Comparing a simulation model with various analytic models of the international diffusion of consumer technology

In this paper we propose and evaluate a method for studying technology adoption at the national level using hybrid simulation. A hybrid simulation model is developed which combines elements of system dynamics and agent-based modelling, and treats nations as adopting agents. International diffusion is modelled as a social system where the adoption of an innovation, or even just growing pressure to adopt an innovation, in one nation can then influence its adoption in others. The model is used to investigate nine different technological innovations for which sufficient international data are available. Using the available empirical data, the method of differential evolution is used to configure the model which allows the parameter space to be explored in an efficient manner, without bias or subjective disagreement. Good agreement is found between the parameters derived in this way and those reported to configure analytic models. For each of the nine innovations, we report the rank order correlation between the actual order of adoption of the innovations by nations and the order predicted by the simulation model. We also report the rank order correlations between the actual order and the order predicted by a much simpler statistical model. Improvements in the rank order correlation are shown when some form of social influence between nations is included, although there is no significant difference in results between the four different types of social influence considered by the simulation. The nine technologies investigated also appear to fall into two groups with significantly different uptake speeds. Advantages and limitations of the approach are discussed along with suggested implications for practice

Simulating the diffusion of technological innovation with an integrated hybrid agent-based system dynamics model

The potential of hybrid models to enhance simulations of the real world is explored. While the scope for design of such models is large, the focus here brings together agent-based (AB) and system dynamics (SD) modelling within a defined architectural framework. Comprising a number of modules, each of which is implemented in a single modelling paradigm, the design of hybrid models looks to exploit the potential from a range of approaches and tools. Coded within a single programming environment, the international diffusion of technological innovation is used as a case study to highlight hybrid simulation model design and implementation. An integrated hybrid simulation design that incorporates feedback between modules in a continuous, fluid, process is employed to develop a model comprising two SD modules and one AB module. The predictions from the hybrid model are compared to known outcomes regarding the national adoption of mobile telephony, fixed internet and fixed broadband. We conclude with some thoughts on the design of hybrid simulation models

Design classes for hybrid simulations involving agent-based and system dynamics models

Hybrid simulation involves the use of multiple simulation paradigms, and is becoming an increasingly common approach to modelling modern, complex systems. Despite growing interest in its use, little guidance exists for modellers regarding the nature and variety of hybrid simulation models. Here, we concentrate on one particular hybrid – that involving agent-based and system dynamics models. Based on an up-to-date review of the literature, we propose three basic types of hybrid agent-based system dynamics simulations, referred to here as interfaced, integrated and sequential hybrid designs. We speculate that the classification presented may also be useful for other classes of hybrid simulations

Green neighbourhoods: the role of big data in low voltage networks’ planning

In this chapter, we aim to illustrate the benefits of data collection and analysis to the maintenance and planning of current and future low voltage net- works. To start with, we present several recently developed methods based on graph theory and agent-based modelling for analysis and short- and long-term prediction of individual households electric energy demand. We show how maximum weighted perfect matching in bipartite graphs can be used for short-term forecasts, and then review recent research developments of this method that allow applications on very large datasets. Based on known individual profiles, we then review agent-based modelling techniques for uptake of low carbon technologies taking into account socio-demographic characteristics of local neighbourhoods. While these techniques are relatively easily scalable, measuring the uncertainty of their results is more challenging. We present confidence bounds that allow us to measure uncertainty of the uptake based on different scenarios. Finally, two case-studies are reported, describing applications of these techniques to energy modelling on a real low-voltage net- work in Bracknell, UK. These studies show how applying agent-based modelling to large collected datasets can create added value through more efficient energy usage. Big data analytics of supply and demand can contribute to a better use of renewable sources resulting in more reliable, cheaper energy and cut our carbon emissions at the same time