Diffusion, Seeding, and the Value of Network Information

Akbarpour, Mohammad; Malladi, Suraj; Saberi, Amin

doi:10.1145/3219166.3219225

Cited by 20 publications

(12 citation statements)

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“…The fact that broadcast generates fewer conversations than seeding under common knowledge need not imply that people learn less-because the direct effect of broadcasting is to inform more people. 4 Nevertheless, the theory opens up the possibility of reversing the ordering one would expect from the "infection-type" models often used to study information transmission (Bass, 1969;Bailey, 1975;Jackson, 2008;Jackson and Yariv, 2011;Aral and Walker, 2012;Akbarpour et al, 2017). In those models, people pass on the information with some probability without being asked, or ask questions without any strategic motive if they don't have the information (again with some probability).…”

Section: Introductionmentioning

confidence: 99%

When Less is More: Experimental Evidence on Information Delivery During India's Demonetization

Banerjee¹,

Breza²,

Chandrasekhar³

et al. 2018

SSRN Journal

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How should policymakers disseminate information: by broadcasting it widely (e.g., via mass media), or letting word spread from a small number of initially informed "seed" individuals? While conventional wisdom suggests delivering information more widely is better, we show theoretically and experimentally that this may not hold when people need to ask questions to fully comprehend the information they were given. In a field experiment during the chaotic 2016 Indian demonetization, we varied how information about demonetization's official rules was delivered to villages on two dimensions: how many were initially informed (broadcasting versus seeding) and whether the identity of the initially informed was publicly disclosed (common knowledge). The quality of information aggregation is measured in three ways: the volume of conversations about demonetization, the level of knowledge about demonetization rules, and choice quality in a strongly incentivized decision dependent on understanding the rules. Our results are consistent with four predictions of a model in which people need others' help to make the best use of announced information, but worry about signaling inability or unwillingness to correctly process the information they have access to. First, if who is informed is not publicized, broadcasting improves all three outcomes relative to seeding. Second, under seeding, publicizing who is informed improves all three outcomes. Third, when broadcasting, publicizing who is informed hurts along all three dimensions. Finally, when who is informed is made public, telling more individuals (broadcasting relative to seeding) is worse along all three dimensions.

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Section: Introductionmentioning

confidence: 99%

When Less is More: Experimental Evidence on Information Delivery During India's Demonetization

Banerjee¹,

Breza²,

Chandrasekhar³

et al. 2018

SSRN Journal

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“…Similar to the exercise in Banerjee et al (2013), which centrality measures perform well in practice for finding the optimal placement of such agents? And, along the lines of Akbarpour et al (2017), how many additional placements would have to be picked at random to prompt a larger diffusion than the optimum? total infection.…”

Section: Discussionmentioning

confidence: 99%

Diffusion in Networks and the Unexpected Virtue of Burstiness

Akbarpour

Jackson

2017

Proceedings of the 2017 ACM Conference on Economics and Computation

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Whether an idea, information, infection, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts", active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns -some are bursty and others are not. Here we show that, if people differ in their activity patterns, bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.

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“…1 Although there is an enormous literature that examines how to partition the nodes of a network into a collection of "communities" (see Fortunato (2010)), much of that literature is motivated entirely by the position of nodes in a graph. 2 This traces back to Lorrain 1 Random seedings can do well in simple contagion processes in which there is no threshold for behavioral choices (Akbarpour, Malladi, and Saberi, 2017). However, if behavior involves a threshold, then our approach significantly outperforms random seedings.…”

Section: Introductionmentioning

confidence: 88%

Behavioral Communities and the Atomic Structure of Networks

Jackson

Storms

2017

SSRN Journal

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We develop a theory of 'behavioral communities' and the 'atomic structure' of networks. We define atoms to be groups of agents whose behaviors always match each other in a set of coordination games played on the network. This provides a microfoundation for a method of detecting communities in social and economic networks. We provide theoretical results characterizing such behavior-based communities and atomic structures and discussing their properties in large random networks. We also provide an algorithm for identifying behavioral communities. We discuss applications including: a method of estimating underlying preferences by observing behavioral conventions in data, and optimally seeding diffusion processes when there are peer interactions and homophily. We illustrate the techniques with applications to high school friendship networks and rural village networks. JEL Classification Codes: D85, D13, L14, O12, Z13

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Diffusion, Seeding, and the Value of Network Information

Cited by 20 publications

References 0 publications

When Less is More: Experimental Evidence on Information Delivery During India's Demonetization

When Less is More: Experimental Evidence on Information Delivery During India's Demonetization

Diffusion in Networks and the Unexpected Virtue of Burstiness

Behavioral Communities and the Atomic Structure of Networks

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