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Simulating Superdiversity

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An invited talk given at the Institute for Research into Superdiversity (IRIS), University of Brimingham, 31st Jan 2017

Abstract:
A simulation to illustrate how the complex patterns of cultural and genetic signals might combine to define what we mean by "groups" of people is presented. In this model both (a) how each individual might define their "in group" and (b) how each individual behaves to others in 'in' or 'out' groups can evolve over time. Thus groups are not something that is precisely defined but is something that emerges in the simulation. The point is to illustrate the power of simulation techniques to explore such processes in a non-prescriptive way that takes the micro-macro distinction seriously and represents them within complex simulations. In the particular simulation presented, groups defined by culture strongly emerge as dominant and ethnically defined groups only occur when they are also culturally defined.

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Simulating Superdiversity

  1. 1. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 1 Simulating Superdiversity Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University
  2. 2. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 2 Acknowledgements • This work came out of a long personal collaboration with David Hales  • A tiny part of the “SCID” project (the Social Complexity of Immigration and Diversity), 2010-2016, funded by the EPSRC under their “Complexity Science for the Real World” call  • In conjunction with the Cathy Marsh Institute for Social Research and the Department for Theoretical Physics at the University of Manchester 
  3. 3. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 3 Aims of Talk • To talk about agent-based social simulation, and its place in social science • To illustrate how social simulation might be used to explore and illustrate issues of diversity • To show both its power and its difficulties • To, hopefully, inspire collaboration for the development of this tool for understanding issues of diversity
  4. 4. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 4 Caveats! • What will be presented is an abstract simulation • This should be treated as a kind of “thought experiment” to suggest ideas, hypotheses etc. • It has not been checked against any observed data and so does not tell us about what happens in observed processes/phenomena • It is merely to show what sort of thing can be put into a simulation… • …with the hope of stimulating collaborations that might develop a model with a better evidential grounding from which conclusions might be drawn
  5. 5. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 5 Structure of Talk 1. About agent-based social simulation 2. A brief bit of historical simulation context 3. About the simulation model set-up 4. The complexity of simulation outcomes 5. How this kind of simulation might be developed into something more serious
  6. 6. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 6 Agent-Based Simulation • Is a computer program • Much like a multi-character game, where each social actor is represented by a different “agent” • These agents can each have very different behaviours and characteristics • Social phenomena (such as social networks) can emerge out of the decisions and interaction of these individual agents (upwards “emergence”) • But, at the same time, the behaviour of individuals can be constrained by “downwards” acting rules and social norms from society and peers • No particular theoretical assumptions are needed!
  7. 7. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 7 System Dynamics, Statistical, or other Mathematical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  8. 8. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 8 System Dynamics, Statistical, or other Mathematical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  9. 9. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 9 System Dynamics, Statistical, or other Mathematical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  10. 10. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 10 System Dynamics, Statistical, or other Mathematical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  11. 11. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 11 System Dynamics, Statistical, or other Mathematical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  12. 12. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 12 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  13. 13. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 13 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  14. 14. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 14 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  15. 15. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 15 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  16. 16. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 16 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  17. 17. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 17 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes
  18. 18. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 18 Individual- or Agent-based simulation Real World Individual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes Agent-
  19. 19. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 19 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation
  20. 20. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 20 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation
  21. 21. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 21 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Specification (incl. rules)
  22. 22. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 22 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Specification (incl. rules)
  23. 23. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 23 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Specification (incl. rules)
  24. 24. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 24 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Specification (incl. rules)
  25. 25. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 25 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Specification (incl. rules)
  26. 26. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 26 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Specification (incl. rules)
  27. 27. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 27 What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (e.g. sets of rules like: if this has happened then do this) • Repeatedly evaluated in parallel to see what happens • Outcomes are inspected, graphed, pictured, measured and interpreted in different ways Simulation Representations of OutcomesSpecification (incl. rules)
  28. 28. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 28 In Vitro vs In Vivo Analogy • 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 vivo • 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 or observation
  29. 29. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 29 The Micro-Macro Link • How do the tendencies, abilities and observed behaviour of individuals… • …relate to the measured aggregate properties of society? • Social Embedding etc. implies this link is complex • Averaging assumptions (a general tendency + random noise) do not capture non-linear interaction • This is often two-way, with society constraining and framing individual action as well as individual constituting society in an emergent fashion • Somewhat-persistent, complicated meso-level structures mediate these effects – these might be key to understanding this
  30. 30. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 30 Micro-Macro Relationships Micro/ Individual data Qualitative, behavioural, social psychological data Theory, narrative accounts Social, economic surveys; Census Macro/ Social data Simulation
  31. 31. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 31 Micro-Macro Relationships Micro/ Individual data Qualitative, behavioural, social psychological data Theory, narrative accounts Social, economic surveys; Census Macro/ Social data Simulation
  32. 32. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 32 Simulations can be very complex • Simulations can be complicated, with lots of detail happing simultaneously to many agents in parallel • This is the point of agent-based simulation, since it allows us to track complicated processes that we could not hold in our mind • There may be emergent phenomena – patterns that appear at the macro level that are not obviously ‘built into’ the structure but result from the processes at the micro level • As well as constraints from the population and surrounding agents on behaviour of individuals • This makes the simulations difficult to understand
  33. 33. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 33 Understanding Simulations • Although complex, simulation outcomes can be inspected in multiple ways at any level of detail • Any number of experiments on the simulation can be performed to test understandings • Population of agents can be measured just as people can be, (but all of them and without error) • However other ways can be more helpful, e.g. – Using different visualisations of the population – Looking at social networks – Following individual agents and generating their ‘stories’
  34. 34. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 34 Historical Context 1: Schelling’s Segregation Model Schelling, Thomas C. 1971. Dynamic Models of Segregation. Journal of Mathematical Sociology 1:143- 186. Rule: each iteration, each dot looks at its 8 neighbours and if, say, less than 30% are the same colour as itself, it moves to a random empty square This was a kind of counter example – it showed that segregation could emerge with low levels of ethnocentrism
  35. 35. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 35 Historical Context 2: Axelrod’s Model of Cultural Change Axelrod, R (1997) The dissemination of culture - A model with local convergence and global polarization. Journal of Conflict Resolution, 41(2):203-226. Rule: each iteration, each patch picks a neighbour, if is sufficiently similar copy one of their ‘values’ Increasing sized patches appear different from each other but uniform inside. Colours above are a summary, ethnicity of patches represented as a string of values
  36. 36. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 36 Historical Context 3: Hammond and Axelrod’s Model of Ethnocentrism Hammond, RA. & Axelrod, R. (2006) The Evolution of Ethnocentrism. Journal of Conflict Resolution, 50(6):1-11. Rules: Colours are different ethnicities: circles cooperate with same color, squares defect with same color, filled-in shapes cooperate with different color, empty shapes defect with different color. If new agents inherit from parents (with some mutation) then ethnocentrism evolves over time
  37. 37. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 37 This model aims to… • …go beyond that of a few, pre-defined sets, but rather allows groupings to emerge and dissolve • That does not pre-determine what constitutes an individual’s “in-group” but lets this develop • That takes seriously the heterogeneity of people • But also how behaviour and groupings result from the social embedding of those individuals within their social environment as a result of their individual experience and interactions • To be a starting point for the development of a more serious model of these issues
  38. 38. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 38 Agents in the model have: • 2 continuous characteristics: their ethnic tag, and a cultural tag – only difference is that cultural tag can be changed! No hard-wired link to behaviour. • Behaviour is specified as to which action (out of 3 possible) an agent takes towards: (a) a member of its in-group (b) a non-member – 3 possible actions no nothing (Sit), donate altruistically (Donate), harm other (Fight) • 2 numbers to determine the extent of their ethnic- and cultural-tolerance • Their score in current round of interactions
  39. 39. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 39 Agents in the model have: • 2 continuous characteristics: their ethnic tag, and a cultural tag – only difference is that cultural tag can be changed! No hard-wired link to behaviour. • Behaviour is specified as to which action (out of 3 possible) an agent takes towards: (a) a member of its in-group (b) a non-member – 3 possible actions no nothing (Sit), donate altruistically (Donate), harm other (Fight) • 2 numbers to determine the extent of their ethnic- and cultural-tolerance • Their score in current round of interactions
  40. 40. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 40 The meaning of actions Before the rounds all agents have a score of 0 In the rounds of the interaction phase when paired • “Bit” (do nothing) no change is made • “Donate” the agent transfers value to the other at a cost to itself (value received 0.2 value cost by sender is 0.1 here) • “Fight” the agent subtracts value from the other at a cost to itself (value lost 1.0 value cost by sender is 0.1 here) Outcome: an agent may imitate (mutable) characteristics from one with a higher score
  41. 41. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 41 In- and Out-group • Agents can behave differently towards other agents, depending on whether other is in their in- group or not (any of the 3 actions can be their behaviour to in-group and to out-group) • Key rule for in-group: the difference in cultural characteristics is less than their cultural tolerance AND if the difference in ethnic characteristics is less than their ethnic tolerance • Note this is not symmetric: A may consider B as part of their in-group but not vice versa (e.g. because B is less tolerant of deviation)
  42. 42. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 42 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics
  43. 43. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 43 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics
  44. 44. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 44 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics
  45. 45. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 45 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics
  46. 46. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 46 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics
  47. 47. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 47 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics
  48. 48. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 48 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Ethnic tolerance
  49. 49. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 49 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Cultural tolerance Ethnic tolerance
  50. 50. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 50 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Cultural tolerance Ethnic tolerance
  51. 51. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 51 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Cultural tolerance Ethnic tolerance
  52. 52. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 52 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Cultural tolerance Ethnic tolerance
  53. 53. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 53 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Cultural tolerance Ethnic tolerance
  54. 54. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 54 Illustration of characteristics, tolerances and in-group Rangeofculturalcharacteristics Range of ethnic characteristics Cultural tolerance Ethnic tolerance
  55. 55. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 55 A visualisation of a population
  56. 56. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 56 A visualisation of a population Each rectangle represents an individual
  57. 57. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 57 A visualisation of a population
  58. 58. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 58 A visualisation of a population
  59. 59. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 59 A visualisation of a population
  60. 60. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 60 A visualisation of a population Projections to 1D
  61. 61. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 61 A visualisation of a population
  62. 62. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 62 A visualisation of a population Cultural Picture only
  63. 63. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 63 A visualisation of a population
  64. 64. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 64 A visualisation of a population Ethnic picture only
  65. 65. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 65 A visualisation of a population
  66. 66. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 66 A visualisation of a population FS “Fight” in- group “Sit” with out- group
  67. 67. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 67 A visualisation of a population
  68. 68. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 68 Behaviour Rules • (Interaction) Several times for each agent: – agent paired with other (in the same patch) • If other is in its in-group: do in-group action to it • If other is not in its in-group: do out-group action to it • (Imitation) Several times for each agent: – agent paired with other (in the same patch) • If other agent has a better score than self: imitate all that agent’s characteristics except ethnicity • (Noisy change) For each agent: – with a small probability randomly change strategy – with a small probability randomly change tolerances – with a small probability randomly change cultural value
  69. 69. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 69 Pairing Is biased in both interaction and imitation phases • A parameter can be set so as to make it more likely an agent will be paired from another in its in- group during the interaction phase (here 50% of the time from own group 50% at random) • Another parameter controls how likely an agent is to be paired with another of its own group during the imitation phase (here 10% of the time from own group, 90% at random)
  70. 70. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 70 Summary of Model • Agents have their own behaviours (Sit, Donate, Fight), different for in- and out-groups • They have their own definitions of their in-group • Ethnic characteristic is fixed, but cultural value characteristic may change • Model goes through interaction, imitation and noisy change phases • No initial correlation between ethnic, cultural values and behaviours (behaviours are always random at the start) • Key process: imitation of an agent doing better
  71. 71. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 71 Example run 1 • Only one patch • 200 agents • A continuous range of ethnic characteristics • Initially random ethnic and cultural characteristics • Initially wide tolerances
  72. 72. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 72
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  81. 81. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 81
  82. 82. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 82 Emergent cooperative group based on culture
  83. 83. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 83 Graphs of example run 1
  84. 84. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 84 Graphs of example run 1 ‘Waves’ of group-based cooperation
  85. 85. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 85 Graphs of example run 1 ‘Waves’ of group-based cooperation Cultural distinctions emerging but not increasing ethnic ones
  86. 86. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 86 One cooperative dynamic found One of the dynamics found in this model is: 1. A group of mutual cooperators happens to form 2. These do very well by mutually donating to each other and hence increasing their score a lot 3. Other agents imitate these, ‘joining’ their group and copying their cooperative strategy 4. So the group grows quickly 5. After a while one agent in the group changes its strategy or group and so gains from
  87. 87. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 87 Example run 2 • Only one patch • 200 agents • 3 differentiated ethnicities • Initial cultures correlated with ethnicity • Initially small tolerances
  88. 88. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 88
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  100. 100. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 100
  101. 101. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 101 Graphs of example run 1
  102. 102. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 102 Cooperation in run 2 • Cooperation does occur, with the strategy to cooperate being imitated • But cooperation is defined by culture AND ethnicity • However no lasting purely ethnically-based cooperation lasts in this model
  103. 103. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 103 Example run 3 • four patches • 100 agents per patch • 6 differentiated ethnicities • Initial cultures and space correlated with ethnicity (so one majority and minority ethnicity in each patch) • Initially small tolerances • No migration – 4 independent patches
  104. 104. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 104
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  121. 121. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 121 A Patch
  122. 122. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 122
  123. 123. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 123
  124. 124. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 124 Each ‘spoke’ is a group of culturally identical agents
  125. 125. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 125
  126. 126. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 126
  127. 127. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 127 Colours indicate behaviour, shape is ethnicity
  128. 128. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 128
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  133. 133. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 133
  134. 134. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 134 Graphs of run 3
  135. 135. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 135 Graphs of run 3 Low cooperative dynamics some aggressive action
  136. 136. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 136 Example run 4 • four patches • 100 agents per patch • 4 differentiated ethnicities • Initial cultures and space correlated with ethnicity (so one majority and minority ethnicity in each patch) • Initially small tolerances • Migration at low rates (0.5%) and comparison between agents on other patches also at low rates (1%)
  137. 137. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 137
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  150. 150. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 150 Graphs of example run 4
  151. 151. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 151 Graphs of example run 4 Good cooperative dynamics but presence of aggressive strategy but unexpressed
  152. 152. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 152 Example run 5 • 5x5 patches • 20 agents per patch • 5 differentiated ethnicities • Initial cultures and space correlated with ethnicity (so one majority on each patch) • Initially small tolerances • Migration at low rates (0.5%) and comparison between agents on other patches also at low rates (1%)
  153. 153. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 153
  154. 154. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 154
  155. 155. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 155
  156. 156. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 156
  157. 157. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 157
  158. 158. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 158
  159. 159. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 159
  160. 160. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 160
  161. 161. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 161
  162. 162. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 162
  163. 163. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 163
  164. 164. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 164 Graphs for run 5
  165. 165. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 165 Graphs for run 5 Cooperative dynamics but much greater variety of behaviours and more expressed aggression
  166. 166. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 166 Summary of model In this model… • Groups, in-groups etc. all ‘fuzzy’ and only identifiable from patterns and processes observed • Cultural groups strongly emerged even when enthicities and cultures separated to start with • Groups are dynamic, new ones forming, growing, decaying all the time • Cooperation maintained despite ‘selfish’ motivation to ‘defect’ and be a parasite • Sometimes ethno-cultural groups • Migration between patches promotes cooperation • The more patches and the smaller the numbers on each patch (also the lower the migration) the greater the variety of behaviours and the more expressed agressive actions there were
  167. 167. Simulating Superdiversity, Bruce Edmonds, Birmingham, January 2017. slide 167 The End The Centre for Policy Modelling: http://cfpm.org These slides will be available at: http://slideshare.net/BruceEdmonds Ad for Workshop! Beyond Schelling and Axelrod: Computational Models of Ethnocentrism and Diversity Manchester June 7-8th Google “Ethnosim2017”

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