Agent Based Modelling (ABM)

An assessment method / framework




An agent-based model (ABM) (also sometimes related to the term multi-agent system or multi-agent simulation) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. ABMs are also called individual-based models.

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Management Domains General
Spatial Resolutions Unknown
Spatial Extents Local (i.e. Catchment or District)
Spatial Dimensions Unknown
Temporal Resolutions Unknown
Temporal Extents Unknown
Steady State or Dynamic Unknown

Input & Output Data

Input Data Formats XLS(X)


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User Information

Operating Systems Unknown
Software Needed Unknown
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Technical Considerations

Analytical Techniques Input/output

Land Journal - Special Issue "Agent-Based Modelling and Landscape Change"

Agent-based Rural Land Use New Zealand model (ARLUNZ) - Landcare Research

Key References

Green, P. (2007). An introduction to agent-based modelling (Dissertation, Master of Business). University of Otago. Retrieved from

Wikipedia -

Bonabeau, E. (2002): Agent-based modeling: Methods and techniques for simulating human systems. PNAS May 14, 2002. Vol 99 (suppl 3) 7280-7287

Associated Case Studies

ABM - Spatial Multi-Agent Simultation of Nitrogen Discharge Trading

Nutrient discharge from intensive farming [nitrogen (N) leached] is a major cause of poor water quality of rivers and lakes in catchments.


ABM - Rangitaiki Catchment

As part of a participatory process scenarios were defined by the working group and assessed using the Agent-Based ARLUNZ model.


Other Key Case Studies

Fraser J. Morgan, Adam J. Daigneault (2015): Estimating Impacts of Climate Change Policy on Land Use: An Agent-Based Modelling Approach. PLOS ONE 10(5): e0127317.

Dake CKG, Manderson AK, Mackay AD 2006. Specification of environmental emission trading options in a spatial multi-agent simulation model of pastoral farming. Paper Presented at the New Zealand Agricultural and Resource Economics Society Conference 25-27 August 2006