Estimating within-school contact networks to understand influenza transmission

Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than nonfriends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS505 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Normal stresses in semiflexible polymer hydrogels

Biopolymer gels such as fibrin and collagen networks are known to develop tensile axial stress when subject to torsion. This negative normal stress is opposite to the classical Poynting effect observed for most elastic solids including synthetic polymer gels, where torsion provokes a positive normal stress. As recently shown, this anomalous behavior in fibrin gels depends on the open, porous network structure of biopolymer gels, which facilitates interstitial fluid flow during shear and can be described by a phenomenological two-fluid model with viscous coupling between network and solvent. Here we extend this model and develop a microscopic model for the individual diagonal components of the stress tensor that determine the axial response of semi-flexible polymer hydrogels. This microscopic model predicts that the magnitude of these stress components depends inversely on the characteristic strain for the onset of nonlinear shear stress, which we confirm experimentally by shear rheometry on fibrin gels. Moreover, our model predicts a transient behavior of the normal stress, which is in excellent agreement with the full time-dependent normal stress we measure.Comment: 12 pages, 8 figure

Information dynamics algorithm for detecting communities in networks

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.Comment: Submitted to "Communication in Nonlinear Science and Numerical Simulation

Friendship Networks

Building upon a long tradition in sociology, economists have recently turned their attention to the analysis of social networks. The present paper adds to this emerging literature by proposing a different approach to social-network formation. As in the model of Jackson and Wolinsky (1996), formation of a link between two individuals requires two-sided investments in the present framework. But in contrast to their approach, where the required investments are exogenously specified and link formation is deterministic, the level of individual investment is a decision variable in the present model and link formation is stochastic. Thus, the probability that a link is formed between two individuals depends on the ``effort" both agents devote to creating the link. These effort levels are chosen noncooperatively via Nash behavior. As in the Jackson-Wolinsky model, indirect links are worth less than direct linkages to other individuals. But, in contrast to their assumption of a smooth benefit decay as link distance increases, the present framework assumes that benefits are zero when more than two links are involved. The model can be viewed as a portrayal of friendship networks. For two individuals to form a friendship, each must exert effort, which could involve inviting the other person to dinner at his house, arranging other types of social outings, or buying gifts on special occasions. Effort creates ``direct" friendships, and the combination of such links leads to ``indirect" friendships. Concretely, a particular individual may invite all of his direct friends to a dinner party at his house, and through socializing with one another, these people enjoy indirect friendships. The paper analyzes the effort externalities that are present in the model, showing the effort levels are universally too low. In addition, the analysis explores the effect of several types of asymmetries on the network structure, as reflected in effort levels and the probability of link formation.social networks

Three Essays on Defending Common-Pool Resources

% !TEX root = ../degeest2017dissertation.tex Environmental protection often relies on cooperation between individuals in uncoordinated groups. In cases such as the management of common-pool resources, individuals must not only monitor and enforce behavior within their group to prevent over-exploitation. They must also contend with external threats on the resource like poaching. This dissertation studies how individuals cooperate to manage shared resources and deter shared threats. The first chapter, Deterring poaching of a common-pool resource , considers the problem of deterring a threat that cannot be perfectly observed. I present results from common pool resource experiments designed to examine the ability of a group of resource users, called insiders, to simultaneously manage their own exploitation and defend their resource from encroachment by outsiders. The insiders can use communication, peer monitoring and sanctions to coordinate their decisions. In addition, they can sanction any outsiders they observe. I vary the insiders\u27 ability to observe and sanction the outsiders from no observability to partial and full observability. I find a striking non-monotonicity between observability of the outsiders and levels of poaching. Poaching was higher under partial monitoring than zero monitoring, and was lower and more stable under full monitoring. Although full observability allowed the insiders to better coordinate their own harvests, they were unable to fully deter poaching because their sanctions were far too low and they were unwilling to punish low levels of poaching. The second chapter, Defending public goods and common-pool resources , studies cooperation and deterrence of a shared threat in different strategic environments. In many real-world social dilemmas, groups of individuals must cooperate to create surplus and defend it from theft. Theft can either foster or discourage collective action. On the one hand, a shared threat can align individual incentives. On the other hand, surplus creation may decrease if individuals are unsure how group members will contribute towards defense. Moreover, there is literature that suggests cooperation is sensitive to whether individual actions confer positive externalities (public goods, PG) or negative externalities (common-pool resources, CPR) on group members -- the cooperation divergence . To examine the relationship between cooperation and defense in different externality settings, I conduct an experiment in which a group of insiders providing a public good or conserving a common-pool resource must coordinate to deter outsiders from stealing the value of their surplus. Our theory predicts that theft will have no different effect on behavior across externality settings. However, I find that it does. Surplus creation is significantly higher in the CPR treatment, while surplus defense is significantly higher in the PG treatment. Across both treatments, I find that the shared threat increases variation within groups, but the effect is more dramatic in the PG treatment. Finally, the third chaper, Enforcement networks in social dilemmas , studies how enforcement emerges and evolves in the first chapter. Sanctions can increase cooperation in social dilemmas, but they impose a high social cost until a credible threat to non-cooperative behavior is established. Moreover, credible threats depend on enforcement structure. For example, small sanctions implemented by many subjects may have a different impact on behavior than the same volume of sanctions meted out by a single subject. In order to understand how credible threats to deviant behavior emerge, it is therefore necessary to study how enforcement structure emerges and evolves in groups. I study enforcement structure by taking a network approach to data from a social dilemma experiment with peer punishment. The exchange of sanctions between subjects can be framed as a directed, weighted network that evolves, enabling us to use tools from network structure to summarize, predict and simulate behavior. I first visualize and summarize the structure of these networks and show that enforcement structure is non-random and tends to cluster around a few individuals. I then model network formation and network efficiency using an empirical framework that separately considers edge formation (a binary sanctioning event) from edge weight (sanction size) and find that subjects respond more to the act of being sanctioned rather than the volume of sanctions. Finally, I recover the underlying Markov process governing enforcement structure and simulate expected long-run behavior. I conclude with a discussion of how my approach can be used to study generalized exchange networks

PLoS One

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups.PR51 RR000169/PR/OCPHP CDC HHS/United StatesR01 HD068335/HD/NICHD NIH HHS/United StatesR24 OD011136/OD/NIH HHS/United StatesR24 RR024396/RR/NCRR NIH HHS/United StatesR24 RR024396/RR/NCRR NIH HHS/United States21857922PMC315393

A Framework for Automatic Behavior Generation in Multi-Function Swarms

17 USC 105 interim-entered record; under review.Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed

Social network structure and the spread of complex contagions from a population genetics perspective

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a non-linear function of the fraction of the individuals' affected neighbors, such a spreading process becomes a "complex contagion". This non-linearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g. for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g. for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex epidemics on real social networks, we present a general framework for analyzing the statistics of contagions with arbitrary non-linear adoption probabilities based on the mathematical tools of population genetics. Our framework provides a unified approach that illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global epidemics become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate