A Computational Model of Immigration and Diversity

1. A Computational Model of Immigration and Diversity Bruce Edmonds Centre for Policy Modelling,Manchester Metropolitan University

2. A €3M, 5-year UK project funded by theUnder their “Complexity in the Real World” Initiative Institute for Social Change&Theoretical Physics Group,University of Manchester Centre for Policy Modelling,Manchester Metropolitan University

3. SCID Researchers • UoM, Institute for Social Change: Ed Fieldhouse Nick Shryane Nick Crossley Yaojun Li Laurence Lessard-Phillips HuwVasey • MMU, Centre for Policy Modelling Bruce Edmonds Ruth Meyer Stefano Picascia • UoM, Dept. for Theoretical Physics Alan McKane Tim Rogers

4. Where this fits in FuturICT • An example of Complexity Science, Social Sciences and ICT combining to model social processes • Specifically to make Complexity Science usefulto the other • Also, to road-test ways of increasing innovation within the Social Sciences • And (when further developed) ideal for exploiting Big Data sources from mobile devices etc. • A demonstration of the kind of approach that might be used for simulating Crime etc.

5. Interfacing Complexity and Social Science Approaches • Physics and Social Science have very different languages, cultures and approaches • We would like the power of approaches and tools of complexity physics but appropriately applied and not in “brave leaps” of abstraction which lose relevance to the observed • (In particular the way that much work in economics involves unrealistic assumptions and a lack of relevance to what is observed) • Thus in SCID simulations, albeit complex ones, will be the common interface and provided a common reference

6. In Vitro vsIn Vivo • In biology there is a well established distinction between what happens in the test tube (in vitro) and what happens in the cell (in vivo) • In vitro is an artificially constrained situation where some of the complex interactions can be worked out… • ..but that does not mean that what happens in vitrowill occur in vivo, since processes not present in vitrocan overwhelm or simply change those worked out in vitro • One can (weakly) detect clues to what factors might be influencing others in vivobut the processes are too complex to be distinguished withoutin vitroexperiments

7. PossibilisticvsProbibilistic • The idea is to map out some of the possiblesocial processes that may happen • Including ones one would not have thought of or ones that have already happened • The global coupling of context-dependent behaviours in society make projecting probabilities problematic • Increases understanding of why processes (such as the spread of a new racket) might happen and the conditions that foster them • Good for analysing risk – how a prediction might go wrong • Can be used for designing early-warning indicators of newly emergent trends • Complementary to statistical models

8. Unravelling the Micro-Macro Link Upward causation – emergence Downward causation – immergence

9. KISS vs. KIDS • KISS: Models that are simple enough to understand and check (rigour) are difficult to directly relate to both macro data and micro evidence (lack of relevance) • KIDS: Models that capture the critical aspects of social interaction (relevance) will be too complex and slow to understand and thoroughly check (lack of rigour) • Butwe need bothrigour and relevance • Mature science connects empirical fit and explanation from micro-level (explanatory and phenomenological models)

10. KISS vs. KIDS as a search strategy

11. The Modelling Approach SNA Model Analytic Model Abstract Simulation Model 1 Abstract Simulation Model 2 Data-Integration Simulation Model Micro-Evidence Macro-Data

12. Aims and Objectives of a DIM • To develop a simulation that integrates as much as possible of the relevant available evidence, both qualitative and statistical (a Data-Integration Model – a DIM) • Regardless of how complex this makes it • A description of a specified kind of situation (not a general theory) that represents the evidence in a single, consistent and dynamicsimulation • This simulation is then a fixed and formal target for later analysis and abstraction

13. But why not just jump straight to simple models? • There are many possible models and you don’t know whyto choose one rather than another, this method provides the underlying reasons • Much social behaviour is context-specific, and this approach allows one to check whether a particular simple model holds when background features/assumptions change • The chain of reference to the evidence is explicit, allowing one to trace their effect and possibly better criticise/improve the model • This approach facilitates the mapping onto qualitative stories/evidence

14. An overview of model structure

15. Basic Elements • 2D grid of locations each of which has either a: household, work place, school, activity 1 centre, activity 2 centre, or empty • People in household going through lifecycle according to the timescale: 1945-2010 (birth, death, migration, partnering, separation, moving out. etc.) • Social network made of: intra-household links, shared activity membership (schools, work, religion, etc.), “friendship” links • Influence occurs over the social network contingent on the state of those involved

16. Population Model • Agents are in households: parents, children etc. of different ages in one location • Initialised from a sample of 1992 BHPS • Agents are born, age, make partnerships have children, move house, separate, die • UK-based moving in/out of region, as well as international immigration/emigration • Rates of all the above estimated from available statistics

17. Agent Characteristics • Age, Ethnicity, location, children, parent, partner, political leaning, date last moved, etc. • The activities it participates in • Its social connections • Plus a memory of facts, e.g.: • “talked about politics with” agent324 blue 1993 • “got desired result from voting” red 1997 • “I am a voter” 2003 • “pissed off with my own party” 2004

18. Immigration and Movement • No special rules for different ethnicities or kinds of people (e.g. class) • Rather composition (household size, income, class, education, civic involvement etc.) derived from survey data • Class and ethnicity come into effect via homophily – people have a tendency to make friends with those similar to themselves (including age, ethnicity, education level, class, location etc.) • This effects the social networks that develop • Which, in turn, effect mutual influence, communication and the spread of social norms

19. Activities Model • As well as households there are activities: schools, places of work, and (currently 2) kinds of activity (church and canoe clubs) • Kids (4-18) attend one of 2 local schools • Those employed (from 16-65) attend a place of work randomly • Activities are joined probabilistically, with choice related to homophily (similarity to existing members)

20. Social Network Model • A “connection” is a relationship where a conversation about politics might occur (but only if the participants are inclined/receptive) • All members of a household are connected; when someone moves out there is a chance of these being dropped as connections • There is a probability of people attending the same activity to be connected (chance varying according to similarity) • There is a chance of spatial neighbours who are most similar being connected • There is a chance of a “Friend of a Friend” becoming a connection • Connections can be dropped

21. Communication and its Effects • Social norms transmitted in pimarily within households (if not contradictory) • Interest in politics transmitted via contact network by interested/involved agents with those who are receptive • Some discussants may be more influential than others • Bias in terms of held beliefs and norms may evolve due to coherence / incoherence in the messages from others • Interest & biases might convert to action if the situation the agent is in is appropriate

22. Approach • Learning process with social scientists, consisting of iterations of: • Rapid prototyping of simulations • Critique and response from social scientists base on evidence • Until the social scientists start becoming (in a small way) informal programmers • Thus prototype is in NetLogo for ease of access and rapidity of adaption • “Production” version will be in Java/Repast

23. Demonstration Run Pictureof World ParametersandControls Indicative GraphsandHistograms SimpleStatistics concerningOutcomes Pseudo-narrative log of eventshappening to a single agent

24. Two Contrasting Sets of Runs “Inner City” set, 20 runs • death-mult 1.2 • immigration-rate 0.035 • density 0.9 • forget-mult 2.28 • drop-friend-prob 0.3 • prob-move-near 0.2 • majority-prop 0.6 • drop-activity-prob 0.15 • int-immigration-rate 0.01 • prob-partner 0.35 • move-prob-mult 0.7 • init-move-prob 2.5 • emmigration-rate 0.055 • birth-mult 1 “Country” set, 20 runs • death-mult 1.5 • immigration-rate 0.005 • density 0.32 • forget-mult 0.56 • drop-friend-prob 0.18 • prob-move-near 0.2 • majority-prop 0.95 • drop-activity-prob 0.065 • int-immigration-rate 0.015 • prob-partner 0.17 • move-prob-mult 0.2 • init-move-prob 2.5 • emmigration-rate 0.15 • birth-mult 0.6

25. Population Makeup “Inner City” set, 20 runs “Country” set, 20 runs

26. Av Local Clustering “Inner City” set, 20 runs “Country” set, 20 runs

27. Same Ethnicity over Links “Inner City” set, 20 runs “Country” set, 20 runs

28. Example Development of Social • Three “snapshots” of the social network from a single run of the “Inner City” version • Darker links are within-household, lighter are other social links • Each link indicates a relationship where if the agents are so minded they might discuss or otherwise influence each other concerning politics, voting etc. • The issue about initialisation is clearly visible here

29. Social Network at 1950

30. Social Network at 1980

31. Social Network at 2010

32. Effect of Immigration Rate on Voting

33. Conclusions • Statistical models give little information about social causation within the context of individuals • But crime cannot be properly understood without the social processes that facilitate or act to reduce it • Crime is not treated as a special social phenomena, but just one kind of behaviour that might arise • A data driven approach to these social process might enable us to understand the prevalence (or relative absence!) or crime • Such simulations are data hungry, so are ideal for using detailed person-by-person data as input • Context-dependent data-mining techniques could well be used in both input data as well as for understanding outputs • This will involve a lot of work, and probably a multi-model approach stretching from cognitive models up to social trends in a chain of models… • …but it is possible!

34. The End