Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$60.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: agent-based modeling; land use change; multi-objective decision-making; reinforcement learning

Document Type: Research Article

Affiliations: 1: Department of Biology,University of Alaska Anchorage, AnchorageAK, USA 2: Department of Geography,Simon Fraser University, BurnabyBritish Columbia, Canada 3: Department of Geography,Memorial University of Newfoundland, St. John'sNewfoundland, Canada

Publication date: May 1, 2011

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more