Modelling Radical Innovation

Modelling Radical Innovation

Dr Christopher WattsResearch 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

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“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…

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Overview• SIMIAN: Novelty / Innovation• 3 examples of generative mechanisms

– Cluster formation– Stratification– Problem solving through searching

• Science models

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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

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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

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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

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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

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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…

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(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”)

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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

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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?

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(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

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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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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

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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?

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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!

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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?