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1 The Social Complexity of Immigration and Diversity 1

2 2 Institutional partners Theoretical Physics Group Centre for Policy Modelling

3 Researchers Nick Crossley Bruce Edmonds Edward Fieldhouse Laurence Lessard- Phillips Yaojun Li Alan McKane Ruth Meyer Tim Rogers Nick Shryane Huw Vasey 3

4 4 SCID aims New methods for complexity science –Integrating analytic and descriptive modelling approaches at different levels of abstraction New social theory –Use the new modelling approaches to Link micro and macro processes and theory Inform policy

5 5 SCID domains Immigration and diversity in relation to –Fundamental social processes Homophily, trust –Socio-economic processes Education, employment –Socio-political processes Political participation, effects of parties

6 Modelling SCID will take modelling approaches from complexity science and apply them to social science issues “Modelling” means different things to different people 6

7 Modelling 7

8 Modelling 8

9 Modelling 9 “Traditional” social science approach: –Observe data, then try to infer the causal process that generated it Statistical modelling (this also applies to qualitative analysis) –Problematic – we’re actually modelling conditional probabilities/statistical distributions; many causal models might ‘fit’ a given set of data –Research design can help – experiments vs. observation

10 Modelling 10 Process modelling approach –Generate data, using a hypothesised model of the causal process Agent-based models & differential equations –Problematic – need to be able to describe in detail the micro-level social processes –Trade-offs required; rigour vs. relevance, simple & abstract vs. complicated & realistic

11 Example of a simple agent-based model Schelling (1969) – residential segregation –Take a 2D grid and randomly populate each square with either a red ‘agent’, a blue ‘agent’, or a vacant space 11

12 Schelling model 12

13 Schelling model Schelling (1969) – residential segregation –Take a 2D grid and randomly populate each square with either a red ‘agent’, a blue ‘agent’, or a vacant space –Social process: Agents are ‘happy’ if at least d of their eight neighbours are of the same colour Each ‘turn’ an agent is picked at random, and, if unhappy, will ‘move’ to a vacant square –Even with low values of d, the grid quickly becomes spatially segregated 13

14 Schelling model of segregation 14 Turn = 0 d = 3

15 Schelling model of segregation 15 Turn = 10 d = 3

16 Schelling model of segregation 16 Turn = 100 d = 3

17 Schelling model This segregation model is a model of how a micro process can become amplified at the macro level –It’s a model of an ‘idea’, not a model of the real world Even this simple model, though, shows how ABMs can allow things like agent-agent interactions, to model processes such as social influence 17

18 Another ABM ‘Boids’ –Agents represent animals that flock/school –Each agent follows simple rules: Stay close to nearby agents but avoid collisions Travel in the direction of nearby agents –Without central control, macro ‘flocking’ emerges as a consequence of the micro rules 18

19 Boids 19 “flock of birds with Blender 2.5 Boid physics”.

20 Boids The micro rules won’t allow one to predict the exact behaviours of individual real- world birds But they do allow insight into the sort of processes that can produce recognisably realistic real-world macro patterns 20


22 Social processes in voting Influences based on social factors seem to be at work in the decision to vote or not SCID will try to specify and model the way these processes operate and influence the decision to vote 22

23 23

24 Household effect on voting 24 Assuming independent choices

25 Household effect on voting 25 Source: Cutts & Fieldhouse 2009, Am J Pol Sci Actual 2001 UK election data

26 Household effect on voting 26 Source: Cutts & Fieldhouse 2009, Am J Pol Sci

27 Data integration model of voting Agent based simulation model –Agents have characteristics, e.g. age, party affiliation, ethnicity, memory. –Agents have behaviours, e.g. voting, discussing politics, making friends. Households –Every agent belongs to a household. Networks –Agents are linked to a varying number of other agents. 27

28 Agent-Based Model (ABM) of Voting 28

29 ABM dynamics Household dynamics –Households form (‘marriage’, ‘immigration’), change (‘birth’, ‘death’, ‘kids moving out’) and dissolve (‘divorce’, ‘emigration’, ‘death’). Network dynamics –Friendships/associations form and dissolve, influenced by Characteristics in common (‘homophily’). Activities in common. Friends in common. 29

30 Agent-Based Model (ABM) of Voting 30

31 ABM dynamics Voting dynamics –Every Nth timestep, agents may vote or abstain, influenced by: The voting behaviour of their cohabiters and friends (‘conformity’, ‘norms’, ‘reaction’). Their past history of voting (‘habit’). Their interest in the outcome (‘rational-choice’). Their desire to communicate / influence others (‘expression’) 31

32 Agent-Based Model of Voting 32

33 Agent-Based Model of Voting 33

34 34

35 Data hungry With so much in the model, we need lots of data to be able to –Micro level - make sensible choices for model parameters, processes and interactions –Macro level – to evaluate whether the model is producing recognisably realistic behaviour Ideally, data not just on individuals, but on networks – who interacts with whom? –Nick Crossley to talk more on this next 35

36 Analysing and simplifying The ABM of voting will be very complex Hopefully it will be realistic (relevance) but it will be hard to fully understand (lack of rigour) We will therefore seek to “model the model” Produce a simpler (less relevant) but more analytically tractable dynamical model (rigour) 36

37 37 Data-Integration Simulation Model Micro-Evidence Macro-Data Abstract Simulation Model 1 Abstract Simulation Model 2 SNA Model Analytic Model Modelling strategy

38 Thanks for listening Project website – Manchester complexity discussion group – One-day workshop –Complexity of evolutionary processes in biology and the behavioural sciences –University of Manchester, 13th June 2011 – 38

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