Computational Models of Social Influence And Collective Behavior

2017 ◽  
pp. 258-280 ◽  
Author(s):  
Robert J. MacCoun
2014 ◽  
Vol 37 (1) ◽  
pp. 83-85
Author(s):  
Alejandro N. García ◽  
José M. Torralba ◽  
Ana Marta González

AbstractBentley et al. bypass the relevance of emotions in decision-making, resulting in a possible over-simplification of behavioral types. We propose integrating emotions, both in the north–south axis (in relation to cognition) as well as in the west–east axis (in relation to social influence), by suggesting a Z-axis, in charge of registering emotional depth and involvement.


2019 ◽  
Author(s):  
Jae-Young Son ◽  
Apoorva Bhandari ◽  
Oriel FeldmanHall

Justice systems delegate punishment decisions to groups in the belief that the aggregation of individuals’ preferences facilitates judiciousness. However, group dynamics may also lead individuals to relinquish moral responsibility by conforming to the majority’s preference for punishment. Across five experiments (N=399), we find Victims and Jurors tasked with restoring justice become increasingly punitive (by as much as 40%) as groups express a desire to punish, with every additional punisher augmenting an individual’s punishment rates. This influence is so potent that knowing about a past group’s preference continues swaying decisions even when they cannot affect present outcomes. Using computational models of decision-making, we test long-standing theories of how groups influence choice. We find groups induce conformity by making individuals less cautious and more impulsive, and by amplifying the value of punishment. However, compared to Victims, Jurors are more sensitive to moral violation severity and less readily swayed by the group. Conformity to a group’s punitive preference also extends to weightier moral violations such as assault and theft. Our results demonstrate that groups can powerfully shift an individual’s punitive preference across a variety of contexts, while additionally revealing the cognitive mechanisms by which social influence alters moral values.


Author(s):  
Christopher Winship

This article explores threshold models of social influence, with a particular focus on the consequences of simple heuristics in the context of social influence on collective decision-making processes. It first provides an overview of social-influence and threshold models before discussing influence cascades on complete and random networks. It then considers cascades in networks that emphasize the importance of social groups in the formation of influence networks, namely random-group networks and generalized-affiliation networks. It also describes cascade-seeding strategies and demonstrates how the structure of influence networks shapes the roles of individual actors (prominent or not) in the generation of collective behavior, including wild cascades of influence.


2019 ◽  
Vol 24 (2) ◽  
pp. 103-120 ◽  
Author(s):  
Michael Muthukrishna ◽  
Mark Schaller

Societies differ in susceptibility to social influence and in the social network structure through which individuals influence each other. What implications might these cultural differences have for changes in cultural norms over time? Using parameters informed by empirical evidence, we computationally modeled these cross-cultural differences to predict two forms of cultural change: consolidation of opinion majorities into stronger majorities, and the spread of initially unpopular beliefs. Results obtained from more than 300,000 computer simulations showed that in populations characterized by greater susceptibility to social influence, there was more rapid consolidation of majority opinion and also more successful spread of initially unpopular beliefs. Initially unpopular beliefs also spread more readily in populations characterized by less densely connected social networks. These computational outputs highlight the value of computational modeling methods as a means to specify hypotheses about specific ways in which cross-cultural differences may have long-term consequences for cultural stability and cultural change.


2018 ◽  
pp. 457-497
Author(s):  
Lizabeth A. Crawford ◽  
Katherine B. Novak

2005 ◽  
Vol 9 (9) ◽  
pp. 424-430 ◽  
Author(s):  
Robert L. Goldstone ◽  
Marco A. Janssen

2022 ◽  
Vol 18 (1) ◽  
pp. e1009772
Author(s):  
Marina Papadopoulou ◽  
Hanno Hildenbrandt ◽  
Daniel W. E. Sankey ◽  
Steven J. Portugal ◽  
Charlotte K. Hemelrijk

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons’ collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior.


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