealth as long as players do not know about the wealth of others. But when players' wealth was visible to others, inequality had a detrimental effect.
Humans prefer relatively equal distributions of resources
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,
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,
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,
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,
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, yet societies have varying degrees of economic inequality
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. To investigate some of the possible determinants and consequences of inequality, here we perform experiments involving a networked public goods game in which subjects interact and gain or lose wealth. Subjects (
n
= 1,462) were randomly assigned to have higher or lower initial endowments, and were embedded within social networks with three levels of economic inequality (Gini coefficient = 0.0, 0.2, and 0.4). In addition, we manipulated the visibility of the wealth of network neighbours. We show that wealth visibility facilitates the downstream consequences of initial inequality—in initially more unequal situations, wealth visibility leads to greater inequality than when wealth is invisible. This result reflects a heterogeneous response to visibility in richer versus poorer subjects. We also find that making wealth visible has adverse welfare consequences, yielding lower levels of overall cooperation, inter-connectedness, and wealth. High initial levels of economic inequality alone, however, have relatively few deleterious welfare effects.
Journal Article
Detecting Emotional Contagion in Massive Social Networks
Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.
Journal Article
Cooperative behavior cascades in human social networks
Theoretical models suggest that social networks influence the evolution of cooperation, but to date there have been few experimental studies. Observational data suggest that a wide variety of behaviors may spread in human social networks, but subjects in such studies can choose to befriend people with similar behaviors, posing difficulty for causal inference. Here, we exploit a seminal set of laboratory experiments that originally showed that voluntary costly punishment can help sustain cooperation. In these experiments, subjects were randomly assigned to a sequence of different groups to play a series of single-shot public goods games with strangers; this feature allowed us to draw networks of interactions to explore how cooperative and uncooperative behaviors spread from person to person to person. We show that, in both an ordinary public goods game and in a public goods game with punishment, focal individuals are influenced by fellow group members' contribution behavior in future interactions with other individuals who were not a party to the initial interaction. Furthermore, this influence persists for multiple periods and spreads up to three degrees of separation (from person to person to person to person). The results suggest that each additional contribution a subject makes to the public good in the first period is tripled over the course of the experiment by other subjects who are directly or indirectly influenced to contribute more as a consequence. These results show experimentally that cooperative behavior cascades in human social networks.
Journal Article
The Collective Dynamics of Smoking in a Large Social Network
The prevalence of smoking has decreased substantially in the United States over the past 30 years. This article examines the extent of person-to-person spread of smoking behavior and the extent to which groups of widely connected people quit together. Smoking behavior spreads through close and distant social ties, groups of interconnected people quit in concert, and smokers are increasingly marginalized socially.
This article examines the extent of person-to-person spread of smoking behavior and the extent to which groups of widely connected people quit together.
Roughly 44.5 million adults were smokers in the United States in 2004,
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and smoking remains the leading preventable cause of death,
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with 440,000 deaths annually.
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Nevertheless, the prevalence of smoking has declined from 45% to 21% over the past four decades.
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Past studies have documented the impact of dyadic social ties on the initiation and cessation of smoking, primarily in young people.
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,
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However, the extent to which smoking depends on how people are embedded in a social network and the extent to which smoking behavior transcends direct dyadic ties are not known. Since diverse phenomena can spread within social . . .
Journal Article