How it works
Here we provide an overview of the modelling approach that is being used.
Our human-behaviour models help decision-makers who want to create better policies and decisions.
Our human behaviour model helps policy makers and network planners in energy, transport and water who want to reduce uncertainty and understand possible scenarios’ impact on investment decisions or policy actions. This can be achieved by being able to understand and model human behaviour more accurately. Unlike current methods such as consumer surveys which do not capture the complexities around consumer decision making, we embed:
Different types of agents, such as sales and information agents, acknowledging that the process of adoption is typically dependent on a complex web of interactions.
A flexible user interface that allows for exploring a whole range of 'what-if' scenarios.
Non-monetary drivers of behaviour, such as relating to agent-held intrinsic values, normative pressures, as well as practical limitations towards adoption.
Inertia in the translation from intention to action, due to the slow diffusion of communication across a population.
The way we develop models
The modelling approach is often iterative, as it is refined based on new data or insights. The diagram below outlines our unique process that provides deep insights on the likely behavioural responses that shape the success of a specific policy or investment in improving the sustainability of our cities.
Map the decision processes - Understand the decision context through literature review and discussions with stakeholders.
Collect information - The validity of an ABM is heavily dependent on the quality of the input data. Therefore, we expend considerable effort on data collection and analysis using tools such as surveys.
Analyse behavioural profiles - Survey datasets are analysed to develop decision-making profiles of different agents, and how they are likely to respond to policy or investment interventions.
Trial ABM - The decision making profiles of agents are coded into a prototype ABM to make sure that the modelled emergent behaviour is realistic representation, and provides useful insights.
Iterate based on feedback - The ABM is refined based on validation, sensitivity analysis and feedback from key stakeholders to ensure robust outputs to inform policy and investment decisions.
Explore policy and investment options - Key implications for decision makers are displayed in a range of user-friendly visual outputs and short reports. The platform provides a policy sandpit to better understand likely outcomes and refine policies within a virtual laboratory.
To what have we applied the models?
We have applied our models in a range of contexts (see background), in particular, the adoption of energy efficiency in buildings, commuters’ choice of transport mode, and householders’ adoption of water-efficient appliances and behaviours.
Why an ABM?
ABM is a computer simulation technique, and Agents are software representations of individuals or organisations and can refer to different types of people and information involved in the decision-making process. To illustrate how this may work, for example purchasing a water efficient appliance is likely to involve interactions amongst householders considering a purchase, sales agents promoting a product, and information sources such as the media. Specific benefits of the ABM approach are:
Being able to describe how the interaction of different types of agents (households, salespeople, media, builders, etc.) lead to the adoption of behaviour.
Capturing a range of behavioural drivers, including ones that aren’t usually captured, especially non-monetary drivers of behaviours.
Because Agents represent individuals, being able to describe the diversity of attributes across a population rather than relying on averages or other statistical techniques.
Being able to describe the different ways that agents may make their decisions, i.e. based on heuristics, social influence and limited information, rather than to assume economic rationality or perfect information.
Consumat theory of human behaviour
There are numerous theories that describe consumer behaviour. Examples are theories about human needs, motivational processes, social comparison theory, social learning theory, the theory of reasoned action which all represent aspects of consumer behaviour. Here we have adopted a meta-theory of consumer behaviour, i.e. the Consumat theory, which encapsulates many other such theories. This meta-model is particularly suitable for developing simulation models. The Consumat theory operates at different scales. Individual behaviour leads to collective (macro) level outcomes, which in turn influence individual scale decision making.
At the individual level household agents are equipped with needs which may be more or less satisfied. These represent behavioural drivers. When confronted with a consumption choice, an agent will evaluate to what extent the choice will satisfy needs, but this will be done under a certain degree of uncertainty. Depending on the degree of need satisfaction and uncertainty, agents use different cognitive processes for making a decision: repetition, deliberation, imitation and social comparison. In practice within simulations, this means that the agents are sorted into four different categories: Repeaters, Optimisers, Inquirers and Imitators.
- Repeaters are not going to "make the consumption choice" and will require circumstances to change in subsequent years to become more engaged and aware enough to shift them to the other decision categories.
- Optimisers, on the other hand, will "make the consumption choice" and will remain a participant in subsequent time steps.
- Inquirers may or may not "make the consumption choice", depending on how satisfied they feel about the offering after they have gathered more information. Hence, increasing the parameter values of "Framing" and “Influencing the influencers” can potentially elevate the level of satisfaction of Inquirers to adopt the WaterFix program in a later time step.
- Imitators copy the behaviours of others in their social network, and thus may or may not choose to “make the consumption choice” depending on how many other households have already adopted the program, based on social normative pressures.
We acknowledge that this description of the Consumat behavioural meta-model has been adapted from various descriptions in literature, including from these references:
- Janssen, M. and W. Jager (1999) "An Integrated Approach to Simulating Behavioural Processes: A Case Study of the Lock-in of Consumption Patterns." Journal of Artificial Societies and Social Simulation 2(2).
- Janssen, M. A. and C. Viek (2001) "Experimentation with household dynamics: the Consumat approach." International Journal of Sustainable Development 4(1): 90-100.
- Janssen, M. A. and W. Jager (2001) "Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics." Journal of Economic Psychology 22(6): 745-772.
- Jager, W. (2006) "Stimulating the diffusion of photovoltaic systems: A behavioural perspective." Energy Policy 34(14): 1935-1943.
- Jager, W. and M. Janssen (2012) An updated conceptual framework for integrated modeling of human decision making: The Consumat II. Complexity in the Real World @ ECCS 2012 - from policy intelligence to intelligent policy. Brussels.
- Jager, W., M. Janssen and M. Bockarjova (2014) Diffusion dynamics of electric cars and adaptive policy: Towards an empirical based simulation. Advances in Intelligent Systems and Computing. 229 AISC: 259-270.
Complexities of choice
To describe the choice of individuals to adopt resource efficient technology or behaviour we need to move beyond any relatively simplistic models of human behaviour and attempt to describe a broader range of complexities. Therefore, we have explored five different lenses on consumer choice:
Media and communications
Household priorities and perceptions
None of these perspectives holds the full answer to describing household decision making yet jointly we think that they provide a useful starting point, and provide design principles for the ABM that we develop. Factors that we consider are shown in the table below.
|Cognitive Biases||It is important to incorporate the latest and most relevant aspects of behavioural science because humans make decisions based on heuristics and are subject to a range of common biases.|
|Social comparisons||Choices are often based on social processes which involve implicit or explicit peer pressures.|
|Imitation||Decisions are often based on heuristics and perhaps the most common one is imitation of peers|
|The role of media||Decisions are influenced by perceptions which in modern society is strongly influenced by media.|
|Limited bandwidth and strict budgets||People will make decisions in contexts with competing demands for time, effort and money.|
|Non-monetary priorities||There are many aspects that people will consider of which are non-monetary and often also non-quantitative such as lifestyle or comfort factors.|
|Decision triggers||Decisions are made only at certain times, and it may be useful to consciously trigger additional decisions in order to speed up the transitions process.|
|Heterogeneity||People make decisions based on individual circumstances and priorities, which vary considerably across a population.|
|The frequency of proactive or passive decisions||The way that choices are presented, and when they are presented, to consumers is critically important for the outcome, especially as it influences the average frequency by which people will make decisions.|
Different types of agents
There are typically three types of agents in our models:
Decision making agents (householders/commuters/building owners): representing the primary decision makers in the model. Community members are the ones that typically decide whether to adopt or not adopt a behaviour or technology.
Decision activation agents: these agents, such as sales agents, marketers or tradespeople, usually representing the promotion of products through targeted interactions.
Influencer agents: these represent the ecosystem of agents that provide recommendations to community members on which product to purchase and thus represents diverse agents such as retailers, tradespeople, councils, online forums and media.
Peer and social network: families, friends and colleagues, neighbours and the local community. These networks influence perceptions, generate peer pressure and offer recommendations.
Commercial building energy efficiency
- Marquez, L, McGregor, J, Seo, S, Walton, A, Moglia, M, Higgins, A, Gardner, J. (2015) Modeling the adoption of energy efficient retrofits by mid-tier commercial buildings, 21st International Congress on Modelling and Simulation (MODSIM2015), Gold Coast, Australia, 29 November - 4 December 2015, http://www.mssanz.org.au/modsim2015/
Residential building energy efficiency
- Moglia M, Podkalicka A, McGregor J (2018) An Agent-Based Model of Residential Energy Efficiency Adoption, Journal of Artificial Societies and Social Simulation 21 (3) 3, https://doi.org/10.18564/jasss.3729
- Moglia, M, Podkalicka, A, Marquez, L, Fiess, S, McGregor, J (2018) RP3028: Mapping the adoption processes of energy efficient products in the residential sector, Low Carbon Living CRC, csiro:EP179597, https://publications.csiro.au/rpr/pub?pid=csiro:EP179597
- Moglia M, Cook S, McGregor J (2017) A review of Agent-Based Modelling of technology diffusion with special reference to residential energy efficiency, Sustainable Cities and Society 31(May 2017), pp. 173-182, https://doi.org/10.1016/j.scs.2017.03.006
- Moglia M, Dawson M, Dixon J, Cameron B (2017) Using Agent-Based Modelling to Identify Effective Water Conservation Policies: RP3035 Factsheet, https://www.researchgate.net/publication/321372383_Using_Agent-Based_Modelling_to_Identify_Effective_Water_Conservation_Policies
- Moglia M, Cook S, Tapsuwan S (2019) RP 3035 Final Report: Modelling Uptake of Water Conservation and Efficiency Measures in Sydney, Low Carbon Living CRC, csiro:EP189668, https://publications.csiro.au/rpr/pub?pid=csiro:EP189668
- Moglia M, Cook S, Tapsuwan S (2018) Promoting Water Conservation: Where to from here? Water 2018, 10(11), 1510; https://doi.org/10.3390/w10111510
- Tapsuwan S, Cook S, Moglia M (2018) Willingness to Pay for Rainwater Tank Features: A Post-Drought Analysis of Sydney Water Users, Water 2018, 10(9), 1199; https://doi.org/10.3390/w10091199
- Cook, S., M. Moglia and S. Tapsuwan (2019). Modelling Suburban Low Carbon Commuter Mode Choices. Melbourne, Low Carbon Living CRC.