TY - GEN
T1 - A network analytic approach to investigating a land-use change agent-based model
AU - Lee, Ju Sung
AU - Filatova, Tatiana
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Agent-based model analysis
KW - Complexity metrics
KW - Graph representation
KW - Land-use change
KW - Network analysis
UR - http://www.scopus.com/inward/record.url?scp=85016128549&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47253-9_20
DO - 10.1007/978-3-319-47253-9_20
M3 - Conference contribution
AN - SCOPUS:85016128549
SN - 9783319472522
T3 - Advances in Intelligent Systems and Computing
SP - 231
EP - 240
BT - Advances in Social Simulation 2015
A2 - de Roo, Gert
A2 - Hoogduin, Lex
A2 - Hemelrijk, Charlotte
A2 - Flache, Andreas
A2 - Verbrugge, Rineke
A2 - Jager, Wander
PB - Springer
T2 - 11th Conference of the European Social Simulation Association, ESSA 2015
Y2 - 14 September 2015 through 18 September 2015
ER -