Conventional tools used to understand energy systems, such as system dynamic models do not cope well with the complexity of consumer energy behaviours. Agent based modelling can be more suitable to represent the complexities of consumer behaviours and their decision making processes in ways that can improve understanding of the demand side of energy systems (Ajmone-Marsan et
al,2012) (Rai et al., 2016). In particular, as discussed earlier, there has been an increase in the last few
years in the application of ABM to the study of consumer behaviour across a range of energy and environmental problems and sustainable energy technologies adoption (Chappin et al., 2012; Kowalska-Pyzalska et al., 2014; McCoy et al., 2014; Jensen et al., 2015; Ringler et al., 2016; Alfaro et al., 2017; Busch et al., 2017). According to Rai (Rai et al., 2016) these models combine three main goals: - to represent behaviour driven models of decision making that depart from the neo-classical rational view; - to incorporate heterogeneous agents and environments through rich datasets in order to provide more realistic setting that could be of help in developing decision-support tools; - to study emergence in consumer systems, in particular how values and beliefs at consumer level (as defined by social psychology and behavioural theories) lead to macro behaviours such as adoption over time and space. While ABM design and evaluation questions can be posed ex-post to evaluate the effects of policies and programmes after implementation, most of the current ABM research on energy related consumer behaviour focuses on policy design problems in ex-ante settings. In this context, agent-based simulations can provide insights into key aspects of the problem under analysis as for example the influence of social networks in shaping consumer's decisions on energy consumption choices or identifying critical aspects that deserve further analysis and focus in future empirical studies. These obtained insights can be used to assess the potential impacts of policies before any action is taken.
Rai (Rai et al., 2016) provides an interesting and simple exemplification of the common elements of ABM that can be easily transposed to the energy field. He suggests a three step approach that is presented in Figure 8. The first step is to specify the factors that determine consumer's behaviours (micro-drivers); these factors are determined by the theory that informs the model. There is a significant variety of theoretical choices that may underpin consumer's behaviour, spanning from
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prospect theory, theory of planned behaviour, utility maximization, threshold behavioural rules and diffusion of innovations (see Chapter 3). The second step is to formalize the factors that determine consumer's behaviour through specific behavioural rules that may be used to determine how an agent will behave under certain circumstances. An ABM model will have to specify how (probability of a specific behaviour) and when an agent will behave. The third step is to represent the outcomes of agent's behaviours through aggregated values over time and space. The outcomes may be represented by adoption curves or switching behaviour. Depending on the model assumptions - that is to say on the chosen theory of behaviour - and on how these are operationalized, the shape of the outcome curves may vary dramatically. Indeed, it is the study of how agent's behaviours may produce emergent or unexpected macro-level outcomes that make ABM a well suited tool for complex systems analysis.
Figure 8. Common elements in ABM (Rai et al., 2016)
a: specification of the general factors that drive the decisions and behaviour, which may be derived from complementary or
competing theories of human behaviour. For instance, a theory of rational choice might emphasize the importance of economic costs and benefits to adoption whereas a theory of social influence will emphasize the importance of having other social contacts who have adopted. b: specification of a specific decision rule, such as the probability of adoption of agent i at time t (Pi,t) specified in the equation. Variables Ei and Ni,t represent the ith agent's economic benefit of adoption and the proportion of
social contacts who have adopted before time t, respectively. A model parameter (a) controls the relative importance of economic versus social influence factors. c: Varying model parameter a yields different emergent outcomes — in this case different adoption curves, which describe the saturation of the technology in the system over time (from (Rai et al., 2016))
Agent based models can be built both for theory testing and predictive modelling of the demand side of energy. ABM can improve both ex ante and ex post policy design and evaluation (Rai et al., 2016). ABM can have different scopes of analysis:
formalization of theory in which case the model is likely to be pitched at a very abstract level; (theory testing)
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description of a wide class of social phenomena (e.g.: behaviour of consumers, development of industrial district);
provision of specific model of particular social situation (e.g.: model of electricity markets).
These different types of agent-based model require rather different approaches to validation (Boero et
al., 2005). Agent-based models can be considered 'valid' if they produce strong fits at model validation
stage, which means a positive answer to the question "did we build the right thing"? "are the results convincing?". Traditional validation seeks to verify whether the model is an accurate representation of the real-world system comparing experimental results and real-world data. However, these traditional methods are not always applicable to agent-based modeling since very often there is no "real system" available for comparison. Validation in agent based modelling focuses on understanding if a model is useful or convincing in the explanation it offers to the problems it seeks to explore. In ABM the real outcome of the model is to be sought in the increased insight and knowledge and not in the experimental results (van Dam et al., 2013). Outcome of agent-based models can be validated through different methods, including (van Dam et al., 2013):
Historic replay;
Face validation through expert consultation;
Literature validation; and
Model replication
When models are developed for theory testing, as it will be the case for the model I will present in Chapter 4, validation might involve a qualitative judgment.
2.5 Conclusions
This chapter has argued and substantiated the complex nature of emerging electricity systems and what are the features and interfaces that make this system complex and challenging to study.
I have argued that ABM is best suited to represent socio-technical system and in particular the energy consumer behaviour thanks to its flexible structure that allow the representations of complex agent systems, including the behaviour of agents, their interactions with the technical system, their social interactions and the context in which they operate
I am interested in studying the emergence in consumer systems, in particular how values and beliefs at consumer level (as defined by social psychology and behavioural theories) lead to macro behaviours such as adoption over time and space.
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My research context is an ex-ante setting where the questions I want to address are related to estimating the effects on emergence of various contextual factors, endogenous social dynamics and possible policy alternatives.
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