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Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets

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Abstract

One emerging area of agent-based modelling is retail markets; however, there are problems with modelling such systems. The vast size of such markets makes individual-level modelling, for example of customers, difficult and this is particularly true where the markets are spatially complex. There is an emerging recognition that the power of agent-based systems is enhanced when integrated with other AI-based and conventional approaches. The resulting hybrid models are powerful tools that combine the flexibility of the agent-based methodology with the strengths of more traditional modelling. Such combinations allow us to consider agent-based modelling of such large-scale and complex retail markets. In particular, this paper examines the application of a hybrid agent-based model to a retail petrol market. An agent model was constructed and experiments were conducted to determine whether the trends and patterns of the retail petrol market could be replicated. Consumer behaviour was incorporated by the inclusion of a spatial interaction (SI) model and a network component. The model is shown to reproduce the spatial patterns seen in the real market, as well as well known behaviours of the market such as the "rocket and feathers" effect. In addition the model was successful at predicting the long term profitability of individual retailers. The results show that agent-based modelling has the ability to improve on existing approaches to modelling retail markets.

Suggested Citation

  • Alison Heppenstall & Andrew Evans & Mark Birkin, 2006. "Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-2.
    • Handle: RePEc:jas:jasssj:2005-29-3
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    Citations

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    Cited by:

    1. He, Zhou & Cheng, T.C.E. & Dong, Jichang & Wang, Shouyang, 2016. "Evolutionary location and pricing strategies for service merchants in competitive O2O markets," European Journal of Operational Research, Elsevier, vol. 254(2), pages 595-609.
    2. Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, vol. 151(C), pages 273-284.
    3. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    4. Sedar Olmez & Jason Thompson & Ellie Marfleet & Keiran Suchak & Alison Heppenstall & Ed Manley & Annabel Whipp & Rajith Vidanaarachchi, 2022. "An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space," Energies, MDPI, vol. 15(11), pages 1-24, May.
    5. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    6. Peter A Johnson & Renee E Sieber, 2011. "An Agent-Based Approach to Providing Tourism Planning Support," Environment and Planning B, , vol. 38(3), pages 486-504, June.
    7. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.
    8. Paul Plummer & Eric Sheppard & Robert Haining, 2012. "Rationality, Stability, and Endogenous Price Formation in Spatially Interdependent Markets," Environment and Planning A, , vol. 44(3), pages 538-559, March.

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