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

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.Agents, Spatial Interaction Model, Retail Markets, Networks

Hybrid modelling of individual movement and collective behaviour

Mathematical models of dispersal in biological systems are often written in terms of partial differential equations (PDEs) which describe the time evolution of population-level variables (concentrations, densities). A more detailed modelling approach is given by individual-based (agent-based) models which describe the behaviour of each organism. In recent years, an intermediate modelling methodology – hybrid modelling – has been applied to a number of biological systems. These hybrid models couple an individual-based description of cells/animals with a PDEmodel of their environment. In this chapter, we overview hybrid models in the literature with the focus on the mathematical challenges of this modelling approach. The detailed analysis is presented using the example of chemotaxis, where cells move according to extracellular chemicals that can be altered by the cells themselves. In this case, individual-based models of cells are coupled with PDEs for extracellular chemical signals. Travelling waves in these hybrid models are investigated. In particular, we show that in contrary to the PDEs, hybrid chemotaxis models only develop a transient travelling wave

The Contribution of Social Simulation in the Advancement of Marketing Issues and Challenges

For some years now, marketers have been praising for a more holistic approach of a company’s marketing efforts across all areas. However, traditional models show serious limitations to address the complexities of managing all of a company’s touch points with a customer. Agent-based modeling (ABM) has opened the door to explore the unfolding behaviors and outputs of an increasingly connected and interactive marketplace. The contribution of this paper is twofold. On the one hand, it provides researchers with a state-of-the-art repository for this strand of research. This facilitates the identification of relevant gaps in the literature and future research avenues. Second, it contributes to assess the way ABM has improved our understanding of the dynamics of markets and its participants when marketing strategies are implemented. Both goals aim at showing the various ways that social simulation has expanded our understanding of marketing and the future research opportunities for both, marketing and computer scientists

Simulating emergent urban form: desakota in China

We propose that the emergent phenomenon know as ?desakota?, the rapidurbanization of densely populated rural populations in the newlydeveloped world, particularly China, can be simulated using agent-basedmodels which combine both local and global features. We argue thatdeskota represents a surprising and unusual form of urbanization wellmatchedto processes of land development that are driven from the bottomup but moderated by the higher-level macro economy. We develop asimple logic which links local household reform to global urban reform,translating these ideas into a model structure which reflects these twoscales. Our model first determines the rate of growth of different spatialaggregates using linear statistical analysis. It then allocates this growth tothe local level using developer agents who determine the transformation ormutation of rural households to urban pursuits based on local land costs,accessibilities, and growth management practices. The model is applied todesakota development in the Suzhou region between 1990 and 2000. Weshow how the global rates of change predicted at the township level in theWuxian City region surrounding Suzhou are tempered by localtransformations of rural to urban land uses which we predict using cellularautomata rules. The model, which is implemented in the RePast 3software, is validated using a blend of data taken from remote sensing andgovernment statistical sources. It represents an example of generativesocial science that fuses plausible behavior with formalized logics matchedagainst empirical evidence, essential in showing how novel patterns ofurbanization such as desakota emerge