Systems-Behavioral Modeling

Represent the Decisions of Agents in Changing Land Use.

An innovation in modeling the impact of climate change on land use is represented by our MABEL (Multi-Agent Based Economic Landscape) Model. The agent-based model (cf. Sallach, 2001; Box, 2002; Gimblett, 2002) simulates the economics of land transitions. MABEL (Alexandridis and Pijanowski, 2002) currently has three types of agents: sellers, buyers and land use planners. Buying and selling is determined by each individual’s economic utility for all potential land use types (urban, agriculture, forest or wetland). The utility function is also Bayesian; thus previous decisions and the probability of a land transaction (to buy, sell or not to sell). Learning is introduced into the utility function enabling the agents to “adapt” the best strategies. The model is parcel based so that the buying and selling of a portion of a parcel is factored into the model. Once the model is initiated, the model updates each individual agent’s utility values in a Bayesian fashion; all values are thus changed during each time step. These values are then saved and are analyzed in relationship to patterns of parcelization and land use.

We intend to explore a variety of methods to parameterize a MABEL-like model for East Africa. Case study areas currently have the requisite information required for a MABEL-like model: parcel, land use/cover data and household survey information for several time periods. Parcel data and associated land use, household survey and biophysical attributes exist for all of the case study sites. We recognize that there will be a variety of different types of agents that will interact in complex hierarchies. In southern Kenya, for example, land use categories exist that both conflict and complement each other in terms of resource management, economic objectives, and environmental sustainability. These include herding, farming, and wildlife-based tourism. Different actors engage in these land uses, and decisions on resource allocation are contingent upon legal, political, and ethnic institutions and relationships. For example, an elected committee administers group ranches in this area with a chairman, secretary and treasurer. This committee manages broad policy issues. Individual members take decisions regarding size of herds, when and where to graze and water livestock, and on cropping strategies, and expected income from each crop, etc. As land managers these individuals will respond differently to climate dynamics, to the relatively static biophysical environment (soils, topography, hydrography), and to cultural traditions, economic opportunities, policy initiatives, and relationships with land managers in other adjacent land use systems.

Capture Society’s Decision Making as it Responds to External Driving Forces.

A third approach to modeling will be game or role playing simulations. Such simulations are long established in the fields of military science and the social sciences. They are designed to allow actors representing key individuals, groups, and institutions to interact to address hypothetical scenarios. The outcomes yield specific results in terms of prospective effects of specified initial conditions and drivers of change. For example in resource conflict simulations, maps illustrate the outcomes of discussion and debate over economic, social, policy, and environmental concerns. Equally important are insights to the process of decision-making by individuals and groups, including alliance formation and application of power or force.

The team will build on an existing role playing simulation of land use change in East Africa (Campbell and Palutikof 1978; Shoemaker 1998) to examine the impact of climate change, defined by output from climate change scenarios and crop-climate models. The databases developed for the project will serve as a basis for updating the information base on which the simulation will be run.

There will be two categories of output important to the project. First, future land use change will be projected based on specified initial conditions and introduced changes in climate and related crop and vegetation conditions. The participants will represent their output in map format. These maps will be compared with those derived from other modeling procedures. Second, the interactions between actors will be documented and used to parameterize the MABEL model. The research team will take notes on communication pathways, strategies and behaviors that result from different types of information/contexts provided them and that we establish goals. We are conducting 2 role playing simulations; one representing individual decisions and another representing a resource manager’s decision regarding land management.

Incorporate Opinions and Beliefs Affecting Land Use Decisions.

Our fourth approach, the integration of individual and group Belief Networks (Gill 2002), will allow the team to combine the results of the agent and role playing simulation as well as serve as another example of modeling. Belief Networks allow users to diagram how they perceive cause and effect relationships between components. The probability of the cause-effect relationship, recorded on a continuous or Likert scale, are introduced in a Markov or Bayesian fashion. As new evidence is introduced to the Belief Network, the probability of certain outcomes changes. For example, as the climate gets warmer and drier, the likelihood that individuals will shift from fruit and vegetables to a row crop such as maize increases.


Alexandidris, K.B., B.C. Pijanowski, and L. Zheng. in review,. Simulating the Sequential Decision-Making Process of Agent-Based Actions in a Multi-Agent Based Economic Landscape (MABEL) Model. Journal of Environment and Resource Economics.

Box, P. 2002. Spatial Units as Agents: Making the Landscape an Equal Player in Agent-Based Simulations. In Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes, edited by R.H. Gimblett. New York: Methuen.

Campbell, D.J., and J.P. Palutikof. 1978. Allocation of Land Resources in Semi-Arid Areas:  A Simulation Based on the East African Experience. Nairobi: Institute for Development Studies, University of Nairobi.

Gimblett, H.R., ed. 2002. Integrating Geographic Information Systems and Agent-Based Modeling Techniques. Oxford and New York: Oxford University Press.

Lei, Z., Pijanowski, B., & Alexandridis, K. (2005). Distributed Modeling Architecture of a Multi Agent-based Behavioral Economic Landscape (MABEL) Model. Simulation and Modeling International, 81(7), 503-515.

Lei, Z., Pijanowski, B., & Olson, J. (2005). Distributed Modeling Architecture of a Multi-Agent-Based Behavioral Economic Landscape (MABEL) Model. Simulation, 81(7), 503-515.

Sallach, D.L., and C.M. Macal. 2001. Introduction: The Simulation of Social Agents. In Social Science Computer Review. Thousand Oaks, CA: Sage Publications.

Schoemaker, P.J.H. 1998. Scenario Planning: A Tool for Strategic Thinking. In Strategic Development: Methods and Models, edited by F.A.O. O’Brien. Chichester, UK: John Wiley and Sons.

Washington-Ottombre, C.; Pijanowski, B.; Campbell, D.; Olson, J.; Maitima, J.; Musili, A.; Kibaki, T.; Kaburu, H.; Hayombe, P.; Owango, E.; Irigia, B.; Gichere, S.; & Mwangi, A. (2010). Using a role-playing game to inform the development of land-use models for the study of a complex socio-ecological system. Agricultural Systems, vol. 103, (2010), p. 117.