Modeling-in-the-middle: bridging the gap between agent-based modeling and multi-objective decision-making for land use change

A spectrum of methods exists for investigating and providing solutions for land use change. These methods can be broadly categorized as either ‘top-down’ or ‘bottom-up’ approaches according to how land use change is modeled and analyzed. Although there has been much research in recent years advancing the use of these techniques for both theoretical and practical applications, integrating top-down and bottom-up approaches for enhancing land use change modeling has received minimal attention. The objective of this study is to address this gap in the literature by bridging the bottom-up simulation of agent-based modeling and the top-down analytical capabilities of multi-objective decision-making by means of a heuristic modeling approach called reinforcement learning (RL). A model is developed in which computer agents representing households and commercial enterprises select locations to inhabit based on population densities and attractivity preferences. The land use change resulting from these dynamics is evaluated by a set of agents representing different stakeholders who are embedded with RL algorithms that allow them to influence the land use change process so that their objectives are addressed. The results demonstrate that bridging bottom-up and top-down models leads to negotiated land use patterns in which the desires and objectives of all individuals are constrained by behaviors of others. This study suggests that a movement toward a ‘modeling-in-the-middle’ approach is desirable to incorporate the real yet conflicting forces that shape land use change and that are rarely considered in unison.

[1]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[2]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[3]  Henk J. Scholten,et al.  An Introduction to Geographical Information Systems , 1995 .

[4]  Steven Johnson,et al.  Emergence: The Connected Lives of Ants, Brains, Cities, and Software , 2001 .

[5]  Michael Batty,et al.  Modelling and prediction in a complex world , 2005 .

[6]  Anne van der Veen,et al.  Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change , 2009, J. Artif. Soc. Soc. Simul..

[7]  Wenwu Tang,et al.  Simulating Complex Adaptive Geographic Systems: A Geographically Aware Intelligent Agent Approach , 2008 .

[8]  I. Benenson MULTI-AGENT SIMULATIONS OF RESIDENTIAL DYNAMICS IN THE CITY , 1998 .

[9]  Keith C. Clarke,et al.  A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area , 1997 .

[10]  Piet Rietveld,et al.  Spatial Dimensions in Multicriteria Analysis , 2019, Spatial Multicriteria Decision Making and Analysis.

[11]  David A. Bennett,et al.  Interactive evolutionary approaches to multiobjective spatial decision making: A synthetic review , 2007, Comput. Environ. Urban Syst..

[12]  Lin Liu,et al.  A bottom‐up approach to discover transition rules of cellular automata using ant intelligence , 2008, Int. J. Geogr. Inf. Sci..

[13]  Scott J. Goetz,et al.  Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model , 2010, Comput. Environ. Urban Syst..

[14]  Stephen J. Carver,et al.  Integrating multi-criteria evaluation with geographical information systems , 1991, Int. J. Geogr. Inf. Sci..

[15]  J. Martel,et al.  Enhancing Geographical Information Systems Capabilities with Multi-Criteria Evaluation Functions , 2003 .

[16]  M. Batty,et al.  Simulating Emergent Urban Form Using Agent-Based Modeling: Desakota in the Suzhou-Wuxian Region in China , 2007 .

[17]  Richard L. Church,et al.  Spatial optimization as a generative technique for sustainable multiobjective land‐use allocation , 2008, Int. J. Geogr. Inf. Sci..

[18]  Moira L. Zellner,et al.  Generating Policies for Sustainable Water Use in Complex Scenarios: An Integrated Land-Use and Water-Use Model of Monroe County, Michigan , 2007 .

[19]  Jacek Malczewski,et al.  GIS and Multicriteria Decision Analysis , 1999 .

[20]  G. Brent Hall,et al.  International Journal of Geographical Information Science , 2022 .

[21]  David A. Bennett,et al.  Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems , 2002 .

[22]  Jacek Malczewski,et al.  GIS-based land-use suitability analysis: a critical overview , 2004 .

[23]  Guy Engelen,et al.  Cellular Automata as the Basis of Integrated Dynamic Regional Modelling , 1997 .

[24]  Richard S. Sutton,et al.  Learning to predict by the methods of temporal differences , 1988, Machine Learning.

[25]  J. Ronald Eastman,et al.  Multi-criteria and multi-objective decision making for land allocation using GIS , 1998 .

[26]  José I. Barredo,et al.  The MOLAND Modelling Framework for Urban and Regional Land Use Dynamics , 2007 .

[27]  J. Jacobs The Death and Life of Great American Cities , 1962 .

[28]  Stan Openshaw,et al.  Artificial intelligence in geography , 1997 .

[29]  Jacek Malczewski,et al.  GIS‐based multicriteria decision analysis: a survey of the literature , 2006, Int. J. Geogr. Inf. Sci..

[30]  Juval Portugali,et al.  Self-Organization and the City , 2009, Encyclopedia of Complexity and Systems Science.

[31]  Richard L. Church,et al.  An interface for exploring spatial alternatives for a corridor location problem , 1992 .

[32]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

[33]  Jean-Christophe Castella,et al.  Combining top-down and bottom-up modelling approaches of land use/cover change to support public policies: Application to sustainable management of natural resources in northern Vietnam , 2007 .

[34]  Richard S. Sutton,et al.  Associative search network: A reinforcement learning associative memory , 1981, Biological Cybernetics.

[35]  Michael Batty,et al.  Urban Systems as Cellular Automata , 1997 .

[36]  Daniel G. Brown,et al.  Evaluating the effects of land‐use development policies on ex‐urban forest cover: An integrated agent‐based GIS approach , 2009, Int. J. Geogr. Inf. Sci..

[37]  Michael Batty,et al.  Possible Urban Automata , 1997 .

[38]  William Rand,et al.  Path dependence and the validation of agent‐based spatial models of land use , 2005, Int. J. Geogr. Inf. Sci..