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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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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
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?