The social-technical-environmental (STE) systems around us are complex adaptive systems.
Interactions between systems’ components, cross-scale feedbacks and learning influence systems’ resilience and the emergence of potential structural changes. Agent-based modelling is the leading computational method to study dynamics of such complex system. Agent-based model (ABM) is “a computerised simulation of a number of decision-makers (agents) and institutions, which interact through prescribed rules” (Farmer and Foley, 2009, in Nature 460: 685-686). Agents could be households, firms, farmers, individuals within an organisation or governments. Institutions may include formal markets, legal rules and policies as well as informal institutions such as social norms and norms in use.
The main added value of ABM as a simulation technique is in its ability to represent the behaviour of socio-economic actors more realistically, accounting for bounded rationality, heterogeneity, interactions, evolutionary learning and out-of-equilibrium dynamics, and to combine this representation with a dynamic heterogeneous representation of the spatial environment. ABMs are used intensively to study dynamics of financial markets, in defence research, to represent biodiversity and species evolution, dynamics within organisations – any research problem that demands a disaggregated representation of a complex system. Within the centre we apply ABMs to study the dynamics of coupled STE systems with applications ranging from modelling housing market dynamics, urbanisation and adverse impacts of climate change (flooding, coastal storms, bushfires), farmers’ adaptation to droughts and resilience of the agricultural sector, assessing a distribution of damage from natural hazards and integrating climate adaptation decisions into damage assessment. In collaboration with other tracks within the centre, there is an opportunity to use ABMs as a tool in participatory modelling, to integrate it with large scale simulation models, employ big data and behavioural data collected via mobile apps to specify agents’ rules in ABMs, to complement conceptual modelling with computational simulation models such as ABMs, and apply various visualization techniques to communicate ABMs results to various audiences.
Who is involved
Dr Nagesh Shukla
- Residential relocation and transport mode choice modelling for large scale ABM: In this project, we proposed a data-driven methodology with artificial neural networks (ANNs) and fuzzy sets (to better represent historical knowledge in an intuitive way) to model travel mode choices. The proposed methodology, models and analyses travel mode choice of an individual trip and its influence on consecutive trips of individuals. The methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performance is compared with the state-of-the-art approaches such as decision trees. This project was funded by T4NSW and BTS.
- Value of health interventions for heroin use: This project aims to assess the net social benefit of the current treatment strategy for drug uses and to evaluate, through modelled scenarios, different combinations of treatment. This will lead to decisions and policy that are better informed about the mix and type of treatments in which governments invest. This project was funded by NHMRC Project Grants (2013-2015) in collaboration with NDARC, University of New South Wales.
- Dutch NWO VENI (similar to ARC DECRA) “Changing climate – changing behaviour: integrating adaptive economic behaviour in land-use models”. Using ABMs, we have developed innovative spatially explicit empirical housing market models that account for adaptive economic behaviour and biases in individual flood risk perceptions. The project also supports the PhD research of Mr. K. de Koning.
- Dutch Knowledge for climate program “The role of individual behaviour and social interactions in adaptation of the agricultural sector to increasing climate induced drought risks”. The project funded the PhD of Dr. R. van Duinen to run an extensive survey on drought adaptation options among farmers and to develop a spatial ABM to study the aggregated impacts of individual adaptation on the resilience of the regional agricultural sector to droughts under climate scenarios.
- Transatlantic Digging Into Data program “MIning Relationships Among variables in large datasets from CompLEx systems” (MIRACLE). The project focused on developing algorithms and web-based analysis and visualization tools that provide automated means of discovering complex relationships in the multidimensional output data from STE ABMs.
- EU FP7 “Knowledge Based Climate Mitigation Systems for a Low Carbon Economy” (COMPLEX). The project funds the PhD research of Ms. L. Niamir to studying aggregated impacts of behavioural changes in energy use on the household level (residential energy demand). The main product of the project is an extensive survey eliciting households’ behavioural changes in energy use (investment, conservation and switching between green and grey energy) and the agent-based energy market model. The prototype to integrate the energy market ABM with a macroeconomic CGE model is developed.
- Combining ABMs and Machine Learning. The PhD Project of Ms S. Abdulkareem “Growing spatial and socio-economic dynamics in empirical agent-based models using artificial intelligence algorithms” tests the impact of various ML methods on the intelligent behaviour of agents and cumulative urban dynamics. She applies ML in ABMs to test the dynamics of disease diffusion.
- Extended supply chains for sustainability. PhD project of Ms Firouzeh Taghikhah. Modelling farmers' choices and consumer behaviour to understand the potential for organic winery in Australia.
- ERC StG project (similar to ARC Future Fellowships) “Scaling up behaviour and autonomous adaptation for macro models of climate change damage assessment” (SCALAR). SCALAR is a 5 year long research program (2018–2023) aiming (1) to develop urban and regional ABMs to study how households and firms in coastal cities may adapt to climate threats caused by riverine or storm surge flooding, and (2) to develop methods to aggregate this individual level adaptation to study its interactions with planned public adaptation by governments and its impact on damage assessments in macro Integrated Assessment Models. We also plan to explore how socio-economic resilience changes over time across scales (individual, community, region, country).
- Agent-based housing markets
- Urban dynamics and land use agent-based models
- Energy markets with heterogeneous agents, role of households in climate mitigation
- Agent-based modelling of agricultural adaptation to droughts
- Urbanisation and adverse impacts of climate change (flooding, coastal storms, bushfires)
- Interactions between private and public adaptation to climate change
- Integration of ABMs with macroeconomic CGE models
- Firouzeh Taghikhah
- Vacancy 1: on modelling housing market ABMs, flooding, risk perception biases and community resilience in NSW. Will require strong programming skills in any language of your choice, knowledge of data analysis and statistical techniques is desirable. To be announced shortly. Additional information by request: Tatiana.Filatova@uts.edu.au
- Vacancy 2: on urbanization and individual climate adaptation decision to natural hazards (comparing flooding and bushfires), will require the development of ABMs (strong programming skills) and ability to develop questionnaires and perform statistical analysis of data. To be announced shortly. Additional information by request: Tatiana.Filatova@uts.edu.au
- Niamir, L., T.Filatova, A. Voinov, H. Bressers (Accepted) ‘Transition to Low-carbon Economy: Assessing Cumulative Impacts of Individual Behavioral Changes’, Energy Policy;
- de Koning, K., Filatova, T. & Bin, O. (2017) 'Bridging the Gap Between Revealed and Stated Preferences in Flood-prone Housing Markets', Ecological Economics (136), pp 1–13;
- de Koning, K., Filatova, T. & Bin, O. (2016) 'Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets', Environmental and Resource Economics, October 26, pp 1–17;
- Filatova, T., J.G. Polhill, S. van Ewijk (2016) 'Regime shifts in coupled socio-environmental systems: Review of modelling challenges and approaches’. Environmental Modelling & Software, 75, p. 333–347;
- Lee, J.-S., T. Filatova, A. Ligmann-Zielinska, B. Hassani-Mahmooei, F. Stonedahl, I. Lorscheid, A. Voinov, J.G. Polhill, Z. Sun, and D.C. Parker (2015), ‘The Complexities of Agent-Based Modeling Output Analysis’, Journal of Artificial Societies and Social Simulation, 2015. 18(4): p. 31;
- van Duinen, R., T. Filatova, W. Jager and A. van der Veen (2015). ‘Going beyond perfect rationality: drought risk, economic choices and the influence of social networks’ The Annals of Regional Science October 2015. 35 pp;
- Elsawah, S., Guillaume, J.H.A., Filatova, T., Rook, J., Jakeman, A.J. (2015). ‘A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models’. Journal of Environmental Management, 151 pp. 500 – 516;
- Filatova, T. (2015) "Empirical agent-based land market: Integrating adaptive economic behaviour in urban land-use models", Computers, Environment and Urban Systems (54), p. 397–41;
- Filatova T., P.H. Verburg, D.C. Parker, C.A. Stannard (2013). “Spatial agent-based models for socio-ecological systems: challenges and prospects”, Environmental Modelling & Software, Volume 45, p. 1-7
- Parker D.C. and T. Filatova (2008) “A conceptual design for a bilateral agent-based land market with heterogeneous economic agents”, Computers, Environment and Urban Systems 32, 454–463
- Shukla, N., Ma, J., Wickramasuriya, R. & Huynh, N. Data-driven modelling and analysis of household travel mode choice. In 20th International Congress on Modelling and Simulation; The Modelling and Simulation Society of Australia and New Zealand Inc: Australia, 2013; pp 92-98.
Huynh, N. N., Shukla, N., Munoz Aneiros, A., Cao, V. & Perez, P. A semi-deterministic approach for modelling of urban travel demand. In International Symposium for Next Generation Infrastructure (ISNGI 2013); Perez, P. & Campbell, A. Eds.; University of Wollongong: Australia, 2014; pp 191-199.
Shukla, N., Ma, J., Wickramasuriya, R., Huynh, N. & Perez, P. Modelling mode choice of individual in linked trips with artificial neural networks and fuzzy representation. In Artificial Neural Network Modelling; Shanmuganathan, S. & Samarasinghe, S., Eds.; Springer International Publishing: Switzerland, 2016; pp 405-422.
MR Namazi-Rad, P Mokhtarian, N Shukla, A Munoz, A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data, Journal of Choice Modelling, 2016
Partners and collaborators
- University of Waterloo (Canada): Prof. Dawn C. Parker, Dr Derek Robinson
- East Carolina University (USA): Prof. Okmyung Bin
- University of Colorado Boulder (USA): Dr Albert Kettner
- The James Hutton Institute (UK): Dr Gary Polhill
- 4TU Resilience Engineering Center (Federation of the 4 Dutch Technical Universities)
- ANU (Australia): Prof. Tony Jakeman
- UNSW (Australia): Dr Sondoss El Sawah
- University of Adelaide (Australia): Prof. Holger Maier
- Indian Institute of Technology (Kharagpur, INDIA): Prof M K Tiwari
Ideas for student projects
Potential MSc and PhD thesis topics
- Developing mobile apps to enable multiwave surveys among households on natural hazards risks they experience
- ABM for Sydney housing market
- For existing and newly planned areas
- Considering natural hazard hotspots (flash flooding, riverine flooding, storm survey, bushfires)
- Spatial ABM to explore urbanisation and housing value dynamics given natural hazard events of various magnitude
- ABMs for energy use dynamics (residential, within organisations)
- ABMs to study climate-driven migration
- ABM to study regional economies dynamics (with a focus on the mutually-dependent location of firms and households)
- Modelling and simulation for sustainable logistics and supply chain management problems
- Modelling and simulation for cross-border logistics for landlocked developing countries