Computational modeling and big data

1. Computational modeling and big data Presentation for discussion forum “Theory and mechanisms of social interaction in the big data era” Son Bernadinet, May 6-8, 2013 Andreas Flache University of Groningen, Department of Sociology – ICS

2. The “big data challenge” Strong version: “ Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. ” Anderson, 2008

3. A weaker version Having “more data” we need “less theory” to understand social processes. Example: predict performance of large organizations from structure of networks between employees (e.g. “small world”) without having to know the “content” of the actual network relations.

4. increasing variance of opinion distribution in some issues in some historical periods (e.g. Sexual morality; attitudes towards poor people; lifestyles; consumption tastes) • Research on small groups: evidence of opinion differences increasing over time (van Knippenberg et al. 2007, Early et al. 2000, Feldman 1969) Why Polarization in political opinions? (DiMaggio et al. 1996, Evans 2003, Fischer et al 2009) Political polarization in the internet (Lazer ea 2009)

5. Why social diversity and polarization? A puzzle? Two powerful and general mechanisms in interpersonal interaction • Homophily the more similar people are, the more they influence each other. • Influence the more people influence each other, the more similar they become. How can there be stable diversity in opinions (e.g. on cultural issues) in a world where nobody is entirely disconnected from influence?

6. A macro-social condition: complex networks Small world networks: local clustering and global integration: does this breed consensus? We argue that this depends on the micro-processes  Flache, A., & Macy, M.W. 2011. Small Worlds and Cultural Polarization. Journal of Mathematical Sociology 35.1. Pp. 146-176. | 6

7. opinion 1 0 social influence Microtheory 1: only homophily and ‘positive’ influence • Following earlier social influence models (French etc) Positive influence: • Move towards weighted average opinion (o) of local neighbours

8. Microtheory 1: only homophily and ‘positive’ influence • Following earlier social influence models (French etc) Positive influence: • Move towards weighted average opinion (s) of local neighbours Homophily: change relational weight w The more similarity opinions i-j, the more influence does j have

9. Microtheory 2: xenophobia and ‘negative’ influence added(c.f. Macy ea, 2003; Baldassari & Bearman, 2007; Fent, Groeber & Schweitzer, 2007;…) Positive and negative influence • local neighbours “pull” or “push” depending on weight wij Homophily and xenophobia: change relational weight w • average distance > zero  positive weight, else negative weight + _ distance

10. Average path length L = 6.29 Average path length L = ∞  padd tie= 0.003 Density D = 0.045 Clustering C = 0.814 Density D = 0.04 Clustering C = 1.0 A computational experiment • Add small proportion of “random ties” between initially disconnected clusters • Compare effects micro-theory 1, and micro-theory 2 • N=100, 20 “caves”, K=2, opinion initially uniform random

11. Microtheory 1: homophily and positive influence Outcome polarization measure P P=0: consensus P=1 Perfect polarization in two opposing factions Adding random ties reduces polarization

12. Microtheory 2: …plus xenophobia and negative influence Outcome polarization measure P P=0: consensus P=1 Perfect polarization in two opposing factions Adding random ties increases polarization

13. Tentative conclusion “Small worlds and polarization” • Knowing the structure is not enough • Prediction of theories of social influence and selection depends decisively on microprocess: • Only homophily and positive influence:  Smaller world fosters consensus • also xenophobia and negative influence:  smaller world can increase polarization

14. Big data and computational modelling • Even if we had “big data” on the structure of social networks between all members of a large population (e.g. mobile phone calls, c.f. Eagle, Macy 2010, Science) • … we need to know how “process sensitive” outcomes are in a given structure (computational modelling) • … we need more and better data to know how people’s opinions affect each other in social interaction. “big data”? • “high quality small data”: lab experiments • 0nline experiments (but little control on subjects) • “sentiment analysis” of online interactions? (not yet sufficiently accurate)

15. Empirical research: experiments (Takács e.a. in progress) We conducted a series of 4 experiments with in total 443 subjects. Overall design: Measure subjects’ opinions on pre-selected issues. • E.g. “0..100 percent of immigrants who come to the Netherlands for economic reasons should receive a residence permit. ” • Pair subjects varying distance on opinions and other characteristics. • Repeated sequence of • exposure to others’ opinions, • (exchange messages to influence each other) • adjust opinions. • Attractions (“weights”) were also measured repeatedly • In some conditions, we manipulated initial attraction • E.g. dictator games, football support, different moral positions

16. Results experiments (1) Negative shift positive shift No effect initial opinion distance on direction of influence

17. Results experiments (2) Also no effect of (induced) initial disliking  No evidence for negative influence in lab.

18. Polarization without negative influence A model based on persuasive argument theory (Mäs, Takács, Flache & Jehn, 2012, Organization Science) • Agents differ by demographic characteristics and by opinion • Opinion is constituted by number of pro- and con arguments held by agents (e.g. arg_vector ++----  opinion = -0.33 ) • Homophily: the more similar agents, the more likely they interact • Influence: if agent i interacts with j, then i adopts one of the arguments currently held by j and ‘forgets’ another argument previously held.

19. Polarization with maximal faultline strength But this only happens when initial opinion is sufficiently correlated with demographic attributes (demographically biased opinions)

20. How “criss-crossing” agents prevent polarization A “criss-crossing” agent shares at least one demographic attribute with each of the subgroups • a small probability of interaction between subgroups always remains • The faultline can never be maximal • Sooner or later arguments will be communicated between opposing subgroups and the system moves into consensus eventually

21. Strong faultline with three ‘criss-crossing’ agents (N=20) Polarization in the short run Consensus in the long run

22. A bold generalization “Big data” on opinion formation are (only?) useful in combination with • (computational) modelling of underlying mechanisms and • “high quality small data”

23. What if we had the ideal “big data”? • Record of all interactions between individual members of a population (who does when interact with whom) • Proxy: geographical residence of all members of the population, other places where they meet (employers, voluntary associations, …) • Their opinions on a range of issues pre- post interaction (or in temporally fine-grained panel) • A range of individual characteristics (demographic…)