Evolutionary dynamics of time-resolved social interactions

Abstract

Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.

Coupled disease–behavior dynamics on complex networks: A review

“…Example Refs social network ER network SF network multilayer networks awareness changing disease outbreak or (and) individual prevention measures [79,78,80,82,110,81,111,112,113] square lattice random network SF network urban network learning rules deciding disease status [85,84,93,117] square lattice ER network SF network adaptive network multilayer networks social network social/self-motivated protection mechanisms impacting disease status or (and) topology structure [124,79,130,128,125,126,129,133,83,87,89,91,115,116,122,127,132] time-varying network independent evolution of both disease and behavior processes [138,139] adaptive network multilayer networks social network topology properties determining disease status or (and) individual behavior [93,117,118,131,134,141,142] connection and dynamics process evolve according to their respective rules [135,136,137]. For example, Summin et al recently explored how to lower the number of vaccin...…”

Pattern transitions in spatial epidemics: Mechanisms and emergent properties

“…Aside from the disease spread itself, human behavior also affects disease prevention-mainly vaccination and several non-pharmaceutical measures [155][156][157][158][159][160][161][162]. Vaccination is a primary public health measure with the potential to prevent the transmission of infectious diseases, and hence reduce morbidity and mortality from infections [163][164][165][166].…”

A Novel Computer-Based Set-Up to Study Movement Coordination in Human Ensembles

“…Some further extensions to our work include the possibility of implementing time-varying topologies to study the effects of dynamically adding/removing connections among interacting participants (Cardillo et al, 2014), and enabling the administrator to provide the players with social cues in real time, based on the quality of their performance (i.e., as measured by the group synchronization index). In addition, it is possible to implement new mathematical models (Snapp-Childs et al, 2011; Zhai et al, 2016) for the VP to perform as joint improviser with other virtual or human agents.…”

“…However, in these papers the features and the level of coordination are not explicitly correlated to the way the players interact (i.e., the structure of their connections), as all the subjects involved share direct visual and auditory coupling with all the others, and no other patterns are considered. Moreover, inevitable social interaction affects the level of coordination in the group (Healey et al, 2005; Kauffeld and Meyers, 2009; Passos et al, 2011; D'Ausilio et al, 2012; Duarte et al, 2012, 2013; Glowinski et al, 2013; Cardillo et al, 2014). Indeed, body movements, friendship relationships, shared feelings, particular affinities, and levels of hierarchy have a significant impact on how each individual in the ensemble chooses her/his preferred partner(s) to interact the most with (Baumeister and Leary, 1995; Mäs et al, 2010; Stark et al, 2013).…”