1. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 1 A Computational Model of Immigration and Diversity Bruce Edmonds Centre for Policy Modelling, Manchester Metropolitan University

2. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 2 A €3M, 5-year UK project funded by the Under 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 3 SCID Researchers UoM, Institute for Social Change: Ed Fieldhouse Nick Shryane Nick Crossley Yaojun Li Laurence Lessard-Phillips Huw Vasey MMU, Centre for Policy Modelling Bruce Edmonds Ruth Meyer Stefano Picascia UoM, Dept. for Theoretical Physics Alan McKane Tim Rogers

4. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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 useful to 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 6 In Vitro vs In 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 vitro will occur in vivo, since processes not present in vitro can overwhelm or simply change those worked out in vitro One can (weakly) detect clues to what factors might be influencing others in vivo but the processes are too complex to be distinguished without in vitro experiments

7. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 7 Possibilistic vs Probibilistic The idea is to map out some of the possible social 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 8 Unravelling the Micro-Macro Link Upward causation – emergence Downward causation – immergence

9. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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) But we need both rigour and relevance Mature science connects empirical fit and explanation from micro-level (explanatory and phenomenological models)

10. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 10 KISS vs. KIDS as a search strategy

11. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 11 The Modelling Approach Data-Integration Simulation Model Micro-Evidence Macro-Data Abstract Simulation Model 1 Abstract Simulation Model 2 SNA Model Analytic Model

12. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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 dynamic simulation This simulation is then a fixed and formal target for later analysis and abstraction

13. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 13 But why not just jump straight to simple models? There are many possible models and you don’t know why to 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 14 An overview of model structure Underlying Data from Surveys about Population Composition etc. Demographics of people in households (both native and immigrant) Homophily effects the social network and membership of organisations etc. Social network effects how individuals influence each other, reinforcing and/or changing existing norms/opinions This effect the behaviours of individuals, which can then be extracted from the simulation as model results and compared with evidence etc.

15. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 23 Demonstration Run Parameters and Controls Pseudo-narrative log of events happening to a single agent Simple Statistics concerning Outcomes Picture of World Indicative Graphs and Histograms

24. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 25 Population Makeup “Inner City” set, 20 runs“Country” set, 20 runs

26. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 26 Av Local Clustering “Inner City” set, 20 runs“Country” set, 20 runs

27. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 27 Same Ethnicity over Links “Inner City” set, 20 runs“Country” set, 20 runs

28. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 29 Social Network at 1950

30. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 30 Social Network at 1980

31. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 31 Social Network at 2010

32. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 32 Effect of Immigration Rate on Voting

33. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 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. Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 34 The End