Coupled Agent-Based and State-and-Transition Simulation Model
Dates
Publication Date
2021-07-08
Start Date
2017-08-18
End Date
2029-06-10
Citation
Miller, B.W., and Frid, L., 2021, Coupled Agent-Based and State-and-Transition Simulation Model: U.S. Geological Survey software release, https://doi.org/10.5066/P9R98PPB.
Summary
Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (i.e., autonomous units, such as wildlife, people, or viruses); agent characteristics, decision-making, adaptive behavior, and mobility; and interactions between agents and their environment. STSMs are flexible and intuitive stochastic models of landscape dynamics that can track scenarios and landscape attributes, and integrate diverse data types. Both can be run spatially and track metrics of management success. Due to the complementarity of these [...]
Summary
Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (i.e., autonomous units, such as wildlife, people, or viruses); agent characteristics, decision-making, adaptive behavior, and mobility; and interactions between agents and their environment. STSMs are flexible and intuitive stochastic models of landscape dynamics that can track scenarios and landscape attributes, and integrate diverse data types. Both can be run spatially and track metrics of management success. Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns. We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park.The coupled approach: 1) streamlined handling of model inputs and outputs; 2) allowed representation of processes at multiple temporal scales; 3) minimized assumptions; and 4) generated spatial and temporal patterns that better reflected agent-environment interactions. These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections.
Click on title to download individual files attached to this item.
ABM & STSM_Metadata.xml “Metadata” Original FGDC Metadata
View
8.09 KB
application/fgdc+xml
ABM_STSM_BADL.zip “ABM & STSM Files and Code”
199.19 MB
application/zip
Related External Resources
Type: Related Primary Publication
Miller, B.W., and Frid, L., 2021, A new approach for representing agent-environment feedbacks: coupled agent-based and state-and-transition simulation models: Landscape Ecology, https://doi.org/10.1007/s10980-021-01282-y.
Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (i.e., autonomous units, such as wildlife, people, or viruses); agent characteristics, decision-making, adaptive behavior, and mobility; and interactions between agents and their environment. STSMs are flexible and intuitive stochastic models of landscape dynamics that can track scenarios and landscape attributes, and integrate diverse data types. Both can be run spatially and track metrics of management success. Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns.
