Staged Models for Interdisciplinary Research

Staged Models for Interdisciplinary Research, Bruce Edmonds et al,., CoSyDy, LEEDS, May 20165. slide 1

Staged Models for Interdisciplinary Research

Bruce Edmonds, et al.Centre for Policy Modelling

Manchester Metropolitan University


Credits I

Research work was done as part of the “SCID” (Social Complexity of Immigration and Diversity) project, (cfpm.org/scid) by:• Luis Fernandez Lafuerza, Louise Dyson, Alan McKane,

Department of Theoretical Physics, University of Manchester

• Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University

• Laurence Lessard-Phillips, Ed Fieldhouse, Institute for Social Change, University of Manchester

This work was funded by the EPSRC under grant number EP/H02171X/1 and is described in ArXiv and forthcoming PLoSOne papers (details at end).


Key Problems We Address

1. Given that everything cannot be adequately represented with a simple model, we are often faced with an invidious choice between:

– A model that is simple enough to be understood (rigour) and analysed but missing key features

– A model that includes all relevant features (relevance) but is too complicated to fully understand

But good science needs both rigour and relevance2. This causes some of the difficulties between

social and formal scientists – social scientists value relevance, formal scientists rigour


Our Basic Approach

• To stage abstraction with an intermediate, complex model, that is then, itself, modelled

• The Data Integration Model (DIM) includes all that is deemed relevant by social scientists

• The simpler models of the DIM are developed by formal scientists but validated against the DIM

Representation

Simplification

DIM


A Complex Model of Voter Behaviour (the DIM)Edmonds, B., Lessard-Phillips, L. and Fieldhouse, E. (2014). A Complex Model of Voter Turnout. CoMSES Computational Model Library. http://openabm.org/model/4368


Development of the DIM

• The idea here is to include all elements for which there is some evidence that they are important

• Even if this results in a very complex model• 52 ‘causal stories’ were extracted from the

literature, including from qualitative social science• A modelling framework which allowed these to be

expressed within a single model was developed• How each of the processes were implemented

and calibrated using evidence in literature• ‘Gaps’ in evidence filled in via ‘expert opinion’


Model Simplification Process

• DIM can be evidence-led without worrying (too much) that the result is over complicated

• DIM treated as if it were any other natural phenomena to be modelled, but much easier to validate against (we can measure it perfectly)

• Formal scientists can be free in throwing out stuff, since relevance can be guaranteed via DIM

• It was an iterative process of examining the micro-level of both, and experimenting with macro-level fit of key dynamics as with modelling anything

• Simpler model can then be more easily analysed


Some of the (micro-level) causation

• Reduced model eliminated: kids, social network, rational consideration, political parties, confounding, household immigration and variable population

• Kept were: habit, civic duty and political interest


Some of the Model Variations

Some of the model variations (selected ones that revealed interesting things about the DIM)


Level of political discussion

Reduced Model Compared with DIM

Turnout Proportion

Just the end values plotted

Different dynamics in the transition region, and somewhat different levels of voter turnout


With Different Kinds of Network

Black = DIM, Red = Simpler with Network from DIM, Blue = with synthetic network (random, same local degree, long-distance links via rewiring)Synthetic Network has bistability in transitionAdding high degree or rewiring in DIM also showed bistability


DIM network approximated with ‘Cave Man’ Network

Network from DIM Clustered ‘Cave Man’ Network (term from Dunbar)


Comparison of DIM, Random and ‘Cave Man’ Networks

M1 DIM model, M2 is reduced model with random network with same av. degree, M2+CN with fixed ‘Cave Man’ network


Fixed vs. Dynamic Networks

Reduced models with fixed network extracted from full model (1β, blue) and with a dynamic network where links between families change (2A, red) Dynamism increases turnout due to diversity of links


Individual vs. Household Immigration

With immigrants of either a lower or higher political interest than natives, individual immigration resulted in higher level of turnout than household immigration, due to asymmetry of influence process

Low Interest Immigrants High Interest Immigrants

IndividualImmigration

FamilyImmigration


Final Comparison

Reduced model + dynamic ‘cave man’ network + household immigration had good fit to DIM turnout dynamics but orders of magnitude faster and amenable to complete checking


What was Learnt?

The comparisons suggested that:• ‘high’ and ‘low’ turnout regimes exist, depending on rate of

discussions• when social network has low degree and high clustering

reproduces the gradual transition seen in the complex model from low to high regimes

• (with an increase in discussion rate, high connectedness would result in bistable transition)

• The network was important:– a clustered network was important– the dynamism of the network resulted in higher turnout levels

• household immigration reduced turnout compared to individual immigration

Social Scientists are now investigating these hypotheses


Conclusions

• Relevance and rigour combined, but via a ‘chain’ of highly related models, not in a single model

• Intermediate abstraction step of a DIM• (Compared to ‘brave’ single-step abstraction)• Each model acts as a check on the other• Leveraging understanding of highly complex

phenomena via a model we do not completely understand (the DIM)

• But was relatively labour intensive• Enabled social and formal scientists to work

effectively together, each to their own strengths – crucial communication via a (complicated) model


Further Simplification Steps!

• We continued this process…• …producing an even simpler

model of the ‘simple model’ that could be solved analytically

• Thus extending the model chain and producing new insights

• and hence new hypotheses about the underlying observed phenomena

Analytically Solvable Model


The End

The papers can be read at: http://arxiv.org/abs/1604.00903 (this work, soon in PLoSOne) and

http://arxiv.org/abs/1508.04024 (the further simplification step, soon in EPJ-B)

The models are at: http://openabm.org/model/4368 and http://openabm.org/model/4686

These slides are at: http://slide-share.net/BruceEdmondsBruce Edmonds: http://bruce.edmonds.nameCentre for Policy Modelling: http://cfpm.org

Science

Staged Models for Interdisciplinary Research