4 research outputs found
Diffusion in Networks and the Unexpected Virtue of Burstiness
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
Behavioral Communities and the Atomic Structure of Networks
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
Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness
Youth experiencing homelessness (YEH) are subject to substantially greater
risk of HIV infection, compounded both by their lack of access to stable
housing and the disproportionate representation of youth of marginalized
racial, ethnic, and gender identity groups among YEH. A key goal for health
equity is to improve adoption of protective behaviors in this population. One
promising strategy for intervention is to recruit peer leaders from the
population of YEH to promote behaviors such as condom usage and regular HIV
testing to their social contacts. This raises a computational question: which
youth should be selected as peer leaders to maximize the overall impact of the
intervention? We developed an artificial intelligence system to optimize such
social network interventions in a community health setting. We conducted a
clinical trial enrolling 713 YEH at drop-in centers in a large US city. The
clinical trial compared interventions planned with the algorithm to those where
the highest-degree nodes in the youths' social network were recruited as peer
leaders (the standard method in public health) and to an observation-only
control group. Results from the clinical trial show that youth in the AI group
experience statistically significant reductions in key risk behaviors for HIV
transmission, while those in the other groups do not. This provides, to our
knowledge, the first empirical validation of the usage of AI methods to
optimize social network interventions for health. We conclude by discussing
lessons learned over the course of the project which may inform future attempts
to use AI in community-level interventions
Innovation and Strategic Network Formation
We study a model of innovation with a large number of firms that create new
technologies by combining several discrete ideas. These ideas can be acquired
by private investment or via social learning. Firms face a choice between
secrecy, which protects existing intellectual property, and openness, which
facilitates learning from others. Their decisions determine interaction rates
between firms, and these interaction rates enter our model as link
probabilities in a learning network. Higher interaction rates impose both
positive and negative externalities on other firms, as there is more learning
but also more competition. We show that the equilibrium learning network is at
a critical threshold between sparse and dense networks. At equilibrium, the
positive externality from interaction dominates: the innovation rate and even
average firm profits would be dramatically higher if the network were denser.
So there are large returns to increasing interaction rates above the critical
threshold. Nevertheless, several natural types of interventions fail to move
the equilibrium away from criticality. One policy solution is to introduce
informational intermediaries, such as public innovators who do not have
incentives to be secretive. These intermediaries can facilitate a
high-innovation equilibrium by transmitting ideas from one private firm to
another