OR/16/022 What is ABM

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Beriro, D, Cave, M, Wragg, J and Hughes, A. 2016. Agent Based Modelling: Initial assessment for use on soil bioaccessibility. British Geological Survey Internal Report, OR/16/022.

Defined as ’actions and interactions of autonomous agents with a view to assessing their effects of the system as a whole’. ‘Agents have behaviours, often described by simple rules, and interactions with other agents, which in turn influence their behaviours’ (Macal and North, 2010[1]). Put simply, it is the computational representation of things moving around a space. It radically differs from other modelling approaches in that the agents move around a landscape so have a position: x, y and possibly z. They follow rules on how they move and, importantly, how they engage with each other and the environment in which they exist.

It has a wide range of uses where a behaviour or movement can be attributed to anything within a system. A recent paper (Farmer and Foley, 2009[2]) has espoused ABM for use in economic modelling. One of the authors use ABM to simulate stock market trading by simulating different trading behaviours and types of institutions. They do admit that a global scale economic model would be beneficial, but an ambitious undertaking.

However ABM is not without its challenges, particularly for Socio-Economic systems (Filatova et al., 2013[3]). Here the authors identify four challenges to successfully undertaking ABM of socio-economic systems: 1. Modelling agent’s behaviour; 2. Sensitivity analysis, verification and validation; 3. Coupling socio-demographic, ecological and biophysical models and 4. Spatial representation. These are all relevant issues to the work described below.

As we have seen ABM is not new, there have been a number of applications of agent based approaches over the last decades, and the approach itself has evolved. Table 1 summarises the most relevant applications to the environmental and human health field. The table also includes papers that are appropriate in terms of movement of humans.

Table 1    Sample of relevant environmental applications using ABM
Application Summary Reference/link
Movement of people around streets and within buildings Using different examples: Tate Gallery and Notting Hill Carnival the movement of people as agents is modelled. Different approaches for modelling movement such as stepwise and random walk are used. Batty, 2003[4]

www.casa.ucl.ac.uk/working_papers/paper61.pdf

Fibrosis in the lung Using ABM to replicate the degeneration of lung tissue from exposure to particulate matter. Brown et al., 2011[5]
Appropriate software for city-based movement Three agent modelling software are evaluated to understand the most appropriate approach for simulating agent movement in cities; seven criteria are applied and three case studies are used to test the approach: evacuation, neighbourhood development and trip origin. Crooks et al, 2008[6]

www.discovery.ucl.ac.uk/15174/1/15174.pdf

Car driver behaviour The response of car drives to real-time information is studied using a hypothetical commute and by feeding in travel information and seeing their response. Dia et al., 2002[7]
Agent based pedestrian model Development of the code STREETS which enables pedestrian movement to be simulated. Schelhorn et al., 1999[8]

www.casa.ucl.ac.uk/streets.pdf

Farming How different aspects of farming (technology, economics, environmental change and policy interventions) affect a community of farmers and their output. Schreinemachers and Berger, 2011[9]
Water resource allocation Linkage of a groundwater model to ABM systems is described and an example represented. Castilla-Rho et al., 2015[10]

References

  1. Macal, C M, and North, M J. 2010. Tutorial on agent-based modelling and simulation. Journal of simulation, 4(3), 151–162.
  2. Farmer, J D, and Foley, D. 2009. The economy needs agent-based modelling. Nature, 460(7256), 685–686.
  3. Filatova, T, Verburg, P H, Parker, D C, and Stannard, C A. 2013. Spatial agent-based models for socio-ecological systems: challenges and prospects. Environmental modelling & software, 45, 1–7.
  4. Batty, M. 2003. Agent-based pedestrian modelling. Advanced spatial analysis: The CASA book of GIS, 81.
  5. Brown, B N, Price, I M, Toapanta, F R, DeAlmeida, D R, Wiley, C A, Ross, T M, and Vodovotz, Y. 2011. An agent-based model of inflammation and fibrosis following particulate exposure in the lung. Mathematical biosciences, 231(2), 186–196.
  6. Crooks, A, Castle, C, and Batty, M. 2008. Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430.
  7. Dia, H. 2002. An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transportation Research Part C: Emerging Technologies, 10(5), 331–349.
  8. Schelhorn, T, O'Sullivan, D, Haklay, M, and Thurstain-Goodwin, M. 1999. STREETS: An agent-based pedestrian model.
  9. Schreinemachers, P, and Berger, T. 2011. An agent-based simulation model of human–environment interactions in agricultural systems. Environmental Modelling & Software, 26(7), 845–859.
  10. Castilla-Rho, J C, Mariethoz, G, Rojas, R, Andersen, M S, and Kelly, B F J. 2015. An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems. Environmental Modelling & Software, 73, 305–323.