Agent Based Modelling (ABM)

An assessment method / framework

Purpose

Purpose?

Description

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.

State of Development Please Select

Development Contact

Scope

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)

Accessibility

Open/Closed Source Please Select

User Information

Operating Systems Unknown
Software Needed Unknown
User Interface Please Select
Ease of Use Please Select

Technical Considerations

Analytical Techniques Input/output
Links

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 http://hdl.handle.net/10523/5858

Wikipedia - https://en.wikipedia.org/wiki/Agent-based_model

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