Ease of scientific discovery over time.

<p>(A) Mean diameter (kilometers) of minor planets discovered, 1802–2008. (B) Mean physical size (g) of mammalian species discovered, 1760–2003. (C) Mean inverse of atomic weight of chemical elements discovered, 1669–2006. Adapted from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002072#pcbi.1002072-Arbesman1" target="_blank">[9]</a>.</p

Cooperation, decision time, and culture: Online experiments with American and Indian participants

<div><p>Two separate bodies of work have examined whether culture affects cooperation in economic games and whether cooperative or non-cooperative decisions occur more quickly. Here, we connect this work by exploring the relationship between decision time and cooperation in American versus Indian subjects. We use a series of dynamic social network experiments in which subjects play a repeated public goods game: 80 sessions for a total of 1,462 subjects (1,059 from the United States, 337 from India, and 66 from other countries) making 13,560 decisions. In the first round, where subjects do not know if connecting neighbors are cooperative, American subjects are highly cooperative and decide faster when cooperating than when defecting, whereas a majority of Indian subjects defect and Indians decide faster when defecting than when cooperating. Almost the same is true in later rounds where neighbors were previously cooperative (a cooperative environment) except decision time among Indian subjects. However, when connecting neighbors were previously not cooperative (a non-cooperative environment), a large majority of both American and Indian subjects defect, and defection is faster than cooperation among both sets of subjects. Our results imply the cultural background of subjects in their real life affects the speed of cooperation decision-making differentially in online social environments.</p></div

Visualization of strategy evolution over time.

<p>Sample runs with randomly chosen saints (top panel) and no saints (middle and bottom panels). Number of leaders = 5, radius of recruitment = 1, <i>s</i>_{<i>j</i> ∉ {<i>Leaders</i>}} ∈ [0,1]. The middle panel shows an example of a run that quickly led to full-scale risky behavior, while the bottom panel shows a more moderate state. Snapshots were taken at four different generations and node color indicates risk propensity. With saints, the network tends towards low risk (even though saints, like leaders, are exempted from global learning, so the population is not learning directly from the saints). Without saints, the network tends towards higher risks than with saints (middle, bottom).</p

Network of connections and designated raid leaders.

<p><b>(A)</b> Randomly generated 91-node network. The “leaders” are highlighted in red. <b>(B)</b> Degree distribution (cumulative frequency) of the Nyangatom friendship network (N = 91) and simulated small-world networks with N = 91, 200, 300, 400, 500.</p

Model dynamics with two types of network-based control strategies.

<p>Control strategies for violence. Mean risk-taking ratio given an intervention of (A) never-violent “saints” or (B) always-violent “devils” in the population. Circles correspond to assigning saints or devils to the network randomly; triangles are when they are assigned to top-degree nodes. Assigning to top-degree nodes is consistently a better strategy in this model.</p

Characterization of model dynamics.

<p><b>(A)</b> Mean risk-taking ratio as a function of recruitment radius for networks of various sizes. Graphs are initialized to have an MRR of 1 at <i>r = 1</i>. <b>(B)</b> Mean risk-taking ratio as a function of initial maximal probability of participating in a raid.</p

Relationships between changes in network measures.

<p>Relationships between changes in network measures.</p

Egocentric network involving an ego with N = 8 alters labeled A, B, …, H.

<p>For clarity, the left panel shows only the ego-alter ties while the right panel shows only alter-alter ties. Closeness is computed as the average strength of the ego-alter ties (left panel) and dividing by 10 to make the range 0 to 1. Analogously, transitivity is computed as the average strength of the alter-alter ties (right-panel, including the 0 strength null ties that are not depicted) and dividing by 10.</p

Regressions of individual outcomes on current and lagged network measures.

<p>Regressions of individual outcomes on current and lagged network measures.</p

Friends as sensors yield early detection of the use of hashtags.

<p>a) Measures of lead times based on simulations of an infection spreading through a network with infection probability and recovery probability on a Barabasi-Albert random network with tail exponent show that a sensor group tends to provide earlier warning than a randomly-chosen control group in smaller samples, but decreasing sampling variation in larger sample sizes means that the statistical likelihood of providing early warning is maximized in moderately-sized samples. b) Observed results for hashtags on Twitter used by 1% of the individuals using a hashtag of each sample. c) Average lead time of first usage of each hashtag in the sensor group vs. the control group for all hashtags used by at least 10 users in each of 5 random samples of 50,000 random users.</p