1. Collective Spatial Actions: Policy and Planning as Emergent Properties of Human Interactions Gilberto Câmara Earth System Science Center, INPE Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/ Las Navas 2010: Cognitive and Linguistic Aspects of Geographic Space

2. What cooperation can achieve... http://www.youtube.com/watch?v=0HrjevD2vhk&feature=related Those were the days…

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

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

5. Collective spatial action: deforestation

6. Fossil fuel Land use change 10 8 6 4 2 1960 20101970 1990 2000 1980 CO 2 emissions (PgC y -1 ) 8.7 1.2 9.9 PgC 12% of total Le Quéré et al. 2009, Nature-geoscience, 2009 Collective spatial action: global change

7. The fundamental question of our time How is the Earth’s environment changing, and what are the consequences for human civilization?

8. We need cooperation at a global level… By the year 2050... 9 billion people: 6 billion tons of GHG and 60 million tons of urban pollutants. Resource-hungry: We will withdraw 30% of available fresh water. Risky living: 80% urban areas, 25% near earthquake faults, 2% in coast lines less than 1 m above sea level.

9. An explicit spatial problem in global change: land change “Land-change science has emerged as a foundational element of global environment change and sustainability science” (Rindfuss et al, “Developing a science of land change”, PNAS, 2004).

10. source: Global Land Project Science Plan (IGBP)

11. Impacts of global land change More vulnerable communities are those most at risk

12. Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources We need spatially explicit models to understand human-environment interactions

13. 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 predict changes resulting from human decisions? What GIScience techniques and tools are needed to model human-environment decision making?

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

15. Modelling collective spatial actions: the complex systems approach 1. Situated individuals (persons, groups, agents) 2. Interaction rules - communication 3. Decision rules - actions 4. Properties of space

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

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

18. 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. 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/

20. 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

21. Agent-Based Modelling: Computing approaches to complex systems Goal Environment Representations Communication Action Perception Communication Gilbert, 2003

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

23. “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

24. “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)

25. Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources Our spatially explicit models need good social theories to guide them

26. We need social theories to understand human- environment interactions  Survey Moran, “Environmental Social Science: Human-Environment Interactions and Sustainability” (2010)  Social simulation Schelling, “Micromotives and macrobehavior” (1978). Batty, “Cities and complexity” (2005).  Game theory von Neumann and Morgenstern, “Theory of games and economic behavior” (1944) Nash, "Equilibrium points in n-person games“ (1950).  Evolutionary dynamics Maynard Smith, ”Evolution and the theory of games” (1982) Axelrod, “Evolution of cooperation” (1988). Novak, “Evolutionary dynamics: exploring the equations of life” (2005).  Institutional analysis Ostrom, “Governing the commons” (1990).

27. Social Simulation: Segregation Segregation is an outcome of individual choices But high levels of segregation indicate mean that people are prejudiced?

28. Schelling’s Model of Segregation...ghettos are formed! If people require more than 1/3 of neighbours to be of the same kind...

29. Game Theory Provides a standard taxonomy for analyzing strategic interactions.

30. Prisoners’ Dilemma Two suspects are caught and put in different rooms (no communication). They are offered the following deal: 1. If both of you confess, you will both get 3 years in prison 2. If you confesses whereas the other does not, you will get 1 year and the other gets 5 years in prison. 3. If neither of you confess, you both will get 2 years in prison.

31. The stag-hunt game: conflict between safety and social cooperation Two hunters want to kill a stag. Success is uncertain and, if it comes, require the efforts of both. On the other hand, either hunter can forsake his partner and catch a hare with a good chance of success.

32. Tragedy of the Commons Assume a common-property resource (exclusion is difficult and joint use involves subtractability) with no property rights. (Pasture open to all) Each herdsman tries to keep as many sheep as possible on the commons. Each tries to maximize gain.

33. Add those sheep! The rational herdsman concludes that he should add another sheep. And another…And another…And so does each herdsman “Ruin is the destination toward which all men rush, each pursuing his own best interest…”

34. Tragedy of the Commons? Everybody ’ s property is nobody ’ s property

35. Is the tragedy of the commons inevitable? Experiments show that cooperation emerges if virtuous interactions exist source: Novak, May and Sigmund (Scientific American, 1995)

36. How can cooperation happen? Nowak MA (2006). “Five rules for the evolution of cooperation” Science 314:1560-1563 (most highly cited multidisciplinary paper – ISI, 1 st quarter 2010) "I would lay down my life for two brothers or eight cousins“ (J.B.S. Haldane)

37. Common pool resources(Elinor Ostrom)

38. The ultimate common pool resource

39. Governing the commons [Ostrom, Science, 2005]

40. Governing the commons: Ostrom´s conditions 1.Clearly defined boundaries 2.Congruence between appropriation and provision rules and local conditions 3.Collective-choice arrangements: 4.Monitoring and graduated Sanctions. 5.Conflict-resolution mechanisms 6.Minimal recognition of rights to organize. 7.Organized governance activities.

41. Agent Space Space Agent Benenson and Torrens, “Geographic Automata Systems”, IJGIS, 2005 (but many questions remain...) Modelling collective spatial actions: potential GIScience contributions

42. Modelling collective spatial actions: some potential GIScience contributions 1. Situated individuals (persons, groups, agents): spatial cognition, spatial analysis, scale in GIS 2. Interaction rules: semantics of communication, mobile computing 3. Decision rules: ontology [of actions, events and processes], spatial analysis 4. Properties of space: spatial analysis, spatial databases, scale, uncertainty, vagueness

43. Scientists and Engineers Photo 51(Franklin, 1952) Scientists build in order to study Engineers study in order to build

44. Spatially-explicit land change models Explain past changes, through the identification of determining factors of land use change; Envision which changes will happen, and their intensity, location and time; Assess how choices in public policy can influence change, by building different scenarios considering different policy options.

45. TerraME: Computational environment for developing nature-society models Cell Spaces Support for cellular automata and agents TerraME: Modular modelling tool [Carneiro, 2006]

46. TerraME´s components Describe spatial structure 1:32:00Mens. 1 1. 1:32:10Mens. 3 2. 1:38:07Mens. 2 3. 1:42:00Mens.4 4.... return value true 1. Get first pair 2. Execute the ACTION 3. Timer =EVENT 4. timeToHappen += period Describe temporal structure Newly implanted Deforesting Slowing down latency > 6 years Iddle Year of creation Deforestation = 100% Describe rules of behaviourDescribe spatial relations [Carneiro, 2006]

47. Governing the commons? ~230 scenes Landsat/year Deforestation in Amazonia How could Brazil reduce deforestation from 27.000 km 2 to 7.000 km 2 in 5 years?

49. Institutional analysis in Amazonia Old Settlements (more than 20 years) Recent Settlements (less than 4 years) Farms Settlements 10 to 20 anos Source: Escada, 2003 Identify different agents and try to model their actions

50. Amazonia: multiscale analysis of land change and beef and milk market chains with TerraME Deforestation Forest Non-forest Clouds/no data INPE/PRODES 2003/2004: São Felix do Xingu

51. Create pasture/ Deforest Speculator/ large/small bad land management money surplus Subsistence agriculture Diversify use Manage cattle Move towards the frontier Abandon/Sell the property Buy new land Settlement/ invaded land Sustainability path (alternative uses, technology) Sustainability path (technology) Agents example: small farmers in Amazonia

52. Create pasture/ plantation/ deforest Speculator/ large/small money surplus/bank loan Diversify use Buy new land Manage cattle/ plantation Buy calves from small Buy land from small farmers Agents example: large farmers in Amazonia

53. Land use Change model Beef and milk market chain model Small farmers Medium and large farmers Land use Change model Small farmers Medium and large farmers Landscape metrics model Pasture degradation model Several workshops in 2007 to define model rules and variables Landscape model: different rules for two main types of agents

54. Landscape model: different rules of behavior at different partitions which also change in time FRENTE MEIO RETAGUARDA Forest Not Forest Deforest River FRONT MIDDLE BACK SÃO FÉLIX DO XINGU - 2006

55. Modeling results 97 to 2006 Observed 97 to 2006

56. Conclusion GlScience can make a significant contribution to global change research, supporting spatially explicit models of human- environment interactions with reasoned scientific basis