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  1. Spatial Dynamical Modelling with TerraME Lectures 4: Agent-based modelling Gilberto Câmara

  2. Agent-based modelling with TerraME

  3. What are complex adaptive systems?

  4. Agent: flexible, interacting and autonomous Agent An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

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

  6. Agents: autonomy, flexibility, interaction football players

  7. Representations Communication Communication Action Perception Environment Agent-Based Modelling Goal Gilbert, 2003

  8. Agents are… Identifiable and self-contained Goal-oriented • Does not simply act in response to the environment Situated • Living in an environment with which interacts with other agents Communicative/Socially aware • Communicates with other agents Autonomous • Exercises control over its own actions

  9. Bird Flocking No central authority: Eachbirdreacts to its neighbor Bottom-up: notpossible to modeltheflock in a global manner. It is necessary to simulatethe INTERACTION betweentheindividuals

  10. Bird Flocking: Reynolds Model (1987) 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

  11. Agents changing the landscape

  12. Characteristics of CA models (1) Self-organising systems with emergent properties: locally defined rules resulting in macroscopic ordered structures. Massive amounts of individual actions result in the spatial structures that we know and recognise;

  13. Characteristics of CA models (1) Wolfram (1984): 4 classes of states: (1) homogeneous or single equilibrium (2) periodic states (3) chaotic states (4) edge-of-chaos: localised structures, with organized complexity.

  14. Bird Flocking Reynolds Model (1987) Animation example

  15. Swarm

  16. Repast

  17. Netlogo

  18. Netlogo

  19. TerraME

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

  21. An Example: The Majority 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, or the cell’s previous state if the neighbours are equally divided between “white” and “black” • White cells change to black if there are five or more black neighbours • Black cells change to white if there are five or more white neighbours • What is the result after 50 iterations? • How long will it take for a stable state to occur?

  22. The Modified Majority Model for Segregation • Include random individual variation • Some individuals are more susceptible to their neighbours than others • In general, white cells with five neighbours change to black, but: • Some “white” cells change to black if there are only four “black” neighbours • Some “white” cells change to black only if there are six “black” neighbours • Variation of individual difference • What happens in this case after 50 iterations and 500 iterations?

  23. 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

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

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

  26. References • J. Zhang. Residential segregation in an all-integrationist world. Journal of Economic Behaviour & Organization, v. 54 pp. 533-550. 2004 • T. C. Shelling. Micromotives and Macrobehavior. Norton, New York. 1978

  27. Zhang: Residential segregation in an all-integrationist world Some studies show thatmostpeopleprefer to live in a non-segregatedsociety. Whythere is somuchsegregation?

  28. Satisfaction

  29. Satisfaction

  30. Agents moving

  31. Agents moving

  32. Agents moving

  33. Simulation

  34. Vizinhança e Segregação

  35. Development of Agent-based models in TerraME

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

  37. 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

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

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

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

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

  42. Explanation and Generative Sufficiency Agent model A1 Macrostructure ? Agent model A2 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.

  43. TerraME extension for agent-based modelling ForEachAgent = function(agents, func, event) nagents = table.getn(agents) for i = 1, nagents do func (agents[i],(event)) end end Replicate = function(agent, nagents) ag = {} for i = 1, nagents do ag[i] = agent() ag[i].id = i end return ag end (contained in file agent.lua)

  44. ABM example Urban Dynamics in Latin American cities: an agent‐based simulation approach Joana Barros

  45. Latin American cities High speed of urban growth (urbanization) Poverty + spontaneous settlements Poor control of policies upon the development process Spatial result: fragmented set of patches, with different morphological patterns often disconnected from each other that mutate and evolve in time.

  46. Peripherization 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.. São Paulo - Brasil Caracas - Venezuela

  47. 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)

  48. The Peripherisation Model • Four modules: Peripherisation module Spontaneous settlements module Inner city processes module Spatial constraints module

  49. Peripherization moduls • 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

  50. 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