A Network Analytic Approach to Investigating a Land-Use Change Agent-Based Model

Ju-Sung Lee, Tatiana Filatova

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Precise analysis of agent-based model (ABM) outputs can be a challenging and even onerous endeavor. Multiple runs or Monte Carlo sampling of one’s model (for the purposes of calibration, sensitivity, or parameter-outcome analysis) often yields a large set of trajectories or state transitions which may, under certain measurements, characterize the model’s behavior. These temporal state transitions can be represented as a directed graph (or network) which is then amenable to network analytic and graph theoretic measurements. Building on strategies of aggregating model outputs from multiple runs into graphs, we devise a temporally constrained graph aggregating state changes from runs and examine its properties in order to characterize the behavior of a land-use change ABM, the RHEA model. Features of these graphs are transformed into measures of complexity which in turn vary with different parameter or experimental conditions. This approach provides insights into the model behavior beyond traditional statistical analysis. We find that increasing the complexity in our experimental conditions can ironically decrease the complexity in the model behavior.
Original languageEnglish
Title of host publicationAdvances in Social Simulation 2015
ISBN (Electronic)978-3-319-47253-9
ISBN (Print)978-3-319-47252-2
Publication statusPublished - 2017
Event11th Annual Social Simulation Conference 2015 - Groningen, Netherlands
Duration: 14 Sept 201518 Sept 2015
Conference number: 11

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference11th Annual Social Simulation Conference 2015
Abbreviated titleSSC 2015


  • Agent-based model analysis
  • Graph representation
  • Network analysis
  • Complexity metrics
  • Land-use change

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