An agent-based simulation of a creative city

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An agent-based simulation of a creative city

  1. 1. ` Agent-Based Modeling for exploring Pakistan’s Urban Dynamics Ammar A. Malik Hilton L. Root Andrew T. Crooks Melanie Swartz SWARMFEST 2013 Orlando, FL
  2. 2. Presentation Outline • Acknowledgement: IFPRI, Pakistan Planning Commission • The Urban Century • Role of Creativity in Urban Development • The Creative City Model • Experiments: Karachi • The Next Steps
  3. 3. Percentage of Urban Population by Size, 1960 Source: UN Stats
  4. 4. Source: UN Stats Percentage of Urban Population by Size, 2011
  5. 5. Source: UN Stats Percentage of Urban Population by Size, 2025
  6. 6. The Expansion of Cities • World Urbanization: 50% in 2006, 75% by 2050. • Every week, more than 1 million people are being added to cities, likely to continue till 2050. • Problems: global warming, pollution/disease, energy. • Solutions: crucibles of civilization, avenues for unleashing entrepreneurial energy.
  7. 7. 0 50 100 150 200 250 300 1951 1961 1972 1981 1990 2001 2010 2020 2030 Millions Urban Rural Pakistan’s Population: Urban vs. Rural Source: UN-Habitat (2008)
  8. 8. Developing Country Megacities Population Growth Comparison 0 2 4 6 8 10 12 14 16 18 1970 1980 1990 2000 2010 Millions Cairo Beijing Jakarta Delhi Karachi Source: United Nations
  9. 9. Why Karachi? • The journey from being the ‘Beirut of South Asia’ to ‘the most violent city on earth’ • “The world’s fastest growing megacity, has grown 80% between 2000 and 2010 to 21m people” (Forbes 2013) • A microcosm of Pakistan, representation of all ethnicities. • Produces 20% of national GDP, 25% of national revenues, handles 95% of foreign trade, retains 45% of employment in large-scale manufacturing (ADB 2005) • Pakistan’s financial and banking hub: hosts 40% of all financial activity and 50% of bank deposits (KSDP 2007)
  10. 10. Creativity & Urban Development
  11. 11. Insights from Literature • Individual or Social? • Creative ideas have “novelty, usefulness and surprise” (Simonton 2012) • Richard Florida’s (2002) “Theory of the Creative Class” o Creative workers, who “draw on complex bodies of knowledge to solve specific problems” associated with prosperity o The 3Ts: Technology, Tolerance & Talent • Human Capital driving long-term economic growth (Barro 2001; Cohen and Soto 2007) o Creative Clusters in cities are formed by free flow of ideas (Andersson 1985)
  12. 12. New Urbanism • Density fosters human interactions, “the loci for development” (Glaeser 2011) o Environmental Efficiency o Education as the “most reliable predictor of urban growth” o Successful cities attract the poor; they thrive on diversity • Vibrant Urban Culture & Public Spaces (Landry 2000) o Cultural and physical amenities attract creative individuals • Jacobs (1961) “Cities happen to be problems in organized complexity, like the life sciences.” o “…the whole is more than the sum of the parts.” (Simon 1962) o Understanding the macro-level from individual-level interaction
  13. 13. Model Purpose • An Urban Laboratory for asking what if questions and testing policy ideas. • To Explain: o The relationship between land-use regulation and creative economy. o When, where and how creative clusters emerge in cities? • To Test Policy Scenarios: o What if land-use zones are altered in favor of mixed land-use? o What if urban mobility or transportation costs change? o What if income inequality across households improves?
  14. 14. The Creative City Model
  15. 15. The Creative City Model • Conceptual model built using Netlogo • Scope of model area representation is a city or urban area • System behaviors: o Impact attributes of and number of agents in model over time o Restrict or enable where agents can interact with the environment • Agent behaviors: o Agents are dynamic and change over time o Interact with other agents o Interact with the environment • Environment behaviors: o Change over time o Impacted by agents
  16. 16. Individual Agents Income Tolerance Education Neighborhood Environment Landuse Neighborhood Rent City Level Factors Population growth rates Brain Drain Observer Controls Mobility restriction Development restrictions Segregation / Tolerance Model Features and Attributes
  17. 17. Model Features and Attributes Individual Agents Assigned at the start. When an agent is “inspired” by partnering with a high creative agent in a creative space, the agent can raise a level. Creativity Level High Med Low Creative Space and Value Based on frequency of visits by medium and high creative agents. Or, based on creative- density. Environment
  18. 18. The Creative City Model Flow elSet up environment (landuse, neighborhoods, creative space, rent) Set up agents with attributes (income, education, tolerance, creativity) Pop Growth and Brain Drain Partner/Inspir e Creativity via Interaction Update Environment Values (Rent, Creative Space) Update displays and check interface values Content and Satisfied ? Check Satisfaction Move yes no stay Update creative value from frequency visit by med and high creative Adjust rents If max creative value, convert neighbor cells to creative space Creative space? Find partner Get inspired? Un couple Is partner medium or high creative? Is place high creative value? Raise Creativity Environment (affordability, occupancy, landuse, neighborhood) Check tolerance level of nearby Start Sim End Sim?
  19. 19. Behavioral Rules Summary Role Behavioral Rule Agent Movement Stop when satisfied (based on environment) and content (based on nearby agents) Agent Interaction Partnering may lead to increased creativity level Environment Values (Density, Rent, Occupancy, Creative Value) Based on density/frequency of agent visit User controls Impact range of movement of agents User interaction Modify values, change display of environment and agents based on attributes, query agents
  20. 20. Basic Model Interface http://malik.gmu.edu/Creativity Inputs Environment Outputs
  21. 21. Model Outputs
  22. 22. • Allowing development typically increases amount of creative space • Restricting movement does not have as big an impact as anticipated • Ability to afford rent in a desired neighborhood and tolerance of the neighbors also have a large impact Parameter Sweep Findings 0 2 4 6 8 10 12 14 0 10 20 30 40 50 60 70 80 90 100 CreativeSpace Tolerance Level Tolerance Level 12.6 12.65 12.7 12.75 12.8 12.85 12.9 12.95 13 13.05 13.1 0 25 50 75 100 200 CreativeSpace Rent Percentage of Income Rent Percentage of Income
  23. 23. 0 2 4 6 8 10 12 14 -10 -5 0 5 10 15 CreativeSpace Brain Drain Brain Drain 0 2 4 6 8 10 12 14 16 18 -10 -5 0 5 10 CreativeSpace Population Growth Rate Population Growth Rate Parameter Sweep Findings • Brain drain and population growth have a large impact on ability to support creative spaces, more so than just size of population.
  24. 24. Typical Model Run http://malik.gmu.edu/Creativity
  25. 25. Application on Karachi
  26. 26. Karachi Experiments Input Parameters Karachi Values* Starting Population 1,800 Population Growth Rate 3 Education 50 Brain Drain 5 Percent Highly Creative 15 Tolerance 30 Income(average) / top10 30,000 / 100,000 Average Rent 12,000 Rent Percentage of Income 40 * Karachi values interpolated based on recent Pew Research Study Experiments* Movement ON/OFF Development ON/OFF Segregation ON/OFF * Run model for period of 10 years for each combination
  27. 27. Business as usual… Key Outputs Today 3 Years 5 Years 10 Years 20 Years Percent Highly Creative 10 7 6 3 1 Gini Coefficient 0.67 0.66 0.69 0.72 0.75 Percent Creative Space 1.8 3.7 6 4.5 4.8 Percent University Edu. 50 38 32 21 15 Average Income (Rs.) 37,000 41,165 45,200 55,013 60,394 Percent Affording Rent 46 45 44 43 45
  28. 28. Karachi Experiments Results Segregation ON Rest. Movement OFF Development OFF (Base) Rest. Movement OFF Development ON Rest. Movement ON Development OFF Rest. Movement ON Development ON Percent Creative Space <1 <1 <1 <1 Percent Afford Rent 35 38 38 38 Percent Creative Population 11.8 12 12 12 Segregation OFF Percent Creative Space 2 1 3 1 Percent Afford Rent 46 93 45 92 Percent Creative Population 12.5 12 13.2 12
  29. 29. Karachi Findings • Few creative clusters emerge, creative space is very low, as expected. • Key issues for Karachi: high brain drain and low tolerance. • Development, or mixed land-use, alone won’t work. • Smart development strengthening neighborhoods and increasing access to creative places fosters creativity. • More experimentation, calibration & interpretation!
  30. 30. The Next Steps • Apply verified theoretical model to Karachi. • GIS Integration, using R for spatial economic data analysis. • Empirically grounded behavioral rules, Karachi fieldwork. • Applying Creative City Model to several real-world cities!
  31. 31. ` Agent-Based Modeling for exploring Pakistan’s Urban Dynamics amalik8@gmu.edu

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