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This is the home page of the MABEL (Multi Agent-based Behavioral Economic Landscape) Model. The model is based on the SWARM modeling package. The model is designed to simulate land use changes over time. Important features of the model include: sequential Markov chain construction, a knowledge base, Bayesian Belief Networks that integrate biophysical and socioeconomic inputs and actions based on a utility function. The model is also spatially explicit, with agents making decisions on how to buy/sell, and configure (split) ownerships parcels and is built to link to the ArcGIS software package.

You can find model descriptions, PowerPoint presentations, video snapshots of the model "in action" and a list of projects that are supporting the development of this model.

Overview Agents in MABEL own land configured as parcels. They make decisions on how to use their land based on inputs from the biophysical environment (e.g., climate), economics (e.g., anticipated crop prices), demographics (e.g., age) and any other socioeconomic parameter (e.g., level of risk adversion). These components are integrated into agent's beliefs about future expectations about potential payoffs (i.e., rewards) from actions. MABEL is a multi-agent based system in that different kinds of agents, distinguished from their land ownership, interact in a pseudo market model. Our current market model is a land bidding routine that allows buyers and sellers of land interact by first annoucing their intentions and then to complete transactions given a set of rules, some of which come from a policy agent (i.e., a local planning commission), that regulate land use transition types and how parcels are divided. Part of the model involves a complex parcel division routine that allows buyer and seller agents to calculate three different spatial metrics related to parcels that would be split. These include a shape and height-to-width ratio metric. The latter is intended to ensure that parcels that are created are not "bowling alley" lots. Bayesian Belief Networks are used to configure the agent's beliefs linking biophysical and socioeconomic inputs with internal beliefs and causal relationships and expected outcomes. We configure the outcomes as a utility function that allows the agent to calculate its maximum expected utility given inputs, their beliefs and their intentions. A learning algorithm is built into the model so that agents can increase rewards from their actions. Technical Dimensions The MABEL model is built upon a distributed client-server architecture using TCP/IP sockets specifically configured to monitor client communication and simulation status. The server coordinates all of the clients (so that they are running synchronously). The BBN agent profiles are kept on the server side as well ensuring that agents across all client simulations are configured identically. The model also integrates code from C, C++, Swarm, scripts from SPSS and ArcGIS. Swarm is run in the LINUX environment on a Windows simulator. Much of the data communication software is written in C. Modeling Environment We have conducted several different types of simulations to test the reliability of MABEL. This includes a Monte Carlo simulation, tests of different BBN configurations, tests of different data sources as inputs (e.g., PUMs) and tests of different parcelization algorithms. We are currently (early 2007) working on developing a set of role playing simulation games and methods that can be used to parameterize MABEL for East Africa. This work will entail conducting games that generate specific outcomes that can be used to configure MABEL in ways that test our understanding of how people respond to climate change in savannah ecosystems.

Simulation Examples These examples are for Long Lake, Grand Traverse County, Michigan, USA. Time (0) Time (1) Example GUI Interfaces This is the server interface that shows status of clients. And the client interface looks like this:

 

Publications

Alexandridis, K. B. C. Pijanowski, Z. Lei. 2007. Assessing Multi-Agent Parcelization Performance in the MABEL Simulation Model using Monte Carlo Replication Experiments. Environment and Planning B.

Alexandridis, K., B. Pijanowski and Z. Lei. 2005. The use of robust and efficient methodologies in agent-base modeling: case studies using repeated measures and behavioral components in the MABEL simulation model. Proceedings of the Agent 2004 Conference on Social Dynamics, Interaction, Reflexivity and Emergence. Argonne National Laboratory and the University of Chicago . 2005, October 3-6, 2004.

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

Alexandridis., K., B. Pijanowski and Z. Lei. 2004. The Use of Robust and Efficient Methodologies in Agent-Based Modeling: Case Studies Using Repeated Measures and Behavioral Components in the MABEL Simulation Model. Agent2004 Conference, Chicago , Illinois . October 5, 2004.

Lei Z., B. Pijanowski and K. Alexandridis. 2003. Simulation and Distributed Architecture in Multi-agent-based Environmental Landscape (MABEL) Model with Swarm. October 2, 2003. Agent2003 Conference. Chicago , Illinois .

Alexandridis, K. T., and B. C. Pijanowski. 2002. Multi Agent-Based Environmental Landscape (MABEL) - An Artificial Intelligence Simulation Model: Some Early Assessments (Staff Paper No. 2002-09). Lansing , MI : Department of Agricultural Economics, Michigan State University . Proceedings of the AERE/EAERE: 2002 World Congress of Environmental and Resource Economists, Monterey , California , June 24-27.

 

 

Last updated by BCP on March 3, 2007

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