Group 3: HUMAN ASPECTS ON CHANGE MODELLING

1.1. Abstract 1.2. State-Of-The-Art, related literature 1.3. Work packages descriptions 1.4. Deliverables, time plan

• “Modeling Human and Institutional Aspects on Multi-Scale LUCC models”

• TerraLib (database, elementary topological operations)
• TerraHS/Haskell (prototype implementation)
• Secondo (database for moving objects),
• Aguila (visualisation)
• TerraME (environmental modelling)
• PCRaster (environmental modelling)
• TerraView (wants to do animation)
• R, aRT
• OGC web services – 52N suite, …

Besides humans and institutions are the major driver of environmental changes [Lambin, Turner et al. 2001; Parker, Berger et al. 2001], models which take in consideration their motivations and the way they take decisions or reason about the environment they are embedded are yet in a very early stage. Several agent theories [Wooldbridge, Jennings 1995] and architectures [Minar 1996; Swarm 2008; NetLogo 2008, Repast 2008] have been proposed on the literature. However, they were not designed for simulation of human or institutional activities in environmental systems. Therefore, they lack on meet the whole requirements of this application.

The Nested-CA model of computation, implemented in the TerraME software, has been designed for multiple scale environmental change model development [Carneiro 2006; Moreira 2008; Carneiro 2008]. However, TerraME provides just a situated view of control in which agents can acts based on their correlated state with the environment state, without the needing for explicit knowledge representation or symbolic processing. This approach supposes that at the model design time, the modeler is capable of enumerate all the possible states of the environment and provide the reaction of each agent to each of these states [Rosenschein, Kaelbling 1995]. Besides [Maes 1990] and [Fasciano 1994; Devigne, D., Mathieu, P., Routier, J.C 2004] have shown that situated agents can have goals and plan, there are no evidences that TerraME agents can do that. Moreover, TerraME has just a very simple remote method invocation mechanism for agent communication that has yet not been used for collaborative agent modeling.

This way, the following issues should be addressed in this work in order to provide an agent architecture for simulating human and institutional behavior in environmental change models: What are the requirements for such agent architecture? How agents can autonomously take decisions? How to simulate goal oriented behavior? How to simulate knowledge based decision taking process? How agents can plan their actions? How to simulate collaborative and competitive behavior?

References:

• Carneiro, T. G. S., Câmara, G., Maretto, R. V., (2008) “Irregular Cellular Spaces: Supporting Realistic Spatial Dynamic Modeling over Geographical Databases”. X Brazilian Symposium on Geoinformatics (GeoInfo 2008). Rio de Janeiro, Brazil .
• Carneiro, T. G. S.. (2006) “Nested-CA: a foundation for multiscale modeling of land use and land change”. PhD Thesis in Computer Science. Sao Jose dos Campos: INPE, 2006. Computer Science Department.
• Devigne, D., Mathieu, P., Routier, J.C. (2004) “Planning for spatially situated agents” Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2004). Volume , Issue , 20-24 Sept.Page(s): 385 – 388
• Fasciano, M. (1994) “Situated agents can have plans”. Proceedings of the twelfth national conference on Artificial intelligence (vol. 2), American Association for Artificial Intelligence, Menlo Park, CA, USA.
• Lambin, E. F., B. L. Turner, et al. (2001). “The causes of land-use and land-cover change: moving beyond the myths.” Global Environmental Change-Human and Policy Dimensions 11(4): 261-269.
• Maes, P. (1990). “Situated Agents Can Have Goals”. Robotics and Autonomous Systems 6: 49-70.
• Minar, N., Burkhart, R., Langton, C., Askenazi, M. (1996) “The Swarm simulation system: A toolkit for building mult-agent simulations”. Working Paper 96-06-042, Santa Fe Institute, Santa Fe.
• Moreira, E.; Aguiar, A. P.; Costa, S.; Câmara, G., (2008), Spatial relations across scales in land change models, in Vinhas, L., ed., X Brazilian Symposium on Geoinformatics, Rio de Janeiro, Brazil.
• Parker, D. C., T. Berger, et al. (2001). “Agent-Based Models of Land-Use and Land-Cover Change.” Report and Review of an International Workshop. L. R. No.6. Irvine, California, USA.
• Rosenschein, S. J., L. P. Kaelbling (1995). “A situated view of representation and control.” Artificial Intelligence 73(149-73).
• Wooldbridge, M.; Jennings, N. R, (1985). Agent theories, architectures, and languages: a survey. Proceedings of the workshop on agent theories, architectures, and languages on Intelligent agents, Amsterdam, The Netherlands.
• Swarm. Swarm Development Group. http://repast.sourceforge.net/repast_3/index.html. Last access: July/2008.
• NetLogo. http://ccl.northwestern.edu/netlogo/. Last access: July/2008.
• Repast. Repast Agent Simulation Toolkit. http://repast.sourceforge.net/repast_3/index.html. Last access: July/2008.

The most import drivers of the Amazon land occupation are economical activities related to the international market of commodities (beef, grains and timber), the extractivism (animals, plants, fruits), the local market, the subsistence agriculture, the miner market, and the housing market. Besides most of these activities are legal and are regulated by laws that attempt to make them sustainable, a great part of these activities are illegal and have a huge impact on the regional economy and land system structure. Actors involved on this black economy include farmers, wood loggers, settlers, grileiros, traffickers, killers, govern corrupted institutions or authorities. We do believe that incorporating these illegal actors and activities into Brazilian Amazon land use change models is an important issue. This way, a lot work has to be done in order to provide answers for the following questions: What are the legal and illegal activities and actors involved in the land occupation process at the Brazilian Amazon? How to simulate negotiation and corruption for land appropriation? What are the alternative scenarios for corruption mitigation?

SANTOS JÚNIOR, R. A. O. . The drug trade, the black economy and society in Western Amazonia. International Social Science Journal, EUA, v. 169, 2001.

Land changes are results from complex social and biophysical systems and their interactions (Turner et al., 1995). Such interactions result from processes that act on different hierarchical levels. At the global scale, the national and international commodities market (beef, grains and timber) drives demand for land change. This way, these changes cannot be adequately understood without knowing their linkages to decisions and structures made elsewhere. In this sense, understanding the role of networks is essential to understanding how to decide about the land use.

A network is a set of items, which we will call vertices or nodes, with connections between them, called edges. Networks exist everywhere and at every scale and can be physical, such as infrastructure networks, and logical ones, such as market chains, linking a certain location to distant consumption or influential sites. Several works argue that networks are important tool to describe complex systems, REFS. <gpm>

We believe which networks can be a important tool to link the actor of land change to global scenarios, as market and police public in land change modeling. However, is necessary to solve some specific challenges:

• Network Architecture, including data structures and algorithms.

• Aguiar, A., Câmara, G., Cartaxo., R.: Modeling Spatial Relations by Generalized Proximity Matrices. V Brazilian Symposium in Geoinformatics - GeoInfo 2003, Campos do Jordão, SP, Brazil (2003)

• Andrade, P. R. (2008). Modelling and simulation of complex systems with geospatial agents.

• Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks.Science , 286, pp. 509-512.

• Barabási, A. L., & Bonabeau, E. (2003). Scale-free networks. Scientific American , 288,50-59.

• Newman, M., Barabási, A. L., & Watts, D. J. (2006). The structure and dynamics of networks. EUA: Princeton University press.

• Moreira, E., Aguiar, A.P., Costa, S., Câmara, G.: Spatial relations across scales in land change models. In: Vinhas, L. (ed.): X Brazilian Symposium on Geoinformatics, GeoInfo 2008. SBC, Rio de Janeiro (2008)

• Watts, D. J. (1999). Small worlds. EUA: Princeton University Press.

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