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. What about the unknown unknowns? source: John Barrow (after David Ruelle) Complexity of the phenomenon Uncertainty on basic equations Solar System Dynamics Meteorology Chemical Reactions Hydrological Models Particle Physics Quantum Gravity Living Systems Global Change Social and Economic Systems

3. 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.

4. Is computing also a natural science? “Information processes and computation continue to be found abundantly in the deep structures of many fields. Computing is not—in fact, never was—a science only of the artificial.” (Peter Denning, CACM, 2007). http://www.red3d.com/cwr/boids/

5. Where does this image come from?

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

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

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

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

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

11. A plague of locusts

12. Collective spatial action: volunteered GI Are Brazilians less cooperative? Less tech-savvy? Does google solve their problems? Are they happy with their public data?

13. Computing is also a natural science Computing studies information flows in natural systems......and how to represent and work with information flows in artificial systems

14. Computing is also a natural science Computing studies information flows in natural systems......and how to represent and work with information flows in artificial systems

15. Collective spatial action: pedestrian modelling Batty, “Agent-Based Pedestrian Modelling”, in: Advanced Spatial Analysis, ESRI Press, 2003. Notting Hill Carnival (London)

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

17. 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?

18. 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.

19. Clocks, clouds or ants? Clocks: deterministic methods Clouds: statistical distributions Ants: emerging behaviour

20. Slides from LANDSAT 197319872000 images: USGS Modelling Human-Environment Interactions How do we decide on the use of natural resources? What are the conditions favoring success in resource mgnt? Can we anticipate changes resulting from human decisions? What GIScience techniques and tools are needed to model human-environment decision making?

21. What are complex adaptive systems?

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

23. 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.

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

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

26. 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)

27. 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)

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

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

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

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

32. 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

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

34. 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/

35. 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

36. Four types of spatial agents Natural agents, artificial environment Artificial agents, artificial environmentArtificial agents, natural environment Natural Agents, natural environment source: Couclelis (2001)

37. “Agent-based modeling meets an intuitive desire to explicitly represent human decision making. (…) However, by doing so, the well-known problems of modeling a highly complex, dynamic spatial environment are compounded by the problems of modeling highly complex, dynamic decision-making. (…) The question is whether the benefits of that approach to spatial modeling exceed the considerable costs of the added dimensions of complexity introduced into the modeling effort. The answer is far from clear and in, my mind, it is in the negative. But then I am open to being persuaded otherwise ”. (from “Why I no longer work with agents”, 2001 LUCC ABM Workshop) Some caution necessary... Helen Couclelis

38. “Complexity is more and more acknowledged to be a key characteristic of the world we live in and of the systems that cohabit our world. It is not new for science to attempt to understand complex systems: astronomers have been at it for millennia, and biologists, economists, psychologists, and others joined them some generations ago. (…) If, as appears to be the case, complexity (like systems science) is too general a subject to have much content, then particular classes of complex systems possessing strong properties that provide a fulcrum for theorizing and generalizing can serve as the foci of attention.” (from “The Sciences of the Artificial”, 1996) Some caution necessary... Herbert Simon (1958)

39. ABM in TerraME: Types and Functions

40. 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

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

42. CellAgent forEachAgentforEachCell forEachConnection forEachNeighbor forEachAgent CellularSpace Society GroupTrajectory DBMS

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

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

45. 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

46. 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

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

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

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

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

51. 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

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

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

54. 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 ?

55. 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 ?

56. 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 ?