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Value of ABM for integrating geographical understanding - Millington Auckland 2013

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Seminar given by James Millington in School of Environment, University of Auckland, May 2013

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Value of ABM for integrating geographical understanding - Millington Auckland 2013

  1. 1. http://www.landscapemodelling.netJames MillingtonDept. of Geography, King’s College LondonThrough Thick and Thin:The value of agent-based modelling forintegrating geographical understanding
  2. 2. http://www.landscapemodelling.net
  3. 3. http://www.landscapemodelling.net
  4. 4. http://www.landscapemodelling.net
  5. 5. http://www.landscapemodelling.net
  6. 6. http://www.landscapemodelling.net
  7. 7. Use of ABM in geographyEstablished Land use/cover change Deadman et al. 2004; An et al. 2005; Evans and Kelley 2008 Urban phenomena and change Haklay et al. 2001; Hochmair 2005; Jayaprakash et al. 2009More recently Crime distribution (Malleson et al. 2012; Malleson 2012) School catchments (Harland & Heppenstall 2012; Millington et al. in review) Crowd dynamics (Torrens 2012; Johansson and Kretz 2012)http://www.landscapemodelling.net
  8. 8. Early adopters in geography Contexts consistent with prior skills andunderstanding (GIS, Computer Science)Little evidence of ABM use to ask questionsarising from social or cultural theoryhttp://www.landscapemodelling.netMovement & Locations Physical Change
  9. 9. Social and cultural aversionThree proposed reasons1. Misconceptions about this ‘modelling’ ABM associated with previously rejectedquantitative methods?2. Failure of ABM to exploit their potential Quantitative generalization etc. often still used3. Models are too ‘thin’ to understand the world Alone maybe, but could be used more ‘thickly’http://www.landscapemodelling.net
  10. 10. Quantitative revolutionStatistical approaches result in:"sad degeneration and routinization of themodelling exercise into mere data crunching,numerical analysis and statistical inferenceinstead of careful theory-building"Harvey (1989 p.213)Prime position of mathematical modelling canmean “conceptualization becomes the slave ofquantification”Sayer (1982, p.75)http://www.landscapemodelling.net
  11. 11. http://www.landscapemodelling.net
  12. 12. Millington et al. (2007)Land use regression modelhttp://www.landscapemodelling.net
  13. 13. http://www.landscapemodelling.net
  14. 14. Modelling differencesStatistical modelling demands: Generalization and Aggregation Quantitative descriptions of relations Homogenization of measured objectshttp://www.landscapemodelling.net
  15. 15. Millington et al. (2007)Land use regression modelhttp://www.landscapemodelling.net
  16. 16. Land use agent-based modelMillington et al. (2008)http://www.landscapemodelling.net
  17. 17. Modelling differencesStatistical modelling demands: Generalization and Aggregation Quantitative descriptions of relations Homogenization of measured objectsAgent-based modelling allows: Abstraction and Disaggregation Autonomous heterogeneous objects Explicit representation of causal powershttp://www.landscapemodelling.net
  18. 18. Agent-based representationAgents: Interactive (with other agents and env.) Autonomous behaviour Multiple attributes Adaptive (behaviour or attributes) Represent multiple levels of organizationEnvironment: Influences and influenced by agentshttp://www.landscapemodelling.net
  19. 19. Agent-based representationhttp://www.landscapemodelling.netGalan et al. (2009)
  20. 20. Agent-based representation Quantitative generalization and aggregation isnot required Abstractions can be based on Interviews Survey results Participant observation Natural language (mental) models encodedusing logical symbolizationhttp://www.landscapemodelling.net
  21. 21. Unfulfilled potentialhttp://www.landscapemodelling.netQuantitativeGeneralizationUtilityMaximizationPerfectRationalityPrediction Forecasting
  22. 22. Thin description Models are too simple and uncoupled fromreality to be relevant for understanding it?Yeah, Models are ‘thinner’ than ethnographers’ thickdescriptionsNah Abstractions are needed for understanding ABM need not be so epistemologically thinhttp://www.landscapemodelling.net
  23. 23. Social and cultural aversionThree proposed reasons1. Misconceptions about this ‘modelling’ ABM associated with previously rejectedquantitative methods?2. Failure of ABM to exploit their potential Quantitative generalization etc. often still used3. Models are too ‘thin’ to understand the world Alone maybe, but could be used more ‘thickly’http://www.landscapemodelling.net
  24. 24. Epistemic RolesHeuristicStructure vs. AgencyNecessary vs. ContingentDialogicMensaticNarrativehttp://www.landscapemodelling.net
  25. 25. Epistemic RolesHeuristicStructure vs. AgencyNecessary vs. ContingentDialogicMensaticNarrativehttp://www.landscapemodelling.net
  26. 26. Recursionhttp://www.landscapemodelling.net
  27. 27. Recursionhttp://www.landscapemodelling.net
  28. 28. Structurationhttp://www.landscapemodelling.netStructure Agency
  29. 29. Social Psychology Theory People hold multiple self-concepts within theirself-identity in a hierarchy (Stryker and Burke 2000) Farmer: Producer, Agri-business person,Conservationist, Diversifier People attempt to express their identitythrough their behaviour Identity changes slowly to match socialnetwork if behaviour cannot match identityhttp://landscapemodelling.net
  30. 30. Farmer Social Psychologyhttp://landscapemodelling.net
  31. 31. Epistemic RolesHeuristicStructure vs. AgencyNecessary vs. ContingentDialogicMensaticNarrativehttp://www.landscapemodelling.net
  32. 32. School Choice and Admissions Distance-based admissions policies hierarchies of school popularity lead to the reproduction of social inequalityhttp://landscapemodelling.net
  33. 33. Empirical Patternshttp://landscapemodelling.net
  34. 34. Empirical Patternshttp://landscapemodelling.netBarking AbbeyWarren
  35. 35. Empirical Patternshttp://landscapemodelling.netBarking AbbeyWarren
  36. 36. Empirical Patternshttp://landscapemodelling.net
  37. 37. Necessary or Contingent? What relationships necessary for patterns? School value-added? Family location constraints? What relationships are contingent? Examine combinations of rules No value-added (nVA), no location constraints (nLC) Value-added (VA), no location constraints (nLC) No value-added (nVA), location constraints (LC) Value-added (VA), movement constraints (LC)http://landscapemodelling.net
  38. 38. http://landscapemodelling.netnLC nVA nLC VALC nVA LC VA
  39. 39. http://landscapemodelling.net20 40 60 80GCSE score0246810A:Pratio20 40 60 80GCSE score0246810A:Pratio
  40. 40. http://landscapemodelling.netA:P ratio2 4 6 8 100020406080100Max.DistanceA:P ratio2 4 6 8 100020406080100Max.Distance
  41. 41. Epistemic RolesHeuristicStructure vs. AgencyNecessary vs. ContingentDialogicMensaticNarrativehttp://www.landscapemodelling.net
  42. 42. Put your model where your mouth isPresent your mental model as a formal modelhttp://www.landscapemodelling.netABM
  43. 43. Participatory Modellinghttp://www.landscapemodelling.netD’Aquino et al. (2003)
  44. 44. Participatory Modellinghttp://www.landscapemodelling.netD’Aquino et al. (2003)
  45. 45. Participatory Modellinghttp://www.landscapemodelling.netD’Aquino et al. (2003)
  46. 46. http://landscapemodelling.netBoundary CrossingDemeritt (2009)ABM
  47. 47. Epistemic RolesHeuristicStructure vs. AgencyNecessary vs. ContingentDialogicMensaticNarrativehttp://www.landscapemodelling.net
  48. 48. Narrative: Beyond Statistics Breeding synchrony in bird colonies Jovanni and Grimm (2008) Proc. R. Soc. Bhttp://landscapemodelling.net
  49. 49. Statistical Summarieshttp://landscapemodelling.netJovani & Grimm (2008)
  50. 50. ABM are event-drivenhttp://landscapemodelling.netNarrativeUnderstandingEvents
  51. 51. What is a narrative?http://landscapemodelling.netNarrative…may move back and forth betweenaccounts of low-level events andsystem level (statistical) summaries toshow how they are linked… is not simply a chronicle of events
  52. 52. Influencing Neighbours (IN)http://landscapemodelling.netIN = 8 IN = 1
  53. 53. Influencing Neighbours (IN)http://landscapemodelling.netIN = 8 IN = 1
  54. 54. Influencing Neighbours in Spacehttp://landscapemodelling.net
  55. 55. Influencing Neighbours in Timehttp://landscapemodelling.net
  56. 56. Narratives of contingenciesMillington et al. (2012)
  57. 57. Thicker ApproachesHeuristicStructure vs. AgencyNecessary vs. ContingentDialogicMensaticNarrativehttp://www.landscapemodelling.net
  58. 58. Outstanding Challenges Improving model representations Identifying and demonstrating causalityClifford (2008)Shared challenges?“appropriate abstraction to understand howstructures underlying mechanisms produceempirical events and the identification andexplanation of how necessity andcontingency combine to produce history”http://www.landscapemodelling.net
  59. 59. Through thick and thinMixed methods Corroborating findings Alternative interpretations Development of theory Expansion of inquiryhttp://www.landscapemodelling.netQuantitative Qualitative+
  60. 60. Through thick and thinMixed methods Corroborating findings Alternative interpretations Development of theory Expansion of inquiry Examining consequences of theories Identifying patterns to seek empiricallyhttp://www.landscapemodelling.netSimulation Qualitative+
  61. 61. Through thick and thinMixed methods Corroborating findings Alternative interpretations Development of theory Expansion of inquiry Examining consequences of theories Identifying patterns to seek empiricallyhttp://www.landscapemodelling.netSimulation Qualitative
  62. 62. Through thick and thinhttp://www.landscapemodelling.netTheoryObservationSimulationIterative Process
  63. 63. Challenges for ‘thick and thin’Scepticismhttp://www.landscapemodelling.net
  64. 64. Challenges for ‘thick and thin’ScepticismSkills Teaching computational concepts ‘Interaction’ expertise rather than ‘contributory’http://www.landscapemodelling.net
  65. 65. Challenges for ‘thick and thin’ScepticismSkills Teaching computational concepts ‘Interaction’ expertise rather than ‘contributory’Resources Time and Energy!http://www.landscapemodelling.net
  66. 66. Acknowledgementshttp://www.landscapemodelling.netjames.millington@kcl.ac.uk@jamesmillington

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