Theory and mechanisms of social interactions in the big data era (May 6-8, 2013)

1. Theory and mechanisms of social interactions in the big data era (May 6-8, 2013) Maxi San Miguel [IFISC (CSIC-UIB)] & Anxo Sánchez [GISC-UC3M]

2. Why are we here?

3. -Robert Axtell (Computational Social Science, George Mason University, Fairfax) -Guillaume Deffuant (Laboratoire d’Ingeniérie pour les Systèmes Complexes, Aubière) -Bruce Edmonds (Centre for Policy Modelling, Manchester Metropolitan University) -Andreas Flache (Department of Sociology, Rijksuniversiteit Groningen) -Cars Hommes (CeNDEF & Department of Quantitative Economics, Univ. Amsterdam) -Alan Kirman (GREQAM, Marseille) -Jurgen Jost (Max Planck Institute for Mathematics in the Sciences, Leipzig) -Tom Lenaerts (Computer Science Department, Université Libre de Bruxelles) -Michael Macy (Department of Sociology, Cornell University, Ithaca) Meeting designers: -Maxi San Miguel (IFISC, CSIC-UIB, Mallorca) -Anxo Sánchez (Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés) Local (IFISC, CSIC-UIB) participants: -Víctor M. Eguílluz -José J. Ramasco -Raúl Toral

4. What is this workshop about? Predicting vs. Understanding in Social Behavior

5. Two conflicting views (I) The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

6. Two conflicting views (II) The focus on data detracts from the need to get a better theoretical grasp on social systems. Note that even the very best of these computational articles are largely focused on existing theories. That's valuable, but it is only one piece of what needs to be done.

7. So, what is the plan? Brainstorm to discuss whether and how the theoretical approach to the mechanisms at work in social contexts can guide research leading to breakthroughs in the social sciences taking into account the availability of big data. Aim:Establish a roadmap for the development of theoretical social sciences in the big data era

8. We believe we need to know: What theories of human behavior and mechanisms of social interactions are well established? Which theories are most desperately needed? How are these theories related to data? Economics, sociology, cognition: Can we really make progress towards an applicable, unified body of knowledge identifying what is needed from these different perspectives?

9. We believe we need to know: Theory and models of emotions, expectations, trust, meaning, their explanatory power in relation to data, and data analysis oriented to assessing these behaviors. Simple versus elaborate models. Details vs universal behavior. When can researchers resort to very simple behavioral rules and when is it needed to include very detailed models. What can be learned from each of them? Proposing theories from observational data, versus designing data collection to validate theories.

10. Proposed tentative schedule Today, 19 h: Trust, emotions, thresholds, emotional waves (Alan Kirman) Tomorrow, 9 h: Expectations (Cars Hommes) Tomorrow, 11 h: Heterogeneity, noise and simulation (Rob Axtell) Tomorrow, 16:30 h: Computational models and big data (Andreas Flache) Tomorrow, 18 h: Context dependence in behavior and data (Bruce Edmonds) Wednesday, 8:30: Interactions, micro-macro and collective effects (Juergen Jost) Wednesday, 10:30: Conclusions (Michael Macy)

11. But:

12. We can change it now We can rearrange it on the fly

13. So, let’s start! In this session the discussion is intended to identify key points for the debate, initial disagreements or alternative viewpoints and to plan the rest of sessions

14. Alan Kirman: Trust, emotions, thresholds, emotional waves Emotions and trust. Big data as indicator of emotional waves. Thresholds: reactions to collective behavior vs reacting to individual ones. Aggregate behavior as resulting from self-organization: emergence vs typical agents.

15. Cars Hommes: Expectations Heterogeneous expectations as a source of instability, in particular in financial/macroeconomic contexts: models and paradigms. Heterogeneous expectations vs heterogeneous agents. Theory verification by experiments and data. More on stylized vs detailed simulations.

16. Robert Axtell: Heterogeneity, noise and simulation Sorting noise from actual human variability in large data sets, in order to incorporate realistic levels of agent heterogeneity into models. Tradeoffs in large numbers of simple agents vs a few complex agents. Data as an indicator of relevant simulation sizes.

17. Andreas Flache: Computational modeling and big data How can computational modelling be fruitfully related to the analysis of big data? Computational modeling to generate different theoretical scenarios and to confront them with the data. What and how much do we need to know about the "content" of social interactions, to predict behavior of social systems from data on their macroscopic structure (e.g. complex network structure)?

18. Bruce Edmonds: Context-dependence in behaviour and data It is well known that many aspects of human cognition and behaviour are sharply context-dependent including: language, preferences, memory, social norms, visual perception, judging trustworthiness and assessing reputation. Thus we would expect to see this in both the data and in our models of human behaviour. However how to do this best is not clear, thus the discussion is on ways forward to possibly: detect the influence of social context, identify it, exploit it in our analysis and generally deal with it.

19. Jürgen Jost: Interactions, micro-macro, and collective effects What behavior can be found empirically, how flexible and adaptive is it, how does it influence and shape the collective processes, and to what degree does it reflect or anticipate those processes? In my opinion, the focus should not be on models of individual behavior, but on those processes of interaction between the different levels. Can there emerge qualitatively different types of dynamics? If so, what triggers them, and what are the relevant bifurcation parameters and threshold variables? Or in social terms, who is following whom, and by what kind of behavior get individuals into a leadership position? What types of collective dynamics 'enslave' the individual degrees of freedom, that is, make the behavior of the individuals simpler than what they might do in isolation?

20. Michael Macy: CONCLUSIONS? -Perspective paper for SCIENCE? A discussion of the role of theory in the social sciences could be of great interest to our readers. I am especially interested in the questions you raised --"Which theories are most desperately needed? How are these theories related to data?" The best next step would be for us to communicate after the meeting in May so that we can discuss what would be included in the manuscript. (B. Jasny, Science) -Roadmap for the development of theoretical social sciences in the big data era? -List of selected hard and important questions to be addressed in the near future?

21. Questions: • Correct ordering of sessions? • What is missing? (Tom?) • What can be shortened? • Suggestions for efficient discussions? • Work groups, lunch dinner, evening, Tuesday informal discussions? • …?...?...?