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Modelling Radical Innovation. Dr Christopher Watts Research Fellow Centre for Research in Social Simulation (CRESS) [email protected] ESRC Research Methods Festival 2010, St Catherine’s College, University of Oxford. “ Radical innovation ” ?. - PowerPoint PPT Presentation
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Modelling Radical Innovation
Dr Christopher WattsResearch Fellow
Centre for Research in Social Simulation (CRESS)[email protected]
ESRC Research Methods Festival 2010,St Catherine’s College, University of Oxford
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…
www.simian.ac.uk3
Overview• SIMIAN: Novelty / Innovation• 3 examples of generative mechanisms
– Cluster formation– Stratification– Problem solving through searching
• Science models
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
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
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
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
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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.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…
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”)
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
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
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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.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?
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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.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
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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?
Journal: Research Policy
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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.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?
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!
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?