scholarly journals A general analysis of boundedly rational learning in social networks

2021 ◽  
Vol 16 (1) ◽  
pp. 317-357 ◽  
Author(s):  
Manuel Mueller-Frank ◽  
Claudia Neri
Keyword(s):  
Social Networks ◽  
Utility Function ◽  
Learning Outcomes ◽  
Network Structure ◽  
Bayesian Updating ◽  
General Analysis ◽  
Differential Treatment ◽  
Normative Foundation ◽  
Rational Learning ◽  
Optimal Action

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we provide a normative foundation for quasi‐Bayesian updating, where a quasi‐Bayesian agent treats others' actions as if they were based only on their private signal. Quasi‐Bayesian updating induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly asymmetric environments. In all other environments, learning fails in networks with a diameter larger than 4. Finally, we consider a richer class of updating behavior that allows for nonstationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems that induce learning for most networks.

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

scholarly journals The Network Structure of Police Misconduct

2019 ◽  
Vol 5 ◽  
pp. 237802311987979 ◽  
Author(s):  
George Wood ◽  
Daria Roithmayr ◽  
Andrew V. Papachristos
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Police Officers ◽  
Police Misconduct ◽  
Police Departments ◽  
Top Down ◽  
Network Ties ◽  
Individual Level ◽  
Factors Associated ◽  
Using Data

Conventional explanations of police misconduct generally adopt a microlevel focus on deviant officers or a macrolevel focus on the top-down organization of police departments. Between these levels are social networks of misconduct. This study recreates these networks using data on 16,503 complaints and 15,811 police officers over a six-year period in Chicago. We examine individual-level factors associated with receiving a complaint, the basic properties of these misconduct networks, and factors related to officer co-naming in complaints. We find that the incidence of police misconduct is associated with attributes including race, age, and tenure and that almost half of police officers are connected in misconduct ties in broader networks of misconduct. We also find that certain dyadic factors, especially seniority and race, strongly predict network ties and the incidence of group misconduct. Our results provide actionable information regarding possible ways to leverage the co-complaint network structure to reduce misconduct.

A General Analysis of Sequential Social Learning

2021 ◽  
Author(s):  
Itai Arieli ◽  
Manuel Mueller-Frank
Keyword(s):  
Social Learning ◽  
Utility Function ◽  
Posterior Probability ◽  
General Analysis ◽  
Sufficient Condition ◽  
Important Class ◽  
General Utility ◽  
Learning Game ◽  
Necessary And Sufficient ◽  
The Given

This paper analyzes a sequential social learning game with a general utility function, state, and action space. We show that asymptotic learning holds for every utility function if and only if signals are totally unbounded, that is, the support of the private posterior probability of every event contains both zero and one. For the case of finitely many actions, we provide a sufficient condition for asymptotic learning depending on the given utility function. Finally, we establish that for the important class of simple utility functions with finitely many actions and states, pairwise unbounded signals, which generally are a strictly weaker notion than unbounded signals, are necessary and sufficient for asymptotic learning.

scholarly journals Misinformation, Radicalization and Hate Through the Lens of Users

2020 ◽  
Author(s):  
Manoel Horta Ribeiro ◽  
Virgílio A. F. Almeida ◽  
Wagner Meira Jr
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Network Structure ◽  
Online Social Networks ◽  
Hate Speech ◽  
Far Right ◽  
Content Creation ◽  
Political Opinion

The popularization of Online Social Networks has changed the dynamics of content creation and consumption. In this setting, society has witnessed an amplification in phenomena such as misinformation and hate speech. This dissertation studies these issues through the lens of users. In three case studies in social networks, we: (i) provide insight on how the perception of what is misinformation is altered by political opinion; (ii) propose a methodology to study hate speech on a user-level, showing that the network structure of users can improve the detection of the phenomenon; (iii) characterize user radicalization in far-right channels on YouTube through time, showing a growing migration towards the consumption of extreme content in the platform.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

Social Networks in the Higher Education Framework- Understanding the University as an Organization

2012 ◽  
pp. 805-824 ◽  
Author(s):  
José Antonio Álvarez Bermejo ◽  
César Bernal Bravo ◽  
Manuel Jesús Rubia Mateos ◽  
Javier Roca Piera
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Social Network ◽  
Learning Outcomes ◽  
Learning Process ◽  
Self Esteem ◽  
Social Aspects ◽  
Restricted Group ◽  
University Degree ◽  
The University

Recent studies are focusing on how social networks impact the learning process and how students organize themselves to face collaborative tasks via these networks, as well as their impact on the learning outcomes of the students. In a number of these studies, learning social aspects are analyzed, showing, among other issues of interest, that participating in social networks positively affects students’ self-esteem. In this article it is shown how this applies to the university model being adopted in Europe. Nowadays, the student is limited by the class and by the restricted group of people enrolled in that same university degree. In which way can the university facilitate that students get to each other so that they can find aspects in common and therefore the set of relationships grows? This chapter shows how our university—Universidad de Almería, UAL—globalizes its campus providing access to every student, as well as how this social network is succeeding.

Simulating Social Network Formation

2011 ◽  
pp. 581-599
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Model And Simulation ◽  
Graph Theoretic Models

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

scholarly journals Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

2020 ◽  
Vol 11 (1) ◽  
pp. 18-24
Author(s):  
Morgan Prust ◽  
Abby Halm ◽  
Simona Nedelcu ◽  
Amber Nieves ◽  
Amar Dhand
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Network Structure ◽  
Network Size ◽  
Personal Network ◽  
Mapping Tool ◽  
Social Network Structure ◽  
Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

scholarly journals Cultural selection shapes network structure

2019 ◽  
Vol 5 (8) ◽  
pp. eaaw0609 ◽  
Author(s):  
Marco Smolla ◽  
Erol Akçay
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Cultural Evolution ◽  
Level Structure ◽  
Population Level ◽  
Trade Off ◽  
Skill Sets ◽  
Individual Level ◽  
Dense Networks ◽  
The Social

Cultural evolution relies on the social transmission of cultural traits along a population’s social network. Research indicates that network structure affects information spread and thus the capacity for cumulative culture. However, how network structure itself is driven by population-culture co-evolution remains largely unclear. We use a simple model to investigate how populations negotiate the trade-off between acquiring new skills and getting better at existing skills and how this trade-off shapes social networks. We find unexpected eco-evolutionary feedbacks from culture onto social networks and vice versa. We show that selecting for skill generalists results in sparse networks with diverse skill sets, whereas selecting for skill specialists results in dense networks and a population that specializes on the same few skills on which everyone is an expert. Our model advances our understanding of the complex feedbacks in cultural evolution and demonstrates how individual-level behavior can lead to the emergence of population-level structure.

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