Agent-based models and social simulation

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. Where does this image come from? 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 Yeastproteins (BarabasiandBoneabau, SciAm, 2003) Scientists in SiliconValley (Fleming and Marx, CalifMngtRew, 2006)

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

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

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

9. Complex adaptative systems How come that an ecosystem with all its diverse species functions and exhibits patterns of regularity? How come that a city with many inhabitants 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. Agent: flexible, interacting and autonomous Agents as basis for complex systems An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

14. Representations Communication Communication Action Perception Environment Agent-Based Modelling Goal 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 Artificial agents, natural environment Artificial agents, artificial environment Natural agents, artificial environment Natural Agents, natural environment fonte: Helen Couclelis (UCSB)

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

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 No central autority Each bird reacts to its neighbor Model based on bottom up interactions http://www.red3d.com/cwr/boids/

25. 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 www.red3d.com/cwr/boids/

26. Agents moving

27. Agents moving

28. Agents moving

29. Schelling segregation model

30. Segregation Some studies show thatmostpeopleprefer to live in a non-segregatedsociety. Whythere is somuchsegregation?

31. Segregation Segregation is anoutcomeof individual choices Buthighlevelsofsegregationindicatemeanthatpeople are prejudiced?

32. 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

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. Tolerancevaluesabove 30%: formationofghettos Schelling’s Model of Segregation

36. ABM in TerraME:Types and Functions

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

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

39. forEachRelative forEachNeighbor forEachCell Agent Cell forEachAgent forEachAgent forEachCell Society CellularSpace Group Trajectory DBMS

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

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

42. Society 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 = 100 capital = 50 capital = 25

43. Society functioncreateAgent (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 = 100 capital = 50 capital = 25

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

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

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

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

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 Conjectures Agent model A1 Macrostructure ? Agent model A2 Spatial segregation Bird flocking Refutation ? Agent model A3