I am Professor in Computational Resilience Economics at the University of Twente (the Netherlands), which I joined in 2010. In September 2017 I also joined University of Technology Sydney (Australia) as Professor of Computational Economic Modeling working with spatial simulation models to study socioeconomic impacts of disasters and emergence of resilience across scales. I was honored to be elected as a Member of the De Jonge Akademie of the Royal Dutch Academy of Sciences (DJA/ KNAW in 2016) and of Social Sciences Council (SWR/KNAW in 2017). From 2009 to 2015 I have been working part-time as an economist at Deltares – the leading Dutch knowledge institute in the field of water management – specializing in economics of climate change, with focus on floods and droughts management.
I am interested in the feedbacks between policies and aggregated outcomes of individual decisions in the context of spatial and environmental policy-making. The issue of social interactions and information diffusion through networks to affect economic behavior is highly relevant here. My research line focuses on exploring how behavioral changes at micro level may lead to critical transitions (tipping points/regime shifts) on macro level in complex adaptive human-environment systems in application to climate change economics. I use agent-based modelling (ABM) combined with social science methods of behavioral data collection on individual decisions and social networks. This research line has been distinguished by the NWO VENI and ERC Starting grants and the Early Career Excellence award of the International Environmental Modeling Society (iEMSs). In 2018 I was invited to serve as the Associate Editor of the Environmental Modelling & Software journal, where I have been a regular Member of the Editorial Board since 2013.
LUXE is a land-use change model featuring different levels of land market implementation. It integrates utility measures, budget constraints, competitive bidding, and market interactions to model land-use change in exurban environment.
The model aims at estimating household energy consumption and the related greenhouse gas (GHG) emissions reduction based on the behavior of the individual household under different operationalizations of the Theory of Planned Behaviour (TPB).
The original model is developed as a tool to explore households decisions regarding solar panel investments and cumulative consequences of these individual choices (i.e. diffusion of PVs, regional emissions savings, monetary savings). We extend the model to explore a methodological question regarding an interpretation of qualitative concepts from social science theories, specifically Theory of Planned Behaviour in a formal code of quantitative agent-based models (ABMs). We develop 3 versions of the model: one TPB-based ABM designed by the authors and two alternatives inspired by the TPB-ABM of Schwarz and Ernst (2009) and the TPB-ABM of Rai and Robinson (2015). The model is implemented in NetLogo.
RHEA aims to provide a methodological platform to simulate the aggregated impact of households’ residential location choice and dynamic risk perceptions in response to flooding on urban land markets. It integrates adaptive behaviour into the spatial landscape using behavioural theories and empirical data sources. The platform can be used to assess: how changes in households’ preferences or risk perceptions capitalize in property values, how price dynamics in the housing market affect spatial demographics in hazard-prone urban areas, how structural non-marginal shifts in land markets emerge from the bottom up, and how economic land use systems react to climate change. RHEA allows direct modelling of interactions of many heterogeneous agents in a land market over a heterogeneous spatial landscape. As other ABMs of markets it helps to understand how aggregated patterns and economic indices result from many individual interactions of economic agents.
The model could be used by scientists to explore the impact of climate change and increased flood risk on urban resilience, and the effect of various behavioural assumptions on the choices that people make in response to flood risk. It can be used by policy-makers to explore the aggregated impact of climate adaptation policies aimed at minimizing flood damages and the social costs of flood risk.
Based on theoretical and empirical considerations, ORVin is developed to understand consumer purchasing behavior regarding organic wines. To gain insight into the process of wine consumption, the theory of planned behavior is considered along with alphabet theory and goal framing theory. This provides a solid theoretical framework for identifying behavioral factors including beliefs, attitudes, norms, habits, and goals that may influence organic wine purchases. The model can be used to examine the effectiveness of different interventions for encouraging households to purchase organic wine instead of conventional wine. ORVin provides a dynamic platform to study the individual reaction of the disaggregated, low-level actors of the system to the hypothetical changes in the wine market such as taxation, marketing campaigns, and promotions. The cumulative impacts of changing behavior are also evaluated with respect to the environment. This model improves users understanding of the complexity of wine purchasing decisions and help them to further interpret and forecast organic wine market.
This model simulates the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain.
Computational social science has seen a shift from theoretical models to empirical agent-based models (ABMs) grounded in data-driven correlations between behavioral factors defining agents’ decisions. The community’s interest in theoretical ABMs with behavioral theories setting stylized rules that guide agents’ actions is fading, especially when it concerns policy-related simulations. However, it remains unclear to what extent theory-driven ABMs mislead, if at all, possible policy decisions when compared to the outcomes of models with empirical micro-foundations. This is especially relevant for pro-environemntal policies that increasingly rely on quantifying cumulative effects of individual behavioral changes, where ABMs are so useful. To address this methodological dilemma, we propose a comparison framework to quantitatively explore the differences between theory- and data-driven ABMs. Inspired by the existing theory-based model, ORVin-T, which studies the individual choice between organic and conventional products, we designed a survey to collect data on individual preferences and purchasing decisions. We then used this extensive empirical microdata to build an empirical twin, ORVin-E, replacing the theoretical assumptions and secondary aggregated data used to parametrize agents’ decision strategies with empirical survey data. We compare the models in terms of key outputs, perform sensitivity analysis and three policy scenario.