Tatiana Filatova is Professor of Computational Economic Modeling at the ISM/FEIT at the University of Technology Sydney, Australia, and at CSTM, University of Twente in the Netherlands. During her PhD (defended in 2009, cum laude) she spent 1.5 years at George Mason University (USA) as a Visiting Researcher. Tatiana’s research focuses on exploring how behavioral changes at micro level may lead to regime shifts on macro level in complex adaptive socio-environmental systems, in particular in application to climate change economics (adaptation and mitigation). Prof. Filatova’s research line pioneered in integrating adaptive economic behavior in environmental simulation models, in particular urban and regional models. Her team uses spatial agent-based models, i.e. a computer code representing how many heterogeneous adaptive economic agents – households, firms, farmers, governmental institutions – make decisions and interact with each other and their environment according to different behavioral rules. Such simulations serve as computational laboratory to explore how urban patterns of development and property prices develop over time, how regional economic losses from disasters, welfare and socio-economic resilience change depending on behavioral rules of agents and policies. Such models heavily rely on economic and social survey data to specify behavioral rules as well as social networks structures. Most of Tatiana’s recent projects focus on cities and adaptation to climate change, with an interest on how socioeconomic impacts of disasters are distributed and how resilience emerges across scales. This research line is distinguished by a number of international awards and total research funding equivalent to 12 mln AUD. Since 2018 Prof. Filatova serves as Associate Editor of the Environmental Modelling & Software journal and is on the Board of the national 4TU.Resilience Engineering Center in the Netherlands.
Awards and Distinctions
2018 EAERE Award in the field of environmental and resource economics
2017 ERC Starting grant, European Research Council excellence pillar (AUD 2.375.000)
2017 Member-elect of the Social Research Council, Royal Netherlands Academy of Arts and Sciences
2016 Member-elect of the Young Academy (DJA), Royal Netherlands Academy of Arts and Sciences
2014 Early Career Excellence Award, International Environmental Modelling and Software Society (iEMSs), San Diego, USA
2013 Prof De Winter Prize for the Best article of the year (AUD 4.000)
2012 Early Career VENI grant, Dutch Science Foundation NWO (AUD 395.000)
2008 Commendation for the Best Paper and Presentation, International Congress on Environmental Modelling and Software (iEMSs’08), Barcelona, Spain
2004 Best Paper and Presentation in Environmental Economics, Regional Sciences Conference, Moscow, Russia
Academic leadership and professional positions of honor
2018-now Scientific Program Manager, strategic research program of the 4 Dutch Technical Universities “Designing Systems for Informed Resilience Engineering” (DeSIRE)
2018-now Member of the Scientific Steering Board “Resilience Engineering” 4TU.Federation
2017-2018 Co-chair of the ‘Science Policy’ Group within the Young Academy, Royal Netherlands Academy of Arts and Sciences
2008-2012 Secretary, International Environmental Modelling and Software Society (iEMSs)
2008-2014 Board Member, iEMSs
2009-now Co-founder of the Special Interest Group on ‘Socio-Ecological Issues and Sustainable Development’ of the European Social Simulation Association (ESSA)
2018-now Associate Editor, Environmental Modelling & Software (EMS), Elsevier
2017-now Editorial Board Member, Socio-Environmental Systems Modelling (SESMO) journal, Open Access
2014-2017 Editorial Board Member, Environmental Modelling & Software (EMS)
2013-2015 Guest Editor of the special issue on “Modelling systemic change in coupled socio-environmental systems (SES)” together with Gary Polhill (The James Hutton Institute, UK) and Maja Schlüter (Stockholm Resilience Center, Sweden). Environmental Modelling & Software journal (EMS, Elsevier)
2011-2012 Guest Editor of the special issue on “Spatial agent-based models for socio-ecological systems” together with D.C. Parker (University of Waterloo, Canada), P.H. Verburg (VU, NL), and C.A. Stannard (The James Hutton Institute, UK). Environmental Modelling & Software journal (EMS, Elsevier)
Can supervise: YES
- Resilient cities
- Agent-based computational models
- Complex systems
- Economics of climate change
- Coupled social-environmental systems and modeling
- Regime shifts in coupled systems
Brugnach, M, Pahl-Wostl, C, Lindenschmidt, KE, Janssen, JAEB, Filatova, T, Mouton, A, Holtz, G, van der Keur, P & Gaber, N 2008, Chapter Four Complexity and Uncertainty: Rethinking the Modelling Activity.View/Download from: Publisher's site
The complexity and uncertainty inherent in environmental models must be considered and managed in an appropriate manner. This chapter presents a conceptual approach to deal with uncertainty, which considers the context of the purpose for which the model is developed. The four major modelling purposes identified - prediction, exploratory analysis, communication and learning - each focus on different modelling characteristics and roles of uncertainty. The notion of uncertainty is broadened, from being an attribute associated with the quality of information to also comprise modellers' beliefs and experiences. The chapter proposes ways in which uncertainty should be handled for each of the four modelling purposes and presents examples that illustrate the concepts. Various sources of uncertainty are considered relevant when modelling complex systems, and each source manifests differently in the data, structure or frame of the model. © 2008 Elsevier B.V. All rights reserved.
de Koning, K & Filatova, T 2020, 'Repetitive floods intensify outmigration and climate gentrification in coastal cities', Environmental Research Letters, vol. 15, no. 3, pp. 034008-034008.View/Download from: Publisher's site
Elsawah, S, Filatova, T, Jakeman, AJ, Kettner, AJ, Zellner, ML, Athanasiadis, IN, Hamilton, SH, Axtell, RL, Brown, DG, Gilligan, JM, Janssen, MA, Robinson, DT, Rozenberg, J, Ullah, IIT & Lade, SJ 2020, 'Eight grand challenges in socio-environmental systems modeling', Socio-Environmental Systems Modelling, vol. 2, pp. 16226-16226.View/Download from: Publisher's site
Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.
Handayani, K, Filatova, T, Krozer, Y & Anugrah, P 2020, 'Seeking for a climate change mitigation and adaptation nexus: Analysis of a long-term power system expansion', APPLIED ENERGY, vol. 262.View/Download from: Publisher's site
Niamir, L, Ivanova, O, Filatova, T, Voinov, A & Bressers, H 2020, 'Demand-side solutions for climate mitigation: Bottom-up drivers of household energy behavior change in the Netherlands and Spain', ENERGY RESEARCH & SOCIAL SCIENCE, vol. 62.View/Download from: Publisher's site
Niamir, L, Kiesewetter, G, Wagner, F, Schöpp, W, Filatova, T, Voinov, A & Bressers, H 2020, 'Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions', Climatic Change.View/Download from: Publisher's site
© 2019, The Author(s). In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary 'agents of change' in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally imp...
Abdulkareem, SA, Augustijn, E-W, Filatova, T, Musial, K & Mustafa, YT 2020, 'Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.', PLoS One, vol. 15, no. 1, pp. e0226483-e0226483.View/Download from: Publisher's site
Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.
Taghikhah, F, Voinov, A, Shukla, N & Filatova, T 2020, 'Exploring consumer behavior and policy options in organic food adoption: Insights from the Australian wine sector', ENVIRONMENTAL SCIENCE & POLICY, vol. 109, pp. 116-124.View/Download from: Publisher's site
Taghikhah, F, Voinov, A, Shukla, N, Filatova, T & Anufriev, M 2020, 'Integrated modeling of extended agro-food supply chains: A systems approach', European Journal of Operational Research.View/Download from: Publisher's site
© 2020 Elsevier B.V. The current intense food production-consumption is one of the main sources of environmental pollution and contributes to anthropogenic greenhouse gas emissions. Organic farming is a potential way to reduce environmental impacts by excluding synthetic pesticides and fertilizers from the process. Despite ecological benefits, it is unlikely that conversion to organic can be financially viable for farmers, without additional support and incentives from consumers. This study models 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. Findings demonstrate the feasibility and superiority of the proposed model over the traditional sustainable supply chain models in incorporating the feedback between consumers and producers and analyzing management scenarios that can urge farmers to expand organic agriculture. Results further indicate that demand-side participation in transition pathways towards sustainable agriculture can become a time-consuming effort if not accompanied by the middle actors between consumers and farmers. In practice, our proposed model may serve as a decision-support tool to guide evidence-based policymaking in the food and agriculture sector.
Abdulkareem, SA, Mustafa, YT, Augustijn, E-W & Filatova, T 2019, 'Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models', GEOINFORMATICA, vol. 23, no. 2, pp. 243-268.View/Download from: Publisher's site
de Koning, K, Filatova, T & Bin, O 2019, 'Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent-Based Model Approach', SOUTHERN ECONOMIC JOURNAL, vol. 85, no. 4, pp. 1159-1179.View/Download from: Publisher's site
de Koning, K, Filatova, T, Need, A & Bin, O 2019, 'Avoiding or mitigating flooding: Bottom-up drivers of urban resilience to climate change in the USA', Global Environmental Change, vol. 59.View/Download from: Publisher's site
© 2019 Elsevier Ltd Coastal areas around the world are urbanizing rapidly, despite the threat of sea level rise and intensifying floods. Such development places an increasing number of people and capital at risk, which calls for public flood management as well as household level adaptation measures that reduce social vulnerability to flooding and climate change. This study explores several private adaptation responses to flood risk, that are driven by various behavioral triggers. We conduct a survey among households in hazard-prone areas in eight coastal states in the USA, of which, some have recently experienced major flooding. While numerous empirical studies have investigated household-level flood damage mitigation, little attention has been given to examining the decision to retreat from flood zones. We examine what behavioral motives drive the choices for flood damage mitigation and relocation separately among property buyers and sellers. Hence, we focus on the drivers that shape demand for future development in flood-prone cities. We find that households' choices to retreat from or to avoid flood zones (1) are highly sensitive to information that provokes people's feelings of fear, and (2) rely on hazardous events to trigger a protective action, which ideally would take place well before these events occur. We highlight that major flooding may cause a potential risk of large-scale outmigration and demographic changes in flood-prone areas, putting more low-income households at risk. Therefore, coordinated policies that integrate bottom-up drivers of individual climate adaptation are needed to increase urban resilience to floods.
Handayani, K, Filatova, T & Krozer, Y 2019, 'The vulnerability of the power sector to climate variability and change: Evidence from Indonesia', Energies, vol. 12, no. 19.View/Download from: Publisher's site
© 2019 by the authors. The power sector is a key target for reducing CO2 emissions. However, little attention has been paid to the sector's vulnerability to climate change. This paper investigates the impacts of severe weather events and changes in climate variables on the power sector in developing countries, focusing on Indonesia as a country with growing electricity infrastructure, yet being vulnerable to natural hazards. We obtain empirical evidence concerning weather and climate impacts through interviews and focus group discussions with electric utilities along the electricity supply chain. These data are supplemented with reviews of utilities' reports and published energy sector information. Our results indicate that severe weather events often cause disruptions in electricity supply—in the worst cases, even power outages. Weather-related power outages mainly occur due to failures in distribution networks. While severe weather events infrequently cause shutdowns of power plants, their impact magnitude is significant if it does occur. Meanwhile, transmission networks are susceptible to lightning strikes, which are the leading cause of the networks' weather-related failures. We also present estimates of financial losses suffered by utilities due to weather-related power disruptions and highlights their adaptation responses to those disruptions.
Handayani, K, Krozer, Y & Filatova, T 2019, 'From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning', Energy Policy, vol. 127, pp. 134-146.View/Download from: Publisher's site
© 2018 The Author(s) This study analyses a diffusion of renewable energy in an electricity system accounting for technological learning. We explore long-term scenarios for capacity expansion of the Java-Bali electricity system in Indonesia, considering the country's renewable energy targets. We apply the Long-range Energy Alternative Planning (LEAP) model with an integration of technological learning. Our results reveal that, at the medium and high pace of technological learning, the total costs of electricity production to achieve the long-term renewable energy target are 4–10% lower than the scenario without considering technological learning. With respect to technology, solar PV and wind become competitive with other types of renewables and nuclear. Moreover, the fulfilment of the renewable energy targets decreases CO2 emissions by 25% compared to the reference scenario. Implications of our results indicate that energy policies should focus on the early deployment of renewables, upgrading the grid capacity to accommodate variable renewable energy, and enabling faster local learning.
© 2018 Often, socio-environmental agent-based models (ABMs) are driven by a host of parameters, and their outputs are similarly multidimensional or vastly high-dimensional. While this complex data and its inter-relationships may be rendered tractable, the task is far from trivial. In this paper, we study the multidimensional outcome space of the socio-environmental, land-use RHEA ABM (Risks and Hedonics in an Empirical Agent-based Land Market), specifically the inter-distances among the outcome measures, and their reducibility using several well-known dimension reduction techniques, variants of multidimensional scalings. In testing the efficacy of several reduction algorithms, we temporally characterize the model's reducibility while exposing changes in behavior across a wide parameter space that can signal sudden or gradual shifts and possible critical transitions. Our findings reveal that the ABM's signature reducibility trends exhibit idiosyncrasies and unexpected non-linearity as well as discontinuities. These non-linearities and discontinuities are indicative of both gradual and sudden shifts, signaling potential propensity to internal perturbations induced by parameter settings. Additionally, we related the outcome space via their inter-distances to the multidimensional input parameter space, effectively assessing outcome "reducibility to model controls". This analysis reveals that the model's sensitivity to parameters is not only temporally dependent, but also can be partitioned by them, some of which suppress variability in this reduction to model controls, raising questions regarding the extent and structure of endogeneity that yields the distinct temporal trends in the relationship between inputs and outputs and their connections to outcome reducibility.
Mehvar, S, Dastgheib, A, Filatova, T & Ranasinghe, R 2019, 'A practical framework of quantifying climate change-driven environmental losses (QuantiCEL) in coastal areas in developing countries', ENVIRONMENTAL SCIENCE & POLICY, vol. 101, pp. 302-310.View/Download from: Publisher's site
Mehvar, S, Filatova, T, Sarker, MH, Dastgheib, A & Ranasinghe, R 2019, 'Climate change-driven losses in ecosystem services of coastal wetlands: A case study in the West coast of Bangladesh', OCEAN & COASTAL MANAGEMENT, vol. 169, pp. 273-283.View/Download from: Publisher's site
Belete, GF, Voinov, A, Arto, I, Dhavala, K, Bulavskaya, T, Niamir, L, Moghayer, S & Filatova, T 2019, 'Exploring low-carbon futures: A web service approach to linking diverse climate-energy-economy models', Energies, vol. 12, no. 15.View/Download from: Publisher's site
© 2019 by the authors. The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the 'appropriate' models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users.
Abdulkareem, SA, Augustijn, E-W, Mustafa, YT & Filatova, T 2018, 'Intelligent judgements over health risks in a spatial agent-based model', INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, vol. 17.View/Download from: Publisher's site
de Koning, K, Filatova, T & Bin, O 2018, 'Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets', ENVIRONMENTAL & RESOURCE ECONOMICS, vol. 69, no. 2, pp. 247-263.View/Download from: Publisher's site
Ghaffarian, S, Kerle, N & Filatova, T 2018, 'Remote sensing-based proxies for urban disaster risk management and resilience: A review', Remote Sensing, vol. 10, no. 11.View/Download from: Publisher's site
© 2018 by the authors. Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direct...
Husby, T, de Groot, HLF, Hofkes, MW & Filatova, T 2018, 'Flood protection and endogenous sorting of households: the role of credit constraints', MITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE, vol. 23, no. 2, pp. 147-168.View/Download from: Publisher's site
Mehvar, S, Filatova, T, Dastgheib, A, de Ruyter van Steveninck, E & Ranasinghe, R 2018, 'Quantifying economic value of coastal ecosystem services: A review', Journal of Marine Science and Engineering, vol. 6, no. 1.View/Download from: Publisher's site
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. T The complexity of quantifying ecosystem services in monetary terms has long been a challenging issue for economists and ecologists. Many case specific valuation studies have been carried out in various parts of the World. Yet, a coherent review on the valuation of coastal ecosystem services (CES), which systematically describes fundamental concepts, analyzes reported applications, and addresses the issue of climate change (CC) impacts on the monetary value of CES is still lacking. Here, we take a step towards addressing this knowledge gap by pursuing a coherent review that aims to provide policy makers and researchers in multidisciplinary teams with a summary of the state-of-the-art and a guideline on the process of economic valuation of CES and potential changes in these values due to CC impacts. The article highlights the main concepts of CES valuation studies and offers a systematic analysis of the best practices by analyzing two global scale and 30 selected local and regional case studies, in which different CES have been valued. Our analysis shows that coral reefs and mangroves are among the most frequently valued ecosystems, while sea-grass beds are the least considered ones. Currently, tourism and recreation services as well as storm protection are two of the most considered services representing higher estimated value than other CES. In terms of the valuation techniques used, avoided damage, replacement and substitute cost method as well as stated preference method are among the most commonly used valuation techniques. Following the above analysis, we propose a methodological framework that provides step-wise guidance and better insight into the linkages between climate change impacts and the monetary value of CES. This highlights two main types of CC impacts on CES: one being the climate regulation services of coastal ecosystems, and the other being the monetary value of services, which is subje...
Mehvar, S, Filatova, T, Syukri, I, Dastgheib, A & Ranasinghe, R 2018, 'Developing a framework to quantify potential Sea level rise-driven environmental losses: A case study in Semarang coastal area, Indonesia', ENVIRONMENTAL SCIENCE & POLICY, vol. 89, pp. 216-230.View/Download from: Publisher's site
Meyfroid, P, Chowdhury, RR, de Bremond, A, Ellis, EC, Erb, K-H, Filatova, T, Garrett, RD, Grove, JM, Heinimann, A, Kuemmerle, T, Kull, CA, Lambin, EF, Landon, Y, de Warow, YLP, Messerli, P, Mueller, D, Nielsen, JO, Peterson, GD, Garcia, VR, Schluter, M, Turner, BL & Verburg, PH 2018, 'Middle-range theories of land system change', GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS, vol. 53, pp. 52-67.View/Download from: Publisher's site
Muelder, H & Filatova, T 2018, 'One theory-many formalizations: Testing different code implementations of the theory of planned behaviour in energy agent-based models', JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, vol. 21, no. 4.View/Download from: Publisher's site
© 2018, University of Surrey. All rights reserved. As agent-based modelling gains popularity, the demand for transparency in underlying modelling assumptions grows. Behavioural rules guiding agents' decisions, learning, interactions and possible changes in these should rely on solid theoretical and empirical grounds. This field has matured enough to reach the point at which we need to go beyond just reporting what social theory we base these rules upon. Many social science theories operate with various abstract constructions such as attitudes, perceptions, norms or intentions. These concepts are rather subjective and remain open to interpretation when operationalizing them in a formal model code. There is a growing concern that how modellers interpret qualitative social science theories in quantitative ABMs may differ from case to case. Yet, formal tests of these differences are scarce and a systematic approach to analyse any possible disagreements is lacking. Our paper addresses this gap by exploring the consequences of variations in formalizations of one social science theory on the simulation outcomes of agent-based models of the same class. We ran simulations to test the impact of four differences: in model architecture concerning specific equations and their sequence within one theory, in factors affecting agents' decisions, in representation of these potentially differing factors, and finally in the underlying distribution of data used in a model. We illustrate emergent outcomes of these differences using an agent-based model developed to study regional impacts of households' solar panel investment decisions. The Theory of Planned Behaviour was applied as one of the most common social science theories used to define behavioural rules of individual agents. Our findings demonstrate qualitative and quantitative differences in simulation outcomes, even when agents' decision rules are based on the same theory and data. The paper outlines a number of critical met...
Niamir, L, Filatova, T, Voinov, A & Bressers, H 2018, 'Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes', Energy Policy, vol. 118, pp. 325-345.View/Download from: Publisher's site
© 2018 The Authors Changing residential energy demand can play an essential role in transitioning to a green economy. Environmental psychology suggests that behavioral changes regarding energy use are affected by knowledge, awareness, motivation and social learning. Data on various behavioral drivers of change can explain energy use at the individual level, but it provides little information about implications for macro energy demand on regional or national levels. We address this challenge by presenting a theoretically-based and empirically-driven agent-based model to track aggregated impacts of behavioral changes among heterogeneous households. We focus on the representation of the multi-step changes in individual energy use behavior and on a quantitative assessment of their aggregated impacts on the regional level. We understand the behavioral complexity of household energy use as a dynamic process unfolding in stages, and explore the barriers for utilizing the full potential of a region for emissions reduction. We suggest a policy mix that facilitates mutual learning among consumers.
de Koning, K, Filatova, T & Bin, O 2017, 'Bridging the Gap Between Revealed and Stated Preferences in Flood-prone Housing Markets', ECOLOGICAL ECONOMICS, vol. 136, pp. 1-13.View/Download from: Publisher's site
Ermolieva, T, Filatova, T, Ermoliev, Y, Obersteiner, M, de Bruijn, KM & Jeuken, A 2017, 'Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location-Specific Premiums in the Netherlands', Risk Analysis, vol. 37, no. 1, pp. 82-98.View/Download from: Publisher's site
© 2016 Society for Risk Analysis As flood risks grow worldwide, a well-designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood-loss-sharing program involving private insurance based on location-specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS-based flood model and a stochastic optimization procedure with respect to location-specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile-related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.
Handayani, K, Krozer, Y & Filatova, T 2017, 'Trade-offs between electrification and climate change mitigation: An analysis of the Java-Bali power system in Indonesia', Applied Energy, vol. 208, pp. 1020-1037.View/Download from: Publisher's site
© 2017 Elsevier Ltd The power sector in many developing countries face challenges of a fast-rising electricity demand in urban areas and an urgency of improved electricity access in rural areas. In the context of climate change, these development needs are challenged by the vital goal of CO 2 mitigation. This paper investigates plausible trade-offs between electrification and CO 2 mitigation in a developing country context, taking Indonesia as a case study. Aligned with the 2015 Paris Agreement, the Government of Indonesia has announced its voluntary pledge to reduce 29% of its GHGs emissions against the business as usual scenario by 2030. 11% of this should be attained by the energy sector. We incorporate the Indonesian Paris pledge into the modelling of capacity expansion of the Java-Bali power system, which is the largest power system in Indonesia. The LEAP model is used for the analysis in this study. Firstly, we validate the LEAP model using historical data of the national electricity system. Secondly, we develop and analyse four scenarios of the Java-Bali power system expansion from the base year 2015 through to 2030. These include a reference scenario (REF) to reflect a continuation of the present energy mix (REF), then a shift from coal to natural gas (NGS) (natural gas), followed by an expansion of renewable energy (REN) and, finally, the least-cost option (OPT). The shift to natural gas decreases future CO 2 emissions by 38.2 million ton, helping to achieve the CO 2 mitigation target committed to. Likewise, an escalation of renewable energy development in the Java-Bali islands cuts the projected CO 2 emissions by 38.9 million ton and, thus, assures meeting the target. The least-cost scenario attains the targeted emission reduction, but at 33% and 52% lower additional costs compared to NGS and REN, respectively. The cost-effectiveness of CO 2 mitigation scenarios range from 14.9 to 41.8 US$/tCO 2 e.
Filatova, T, Polhill, JG & van Ewijk, S 2016, 'Regime shifts in coupled socio-environmental systems: Review of modelling challenges and approaches', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 75, pp. 333-347.View/Download from: Publisher's site
van Duinen, R, Filatova, T, Jager, W & van der Veen, A 2016, 'Going beyond perfect rationality: drought risk, economic choices and the influence of social networks', ANNALS OF REGIONAL SCIENCE, vol. 57, no. 2-3, pp. 335-369.View/Download from: Publisher's site
Polhill, JG, Filatova, T, Schluter, M & Voinov, A 2016, 'Modelling systemic change in coupled socio-environmental systems', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 75, pp. 318-332.View/Download from: Publisher's site
Polhill, JG, Filatova, T, Schluter, M & Voinov, A 2016, 'Preface to the thematic issue on modelling systemic change in coupled socio-environmental systems', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 75, pp. 317-317.View/Download from: Publisher's site
Elsawah, S, Guillaume, JHA, Filatova, T, Rook, J & Jakeman, AJ 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, vol. 151, pp. 500-516.View/Download from: Publisher's site
Filatova, T 2015, 'Empirical agent-based land market: Integrating adaptive economic behavior in urban land-use models', COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, vol. 54, pp. 397-413.View/Download from: Publisher's site
van Duinen, R, Filatova, T, Geurts, P & van der Veen, A 2015, 'Coping with drought risk: empirical analysis of farmers' drought adaptation in the south-west Netherlands', REGIONAL ENVIRONMENTAL CHANGE, vol. 15, no. 6, pp. 1081-1093.View/Download from: Publisher's site
van Duinen, R, Filatova, T, Geurts, P & van der Veen, A 2015, 'Empirical Analysis of Farmers' Drought Risk Perception: Objective Factors, Personal Circumstances, and Social Influence', RISK ANALYSIS, vol. 35, no. 4, pp. 741-755.View/Download from: Publisher's site
Hasselmann, K, Cremades, R, Filatova, T, Hewitt, R, Jaeger, C, Kovalevsky, D, Voinov, A & Winder, N 2015, 'Free-riders to forerunners', NATURE GEOSCIENCE, vol. 8, no. 12, pp. 895-898.View/Download from: Publisher's site
Lee, J-S, Filatova, T, Ligmann-Zielinska, A, Hassani-Mahmooei, B, Stonedahl, F, Lorscheid, I, Voinov, A, Polhill, G, Sun, Z & Parker, DC 2015, 'The Complexities of Agent-Based Modeling Output Analysis', JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, vol. 18, no. 4.View/Download from: Publisher's site
Filatova, T 2014, 'Market-based instruments for flood risk management: A review of theory, practice and perspectives for climate adaptation policy', ENVIRONMENTAL SCIENCE & POLICY, vol. 37, pp. 227-242.View/Download from: Publisher's site
Huang, Q, Parker, DC, Filatova, T & Sun, S 2014, 'A review of urban residential choice models using agent-based modeling', ENVIRONMENT AND PLANNING B-PLANNING & DESIGN, vol. 41, no. 4, pp. 661-689.View/Download from: Publisher's site
Sun, S, Parker, DC, Huang, Q, Filatova, T, Robinson, DT, Riolo, RL, Hutchins, M & Brown, DG 2014, 'Market Impacts on Land-Use Change: An Agent-Based Experiment', ANNALS OF THE ASSOCIATION OF AMERICAN GEOGRAPHERS, vol. 104, no. 3, pp. 460-484.View/Download from: Publisher's site
Voinov, A & Filatova, T 2014, 'Pricing strategies in inelastic energy markets: can we use less if we can't extract more?', FRONTIERS OF EARTH SCIENCE, vol. 8, no. 1, pp. 3-17.View/Download from: Publisher's site
Filatova, T, Verburg, PH, Parker, DC & Stannard, CA 2013, 'Spatial agent-based models for socio-ecological systems: Challenges and prospects', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 45, pp. 1-7.View/Download from: Publisher's site
Huang, Q, Parker, DC, Sun, S & Filatova, T 2013, 'Effects of agent heterogeneity in the presence of a land-market: A systematic test in an agent-based laboratory', COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, vol. 41, pp. 188-203.View/Download from: Publisher's site
Robinson, DT, Sun, S, Hutchins, M, Riolo, RL, Brown, DG, Parker, DC, Filatova, T, Currie, WS & Kiger, S 2013, 'Effects of land markets and land management on ecosystem function: A framework for modelling exurban land-change', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 45, pp. 129-140.View/Download from: Publisher's site
Filatova, T, Mulder, JPM & van der Veen, A 2011, 'Coastal risk management: How to motivate individual economic decisions to lower flood risk?', OCEAN & COASTAL MANAGEMENT, vol. 54, no. 2, pp. 164-172.View/Download from: Publisher's site
Katsanevakis, S, Stelzenmueller, V, South, A, Sorensen, TK, Jones, PJS, Kerr, S, Badalamenti, F, Anagnostou, C, Breen, P, Chust, G, D'Anna, G, Duijn, M, Filatova, T, Fiorentino, F, Hulsman, H, Johnson, K, Karageorgis, AR, Kroencke, I, Mirto, S, Pipitone, C, Portelli, S, Qiu, W, Reiss, H, Sakellariou, D, Salomidi, M, van Hoof, L, Vassilopoulou, V, Vega Fernandez, T, Voege, S, Weber, A, Zenetos, A & ter Hofstede, R 2011, 'Ecosystem-based marine spatial management: Review of concepts, policies, tools, and critical issues', OCEAN & COASTAL MANAGEMENT, vol. 54, no. 11, pp. 807-820.View/Download from: Publisher's site
Filatova, T, Voinov, A & van der Veen, A 2011, 'Land market mechanisms for preservation of space for coastal ecosystems: An agent-based analysis', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 26, no. 2, pp. 179-190.View/Download from: Publisher's site
Filatova, T, Parker, D & van der Veen, A 2009, 'Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change', JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, vol. 12, no. 1.
Filatova, T, van der Veen, A & Parker, DC 2009, 'Land Market Interactions between Heterogeneous Agents in a Heterogeneous Landscape-Tracing the Macro-Scale Effects of Individual Trade-Offs between Environmental Amenities and Disamenities', CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS-REVUE CANADIENNE D AGROECONOMIE, vol. 57, no. 4, pp. 431-457.View/Download from: Publisher's site
Lee, JS & Filatova, T 2017, 'A network analytic approach to investigating a land-use change agent-based model' in Advances in Social Simulation 2015, Springer, Germany, pp. 231-240.View/Download from: Publisher's site
© Springer International Publishing AG 2017. Precise analysis of agent-based model (ABM) outputs can be a challenging and even onerous endeavor. Multiple runs or Monte Carlo sampling of one's model (for the purposes of calibration, sensitivity, or parameter-outcome analysis) often yields a large set of trajectories or state transitions which may, under certain measurements, characterize the model's behavior. These temporal state transitions can be represented as a directed graph (or network) which is then amenable to network analytic and graph theoretic measurements. Building on strategies of aggregating model outputs from multiple runs into graphs, we devise a temporally constrained graph aggregating state changes from runs and examine its properties in order to characterize the behavior of a land-use change ABM, the RHEA model. Features of these graphs are transformed into measures of complexity which in turn vary with different parameter or experimental conditions. This approach provides insights into the model behavior beyond traditional statistical analysis. We find that increasing the complexity in our experimental conditions can ironically decrease the complexity in the model behavior.
Niamir, L & Filatova, T 2017, 'Transition to low-carbon economy: Simulating nonlinearities in the electricity market, Navarre region, Spain' in Advances in Social Simulation 2015, Springer, Germany, pp. 321-327.View/Download from: Publisher's site
Coupled climate-economy systems are complex adaptive systems. While changes and out-of-equilibrium dynamics are in the essence of such systems, this dynamics can be of a very different nature. Specifically, it can take a form of either gradual marginal developments along a particular trend or exhibit abrupt nonmarginal shifts . Nonlinearities, thresholds, and irreversibility are of particular importance when studying coupled climate-economy systems. Strong feedbacks between climate and economy are realized through energy: economy requires energy for literary every sector, for development in literary any sector, while emissions need to stabilize and be even reduced to avoid catastrophic climate change . Possibilities of passing some thresholds that may drive these climate-energy-economy (CEE) systems in a completely different regime need to be explored. However, currently available models are not always suitable to study nonlinearities, paths involving critical thresholds and irreversibility . To be able to formulate an appropriate energy policy for this complex adaptive CEE system, policymakers should ideally have decision support tools that are able to foresee changes in energy market over the coming decades to plan ahead accordingly. Many macro models, that assume rational representative agent with static behavior, are designed to study marginal changes only. So there is a need for models that are able to capture nonlinear changes and their emergence.
Parker, DC, Brown, DG, Filatova, T, Riolo, R, Robinson, DT & Sun, S 2012, 'Do land markets matter? A modeling ontology and experimental design to test the effects of land markets for an agent-based model of ex-urban residential land-use change' in Agent-Based Models of Geographical Systems, pp. 525-542.View/Download from: Publisher's site
© Springer Science+Business Media B.V. 2012. Urban sprawl is shaped by various geographical, ecological and social factors under the influence of land market forces. When modeling this process, geographers and economists tend to prioritize factors most relevant to their own domain. Still, there are very few structured systematic comparisons exploring how the extent of process representation affects the models' ability to generate extent and pattern of change. This chapter aims to explore the question of how the degree of representation of land market processes affects simulated spatial outcomes. We identify four distinct elements of land markets: resource constraints, competitive bidding, strategic behavior, and endogenous supply decisions. Many land-use-change models include one or more of these elements; thus, the progression that we designed should facilitate analysis of our results in relation to a broad range of existing land-use-change models, from purely geographic to purely economic and from reduced form to highly structural models. The description of the new agent-based model, in which each of the four levels of market representation can be gradually activated, is presented. The behavior of suppliers and acquirers of land, and the agents' interactions at land exchange are discussed in the presence of each of the four land-market mechanisms.
Niamir, L, Ivanova, O, Filatova, T & Voinov, A 2018, 'Tracing Macroeconomic Impacts of Individual Behavioral Changes through Model Integration', IFAC-PapersOnLine, IFAC Workshop on Integrated Assessment Modelling for Environmental System, Brescia, Italy, pp. 96-101.View/Download from: Publisher's site
© 2018 The discourse on climate change stresses the importance of individual behavioral changes and shifts in social norms to assist both climate mitigation efforts worldwide. A design of an effective and efficient climate policy calls for decision support tools that are able to quantify cumulative impacts of individual behaviour and can integrate bottom-up processes into the traditional decision support tools. We propose an integrated system of models that combines strengths of macro and micro approaches to trace the cross-scale feedbacks in socio-economic processes in residential energy markets at provincial and national scales. This paper explores the feasibility of such hybrid models to study dynamic effects of climate change mitigation policy measures targeted at changes in residential energy use practices. We present an example of an agent-based energy model (BENCH) integrated with a EU-EMS computable general equilibrium model. We discusses methodological advancements and open challenges with respect to the integrated system of models.
Abdulkareem, SA, Augustijn, EW, Musial, K, Mustafa, YT & Filatova, T 2018, 'The impact of social versus individual learning for agents' risk perception during epidemics', Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, IEEE 14th International Conference on e-Science (e-Science), IEEE, Amsterdam, Netherlands, pp. 297-298.View/Download from: Publisher's site
© 2018 IEEE. Epidemics have always been a source of concern to people, both at the individual and government level. To fight outbreaks effectively, we need advanced tools that enable us to understand the factors that influence the spread of life-threatening diseases.
Filatova, T & Bin, O 2013, 'Changing Climate, Changing Behavior: Adaptive Economic Behavior and Housing Markets Responses to Flood Risks', ADVANCES IN SOCIAL SIMULATION, 9th Conference of the European-Social-Simulation-Association (ESSA), SPRINGER-VERLAG BERLIN, Warsaw Sch Econ, Warsaw, POLAND, pp. 249-258.View/Download from: Publisher's site
Levy, S, Martens, K, Van Der Heijden, R & Filatova, T 2013, 'Negotiated heights: An agent-based model of density in residential patterns', Proceedings of CUPUM 2013: 13th International Conference on Computers in Urban Planning and Urban Management - Planning Support Systems for Sustainable Urban Development, pp. 1-20.
Filatova, T & Polhill, G 2012, 'Shocks in coupled socio-ecological systems: What are they and how can we model them?', iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, pp. 2619-2630.
Coupled socio-ecological systems (SES) are complex systems characterized by self-organization, non-linearities, interactions among heterogeneous elements within each subsystem, and feedbacks across scales and among subsystems. When such a system experiences a shock or a crisis, the consequences are difficult to predict. In this paper we first define what a shock or a crisis means for SES. Depending on where the system boundary is drawn, shocks can be seen as exogenous or endogenous. For example, human intervention in environmental systems could be seen as exogenous, but endogenous in a socio-environmental system. This difference in the origin and nature of shocks has certain consequences for coupled SES and for policies to ameliorate negative consequences of shocks. Having defined shocks, the paper then focuses on modelling challenges when studying shocks in coupled SES. If we are to explore, study and predict the responses of coupled SES to shocks, the models used need to be able to accommodate (exogenous) or produce (endogenous) a shock event. Various modelling choices need to be made. Specifically, the 'sudden' aspect of a shock suggests the time period over which an event claimed to be a shock occurred might be 'quick'. What does that mean for a discrete event model? Turning to magnitude, what degree of change (in a variable or set of variables) is required for the event to be considered a shock? The 'surprising' nature of a shock means that none of the agents in the model should expect the shock to happen, but may need rules enabling them to generate behaviour in exceptional circumstances. This requires a certain design of the agents' decision-making algorithms, their perception of a shock, memory of past events and formation of expectations, and the information available to them during the time the shock occurred.
Parker, DC, Sun, S, Filatova, T, Magliocca, N, Huang, Q, Brown, DG & Riolo, R 2012, 'The implications of alternative developer decision-making strategies on land-use and land-cover in an agent-based land market model', iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, pp. 2521-2528.
Land developers play a key role in land-use and land cover change, as they directly make land development decisions and bridge the land and housing markets. Developers choose and purchase land from rural land owners, develop and subdivide land into parcel lots, build structures on lots, and sell houses to residential households. Developers determine the initial landscaping states of developed parcels, affecting the state and future trajectories of residential land cover, as well as land market activity. Despite their importance, developers are underrepresented in land use change models due to paucity of data and knowledge regarding their decision-making. Drawing on economic theories and empirical literature, we have developed a generalized model of land development decision-making within a broader agent-based model of land-use change via land markets. Developer's strategies combine their specialty in developing of particular subdivision types, their perception of and attitude towards market uncertainty, and their learning and adaptation strategies based on the dynamics of the simulated land and housing markets. We present a new agent-based land market model that includes these elements. The model will be used to experiment with these different development decision-making methods and compare their impacts on model outputs, particularly on the quantity and spatial pattern of resultant land use changes. Coupling between the land market and a carbon sequestration model, developed for the larger SLUCE2 project, will allow us, in future work, to examine how different developer's strategies will affect the carbon balance in residential landscapes.
Van Duinen, R, Filatova, T & Van Der Veen, A 2012, 'The role of social interaction in farmers' climate adaptation choice', iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, pp. 2493-2504.
Adaptation to climate change might not always occur, with potentially catastrophic results. Success depends on coordinated actions at both governmental and individual levels (public and private adaptation). Even for a "wet" country like the Netherlands, climate change projections show that the frequency and severity of droughts are likely to increase. Freshwater is an important factor for agricultural production. A deficit causes damage to crop production and consequently to a loss of income. Adaptation is the key to decrease farmers' vulnerability at the micro level and the sector's vulnerability at the macro level. Individual adaptation decision-making is determined by the behavior of economic agents and social interaction among them. This can be best studied with agentbased modelling. Given the uncertainty about future weather conditions and the costs and effectiveness of adaptation strategies, a farmer in the model uses a cognitive process (or heuristic) to make adaptation decisions. In this process, he can rely on his experiences and on information from interactions within his social network. Interaction leads to the spread of information and knowledge that causes learning. Learning changes the conditions for individual adaptation decisionmaking. All these interactions cause emergent phenomena: The diffusion of adaptation strategies and a change of drought vulnerability of the agricultural sector. In this paper, we present a conceptual model and the first implementation of an agent-based model. The aim is to study the role of interaction in a farmer's social network on adaptation decisions and on the diffusion of adaptation strategies and vulnerability of the agricultural sector. Micro-level survey data will be used to parameterize agents' behavioral and interaction rules at a later stage. This knowledge is necessary for the successful design of public adaptation strategies, since governmental adaptation actions need to be fine-tuned to private adaptation be...
Filatova, T, Parker, DC & Van Der Veen, A 2011, 'The implications of skewed risk perception for a dutch coastal land market: Insights from an agent-based computational economics model', Agricultural and Resource Economics Review, pp. 405-423.View/Download from: Publisher's site
Dutch coastal land markets are characterized by high amenity values but are threatened by potential coastal hazards, leading to high potential damage costs from flooding. Yet, Dutch residents generally perceive low or no flood risk. Using an agent-based land market model and Dutch survey data on risk perceptions and location preferences, this paper explores the patterns of land development and land rents produced by buyers with low, highly skewed risk perceptions. We find that, compared to representative agent and uniform risk perception models, the skewed risk perception distribution produces substantially more, high-valued development in risky coastal zones, potentially creating economically significant risks triggered by the current Dutch flood protection policy. © 2011 Northeastern Agricultural and Resource Economics Association.
Filatova, T, Parker, DC & van der Veen, A 2008, 'Introducing Preference Heterogeneity into a Monocentric Urban Model: An Agent-Based Land Market Model', SIMULATING INTERACTING AGENTS AND SOCIAL PHENOMENA: THE SECOND WORLD CONGRESS, 2nd World Congress on Social Simulation, SPRINGER-VERLAG TOKYO, George Mason Univ, Washington, DC, pp. 103-+.View/Download from: Publisher's site
Robinson, DT, Filatova, T, Sun, S, Riolo, RL, Brown, DG, Parker, DC, Hutchins, M, Currie, WS & Nassauer, JI 2010, 'Integrating land markets, land management, and ecosystem function in a model of land change', Modelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010, pp. 782-790.
We present the conceptual design of a new land-change modelling framework that builds on previous land-change research and models (i.e. ALMA, SOME, DEED). The design integrates agents of land change, land-market mechanisms, land-management behaviour and its ecosystem impacts, and land-policy scenarios into a single framework that can be used to address questions about land-change processes in exurban environments. The framework is implemented in Java, built using the Repast Simphony agent-based libraries within the Eclipse integrated development environment. The framework serves as a platform for integrating human and natural processes, as well as data that include social surveys of residential landscape and neighbourhood preferences as well as landmanagement behaviours, ecological field measurements of biomass in residential property parcels, interpretations of historical air photographs, and economic and household data acquired from local governments in Southeastern Michigan. The purpose of the framework is to provide an overarching design that can be extended into specific model implementations that evaluate, among other questions, how policy, land-management preferences, and land-market dynamics affect land-use and land-cover change patterns and subsequent carbon storage and flux.
Filatova, T, Van Der Veen, A & Parker, DC 2009, 'Why does individual risk perception matter in land use modeling? Combining survey data and agent-based land market model', Conference Proceedings - 6th Conference of the European Social Simulation Association, ESSA 2009.
This paper aims to understand the effects of biases in individual flood risk perception on aggregated land use patterns and their implications for macro policy. We develop a spatially explicit land market model and param-eterize individual risk perceptions with data from a survey held in the Nether-lands in 2008. Two sets of experiments are presented. A model with heteroge-neous agents produces qualitatively different results compared to a model with homogeneous agents. Individuals with low flood risk perception drive urban developments into the economically inefficient zone and leading to the increas-ing potential damage.
Filatova, T & Van Der Veen, A 2008, 'Mapping survey data into agents' behavioral rules for ABMs: Motivation and challenges', Proc. iEMSs 4th Biennial Meeting - Int. Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008, pp. 2077-2079.
Haase, D, Kabisch, S, Haase, A, Filatova, T, Van Der Veen, A, Tötzer, T, Loibl, W, Scatasta, S, Schetke, S, Zuin, A & Von Walter, F 2008, 'Actors and factors - Bridging social science findings and urban land use change modeling', Proc. iEMSs 4th Biennial Meeting - Int. Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008, pp. 2059-2073.
Recent uneven land use dynamics in urban areas resulting from demographic change, economic pressure and the cities' mutual competition in a globalising world challenge both scientists and practitioners, among them social scientists, modellers and spatial planners. Processes of growth and decline specifically affect the urban environment, the requirements of the residents on social and natural resources. Social and environmental research is interested in a better understanding and ways of explaining the interactions between society and landscape in urban areas. And it is also needed for making life in cities attractive, secure and affordable within or despite of uneven dynamics. The position paper upon "Actors and factors - bridging social science findings and urban land use change modeling" presents approaches and ideas on how social science findings on the interaction of the social system (actors) and the land use (factors) are taken up and formalised using modelling and gaming techniques. It should be understood as a first sketch compiling major challenges and proposing exemplary solutions in the field of interest.
Filatova, T, Van Der Veen, A & Voinov, A 2008, 'An agent-based model for exploring land market mechanisms for coastal zone management', Proc. iEMSs 4th Biennial Meeting - Int. Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008, pp. 792-799.
This paper presents an agent-based model of a land market (ALMA-C) to simulate the emergence of land prices and urban land patterns from bottom-up. Our model mimics individual decisions to buy and to sell land depending on economic, sociological and political factors as well as on the characteristics of the spatial environment. To this we add ecological and environmental considerations and focus on the question of how individual land use decisions can be affected to reduce the pressure on the coastal zone ecosystem functions. A series of model experiments helps visualize and explore how economic incentives at a land market can influence the spatial distribution of activities and land prices in a coastal zone. We demonstrate that economic incentives do affect urban form and pattern, land prices and welfare measures. However, they may not always be sufficient to reduce the pressure on coastal zone ecosystems.
Filatova, T, Parker, DC & van der Veen, A 2007, 'Agent-based land markets: Heterogeneous agents, land prices and urban land use change', Proceedings of the 4th Conference of the European Social Simulation Association, ESSA 2007, pp. 263-275.
© Title Proceedings of the 4th Conference of the European Social Simulation Association, ESSA 2007. All rights reserved. We construct a spatially explicit agent-based model of a bilateral land market. Heterogeneous agents form their bid and ask prices for land based on the utility that they obtain from a certain location (house/land) and based on the state of the market (an excess of demand or supply). We underline the distinction between bid /ask price and individual willingness to pay/to accept and show that variations between them that reflect market conditions can influence land prices. Agents sort among locations with respect to distance from the city center and environmental spatial externalities. Aggregated outcomes such as land patterns and land prices are produced by the model. The basic model of buyers and sellers trading land in the urban area produces results identical to the monocentric urban model. However, more complex dynamics appears when environmental amenities and market-adjustment variable influence the formation of land prices.
Anastasio, P, Brugnach, M, Chiew, F, Filatova, T, Gray, B, Holtz, G, Lindenschmidt, KE, Marques, JC, Pahl-Wostl, C, Pardal, M, Refsgaard, JC, Reichl, J, Van Der Keur, P, Van Der Veen, A & Verdelhos, T 2006, 'Workshop 8: Complexity and uncertainty and a new role for models', Proceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software".
Filatova, T & Van Der Veen, A 2006, 'Microeconomic motives of land use change in coastal zone area: Agent based modelling approach', Proceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software".
Economic growth causes growing urbanization, extension of tourist sector, infrastructure and change of natural landscape. These processes of land use change attract even more attention if they take place in coastal zone area. In that case not only the efficient allocation and preservation of natural area, but also reduction of potential damage from flooding is important. Driven forces of land use at macro and micro levels should be taken into account. This paper presents an agent based model (ABM), which is designed to simulate land use change in coastal zone area based of human behaviour. The aim is to understand motives, types of connections and interactions between different actors and natural environment in order to get a feeling how different policy options and natural conditions might affect land use configuration. Microeconomic motives of land use decisions are in the focus of the research. Individual land use decisions are guided by economic and geomorphologic conditions, spatial planning and coastal protection policy. Each location choice is done according to a set of defined rules and land attributes. Space is represented as a grid of cells. Self-interested economic agents interact with each other trying to benefit from a certain type of land-use. We introduce the perception of risk of flooding in the model of land use as an innovative aspect of ABM simulations for water management problems. Based on decisions of spatially distributed individual economic agents operating in a policy framework, the model produces aggregated land-use patterns as an outcome. Understanding the factors that affect land use decisions will help policy makers design incentives to achieve policy objectives in coastal zone area. The proposed ABM will be applied to a study area in the province of North Holland in the Netherlands.
I work with vast number of external partners in Europe, USA, Canada, South-East Asia. Throughout my career I worked also outside academia in the world leading applied research institute on water management – Deltares (the Netherlands).