1. Modelling Radical Innovation Dr Christopher Watts Research Fellow Centre for Research in Social Simulation (CRESS) c.watts@surrey.ac.uk ESRC Research Methods Festival 2010, St Catherine’s College, University of Oxford

2. www.simian.ac.uk2 “ Radical innovation ” ? How “radical” can an innovation be and still diffuse? –Groups like familiar things –Groups dominated by a minority But we still need novel solutions! Good ideas may lie outside the group…

3. www.simian.ac.uk3 Overview SIMIAN: Novelty / Innovation 3 examples of generative mechanisms –Cluster formation –Stratification –Problem solving through searching Science models

4. www.simian.ac.uk4 About SIMIAN Funded by: –ESRC National Centre for Research Methods 3 sub-projects shared between Surrey and Leicester: –Repeated Interaction –Novelty (Innovation) –Norms Outcomes: –Training courses –“Demonstrator” simulations –3 books

5. www.simian.ac.uk5 The book Working title: “Tools for Rethinking Innovation” Use simulation models to illustrate some contrasting ideas about innovation generation, diffusion and impact Chapters bring together different perspectives –Science Models & Search in Social Networks Social Network Analysis + Bibliometrics + Organisational Learning –Adopting & Adapting Diffusion of Innovations + Actor-Network Theory / Sociology of translations –Creative Destruction Evolutionary Economics + Complexity Science

6. www.simian.ac.uk6 Methodology for Social Simulation Empirical patterns –Scientists (and other academics) are: clustered stratified problem solving / conducting searches Why? –Identify possible generative mechanisms Sociology, social psychology, economics, statistical mechanics… Represent in a computer simulation –Micro-level agent behaviour –Reproduce empirical patterns / macro-level behaviour Address “what-if?” questions; policy decisions Middle-range models – not too abstract, but not facsimiles of reality

7. www.simian.ac.uk7 Examples (1): Cultural group formation People prefer to interact with those similar to themselves (“homophily”) Interactions lead to imitation …which leads to more similarity Result: Homogeneous groups emerge amongst initial diverse

8. www.simian.ac.uk8 Clustering: the evidence Contents: –disciplines; fields; subfields; issues Social: –cliques, elites; co-authors / collaborators; journal boards; conferences Institutional: –universities, faculties, departments, groups / centres, individuals

9. www.simian.ac.uk9 Clustering: The Implications Being in the cluster vs. Spanning boundaries Pooling resources; Promoting trust Excluding outsiders; Promoting “groupthink” Easier to find recognition from peers Harder to break away? Innovations more likely to come from “boundary spanning”? –Novel combinations can come from interdisciplinary work –But boundary spanners need to be accepted by the group…

10. www.simian.ac.uk10 (2): Growth with Preferential Attachment Grow a network by adding one person at a time –Each new person links to one person already present in network That person is chosen with preference for links Result: the numbers of links per person forms a particular distribution (“scale-free”)

11. www.simian.ac.uk11 The Matthew Effect Rich-get-richer / Cumulative advantage principle –“For to all those who have, more will be given, and they will have an abundance; but from those who have nothing, even what they have will be taken away.” (Matthew 25:29, New RSV) Identifiable in sciences (Merton) –Nobel Prize winners & their students –Co-author reputation –Citations

12. www.simian.ac.uk12 Stratification: the evidence A minority accounts for a majority of importance –# publications, # citations, # coauthors, funds… –Individuals, institutions, countries –Across disciplines, countries

13. www.simian.ac.uk13 Stratification: The Implications Success attracts resources (causes more success…) –Elite control over what gets researched? –Lack of exploration? Get into a field via Citation Classics, big-name authors Does overall production vary with distribution of production? –Would egalitarian redistribution of wealth help overall?

14. www.simian.ac.uk14 (3): Heuristic Search Methods “Heuristic” = “Rules of thumb” –Not guaranteed to find the best solution –May be worse than random guesses! Finds reasonably good solutions in a reasonably short time –“Bounded rationality” (H. Simon) E.g. hill climbing on a “fitness landscape” –Step in a random direction –If fitness (height) worse then step back, else adopt new position –Repeat until fitness good enough Analogies with human problem solving?

15. www.simian.ac.uk15 Exploration versus Exploitation Balance –Too narrow? - Better areas missed –Too widely? - Ideas found not made use of Does preference for similarity help search? –Creates groups which focus attention –Creates cultural boundaries inhibiting diffusion Does cumulative advantage help? –Summarises field through “citation classics” –Elite excludes outsiders’ good ideas

16. www.simian.ac.uk16 Science Models Simulate academic publication For each new paper select: –Authors –References –Contents a “fitness” value –Reviewers Record patterns (papers per author etc.) Validate (partly) with bibliometric data

17. www.simian.ac.uk17 Bibliometric data Electronic databases –Web of Science; Scopus Patterns –Geometric growth of a field Derek DS Price discovered this with a tape measure! Networks –Who co-authors with whom –Which paper cites which other papers (Performance?) Metrics –E.g. hirsch index –RAE/REF? University policy?

18. www.simian.ac.uk18 Experiment 1 Treat writing as attempt to search a fitness landscape Evaluate effect on search performance of varying organisational policies –Rich (publications, citations) get richer –Preference for similarity

19. www.simian.ac.uk19 Experiment 2 Does varying the landscape’s properties (esp. “difficulty”) alter the emergent distributions and network structure? Should we model an extrinsically sourced landscape at all? –100% Socially constructed sciences?

20. www.simian.ac.uk20 Early findings There is more than one way to generate a plausible-looking cumulative-advantage pattern in citations Some methods give better search performance than others The difference in the descriptions of these methods can be quite subtle –Easy for modellers to make mistakes!

21. www.simian.ac.uk21 Science models & Search Models of science can combine 3 generative mechanisms –Preference for similarity >>> Clustering –Rich-get-richer >>> Stratification –Heuristic search >>> Problem solving These affect the balance between exploration and exploitation Hence they affect problem-solving performance Implications for science policy and academic publishing practices?