1. Agent-based models and social simulation Gilberto Câmara Tiago Carneiro Pedro Andrade Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/

2. Where does this image come from?

3. Map of the web (Barabasi) (could be brain connections)

4. Information flows in Nature Ant colonies live in a chemical world

5. Conections and flows are universal Yeast proteins (Barabasi and Boneabau, SciAm, 2003) Scientists in Silicon Valley (Fleming and Marx, Calif Mngt Rew, 2006)

6. Information flows in the brain Neurons transmit electrical information, which generate conscience and emotions

7. Information flows generate cooperation White cells attact a cancer cell (cooperative activity) Foto: National Cancer Institute, EUA http://visualsonline.cancer.gov/

8. Information flows in planet Earth Mass and energy transfer between points in the planet

9. Complex adaptative systems How come that a city with many inhabitants functions and exhibits patterns of regularity? How come that an ecosystem with all its diverse species functions and exhibits patterns of regularity?

10. What are complex adaptive systems? Systems composed of many interacting parts that evolve and adapt over time. Organized behavior emerges from the simultaneous interactions of parts without any global plan.

11. What are complex adaptive systems?

12. Universal Computing Computing studies information flows in natural systems......and how to represent and work with information flows in artificial systems

13. Agents as basis for complex systems Agent: flexible, interacting and autonomous An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

14. Agent-Based Modelling Goal Space Representations Communication Action Perception Communication Gilbert, 2003

15. Agents: autonomy, flexibility, interaction Synchronization of fireflies

16. Why is it interesting? Structure structure is emergent from agent interaction this can be directly modeled Agency agents have goals, beliefs and act this can be directly modeled Dynamics things change, develop, evolve agents move (in space and social location) and learn these can be directly modeled Source: (Gilbert, 2006)

17. Is it qualitative or quantitative? Agent-based models can handle all types of data quantitative attributes age size of organization qualitative ordinal or categorical (e.g. ethnicity), relational (e.g. I am linked to him and her) vague A sends B a message about one time in three Source: (Gilbert, 2006)

18. It has been used in different areas of science economy sociology archaeology ecology linguistics political sciences...

19. Source: http://www.leggmason.com/thoughtleaderforum/2004/conference/transcripts/arthur_trans.asp

20. Agents changing the landscape An individual, household, or institution that takes specific actions according to its own decision rules which drive land-cover change.

21. Four types of agents Natural agents, artificial environment Artificial agents, artificial environment Artificial agents, natural environment Natural Agents, natural environment fonte: Helen Couclelis (UCSB)

22. Four types of agents Natural agents, artificial environment Artificial agents, artificial environment Artificial agents, natural environment Natural Agents, natural environment fonte: Helen Couclelis (UCSB) e-science Engineering Applications Behavioral Experiments Descriptive Model

23. Is computer science universal? Modelling information flows in nature is computer science http://www.red3d.com/cwr/boids/

24. Bird Flocking (Reynolds) Example of a computational model 1. No central autority 2. Each bird reacts to its neighbor 3. Model based on bottom up interactions http://www.red3d.com/cwr/boids/

25. Bird Flocking: Reynolds Model (1987) www.red3d.com/cwr/boids/ Cohesion: steer to move toward the average position of local flockmates Separation: steer to avoid crowding local flockmates Alignment: steer towards the average heading of local flockmates

26. Agents moving

29. Schelling segregation model

30. Segregation Some studies show that most people prefer to live in a non-segregated society. Why there is so much segregation?

31. Segregation Segregation is an outcome of individual choices But high levels of segregation indicate mean that people are prejudiced?

32. Schelling’s Model of Segregation < 1/3 Micro-level rules of the game Stay if at least a third of neighbors are “kin” Move to random location otherwise

33. Schelling’s Model of Segregation Schelling (1971) demonstrates a theory to explain the persistence of racial segregation in an environment of growing tolerance If individuals will tolerate racial diversity, but will not tolerate being in a minority in their locality, segregation will still be the equilibrium situation

34. Schelling Model for Segregation Start with a CA with “white” and “black” cells (random) The new cell state is the state of the majority of the cell’s Moore neighbours White cells change to black if there are X or more black neighbours Black cells change to white if there are X or more white neighbours How long will it take for a stable state to occur?

35. Schelling’s Model of Segregation Tolerance values above 30%: formation of ghettos

36. ABM in TerraME: Types and Functions

37. Agent Space Space Agent TerraME: nature-society modelling T. Carneiro, P. Andrade, et al., “An extensible toolbox for modeling nature-society interactions”. Enviromental Modelling and Software, 2013 (Two PhDs). Nature represented in cellular spaces, society represented as agents

38. Geometry Cellular Space Social Network Object Types in TerraLib ecosystem: new tools, new types Coverage Time Series Trajectory Event Agent 2002 2010 2014

39. CellAgent forEachAgentforEachCell forEachRelative forEachNeighbor forEachAgent CellularSpace Society GroupTrajectory DBMS

40. agents = cell:getAgents() if #(agents) == 0 then -- empty agent:leave(oldcell) agent:enter(cell) end Agents within cells

41. Society 上海宋 ABC ACA AAC CCC BBC CBB CAC BBA CCB CBA AAA BAB

42. createAgent = function(capital) return Agent { capital = capital, --... } end data = {} data[1] = 100; data[2] = 50; data[3] = 25 mag = Society(createAgent, data) mag = Society(createAgent, 50) capital = 100capital = 50capital = 25 Society

43. function createAgent (capital) person = Agent { init = function (self), --... } end data = {} data[1] = 100; data[2] = 50; data[3] = 25 mag = Society(createAgent, data) mag = Society(createAgent, 50) capital = 100capital = 50capital = 25 Society

44. CCC BBC CBB CAC BBA CCB CBA ABC ACA AAC AAA BAB Group

45. g = Group{mag, function(agent) return agent. capital > 40 end, function(a1, a2) return a1.capital > a2.capital end } capital = 100capital = 50capital = 25 Group

46. forEachAgent(mag, function(agent) agent.capital = agent.capital + 100 end) capital = 200capital = 150capital = 125 capital = 100capital = 50capital = 25 Traversing the Society

47. Emergence source: (Bonabeau, 2002) “Can you grow it?” (Epstein; Axtell; 1996)

48. Epstein (Generative Social Science)  If you didn´t grow it, you didn´t explain its generation  Agent-based model  Generate a macro- structure  Agents = properties of each agent + rules of interaction  Target = macrostruture M that represents a plausible pattern in the real-world

49. Scientific method Science proceeds by conjectures and refutations (Popper)

50. Explanation and Generative Sufficiency Macrostructure Spatial segregation Bird flocking Agent model A1 Agent model A2 Agent model A3 ? Refutation Conjectures ?

51. Explanation and Generative Sufficiency Macrostructure Occam´s razor: "entia non sunt multiplicanda praeter necessitatem", or "entities should not be multiplied beyond necessity ". Agent model A1 Agent model A2 ?

52. Explanation and Generative Sufficiency Macrostructure Popper´s view "We prefer simpler theories to more complex ones because their empirical content is greater and because they are better testable" Agent model A1 Agent model A2 ?

53. Explanation and Generative Sufficiency Macrostructure Einstein´s rule: The supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience" "Theories should be as simple as possible, but no simpler. Agent model A1 Agent model A2 ?

54. Urban Growth in Latin American cities: exploring urban dynamics through agent-based simulation Joana Xavier Barros 2004

55. Latin American cities High rates of urban growth (rapid urbanization) Poverty + spontaneous settlements (slums) Poor control of public policies on urban development Fragmented urban fabric with different and disconnected morphological patterns that evolve and transform over time.

56. Peripherization São Paulo - Brasil Caracas - Venezuela Process in which the city grows by the addition of low‐income residential areas in the peripheral ring. These areas are slowly incorporated to the city by spatial expansion, occupied by a higher economic group while new low‐income settlements keep emerging on the periphery..

57. Urban growth “Urban sprawl” in United States “Urban sprawl”in Europe (UK) Peripherization in Latin America (Brazil)

58. Research question How does this process happen in space and time? How space is shaped by individual decisions?  Complexity approach Time + Space  automata model Social issues  agent ‐ based simulation )

59. Model: Growth of Latin American cities Peripherisation module Spontaneous settlements module Inner city processes module Spatial constraints module

60. Peripherization module  reproduces the process of expulsion and expansion by simulating the residential locational processes of 3 distinct economic groups.  assumes that despite the economic differences all agents have the same locational preferences. They all want to locate close to the best areas in the city which in Latin America means to be close to high‐income areas  all agents have the same preferences but different restrictions

61. Peripherization module: rules 1. proportion of agents per group is defined as a parameter 2. high‐income agent –can locate anywhere 3. medium‐income agent –can locate anywhere except on high‐income places 4. low‐income agent –can locate only in the vacant space 5. agents can occupy another agent’s cell: then the latter is evicted and must find another

62. Peripherization module: rules

63. Spatial pattern: the rules do not suggests that the spatial outcome of the model would be a segregated pattern Approximates the spatial structure found in the residential locational pattern of Latin American cities multiple initial seeds ‐resembles certain characteristics of metropolitan areas

64. Comparison with reality  Maps of income distribution for São Paulo, Brazil (census 2000)  Maps A and B: quantile breaks (3 and 6 ranges)  Maps C and D: natural breaks (3 and 6 ranges)  No definition of economic groups or social classes