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Modelling Radical Innovation

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. “ Radical innovation ” ?.

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Modelling Radical Innovation

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  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. “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… www.simian.ac.uk

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

  4. 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 www.simian.ac.uk

  5. 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 www.simian.ac.uk

  6. 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 www.simian.ac.uk

  7. 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 www.simian.ac.uk

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

  9. 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… www.simian.ac.uk

  10. (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”) www.simian.ac.uk

  11. 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 www.simian.ac.uk

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

  13. 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? www.simian.ac.uk

  14. (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? www.simian.ac.uk

  15. 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 www.simian.ac.uk

  16. 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 www.simian.ac.uk

  17. 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? www.simian.ac.uk

  18. 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 www.simian.ac.uk

  19. 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? www.simian.ac.uk

  20. 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! www.simian.ac.uk

  21. 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? www.simian.ac.uk

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