Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas (eBook)

Rense Corten (Autor)

eBook Download: EPUB

2014
John Wiley & Sons (Verlag)
978-1-118-76294-3 (ISBN)

Infos

Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas shows students, researchers, and professionals how to use computation methods, rather than mathematical analysis, to answer research questions for an easier, more productive method of testing their models. Illustrations of general methodology are provided and explore how computer simulation is used to bridge the gap between formal theoretical models and empirical applications. An accompanying website supports the text.

Autor

Rense Corten Department of Sociology, Utrecht University, The Netherlands benefit from the novel approaches presented in this book.

Inhalt

Preface ix

1 Introduction 1

1.1 Social dilemmas and social networks 1

1.1.1 Cooperation and social networks 4

1.1.2 Coordination and social networks 5

1.2 Dynamic networks, co-evolution, and research questions 6

1.3 Social networks and social dilemmas between sociology and economics 9

1.4 Approach: Models, simulation, and empirical tests 10

1.4.1 Theoretical models 13

1.4.2 Empirical approach 14

1.5 Description of the remaining chapters 15

References 17

2 Consent or conflict: Co-evolution of coordination and networks 23

2.1 Introduction 23

2.1.1 Polarization, conflict, and coordination 24

2.1.2 Coordination and social networks 26

2.2 The model 28

2.3 Stable states 29

2.4 Simulation design 32

2.5 Simulation results 35

2.5.1 Predicting stable states I: Polarization 36

2.5.2 Predicting stable states II: Efficiency 39

2.6 Conclusions and discussion 41

References 42

3 Cooperation and reputation in dynamic networks 47

3.1 Introduction 47

3.1.1 Cooperation and network effects 48

3.1.2 The case for network dynamics 49

3.1.3 Learning in networks 50

3.1.4 Related theoretical literature 51

3.2 The model 52

3.2.1 Formalization of the problem 52

3.2.2 Individual strategies 54

3.2.3 Reputation 56

3.2.4 Network decisions 58

3.2.5 Convergence 59

3.3 Analysis of the model 60

3.3.1 Dynamics of behavior with two actors 60

3.3.2 Stable states in fixed networks 61

3.3.3 Stable states in dynamic networks 63

3.4 Setup of the simulation 65

3.4.1 Dependent variables 66

3.4.2 Parameters of the simulation 66

3.4.3 Initial conditions of the simulation 67

3.4.4 Convergence of the simulation 68

3.5 Simulation results 68

3.5.1 Results for fixed networks 68

3.5.2 Results for dynamic networks 70

3.6 Conclusions and discussion 73

References 77

4 Co-evolution of conventions and networks: An experimental study 81

4.1 Introduction 81

4.1.1 Coordination, conventions, and networks 82

4.1.2 An experimental approach 85

4.2 Model and simulation 86

4.2.1 The model 86

4.2.2 Analytic results 88

4.2.3 Simulation 90

4.2.4 Overview of micro-level and macro-level hypotheses 93

4.3 Experimental design 96

4.4 Results 97

4.4.1 Macro-level results 97

4.4.2 Individual behavior I: Decisions in the coordination game 101

4.4.3 Individual behavior II: Linking decisions 104

4.5 Conclusions and discussion 107

References 109

5 Alcohol use among adolescents as a coordination problem in a dynamic network 113

5.1 Introduction 113

5.1.1 Coordination, influence, and alcohol use 115

5.1.2 Approaches to the study of selection and influence 117

5.2 Predictions 120

5.3 Data 123

5.3.1 Data collection 123

5.3.2 Variables and measures 123

5.4 Methods of analysis 125

5.5 Results 126

5.5.1 Descriptive results 126

5.5.2 Multilevel regression using combined network measures 130

5.5.3 Multilevel regression using non-reciprocated friendship ties 132

5.5.4 Additional analyses 132

5.6 Conclusions 134

References 136

6 Conclusions 139

6.1 Summary of the findings 139

6.2 Theory, computer simulation, and empirical tests 142

6.3 Suggestions for further research 145

6.3.1 Theoretical extensions 145

6.3.2 Suggestions for empirical studies 148

References 149

Appendix A: Instructions used in the experiment 151

Appendix B: The computer interface used for the experiment 159

Reference 167

Index 169

Rezensionen

"Corten's book provides a very nice example of a
theorizing-modelling-empirical testing cycle which can advance
sociological investigation. This is a worth-reading book for every
scholar who wants to understand the research life cycle in
computational sociology. It is methodologically rigorous as well as
inspirational and enlightening." (Journal of
Artificial Societies and Social Simulation, 1 May 2014)

Textauszug

2

Consent or conflict: Co-evolution of coordination and networks*

2.1 Introduction

This chapter takes the first steps of the approach outlined in Chapter 1—specifying a model and deriving implications from this model using mathematical analysis and simulation. Substantively, this chapter revolves around the theme of coordination problems and social conflict and follows the following setup. First, I introduce coordination problems as a topic for social research, its relation to social conflicts, and discuss why it is important to model coordination problems as embedded in dynamic networks. Next, I introduce a formal game-theoretical model for such problems and derive some analytic results on stable states of this model. As is characteristic for this type of models, however, the analytic results leave many open questions. Therefore, the final step in this chapter is to apply computer simulation to further study the behavior of the model under different circumstances.

A long research tradition in sociology and social psychology has shown that social networks play an important mediating role in the diffusion of behaviors and opinions through a society. In many different contexts, people are influenced by those with whom they interact (Erickson 1988; Marsden and Friedkin 1993; Merton 1968). Empirical examples of such processes include peer pressure among adolescents (Davies and Kandel 1981), diffusion of innovations (Coleman et al. 1957; Valente 2005), rebellion, and collective action (Gould 1991, 1993; Opp and Gern 1993). These findings are relevant for the study of polarization, described as the social or ideological separation of a society into two or more groups (Esteban and Scheider 2008), because the adaptation process might increase agreement within groups, while it deepens disagreement between groups. The extent to which a society will polarize into possibly opposing camps is likely to be influenced by the patterns of social relations through which members of the society influence each other and through which opinions, behaviors, and ideologies diffuse.

It is important to realize that social networks are not always static but can be altered by actors consciously selecting their relations. At least in part, this selection processes is based on behavioral traits of others; sociological research shows that people tend to choose their friends among those who behave and think as themselves (a process known as “homophily”; see Lazarsfeld and Merton 1954; McPherson et al. 2001; Zeggelink et al. 1996). The combination of influence via networks and selective network formation processes suggests that polarization can occur through two different processes—on the one hand, polarization may occur because behavior or opinions cluster locally within networks and, on the other, a society may segregate socially because people with different behaviors or opinions tend to avoid each other.

Our study of polarization takes into account that social and ideological or behavioral alienation between groups develop interdependently. In other words, the degree to which polarization on a behavioral trait occurs depends on patterns of social ties, but this social structure itself is also influenced by behavioral choices. We aim at a theoretical understanding of the interplay between polarization of behavior and social structure. We develop a model in which actors are involved in interactions with others with whom they have social relations, while this social network itself is subject to change by the actors. This model predicts how the likelihood of polarization of behavioral outcomes depends on the social structure of a society at the time actors have to decide on a certain form of collective action or have to develop an opinion on some issue that becomes salient. In addition to problems in which persistent disagreement constitutes a clear potential for conflict, the model captures other types of processes from which conventions emerge, such as lifestyle choices of pupils in school classes. In such networks, persistent disagreement is not problematic per se. In Chapter 5, we will apply our model to such a situation. In the current chapter, however, I will stick to the theme of polarization in societies.

2.1.1 Polarization, conflict, and coordination

Group mobilization and group formation have previously been modeled in different ways, for example, as social influence processes (Axelrod 1997),1 as multi-person Prisoner's Dilemmas (Takács 2001), or as collective action problems (Gould 1999). Identification with a group can also be considered a multi-person coordination problem in the sense that belonging to a group and making the same choice as others is more important than what choice is actually made (Hardin 1995). In group identification, one prefers to join a group if others do the same because benefits can be expected from group membership itself. There is little sense in speaking English if everyone else speaks French; similarly, it may not be beneficial to identify as Serbian if everyone else identifies as Yugoslavian. However, if enough people start to call themselves Serbs, it becomes attractive to join this group.

It can be argued that not only identification with a group, but also group action is often mainly a matter of coordination. Usually, group mobilization and other forms of collective action are seen as free-rider problems. According to the “logic of collective action” (Olson 1971), individual group members should not be expected to contribute to collective efforts unless they have individual (selective) incentives that compensate their efforts. Hence, collective action should not occur in most cases because every individual has reasons to free ride on the other group members. This prediction seems at odds with the real-world observation that group action does occur in many instances, from voting to mass demonstrations and collective violence. This led some scholars to argue that coordination rather than cooperation is the basic strategic interaction that underlies group action. According to Hardin (1995), the power resulting from mass action can diminish the costs of joining to a level that is sufficiently low to reduce the free-rider problem to a problem of coordination (see Heckathorn 1996; Macy 1991; Marwell and Oliver 1993). Others (Chwe 2001; Gould 1995; Klandermans 1988; Lohmann 1994) have also pointed at the importance of coordination in collective phenomena such as rebellion, uprisings, and union participation. Therefore, by modeling collective action as a coordination problem we abstract from free-riding problems and focus on settings in which actors have an incentive to join if enough others do so as well.

Modeling group identification and group mobilization as a coordination problem is not to say that actors are indifferent between behavioral alternatives as long as they coordinate with others. The coordination game that is the backbone of our model assumes that behavioral alternatives can be ranked, even if coordination with others still has priority. Consider the simple coordination game as displayed in Figure 2.1, with c = 8. The actors have two choices, L(EFT) or R(IGHT). The game has two Nash equilibria, (R,R) and (L,L), in each of which actors do not wish to deviate as long as the other actor does not deviate. However, (L,L) yields higher payoffs for both actors and is termed efficient or payoff dominant. The other equilibrium (R,R) is attractive in the sense that it is less risky—if an actor assumes that the other actor chooses R and L with equal probabilities, the expected payoff of choosing R is higher than of choosing L. Therefore, (R,R) is risk dominant (Harsanyi and Selten 1988). For our applications, choosing L may represent joining an uprising in order to accomplish a regime change, while choosing R is to stick to the status quo. Joining the uprising is risky if you are not sure that others will also do so.

Figure 2.1 The two-person version of the multi-person coordination game with payoffs as in the simulation b < c < a < d; (a − b) > (d − c); c = 4 or c = 8.

A consequence of choosing a multi-person version of the coordination game as described above implies that we can not only provide predictions on the likelihood and extent of polarization, but also on the extent to which actors coordinate on the efficient equilibrium. However, the model does not predict how the likelihood of the emergence of violent conflict depends on the polarization that might arise in a population. Rather, the theory assumes that polarization into separated but internally coordinated groups provides potential for violent group conflict, while the model specifies the conditions under which a polarized situation is more or less likely to occur.

As a measure of polarization, we use the two-group version of the index for qualitative variation (IQV; Mueller and Schuessler 1961, pp. 177–179; see also Agresti and Agresti 1978), which is defined as 4p(L)(1 − p(L)), in which p(L) is the proportion of actors in the population choosing L. The measure implies that polarization is 0 if p(L) = 0 or p(L) = 1, while it reaches its maximal value 1 for p(L) = . This measure is the standardized...

Produkt-Details

• Erscheint lt. Verlag: 29.1.2014
• Reihe/Serie: Wiley Series in Computational and Quantitative Social Science
• Reihe/Serie: Wiley Series in Computational and Quantitative Social Science
• Reihe/Serie: Wiley Series in Computational and Quantitative Social Science
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung
• Themenwelt: Technik
• Schlagworte: Ökonometrie u. statistische Methoden • Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas • cooperation problems • Econometric & Statistical Methods • Empirical Studies • Ökonometrie u. statistische Methoden • Prisoner's Dilemmas • Rense Corten • Research Methodologies • social networks and behavior in social dilemmas • Sociology • Soziologie • Soziologische Forschungsmethoden • Statistics • Statistics for Social Sciences • Statistik • Statistik in den Sozialwissenschaften • theoretical network models
• ISBN-10: 1-118-76294-0 / 1118762940
• ISBN-13: 978-1-118-76294-3 / 9781118762943