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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|Spatial Extents||Local (i.e. Catchment or District)|
|Steady State or Dynamic||Unknown|
|Input Data Formats||XLS(X)|
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Land Journal - Special Issue "Agent-Based Modelling and Landscape Change"
Agent-based Rural Land Use New Zealand model (ARLUNZ) - Landcare Research
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
Nutrient discharge from intensive farming [nitrogen (N) leached] is a major cause of poor water quality of rivers and lakes in catchments.
As part of a participatory process scenarios were defined by the working group and assessed using the Agent-Based ARLUNZ model.
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