Before joining the School of Information, Systems and Modeling and leading the Center on Persuasive Systems for Wise Adaptive Living (PERSWADE) at the Faculty of Engineering and IT, Alexey was professor of Spatio-Temporal Systems Modeling for Sustainability Science at ITC, University of Twente in the Netherlands. Prior to that he was coordinating the Chesapeake Research Consortium Community Modeling Program, and was also Principal Research Scientist at John's Hopkins University, USA. He has spent one year with the AAAS Science and Technology Fellowship program working with the US Army Corps of Engineers. For over ten years he was with the Institute for Ecological Economics, Univ. of Maryland and Vermont. He has his MSc and PhD from Moscow State University, Russia.
See also my personal webpage for more information.
Dr Voinov is the director of the Research Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE)
Associate Editor of the Journal for Environmental Modeling and Software and past-president of the International Environmental Modeling and Software Society.
Can supervise: YES
My academic interests evolve around spatial dynamic modeling of socio-environmental systems and sustainability science in application to decision support and policy making. In particular I am interested in
Simulation modeling of environmental and economic systems
- Landscape and watershed modeling, spatial models
- Systems analysis in ecology and economics
- Integrated modeling
Environmental management and decision support
- Participatory modeling
- Environmental policy and planning
- Sustainable energy, bio-energy
- Water-energy-land nexus
- Transitions to low-carbon economy
I am a keen advocate of stakeholder involvement in modeling and decision making.
I teach courses on systems modeling, in particular in application to socio-environmental modeling.
I wrote a book on "Systems Science and Modeling for Ecological Economics" (Academic Press/Elsevier)
The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. This is a challenging task for which modelling and software have become key instruments used to promote sustainability and improve environmental decision processes. This role can especially be one that facilitates systematic integration of various knowledge and data, that fosters learning and helps to make predictions. This book presents the current state of the art in environmental modelling and software and identifies the future challenges in the field. This opening chapter provides an introduction to the topic, the objectives of the book and an outline of its chapters. Modelling can perform a range of valuable roles, from being a process of sharing and structuring knowledge to providing a means of investigating tradeoffs or increasing system understanding. Without full appreciation of their limitations and capabilities, however, there is a risk of models being misused or their outputs misinterpreted. On the other hand, model uncertainty cannot be totally eliminated but it can be understood, communicated and managed. The common problems in modelling that must be understood by modellers and users, and approaches to address them are discussed in the first few chapters. This section of chapters highlights the need for better standards in modelling practice, appropriate handling of uncertainty and improvement of model usability. The next section of the book explores generic and sectoral issues in modelling in the context of the state of the art in modelling tools and approaches, and thereby identifies future research, development and practice needs. Challenges pervasive in various modelling fields include the need for more credible and purposeful models, for better uncertainty management and for more support of an open and collaborative modelling process. Overall a much stronger emphasis on the modelling and software process is warranted. © ...
McIntosh, BS, Giupponi, C, Voinov, AA, Smith, C, Matthews, KB, Monticino, M, Kolkman, MJ, Crossman, N, van Ittersum, M, Haase, D, Haase, A, Mysiak, J, Groot, JCJ, Sieber, S, Verweij, P, Quinn, N, Waeger, P, Gaber, N, Hepting, D, Scholten, H, Sulis, A, van Delden, H, Gaddis, E & Assaf, H 2008, Chapter Three Bridging the Gaps Between Design and Use: Developing Tools to Support Environmental Management and Policy.View/Download from: Publisher's site
Integrated assessment models, decision support systems (DSS) and Geographic Information Systems (GIS) are examples of a growing number of computer-based tools designed to provide decision and information support to people engaged in formulating and implementing environmental policy and management. It is recognised that environmental policy and management users are often not as receptive to using such tools as desired but that little research has been done to uncover and understand the reasons. There is a diverse range of environmental decision and information support tools (DISTs) with uses including organisational and participatory decision support, and scientific research. The different uses and users of DISTs each present particular needs and challenges to the tool developers. The lack of appreciation of the needs of end-users by developers has contributed to the lack of success of many DISTs. Therefore it is important to engage users and other stakeholders in the tool development process to help bridge the gap between design and use. Good practice recommendations for developers to involve users include being clear about the purpose of the tool, working collaboratively with other developers and stakeholders, and building social and scientific credibility. © 2008 Elsevier B.V. All rights reserved.
Wenkel, KO, Wieland, R, Mirschel, W, Schultz, A, Kampichler, C, Kirilenko, A & Voinov, A 2008, Chapter Sixteen Regional Models of Intermediate Complexity (REMICs) - A New Direction in Integrated Landscape Modelling.View/Download from: Publisher's site
The landscape or regional scale of analysis poses certain challenges and new possibilities for better understanding of space and time-related processes and changes. These changes can be caused by exogenous processes (e.g. climate change, dynamics on the global market, or technological advances), or may be a result of endogenous regional processes (e.g. dynamics of land use changes). The regional scale is regarded as an area from about a 100 km2 up to 1000 km2. The main problem is that at this scale we can still distinguish the signals from a variety of local processes and cannot ignore them by averaging, while the sheer number of these processes replicated over a large enough territory makes the system extremely complex, structurally diverse, and ecologically heterogeneous. This complexity is further exacerbated by enhanced uncertainty in data that spans time and space. To cope with these problems we suggest an approach that promotes models of intermediate complexity. An important feature of Regional Models of Intermediate Complexity (REMICs) is that they are characterised by a lower degree of detail in the description of process dynamics, but a higher number of interacting components. The implementation of REMICs calls for specific modelling tools that can handle the spatial heterogeneity of GIS and can offer analytical capabilities of both process-based and statistical modelling. One such tool, the Spatial Analysis and Modeling Toolbox (SAMT) is introduced. SAMT is an open source package that can be used to develop decision support systems (DSS) that can help planners and decision makers better understand the complex reactions of the landscape to various forcings. © 2008 Elsevier B.V. All rights reserved.
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...
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
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.
Pileggi, SF & Voinov, A 2019, 'PERSWADE-CORE: a core ontology for communicating socio-environmental and sustainability science', IEEE Access, vol. 7, pp. 127177-127188.View/Download from: Publisher's site
Voinov, A, Morales, J & Hogenkamp, H 2019, 'Analyzing the social impacts of scooters with geo-spatial methods.', Journal of Environmental Management, vol. 242, pp. 529-538.View/Download from: Publisher's site
Scooters, or gasoline powered two-wheelers, are becoming increasingly popular in the Netherlands. They provide fast, independent and affordable transportation, especially in urban congested areas. Unfortunately, they also have considerable adverse impacts on the environment and human health. The three most prominent impacts are associated with air pollution, noise pollution and traffic accidents. While the total contribution of emissions by scooters is relatively small compared to total traffic related emissions, they have a disproportionally large impact on their direct environment, especially when sharing roads with bicycles as in the Netherlands, where they are characterized as super-polluters. A scoping GIS based assessment, using theoretical and available secondary data, could identify routes with highest likelihood of scooter presence to estimate exhaust and noise impacts and related traffic accidents. Estimated are provided for the total population, and the number of childcare facilities within the impact areas. For future projections four different scenarios are analyzed. For the case study of the town of Enschede in the Netherlands the present noise/exhaust environmental impact of scooters is affecting at least 30% of the population and in the future this number can increase to 38%-53%.
Glynn, PD, Voinov, AA, Shapiro, CD & White, PA 2018, 'Response to Comment by Walker et al. on "From Data to Decisions: Processing Information, Biases, and Beliefs for Improved Management of Natural Resources and Environments"', EARTHS FUTURE, vol. 6, no. 5, pp. 762-769.View/Download from: Publisher's site
Gray, S, Voinov, A, Paolisso, M, Jordan, R, BenDor, T, Bommel, P, Glynn, P, Hedelin, B, Hubacek, K, Introne, J, Kolagani, N, Laursen, B, Prell, C, Schmitt Olabisi, L, Singer, A, Sterling, E & Zellner, M 2018, 'Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling.', Ecological applications : a publication of the Ecological Society of America, vol. 28, no. 1, pp. 46-61.View/Download from: Publisher's site
Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human-environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.
Jordan, R, Gray, S, Zellner, M, Glynn, PD, Voinov, A, Hedelin, B, Sterling, EJ, Leong, K, Olabisi, LS, Hubacek, K, Bommel, P, BenDor, TK, Jetter, AJ, Laursen, B, Singer, A, Giabbanelli, PJ, Kolagani, N, Carrera, LB, Jenni, K & Prell, C 2018, 'Twelve Questions for the Participatory Modeling Community', Earth's Future, vol. 6, no. 8, pp. 1046-1057.View/Download from: Publisher's site
©2018. The Authors. Participatory modeling engages the implicit and explicit knowledge of stakeholders to create formalized and shared representations of reality and has evolved into a field of study as well as a practice. Participatory modeling researchers and practitioners who focus specifically on environmental resources met at the National Socio-Environmental Synthesis Center (SESYNC) in Annapolis, Maryland, over the course of 2 years to discuss the state of the field and future directions for participatory modeling. What follows is a description of 12 overarching groups of questions that could guide future inquiry.
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.
Voinov, A, Çöltekin, A, Chen, M & Beydoun, G 2018, 'Virtual geographic environments in socio-environmental modeling: a fancy distraction or a key to communication?', International Journal of Digital Earth, vol. 11, pp. 408-419.
Voinov, A, Jenni, K, Gray, S, Kolagani, N, Glynn, PD, Bommel, P, Prell, C, Zellner, M, Paolisso, M, Jordan, R, Sterling, E, Schmitt Olabisi, L, Giabbanelli, PJ, Sun, Z, Le Page, C, Elsawah, S, BenDor, TK, Hubacek, K, Laursen, BK, Jetter, A, Basco-Carrera, L, Singer, A, Young, L, Brunacini, J & Smajgl, A 2018, 'Tools and methods in participatory modeling: Selecting the right tool for the job', Environmental Modelling and Software, vol. 109, pp. 232-255.View/Download from: Publisher's site
© 2018 Elsevier Ltd Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process.
Wei, F, Costanza, R, Dai, Q, Stoeckl, N, Gu, X, Farber, S, Nie, Y, Kubiszewski, I, Hu, Y, Swaisgood, R, Yang, X, Bruford, M, Chen, Y, Voinov, A, Qi, D, Owen, M, Yan, L, Kenny, DC, Zhang, Z, Hou, R, Jiang, S, Liu, H, Zhan, X, Zhang, L, Yang, B, Zhao, L, Zheng, X, Zhou, W, Wen, Y, Gao, H & Zhang, W 2018, 'The Value of Ecosystem Services from Giant Panda Reserves.', Current biology : CB, vol. 28, no. 13, pp. 2174-2180.e7.View/Download from: Publisher's site
Ecosystem services (the benefits to humans from ecosystems) are estimated globally at $125 trillion/year [1, 2]. Similar assessments at national and regional scales show how these services support our lives . All valuations recognize the role of biodiversity, which continues to decrease around the world in maintaining these services [4, 5]. The giant panda epitomizes the flagship species . Its unrivalled public appeal translates into support for conservation funding and policy, including a tax on foreign visitors to support its conservation . The Chinese government has established a panda reserve system, which today numbers 67 reserves [8, 9]. The biodiversity of these reserves is among the highest in the temperate world , covering many of China's endemic species . The panda is thus also an umbrella species -protecting panda habitat also protects other species. Despite the benefits derived from pandas, some journalists have suggested that it would be best to let the panda go extinct. With the recent downlisting of the panda from Endangered to Vulnerable, it is clear that society's investment has started to pay off in terms of panda population recovery [13, 14]. Here, we estimate the value of ecosystem services of the panda and its reserves at between US$2.6 and US$6.9 billion/year in 2010. Protecting the panda as an umbrella species and the habitat that supports it yields roughly 10-27 times the cost of maintaining the current reserves, potentially further motivating expansion of the reserves and other investments in natural capital in China.
Zhao, F, Wu, Y, Qiu, L, Sivakumar, B, Zhang, F, Sun, Y, Sun, L, Li, Q & Voinov, A 2018, 'Spatiotemporal features of the hydro-biogeochemical cycles in a typical loess gully watershed', Ecological Indicators, vol. 91, pp. 542-554.View/Download from: Publisher's site
© 2018 Elsevier Ltd Hydrological and biogeochemical processes are essential for material and energy exchange among climate-soil-plant systems and, thus, play an important role in terrestrial ecosystems. In particular, the water-carbon dynamics determine the status and change of ecosystems. Therefore, understanding the spatiotemporal features of the water and carbon cycles is of great importance for watershed ecosystem management. This study employed a newly coupled hydro-biogeochemical model (SWAT-DayCent) to investigate the spatiotemporal characteristics and evolution of the water cycle (evapotranspiration (ET), soil water, and water yield) and carbon cycle (net primary productivity (NPP), soil organic carbon (SOC)) in a typical loess gully watershed (the Jinghe River Basin, JRB) on the Loess Plateau of China during the period of 2000–2010. The satisfactory performance of the coupled model demonstrates that the SWAT-DayCent model is capable of simulating hydro-biogeochemical processes at the watershed scale in the Loess Plateau region. The spatial distributions of hydro-biogeochemical components varied significantly over the JRB—a decreasing gradient from south to north in hydrological variables and NPP, a higher SOC in the western margin than other parts, and a general increasing trend for all the five components in the southeastern part. Temporally, the hydrological variables showed a slightly decreasing trend, the NPP underwent a slight upward trend, but the SOC decreased significantly in the whole basin under the current climate conditions. The correlation analysis between hydrologic components and carbon cycle indicated that the water cycle may have synergies with NPP but may exert little influence on SOC. Overall, our quantitative analyses over time and space can be informative in soil and water conservation practices and ecosystem service enhancement in the JRB specifically and other parts of the Loess Plateau region as well.
Arodudu, O, Helming, K, Wiggering, H & Voinov, A 2017, 'Bioenergy from low-intensity agricultural systems: An energy efficiency analysis', Energies, vol. 10, no. 1, pp. 1-18.View/Download from: Publisher's site
In light of possible future restrictions on the use of fossil fuel, due to climate change obligations and continuous depletion of global fossil fuel reserves, the search for alternative renewable energy sources is expected to be an issue of great concern for policy stakeholders. This study assessed the feasibility of bioenergy production under relatively low-intensity conservative, eco-agricultural settings (as opposed to those produced under high-intensity, fossil fuel based industrialized agriculture). Estimates of the net e nergy gain (NEG) and the energy return on energy invested (EROEI) obtained from a life cycle inventory of the energy inputs and outputs involved reveal that the energy efficiency of bioenergy produced in low-intensity eco-agricultural systems could be as much as much as 448.5-488.3 GJ·ha -1 of NEG and an EROEI of 5.4-5.9 for maize ethanol production systems, and as much as 155.0-283.9 GJ·ha -1 of NEG and an EROEI of 14.7-22.4 for maize biogas production systems. This is substantially higher than for industrialized agriculture with a NEG of 2.8-52.5 GJ·ha -1 and an EROEI of 1.2-1.7 for maize ethanol production systems, as well as a NEG of 59.3-188.7 GJ·ha -1 and an EROEI of 2.2-10.2 for maize biogas production systems. Bioenergy produced in low-intensity eco-agricultural systems could therefore be an important source of energy with immense net benefits for local and regional end-users, provided a more efficient use of the co-products is ensured.
Arodudu, O, Helming, K, Wiggering, H & Voinov, A 2017, 'Towards a more holistic sustainability assessment framework for agro-bioenergy systems - A review', ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, vol. 62, pp. 61-75.View/Download from: Publisher's site
Arodudu, OT, Helming, K, Voinov, A & Wiggering, H 2017, 'Integrating agronomic factors into energy efficiency assessment of agro-bioenergy production – A case study of ethanol and biogas production from maize feedstock', Applied Energy, vol. 198, pp. 426-439.View/Download from: Publisher's site
© 2017 Elsevier Ltd Previous life cycle assessments for agro-bioenergy production rarely considered some agronomic factors with local and regional impacts. While many studies have found the environmental and socio-economic impacts of producing bioenergy on arable land not good enough to be considered sustainable, others consider it still as one of the most effective direct emission reduction and fossil fuel replacement measures. This study improved LCA methods in order to examine the individual and combined effects of often overlooked agronomic factors (e.g. alternative farm power, seed sowing, fertilizer, tillage and irrigation options) on life-cycle energy indicators (net energy gain-NEG, energy return on energy invested-EROEI), across the three major agro-climatic zones namely tropic, sub-tropic and the temperate landscapes. From this study, we found that individual as well as combined effects of agronomic factors may improve the energy productivity of arable bioenergy sources considerably in terms of the NEG (from between 6.8 and 32.9 GJ/ha to between 99.5 and 246.7 GJ/ha for maize ethanol; from between 39.0 and 118.4 GJ/ha to between 127.9 and 257.9 GJ/ha for maize biogas) and EROEI (from between 1.2 and 1.8 to between 2.1 and 3.0 for maize ethanol, from between 4.3 and 12.1 to between 15.0 and 33.9 for maize biogas). The agronomic factors considered by this study accounted for an extra 7.5–14.6 times more of NEG from maize ethanol, an extra 2.2–3.3 times more of NEG from maize biogas, an extra 1.7 to 1.8 times more of EROEI from maize ethanol, and an extra 2.8–3.5 times more of EROEI from maize biogas respectively. This therefore underscores the need to factor in local and regional agronomic factors into energy efficiency and sustainability assessments, as well as decision making processes regarding the application of energy from products of agro-bioenergy production.
Belete, GF, Voinov, A & Laniak, GF 2017, 'An overview of the model integration process: From pre-integration assessment to testing', Environmental Modelling and Software, vol. 87, pp. 49-63.View/Download from: Publisher's site
© 2016 Elsevier Ltd Integration of models requires linking models which can be developed using different tools, methodologies, and assumptions. We performed a literature review with the aim of improving our understanding of model integration process, and also presenting better strategies for building integrated modeling systems. We identified five different phases to characterize integration process: pre-integration assessment, preparation of models for integration, orchestration of models during simulation, data interoperability, and testing. Commonly, there is little reuse of existing frameworks beyond the development teams and not much sharing of science components across frameworks. We believe this must change to enable researchers and assessors to form complex workflows that leverage the current environmental science available. In this paper, we characterize the model integration process and compare integration practices of different groups. We highlight key strategies, features, standards, and practices that can be employed by developers to increase reuse and interoperability of science software components and systems.
Belete, GF, Voinov, A & Morales, J 2017, 'Designing the Distributed Model Integration Framework – DMIF', Environmental Modelling and Software, vol. 94, pp. 112-126.View/Download from: Publisher's site
ï¿½ 2017 Elsevier Ltd We describe and discuss the design and prototype of the Distributed Model Integration Framework (DMIF) that links models deployed on different hardware and software platforms. We used distributed computing and service-oriented development approaches to address the different aspects of interoperability. Reusable web service wrappers were developed for technical interoperability models created in NetLogo and GAMS modeling languages. We investigated automated semantic mapping of text-based input-output data and attribute names of components using word overlap semantic matching algorithms and using an openly available lexical database. We also incorporated automated unit conversion in semantic mediation by using openly available ontologies. DMIF helps to avoid significant amount of reinvention by framework developers, and opens up the modeling process for many stakeholders who are not prepared to deal with the technical difficulties associated with installing, configuring, and running various models. As a proof of concept, we implemented our design to integrate several climate-energy-economy models.
Glynn, PD, Voinov, AA, Shapiro, CD & White, PA 2017, 'From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments', EARTHS FUTURE, vol. 5, no. 4, pp. 356-378.View/Download from: Publisher's site
Qi, M, Sun, T, Zhang, H, Zhu, M, Yang, W, Shao, D & Voinov, A 2017, 'Maintenance of salt barrens inhibited landward invasion of Spartina species in salt marshes', Ecosphere, vol. 8, no. 10.View/Download from: Publisher's site
© 2017 Qi et al. Spartina spp. (cordgrasses) often dominates intertidal mudflats and/or low marshes. The landward invasion of these species was typically thought to be restrained by low tidal inundation frequencies and interspecific competition. We noticed that the reported soil salinity levels in some salt marshes were much higher than those at the mean higher high water level, which might inhibit the landward invasion of cordgrass. To test this possibility, we transplanted Spartina alterniflora across an elevational gradient in an invaded salt marsh in the Yellow River Delta National Nature Reserve, where a salt accumulation zone (i.e., salt barren) was previously observed. We found that S. alterniflora was significantly inhibited by the salt barren in high marsh regions, although it performed better at upland and low marsh regions. A common garden experiment further elucidated that S. alterniflora performed best at low salinity levels and that this species is less sensitive to inundation frequency. Our results indicated that the salt barren inhibited the landward invasion of S. alterniflora in salt marshes and provided a natural barrier to protect the upland from invasion. Though field observations suggest that S. alterniflora could propagate along tidal channels, which provide low-salinity corridors for the dispersal of propagules, natural salt barrens can inhibit the landward invasion of Spartina in salt marshes. However, artificial disturbances that break the salt barren band in salt marshes (e.g., artificial ditches) might accelerate the invasion of Spartina spp. This new finding should alert salt marsh managers to pay attention to artificial ditches and/or other human activities when attempting to control Spartina invasion.
Argent, RM, Sojda, RS, Guipponi, C, McIntosh, B, Voinov, AA & Maier, HR 2016, 'Best practices for conceptual modelling in environmental planning and management', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 80, pp. 113-121.View/Download from: Publisher's site
Belete, GF & Voinov, A 2016, 'Exploring temporal and functional synchronization in integrating models: A sensitivity analysis', COMPUTERS & GEOSCIENCES, vol. 90, pp. 162-171.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
Turner, KG, Anderson, S, Gonzales-Chang, M, Costanza, R, Courville, S, Dalgaard, T, Dominati, E, Kubiszewski, I, Ogilvy, S, Porfirio, L, Ratna, N, Sandhu, H, Sutton, PC, Svenning, J-C, Turner, GM, Varennes, Y-D, Voinov, A & Wratten, S 2016, 'A review of methods, data, and models to assess changes in the value of ecosystem services from land degradation and restoration', ECOLOGICAL MODELLING, vol. 319, pp. 190-207.View/Download from: Publisher's site
Voinov, A, Kolagani, N & McCall, MK 2016, 'Preface to this Virtual Thematic Issue: Modelling with Stakeholders II', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 79, pp. 153-155.View/Download from: Publisher's site
Voinov, A, Kolagani, N, McCall, MK, Glynn, PD, Kragt, ME, Ostermann, FO, Pierce, SA & Ramu, P 2016, 'Modelling with stakeholders - Next generation', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 77, pp. 196-220.View/Download from: Publisher's site
Zhang, L, Yang, Z, Voinov, A & Gao, S 2016, 'Nature-inspired stormwater management practice: The ecological wisdom underlying the Tuanchen drainage system in Beijing, China and its contemporary relevance', LANDSCAPE AND URBAN PLANNING, vol. 155, pp. 11-20.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
Lin, H, Batty, M, Jorgensen, SE, Fu, B, Konecny, M, Voinov, A, Torrens, P, Lu, G, Zhu, A-X, Wilson, JP, Gong, J, Kolditz, O, Bandrova, T & Chen, M 2015, 'Virtual Environments Begin to Embrace Process-based Geographic Analysis', TRANSACTIONS IN GIS, vol. 19, no. 4, pp. 493-498.View/Download from: Publisher's site
van Duren, I, Voinov, A, Arodudu, O & Firrisa, MT 2015, 'Where to produce rapeseed biodiesel and why? Mapping European rapeseed energy efficiency', RENEWABLE ENERGY, vol. 74, pp. 49-59.View/Download from: Publisher's site
Voinov, A, Arodudu, O, van Duren, I, Morales, J & Qin, L 2015, 'Estimating the potential of roadside vegetation for bioenergy production', JOURNAL OF CLEANER PRODUCTION, vol. 102, pp. 213-225.View/Download from: Publisher's site
Arodudu, O, Ibrahim, E, Voinov, A & van Duren, I 2014, 'Exploring bioenergy potentials of built-up areas based on NEG-EROEI indicators', ECOLOGICAL INDICATORS, vol. 47, pp. 67-79.View/Download from: Publisher's site
Firrisa, MT, van Duren, I & Voinov, A 2014, 'Energy efficiency for rapeseed biodiesel production in different farming systems', ENERGY EFFICIENCY, vol. 7, no. 1, pp. 79-95.View/Download from: Publisher's site
Gaddis, EJB, Voinov, A, Seppelt, R & Rizzo, DM 2014, 'Spatial Optimization of Best Management Practices to Attain Water Quality Targets', WATER RESOURCES MANAGEMENT, vol. 28, no. 6, pp. 1485-1499.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
Voinov, A, Seppelt, R, Reis, S, Nabel, JEMS & Shokravi, S 2014, 'Values in socio-environmental modelling: Persuasion for action or excuse for inaction', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 53, pp. 207-212.View/Download from: Publisher's site
Bennett, ND, Croke, BFW, Guariso, G, Guillaume, JHA, Hamilton, SH, Jakeman, AJ, Marsili-Libelli, S, Newham, LTH, Norton, JP, Perrin, C, Pierce, SA, Robson, B, Seppelt, R, Voinov, AA, Fath, BD & Andreassian, V 2013, 'Characterising performance of environmental models', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 40, pp. 1-20.View/Download from: Publisher's site
Boomer, KMB, Weller, DE, Jordan, TE, Linker, L, Liu, Z-J, Reilly, J, Shenk, G & Voinov, AA 2013, 'Using Multiple Watershed Models to Predict Water, Nitrogen, and Phosphorus Discharges to the Patuxent Estuary', JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, vol. 49, no. 1, pp. 15-39.View/Download from: Publisher's site
Kelly (Letcher), RA, Jakeman, AJ, Barreteau, O, Borsuk, ME, ElSawah, S, Hamilton, SH, Henriksen, HJ, Kuikka, S, Maier, HR, Rizzoli, AE, van Delden, H & Voinov, AA 2013, 'Selecting among five common modelling approaches for integrated environmental assessment and management', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 47, pp. 159-181.View/Download from: Publisher's site
Laniak, GF, Olchin, G, Goodall, J, Voinov, A, Hill, M, Glynn, P, Whelan, G, Geller, G, Quinn, N, Blind, M, Peckham, S, Reaney, S, Gaber, N, Kennedy, R & Hughes, A 2013, 'Integrated environmental modeling: A vision and roadmap for the future', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 39, pp. 3-23.View/Download from: Publisher's site
Laniak, GF, Rizzoli, AE & Voinov, A 2013, 'Thematic Issue on the Future of Integrated Modeling Science and Technology Preface', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 39, pp. 1-2.View/Download from: Publisher's site
Seppelt, R, Bankamp, D, Voinov, AA & Rizzoli, A 2013, '6th International Congress on Environmental Modelling and Software (iEMSs): "Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty": A congress report', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 43, pp. 160-162.View/Download from: Publisher's site
Krueger, T, Page, T, Smith, L & Voinov, A 2012, 'A guide to expert opinion in environmental modelling and management', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 36, pp. 1-3.View/Download from: Publisher's site
Naimi, B & Voinov, A 2012, 'StellaR: A software to translate Stella models into R open-source environment', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 38, pp. 117-118.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
McIntosh, BS, Ascough, JC, Twery, M, Chew, J, Elmahdi, A, Haase, D, Harou, JJ, Hepting, D, Cuddy, S, Jakeman, AJ, Chen, S, Kassahun, A, Lautenbach, S, Matthews, K, Merritt, W, Quinn, NWT, Rodriguez-Roda, I, Sieber, S, Stavenga, M, Sulis, A, Ticehurst, J, Volk, M, Wrobel, M, van Delden, H, El-Sawah, S, Rizzoli, A & Voinov, A 2011, 'Environmental decision support systems (EDSS) development - Challenges and best practices', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 26, no. 12, pp. 1389-1402.View/Download from: Publisher's site
Gaddis, EJB & Voinov, A 2010, 'Spatially Explicit Modeling of Land Use Specific Phosphorus Transport Pathways to Improve TMDL Load Estimates and Implementation Planning', WATER RESOURCES MANAGEMENT, vol. 24, no. 8, pp. 1621-1644.View/Download from: Publisher's site
Gaddis, EJB, Falk, HH, Ginger, C & Voinov, A 2010, 'Effectiveness of a participatory modeling effort to identify and advance community water resource goals in St. Albans, Vermont', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 25, no. 11, pp. 1428-1438.View/Download from: Publisher's site
Voinov, AA, Deluca, C, Hood, RR, Peckham, S, Sherwood, CR & Syvitski, JPM 2010, 'A community approach to earth systems modeling', Eos, vol. 91, no. 13, pp. 117-118.View/Download from: Publisher's site
Jakeman, AJ, Rizzoli, AE & Voinov, AA 2009, 'Outstanding reviewers for environmental modelling and software in 2008', Environmental Modelling and Software, vol. 24, no. 10, pp. 1137-1138.View/Download from: Publisher's site
Jakeman, AJ, Rizzoli, AE & Voinov, AA 2008, 'Outstanding reviewers for environmental modelling and software in 2007', Environmental Modelling and Software, vol. 23, no. 12, p. 1343.View/Download from: Publisher's site
Voinov, A 2008, 'Understanding and communicating sustainability: Global versus regional perspectives', Environment, Development and Sustainability, vol. 10, no. 4, pp. 487-501.View/Download from: Publisher's site
While there is no single definition of sustainability, most would agree that it implies that a system is to be maintained at a certain level, held within certain limits. Sustainability denies run-away growth, but it also precludes any substantial set backs or cuts. This sustainability path is hard to reconcile with the renewal cycle that can be observed in most living systems developing according to their natural intrinsic mechanisms. Besides, since different human dominated systems are in significantly different states and stages of development, sustaining those states assumes maintaining social disparities in perpetuity. This creates a challenge in communicating the ideas of sustainability in different regions. Systems are parts of hierarchies where systems of higher levels are made of subsystems from lower levels. Renewal in components is an important factor of adaptation and evolution. But then sustainability of a system borrows from sustainability of a supra-system and rests upon lack of sustainability in subsystems. Therefore by sustaining certain systems beyond their renewal cycle, we decrease the sustainability of larger, higher level systems. The only way to resolve this contradiction is to agree that the biosphere as a whole with humans as one of its components is the only system which sustainability we are to seek. © 2007 Springer Science+Business Media B.V.
Voinov, A & Gaddis, EJB 2008, 'Lessons for successful participatory watershed modeling: A perspective from modeling practitioners', ECOLOGICAL MODELLING, vol. 216, no. 2, pp. 197-207.View/Download from: Publisher's site
Zharova, N, Sfriso, A, Pavoni, B & Voinov, A 2008, 'Analysis of annual fluctuations of C. nodosa in the Venice lagoon: Modeling approach', ECOLOGICAL MODELLING, vol. 216, no. 2, pp. 134-144.View/Download from: Publisher's site
Gaddis, EJB, Vladich, H & Voinov, A 2007, 'Participatory modeling and the dilemma of diffuse nitrogen management in a residential watershed', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 22, no. 5, pp. 619-629.View/Download from: Publisher's site
Huntington, HP, Hamilton, LC, Nicolson, C, Brunner, R, Lynch, A, Ogilvie, AEJ & Voinov, A 2007, 'Toward understanding the human dimensions of the rapidly changing arctic system: insights and approaches from five HARC projects', REGIONAL ENVIRONMENTAL CHANGE, vol. 7, no. 4, pp. 173-186.View/Download from: Publisher's site
Voinov, A, Costanza, R, Fitz, C & Maxwell, T 2007, 'Patuxent landscape model: 2. Model development - Nutrients, plants, and detritus', WATER RESOURCES, vol. 34, no. 3, pp. 268-276.View/Download from: Publisher's site
Argent, RM, Voinov, A, Maxwell, T, Cuddy, SM, Rahman, JM, Seaton, S, Vertessy, RA & Braddock, RD 2006, 'Comparing modelling frameworks - A workshop approach', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 21, no. 7, pp. 895-910.View/Download from: Publisher's site
Moiseenko, TI, Voinov, AA, Megorsky, VV, Gashkina, NA, Kudriavtseva, LP, Vandish, OI, Sharov, AN, Sharova, Y & Koroleva, IN 2006, 'Ecosystem and human health assessment to define environmental management strategies: The case of long-term human impacts on an Arctic lake', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 369, no. 1-3, pp. 1-20.View/Download from: Publisher's site
Rizzo, DM, Mouser, PJ, Whitney, DH, Mark, CD, Magarey, RD & Voinov, AA 2006, 'The comparison of four dynamic systems-based software packages: Translation and sensitivity analysis', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 21, no. 10, pp. 1491-1502.View/Download from: Publisher's site
Kuhnert, M, Voinov, A & Seppelt, R 2005, 'Comparing raster map comparison algorithms for spatial modeling and analysis', PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, vol. 71, no. 8, pp. 975-984.View/Download from: Publisher's site
Voinov, A, Bromley, L, Kirk, E, Korchak, A, Farley, J, Moiseenko, T, Krasovskaya, T, Makarova, Z, Megorski, V, Selin, V, Kharitonova, G & Edson, R 2004, 'Understanding human and ecosystem dynamics in the Kola Arctic: A participatory integrated study', ARCTIC, vol. 57, no. 4, pp. 375-388.
Rizzoli, A, Voinov, A & Jakeman, A 2003, 'Introducing EMS ShortComs - presenting results, making a difference: is there a better way to publish in the 21st century?', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 18, no. 7, pp. 595-596.View/Download from: Publisher's site
Costanza, R, Voinov, A, Boumans, R, Maxwell, T, Villa, F, Wainger, L & Voinov, H 2002, 'Integrated ecological economic modeling of the Patuxent River watershed, Maryland', ECOLOGICAL MONOGRAPHS, vol. 72, no. 2, pp. 203-231.View/Download from: Publisher's site
Costanza, R, Voinov, A, Boumans, R, Maxwell, T, Villa, F, Wainger, L & Voinov, H 2002, 'Integrated Ecological Economic Modeling of the Patuxent River Watershed, Maryland', Ecological Monographs, vol. 72, no. 2, pp. 203-203.View/Download from: Publisher's site
Seppelt, R & Voinov, A 2002, 'Optimization methodology for land use patterns using spatially explicit landscape models', ECOLOGICAL MODELLING, vol. 151, no. 2-3, pp. 125-142.View/Download from: Publisher's site
Voinov, A 2002, 'Teaching and learning ecological modeling over the web: a collaborative approach', CONSERVATION ECOLOGY, vol. 6, no. 1.
Boumans, RM, Villa, F, Costanza, R, Voinov, A, Voinov, H & Maxwell, T 2001, 'Non-spatial calibrations of a general unit model for ecosystem simulations', ECOLOGICAL MODELLING, vol. 146, no. 1-3, pp. 17-32.View/Download from: Publisher's site
Zharova, N, Sfriso, A, Voinov, A & Pavoni, B 2001, 'A simulation model for the annual fluctuation of Zostera marina biomass in the Venice lagoon', AQUATIC BOTANY, vol. 70, no. 2, pp. 135-150.View/Download from: Publisher's site
Voinov, A, Costanza, R, Wainger, L, Boumans, R, Villa, F, Maxwell, T & Voinov, H 1999, 'Patuxent landscape model: integrated ecological economic modeling of a watershed', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 14, no. 5, pp. 473-491.View/Download from: Publisher's site
Voinov, AA, Voinov, H & Costanza, R 1999, 'Surface water flow in landscape models: 2. Patuxent watershed case study', ECOLOGICAL MODELLING, vol. 119, no. 2-3, pp. 211-230.View/Download from: Publisher's site
Voinov, AA 1998, 'Paradoxes of sustainability', ZHURNAL OBSHCHEI BIOLOGII, vol. 59, no. 2, pp. 209-218.
Voinov, A, Fitz, C & Costanza, R 1997, 'Landscape model provides management tool', GIS World, vol. 10, no. 3, pp. 48-50.
Smith, CL & Voinov, A 1996, 'Resource management: Can it sustain pacific northwest fishery and forest systems?', Ecosystem Health, vol. 2, no. 2, pp. 156-158.
The relative effectiveness of resource management regimes is widely discussed. Sustainability and ecosystem health are two dimensions upon which the effect of management is judged. Evaluating resource management requires long time spans. We look at the impact of management on fish and forest resources by taking a life cycle approach to the exploitation of natural capital. Russian ethnographer Gumilev (1990) describes the process of how human systems go through a set of phases that parallel the birth, growth, maturity, and death stages of the life cycle. The process of adaptive renewal proposed by Holling (1992), too, has life cycle characteristics. The primary variables used to represent the phases of the renewal cycle are the amount of capital that is accumulated and the connectedness in the system. We apply the renewal cycle to a fishery and forestry example in the U.S. Pacific Northwest to see how management regimes alter the capital stock of these systems. In these two examples, 90% of the natural capital is lost or projected to be lost over a century and a half of exploitation. The management regime in both cases evolves toward greater inflexibility. Based on these two examples, resource management does not seem to lead to sustainability or ecosystem health. © 1996 Blackwell Science, Inc.
Voinov, A, Cibuzar, A & Nawrocki, T 1994, 'Sustainable development on a watershed scale russian case study—pronya river', Lake and Reservoir Management, vol. 9, no. 1, pp. 46-50.View/Download from: Publisher's site
A user-friendly package for simulations of wind-induced currents and dispersion of non-conservative pollutants in aquatic media is discussed. The hydrodynamics are modelled by a stationary shallow-water approximation of the Eckman type. The generated patterns of currents are fed into the 2-D advection-diffusion model to calculate the concentration fields of a pollutant coming with inflows or injected directly into the water body. The package runs on IBM compatible PCs with a mathcoprocessor being very desirable. The package is simple to learn. It may be useful for preliminary qualitative analysis of water pollution, as well as for education and demonstration purposes. © 1993 Elsevier Science Publishers Ltd.
VOINOV, AA & ZHAROVA, NA 1991, 'AUTOMATIZATION SYSTEM FOR SIMULATION OF AQUATIC BODIES - SPATIALLY HETEROGENEOUS ECOSYSTEMS', ZHURNAL OBSHCHEI BIOLOGII, vol. 52, no. 6, pp. 868-884.
VOINOV, AA & SVIREZHEV, YM 1981, 'STABILITY OF A SIMPLE FRESH-WATER ECOSYSTEM MODEL', ZHURNAL OBSHCHEI BIOLOGII, vol. 42, no. 6, pp. 936-940.
Voinov, A, Castilla Rho, J, Perez, P & Kenny, D 2020, 'Integrated Ecological Economic Modeling: What is it good for?' in Creating Wellbeing Futures: A Research and Action Agenda for Ecological Economics.
Climate disruption, overpopulation, biodiversity loss, the threats of financial collapse, large-scale damage to our natural and social environments and eroding democracy are all becoming critically important concerns. The editors of this timely book assert that these problems are not separate, but all stem from our overreliance on an out-dated approach to economics that puts growth of production and consumption above all else.
Ecological economics can help create the future that most people want – a future that is prosperous, just, equitable and sustainable. This forward-thinking book lays out an alternative approach that places the sustainable wellbeing of humans and the rest of nature as the overarching goal. Each of the book’s chapters, written by a diverse collection of scholars and practitioners, outlines a research and action agenda for how this future can look and possible actions for its realization.
Sustainable Wellbeing Futures will be of value to academics and students researching environmental and ecological economics, as well as individuals interested in gaining a greater understanding of the concept of a wellbeing future and how we might act to achieve it.
Beydoun, G, Voinov, A & Sugumaran, V 2018, 'Beyond Service-Oriented Architectures' in Sugumaran, V (ed), Developments and Trends in Intelligent Technologies and Smart Systems, IGI Global, USA, pp. 16-25.View/Download from: Publisher's site
Predictions for Service Oriented Architectures (SOA) to deliver transformational results to the role and capabilities of IT for businesses have fallen short. Unforeseen challenges have often emerged in SOA adoption. They fall into two categories: technical issues stemming from service components reuse difficulties and organizational issues stemming from inadequate support or understanding of what is required from the executive management in an organization to facilitate the technical rollout. This paper first explores and analyses the hindrances to the full exploitation of SOA. It then proposes an alternative service delivery approach that is based on even a higher degree of loose coupling than SOA. The approach promotes knowledge services and agent-based support for integration and identification of services. To support the arguments, this chapter sketches as a proof of concept the operationalization of such a service delivery system in disaster management.
© 2019 Elsevier B.V. Diagrams are probably the next most widely used type of models after mental and verbal models. Intuitively when presenting a model we tend to start drawing diagrams to explain the assumptions and simplifications made. With the advent of new software tools it becomes easier to use the computer to design these conceptual diagrams, especially since these software packages in many cases can convert the diagrams into computer code and generate numerical models for further analysis. Most of the systems dynamics software (such as Stella, Madonna, Powersim, Simile and others) can be readily used to put together conceptual models and flow diagrams. More advanced tools are based on the UML (Unified Modeling Language) approach and can generate computer code in Java, PHP, C++, Python, and other languages.
© 2019 Elsevier B.V. Parameters are an important component of any model. In the most general sense parameters drive the system that we observe. When we model a system we try to reproduce its behavior in terms of certain values, which we observe and which we call state variables, or output variables. These are the outputs from the model. These outputs are determined by inputs that go into the model, and by the model internal organization, wiring. Generally, any independent quantity that is used to describe and run a model can be called a parameter. Parameterization is the process of finding the right parameter values for a model. The values for parameters are either found by measuring them in experiments or they are chosen such that the model output matches the observed system behavior as much as possible. This process is also known as calibration.
© 2019 Elsevier B.V. Formulating the model equations and making them run is just the beginning of the modeling process. First you need to make the model output represent data as well as possible. This can be achieved in part by tweaking the parameters of the model. This is the process of model calibration. Then we need to check that the model really does what it was designed to do. This model testing may assume various procedures, and stages, some of which are called validation and verification. For example, we may want to double check that the model is based on correct assumptions, that the code has no bugs, and that the output is properly presented and interpreted. This would be the model verification stage. Or we may want to run the model on an independent set of input data and see how it performs then. That will be called the validation process in some cases. There is still some confusion on terminology and sometimes the words validation and verification are used interchangeably. In any case these are extremely important stages of model analysis that are required to prove the quality of the model, however neither of the formal methods of model analysis should be overestimated in determining the model usability. After all, the model is good as long as it helps achieve the goals of the project. The overall model performance is more important than how well it did on individual tests and comparisons.
© 2019 Elsevier B.V. In the modular approach we do not intend to design a unique general model. Instead, the goal is to offer a framework that can be easily extended and is flexible to be modified. A module that performs best in one case may not be sufficient in another. The goals and scale of a particular study may require a completely different set of modules that will be invoked and further translated into a working model. There is a certain disparity between the software developer and the researcher views upon models and modules. For a software developer, a module is an entity, a black box, which should be as independent as possible, and should be as easy as possible to combine with other modules. This is especially true for the federation approach to modular modeling and is well demonstrated by the web-based modeling systems. The utility of such applications may be marginal from the research viewpoint.
© 2017 Elsevier Inc. All rights reserved. Sustainability is a wicked problem, which is hard to define in a unique way. It cannot be solved and should be treated in a participatory approach involving as many stakeholders in the process as possible. Participatory modeling is an efficient method for dealing with wicked problems. It involves stakeholders in an open-ended process of shared learning and can be essential for developing sustainable technologies. While there may be various levels of participation, the process evolves around a model of the system at stake. The model is built in interaction with the stakeholders; it provides formalism to synchronize stakeholder thinking and knowledge about the system and to move toward consensus about the possible decision making.
Voinov, AA, Glazyrina, IP, Pavoni, B & Zharova, NA 2017, 'Environmental management in uncertain economies' in Growing Pains: Environmental Management in Developing Countries, pp. 148-159.
Voinov, AA, Glazyrina, IP, Pavoni, B & Zharova, NA 2017, 'Environmental management, crime and information: A Russian case study' in Growing Pains: Environmental Management in Developing Countries, pp. 117-129.
Voinov, A & Gaddis, EB 2016, 'Values in participatory modeling: Theory and practice' in Environmental Modeling with Stakeholders: Theory, Methods, and Applications, pp. 47-63.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2017. In this chapter, we reflect on some of our experiences as modelers engaged in participatory modeling by outlining some of the lessons we have learned. Specifically, we outline best practices for modelers seeking to engage in the process, identify trade-offs in evaluating model results, and present a call for future research to explicitly incorporate values in the process.
Hasselmann, K & Voinov, A 2013, 'The actor-driven dynamics of decarbonization' in Reframing the Problem of Climate Change: From Zero Sum Game to Win-Win Solutions, pp. 131-159.View/Download from: Publisher's site
© 2008 Elsevier B.V. All rights reserved. Recent focus on ecological management that is adaptive, participatory, and collaborative has given rise to new approaches to scientific research and the incorporation of stakeholder knowledge and values into scientific models used for decision making. Participatory modeling incorporates input from stakeholders and decision makers into scientific models that support decisions involving complex ecological questions. The process supports democratic principles, is educational, integrates social and natural processes, can legitimate a local decision-making process, and can lead participants to be instrumental in implementing an agreed agenda. Modeling tools employed include indices, statistical models, spatial models, temporal models, and spatially explicit dynamic models. Stakeholder participants engage in the modeling research process in the form of model selection and development, data collection and integration, scenario development, interpretation of results, and development of policy alternatives. Variations of participatory modeling are distinguished by who initiates the process, how stakeholders are enlisted and engaged in the process, the breadth of research questions addressed, and the mechanism by which modeling results are incorporated into decision making. Criteria of successful participatory modeling include scientific credibility, objectivity, transparency, understanding uncertainty, model adaptability, representative involvement, incorporation of stakeholder knowledge, and influence on decision making.
© 2008 Elsevier B.V. All rights reserved. Diagrams are probably the next most widely used type of models after mental and verbal models. Intuitively when presenting a model we tend to start drawing diagrams to explain the assumptions and simplifications made. With the advent of new software tools, it becomes easier to use the computer to design these conceptual diagrams, especially since these software packages in many cases can convert the diagrams into computer code and generate numerical models for further analysis. Most of the system dynamics software (such as Stella, Madonna, Powersim, Simile, and others) can be readily used to put together conceptual models and flow diagrams. More advanced tools are based on the Unified Modeling Language (UML) approach and can generate computer code in Java, PHP, C++, Python, and other languages.
© 2008 Elsevier B.V. All rights reserved. Parameters are an important component of any model. In the most general sense, parameters drive the system that we observe. When we model a system we try to reproduce its behavior in terms of certain values, which we observe and which we call state variables, or output variables. These are the outputs from the model. These outputs are determined by inputs that go into the model, and by the model internal organization, the wiring. Generally, any quantity that is used to describe and run a model can be called a parameter.
© 2008 Elsevier B.V. All rights reserved. Formulating the model equations and making them run is just the beginning of the modeling process. First you need to make the model output represent data as well as possible. This can be achieved in part by tweaking the parameters of the model. This is the process of model calibration. Then we need to check that the model really does what it was designed to do. This model testing may assume various procedures, and stages, some of which are called validation and verification. For example, we may want to double check that the model is based on correct assumptions, that the code has no bugs, and that the output is properly presented and interpreted. This would be the model verification stage; or we may want to run the model on an independent set of input data and see how it performs then, which will be called the validation process in some cases. There is still some confusion on terminology and sometimes the words validation and verification are used interchangeably. In any case these are extremely important stages of model analysis that are required to prove the quality of the model; however, neither of the formal methods of model analysis should be overestimated in determining the model usability. After all, the model is good as long as it helps achieve the goals of the project. The overall model performance is more important than how well it did on individual tests and comparisons.
© 2008 Elsevier B.V. All rights reserved. Most of the ecological modeling today is based on computer numerical models. Therefore you cannot imagine modeling without software. There are numerous software tools that can be useful for ecological modeling. We can distinguish between modeling languages, extendable modeling systems, particular modeling systems and models, and extendable models. When deciding which software tools are most appropriate for particular modeling tasks it is important to consider the objectives of the modeling effort, the skills of the modeling team, the budget constraints, and the project timeline.
© 2008 Elsevier B.V. All rights reserved. In the modular approach we do not intend to design a unique general model. Instead, the goal is to offer a framework that can be easily extended and is flexible to be modified. A module that performs best in one case may not be sufficient in another. The goals and scale of a particular study may require a completely different set of modules that will be invoked and further translated into a working model. There is a certain disparity between the software developer and the researcher views upon models and modules. For a software developer, a module is an entity, a black box, which should be as independent as possible, and should be as easy as possible to combine with other modules. This is especially true for the federated approach to modular modeling and is well demonstrated by the web-based modeling systems. For a researcher a model is predominantly a tool for understanding the system. By plugging together a number of black boxes, for which specifics and behavior is obscure and hardly understood, we do not significantly increase our knowledge about the system. The results generated are difficult to interpret when there is not enough understanding of the processes that are actually modeled. The decomposition of such systems requires careful analysis of spatial and temporal scales of processes considered and is very closely related to specific goals of the model built. In this context the modular approach can be useful if the focus is shifted from reusability and 'plug-and-play', to transparency, analysis and hierarchical description of various processes and system components. With the modules being transparent and open for experiment and analysis, the researcher can better understand the specifics of the model formalism that is inherited. It is then easier to decide whether a module is suitable or if it should be modified and tuned to the specific goals of a particular study. Modular systems thinking is the way to achieve...
Giabbanelli, PJ, Castellani, B, Voinov, AA & Törnberg, P 2019, 'Ideal, best, and emerging practices in creating artificial societies', Simulation Series, Spring Simulation Conference, IEEE, Tucson, AZ, USA.View/Download from: Publisher's site
© 2019 Society for Modeling & Simulation International (SCS). Artificial societies used to guide and evaluate policies should be built by following “best practices”. However, this goal may be challenged by the complexity of artificial societies and the interdependence of their sub-systems (e.g., built environment, social norms). We created a list of seven practices based on simulation methods, specific aspects of quantitative individual models, and data-driven modeling. By evaluating published models for public health with respect to these ideal practices, we noted significant gaps between current and ideal practices on key items such as replicability and uncertainty. We outlined opportunities to address such gaps, such as integrative models and advances in the computational machinery used to build simulations.
Glynn, P, Shapiro, CD & Voinov, A 2018, 'Records of Engagement and Decision Tracking for Adaptive Management and Policy Development', 2018 IEEE INTERNATIONAL SYMPOSIUM ON TECHNOLOGY AND SOCIETY (IEEE ISTAS 2018), International Symposium on Technology and Society, IEEE, George Washington Univ, Sch Engn & Appl Sci, Washington, DC, pp. 81-87.View/Download from: Publisher's site
Anjum, M, Voinov, A, Castilla Rho, J & Pileggi, SF 2019, 'Understanding mental models through a moderated framework for serious discussion', 23rd International Congress on Modelling and Simulation, Canberra.
Dupen, P, Castilla Rho, J & Voinov, A 2019, 'Model-enabled community engagement in a mining approval process', 23rd International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, Canberra.
Participatory Modelling (PM) can help regulators and communities move toward more positive futures by making stakeholder engagement more meaningful, efficient, and informative (Sterling et al, 2019). We are partnering with industry and agency groups to drive two major innovations in this space: (1) developing a standardised, web-enabled reporting structure for PM processes, and (2) using “management flight simulators”. We are developing these tools to provide an objective, transparent and flexible process where a diverse group of stakeholders can rapidly understand and meaningfully contribute their local knowledge to an early-stage mining or energy development proposals. There are many types and variations of PM, and their value has been amply demonstrated in natural resource management and protection contexts (Voinov et al, 2018). One of the difficulties limiting a wider adoption of PM is the lack of consistent reporting about the engagement processes to enable others to avoid pitfalls and replicate successes (Glynn et al, 2017). This issue led Glynn et al (2018) to call for a new type of record to document PM processes and outcomes, which they term Records of Engagement (RoE). We are responding to this challenge within an important real-world application, through which we explore how tools such as discourse analysis, mental model maps and data visualisation can be combined to create RoEs that capture and communicate the complex information and relationships uncovered during a given PM case-study. Using the experience gathered in this application, we will develop an adaptable RoE template and guidelines to encourage the adoption of RoEs in future collaborative modelling projects. Effective and useful RoE’s require an electronic and highly adaptable format, and creatively apply information visualisation tools to communicate complex information, trends and ideas. Fundamental beliefs of the stakeholders and engagement leaders such as their world view, knowledge abo...
Kenny, D, Voinov, A & Castilla Rho, J 2019, 'Persuasion, influence, and participatory modelling in socio-ecological systems: A framework for action', 23rd International Congress on Modelling and Simulation, Canberra.
Taghikhah, F, Raffe, WL, Mitri, G, Toit, SD, Voinov, A & Garcia, JA 2019, 'Last Island: Exploring Transitions to Sustainable Futures through Play', ACM International Conference Proceeding Series, Australasian Computer Science Week Multiconference, ACM, Sydney, Australia, pp. 1-7.View/Download from: Publisher's site
© 2019 Association for Computing Machinery. A serious game was designed and developed with the goal of exploring potential sustainable futures and the transitions towards them. This computer-assisted board game, Last Island, which incorporates a system dynamics model into a board game's core mechanics, attempts to impart knowledge and understanding on sustainability and how an isolated society may transition to various futures to a non-expert community of players. To this end, this collaborativecompetitive game utilizes the Miniworld model which simulates three variables important for the sustainability of a society: Human population, economic production and the state of the environment. The resulting player interaction offers possibilities to collectively discover and validate potential scenarios for transitioning to a sustainable future, encouraging players to work together to balance the model output while also competing on individual objectives to be the individual winner of the game.
Bakhanova, E, Voinov, A, Raffe, W & Garcia Marin, J 2019, 'Gamification of participatory modeling in the context of sustainable development: existing and new solutions', 23rd International Congress on Modelling and Simulation, Canberra.
Serious games and gamification tools have gradually expanded their application in participatory settings, while already being widely used in the context of sustainable development in general. Their popularity is explained by their ability to create an engaging and experimental environment, which evokes critical thought, meaningful interaction between the participants and experience-based learning.
Although game design principles and tools are, to a large extent, universal, their application differs from one field to another. The simulation modelling field has a long history of using game elements to make complicated models more user-friendly and understandable for wider audiences. Management flight simulators, microworlds, policy exercises and strategic simulations are among the most common examples. Meanwhile, the urban planning field often makes use of interactive 3D maps, including the most recent advancements in applying XR technologies to make the interaction with the system more tactile and collaborative in a multi- user setting. Serious games are used in participatory projects as a supplementary approach to provoking discussion among the stakeholders and stimulating critical thinking. Gamification in the participatory modeling field is commonly used at the initial and final stages of the process or by incorporating a role playing component into the process (e.g. in companion modeling and social simulations). Based on the existing research, we have two main observations: (1) in each of the above-mentioned fields there are traditional ways of using gamification and visualization instruments and there is a lack of 'cross-pollination' between various application fields in terms of choosing gamification tools, (2) gamification tools are commonly used at one or two stages of participatory modeling process but rarely over the entire process of participatory modeling. We suggest that by introducing more gamification elements throughout the whole PM process we can ...
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.
Argent, RM, Sojda, RS, Guipponi, C, McIntosh, B & Voinov, AA 2014, 'Best practice in conceptual modelling for environmental software development', Proceedings - 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling, iEMSs 2014, pp. 2377-2386.
Conceptual modelling is used in many fields with a varying degree of formality. In environmental applications, conceptual models are used to express relationships, explore and test ideas, check inference and causality, identify knowledge and data gaps, synchronize mental models and build consensus, and to highlight key or dominant processes. Conceptual model representations range from simple box and line interaction diagrams, through interaction representations and causal models, to complicated formal representations of the relationships between actors or entities, or between states and processes. Due to their sometimes apparent simplicity, the development and use of a conceptual model is often an attractive option when tackling an environmental problem where the system is either not well understood, or where the understanding of the system is not shared amongst stakeholders. However, we have experienced many examples where conceptual modelling has failed to live up to the promises of managing complexity and aiding decision making. This paper explores the development and application of conceptual modelling to environmental problems, and identifies a range of best practices for environmental scientists and managers that include considerations of stakeholder participation, model development and representation, integration of different and disparate conceptual models, model maturation, testing, and transition to application within the problem situation.
Belete, GF & Voinov, A 2014, 'Integration of models for low carbon economy', Proceedings - 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling, iEMSs 2014, pp. 1897-1904.
Designing the transition to low carbon economy is a very complex task that touches upon a wide variety of climate-energy-economic systems. We need to explore the various possible climate mitigation scenarios at different temporal and spatial scales. However, due to the diversity of the involved disciplines it is difficult to find one complete and unified modeling approach that works equally well in all those different domains. As a result we have to select 'appropriate' models, which represent only specific aspects of the scenarios and assemble them 'coherently'. In this research we have identified some challenges in integrating multidisciplinary models; and have developed a conceptual design for a multidisciplinary model integration framework that can harmonize the technical, semantic, and dataset aspects of interoperability.
Betete, GF, Voinov, A & Holst, N 2014, 'An architecture for integration of multidisciplinary models', Proceedings - 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling, iEMSs 2014, pp. 1251-1259.
Integrating multidisciplinary models requires linking models: that may operate at different temporal and spatial scales; developed using different methodologies, tools and techniques; different levels of complexity; calibrated for different ranges of inputs and outputs, etc. On the other hand, integration of models requires us to address technical, semantic, and dataset aspects of interoperability. So we need a genuine techniques that enable us to integrate various domain specific models for interdisciplinary study. In this research work, we investigated best practices of System Integration, Enterprise Application Integration, and Integration Design Patterns. We developed an architecture of a multidisciplinary model integration framework that brings these three aspects of integration together. Service-oriented-based platform independent architecture that enables to establish loosely coupled dependency among various models is presented.
Kolagani, N, Ramu, P, Voinov, AA, Gali, R & Rao, CL 2014, 'Educating stakeholders about the need for water balance using a participatory modeling framework', Proceedings - 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling, iEMSs 2014, pp. 1105-1112.
Groundwater resources in many villages of the developing countries have been undergoing rapid decline over last few decades due to their unsustainable large scale exploitation. One of the main reasons for such overexploitation is the inability of village communities to collectively visualize and understand clearly the long term implications of such overexploitation. There is hence an urgent need to create awareness among stakeholders about the unsustainable nature of such overexploitation and to facilitate sustainable usage. Spatio-temporal participatory modeling of their water management practices can help greatly in promoting such awareness among village communities. These modeling tools can then be used by these stakeholders to analyze various future scenarios and plan their actions in an informed way. In this paper, a participatory modeling framework for carrying out water balance studies at village level is proposed and is demonstrated using case study of a South Indian village. Stakeholders analyzed their past actions and future plans using simulations. Classes needed for simulation and rules for their behaviour, such as what influences the decision of a farmer to sow a crop or to sink a well, were gathered through discussions with knowledgeable stakeholders. An open source Geographical Information System. 'Quantum GIS', extended using Python programming was used as the platform for carrying out and visually presenting these spatio-temporal simulations to the stakeholders.
Biesecker, M, Erion, R, Hay, CH, Henebry, GM, Johnston, CA, Kjaersgaard, JH, Shmagin, BA, Van Der Sluis, E, Capehart, W, Kirilenko, AE, Krakauer, NY, Sweeney, M & Voinov, AA 2012, 'UNCERTAINTY OF HYDROLOGIC EVENTS UNDER SOUTH DAKOTA'S CHANGING CONDITIONS: A RESEARCH AGENDA', PROCEEDINGS OF THE SOUTH DAKOTA ACADEMY OF SCIENCE, VOL 91, 97th Annual Meeting of the South-Dakota-Academy-of-Science, SOUTH DAKOTA ACAD SCIENCE, Univ S Dakota, Muenster Univ Ctr, Vermillion, SD, pp. 257-259.
ElSawah, S, Haase, D, Van Delden, H, Pierce, S, ElMahdi, A, Voinov, AA & Jakeman, AJ 2012, 'Using system dynamics for environmental modelling: Lessons learnt from six case studies', iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, pp. 1367-1374.
System dynamics modelling includes a set of conceptual and numerical methods that are used to understand the structure and behaviour of complex systems, such as socio-ecological systems. A system dynamics model represents the causal relationships, feedback loops, and delays that are thought to generate the system behaviour. System dynamics is widely used for developing environmental models and decision support systems. However, little attention has been given to reflecting on modelling exercises in terms of the utility of system dynamics, its strengths and limitations, experienced during modelling and implementation challenges. These practical lessons are useful for guiding modellers on deciding when and how to use system dynamics. The purpose of this paper is to shed some light on these issues drawing on experience from six case studies. Case studies demonstrate a wide range of applications (e.g. land use, groundwater management, urban water systems), tools, modelling approaches (e.g. coupled, integrated), and computational software.
Sojda, RS, Chen, SH, El Sawah, S, Guillaume, JHA, Jakeman, AJ, Lautenbach, S, McIntosh, BS, Rizzoli, AE, Seppelt, R, Struss, P, Voinov, AA & Volk, M 2012, 'Identifying the decision to be supported: A review of papers from environmental modelling and software', iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, pp. 73-80.
Two of the basic tenets of decision support system efforts are to help identify and structure the decisions to be supported, and to then provide analysis in how those decisions might be best made. One example from wetland management would be that wildlife biologists must decide when to draw down water levels to optimise aquatic invertebrates as food for breeding ducks. Once such a decision is identified, a system or tool to help them make that decision in the face of current and projected climate conditions could be developed. We examined a random sample of 100 papers published from 2001-2011 in Environmental Modelling and Software that used the phrase "decision support system" or "decision support tool", and which are characteristic of different sectors. In our review, 41% of the systems and tools related to the water resources sector, 34% were related to agriculture, and 22% to the conservation of fish, wildlife, and protected area management. Only 60% of the papers were deemed to be reporting on DSS. This was based on the papers reviewed not having directly identified a specific decision to be supported. We also report on the techniques that were used to identify the decisions, such as formal survey, focus group, expert opinion, or sole judgment of the author(s). The primary underlying modelling system, e.g., expert system, agent based model, Bayesian belief network, geographical information system (GIS), and the like was categorised next. Finally, since decision support typically should target some aspect of unstructured decisions, we subjectively determined to what degree this was the case. In only 23% of the papers reviewed, did the system appear to tackle unstructured decisions. This knowledge should be useful in helping workers in the field develop more effective systems and tools, especially by being exposed to the approaches in different, but related, disciplines. We propose that a standard blueprint for reporting on DSS be developed for consideration b...
Gaddis, E, Adams, C & Voinov, A 2010, 'Effective engagement of stakeholders in Total Maximum Daily Load development and implementation', Modelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010, pp. 530-538.
Total Maximum Daily Loads (TMDLs) identify the maximum amount of pollution that a water body can receive and still support its designated uses and allocates the maximum load to specific sources in the watershed. In the United States, The Clean Water Act requires public participation in the process of TMDL development. This requirement has been met through simple presentation of results at public meetings, strategic partnerships with key stakeholders, and/or to advisory committees in which stakeholders participate in critical decisions about TMDL definition and implementation. These decisions include model selection and assumptions, selection of water quality endpoints, load allocations, TMDL review, and implementation planning. In this article, we discuss the benefits and challenges of early and targeted engagement of stakeholders in TMDL development through a participatory modelling process based on our experience in Utah and Vermont.
Voinov, A 2010, ''Integronsters' and the special role of data', Modelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010, pp. 1139-1149.
In many cases model integration treats models as software components only, ignoring the fluid relationship between models and reality, the evolving nature of models and their constant modification and re-calibration. As a result, with integrated models we find increased complexity, where changes that used to impact only relatively contained models of subsystems, now propagate throughout the whole integrated system. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, when efficiency is supposed to be gained from independent development of modules. Treating models only as software in solving the integration challenge may give birth to 'integronsters' - constructs that are perfectly valid as software products but ugly and useless as models. We argue that one possible remedy is to learn to use data as modules and integrate them into the models. Then the data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a moduleindependent baseline dynamics, which is then incremented when scenarios are to be run. In this case it is not the model output that is directed into the next model input, but model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. The Chesapeake Bay Program suite of models is used to illustrate these problems and the possible solutions.
Cox, W, Cardwell, H & Voinov, A 2008, 'SVP as a short term planning tool: Preliminary results of a pilot study', World Environmental and Water Resources Congress 2008: Ahupua'a - Proceedings of the World Environmental and Water Resources Congress 2008.View/Download from: Publisher's site
Shared vision planning encompasses the basic principles of traditional planning but differs from the conventional approach in its fundamental reliance on stakeholder collaboration in a process of mutual learning and discovery as facilitated by a collaboratively developed model of the system. This collaborative approach seeks to define issues and problems, identify values and interests, and explore alternative strategies for resolving conflict and solving problems. This paper reports on the preliminary results of a pilot study initiated by the U.S. Army Corps of Engineers' Institute for Water Resources to investigate the feasibility of SVP as a planning tool in a short-term, small-scale context in support of regulatory programs and local water planning. Experience with the pilot study conducted in the James River Basin of Virginia to date suggests significant challenges to application of SVP in a short-term, small-scale planning environment. Engaging a full range of stakeholders has been hindered by restrictions imposed by the short time frame, and scale limitations created stakeholder doubt about the validity and usefulness of the process. The fact that the pilot study was presented as a limited exercise caused it to be viewed as a threat to prospects for future, larger-scale planning studies in the Basin. This experience illustrates the importance of pre-existing conditions to the success of SVP and demonstrates the special challenges that impact use of SVP in situations involving limited time and scope. © 2008 ASCE.
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.
Voinov, A, Arctur, D, Zaslavskiy, I & Ali, S 2008, 'Community-based software tools to support participatory modelling: A vision', 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. 766-774.
Environmental management depends on analysis of complex dynamics and spatial relationships of ecological and socio-economic systems. Modelling, when used to conduct such analyses, is recognized as an effective decision support tool in environmental management. Modelling conducted in a participatory fashion, involving stakeholders in various stages of model building and data processing has evolved as an efficient method for conflict resolution and decision-making. However, successful participatory modelling efforts require specific software and computer tools that are not available or accessible for stakeholders. There is a clear need for specialized modelling and data processing infrastructure that would allow comprehensive environmental simulations, based on limited computer programming skills, computer power, and data availability. We are developing a software framework of model and data modules to enable various stakeholders to tap into the recent and ongoing advances in environmental modelling, and high-quality data available on the Internet. The proposed framework would allow managers and planners to run simulations of policy scenarios and utilize state-of-the-art algorithms to develop and evaluate policy alternatives. The web-based modelling framework is based on the following components: A web-based domain-specific interface which facilitates the development, configuration, and execution of models applicable to region-specific watershed issues; A data-finder and transformer unique to the landscape modelling framework that lever-ages relevant Open GIS catalogue, RDF, and GRID resource discovery standards; A module composer that uses a module pool and guided composition of modules based on expert rules, which are either automatically acquired or input from human users, to guide the simulation-modelling process; and A semi-automatic model calibrator and verifier to deliver high quality simulation models. The framework's core components, i.e. model composition...
Voinov, A, Zaslavskiy, I, Arctur, D, Duffy, C & Seppelt, R 2008, 'Community modelling, and data-model interoperability', 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. 2035-2050.
Community modelling is a promising paradigm to develop complex evolving and adaptable modelling systems that can share methods, data and models more easily within specialized communities. Why then are cooperative modelling communities still quite rare and do not propagate easily? Why has open source been so successful for software development, yet open models are still quite exotic? One difference between software and models is that software shares some common language. Models often use very different principles, theories, and semantics. For example hydrodynamic models, ecological models, and decision support models may have limited commonalities, In these cases, the disciplinary problem being solved may be the impediment to communication and to development of effective community tools these principles to another; it becomes difficult for one model to talk to another one. Similar problems prevail in data operations, when data sets (which are also models of sort) are hard to integrate with other data. An issue of contemporary interest is how will community data and models be implemented within environmental observatories. The environmental observatory may are become the ultimate driver for advancing research with a clear need for interoperability standards and functionality. There are at least three facets to the problem: • Lack of common modelling and software tools to enable modularity and connectivity; • Insufficient community understanding or access to basic tools; • Lack of social motivation and communication skills to enable communal work and sharing environments. The goals of this paper are to explore these areas with respect to the following points: • Understand the interoperability needs of the community for data and models within a participatory and collaborative framework; • Discuss research scenarios that would benefit from interoperability and explore interoperability architecture and standards supporting these scenarios; • Explore environmental syste...
Lautenbach, S, Voinov, A & Seppelt, R 2006, 'Localization effects of land use change on hydrological models', Proceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software".
Semi-distributed hydrological models generally have the advantages of short calculation times, comparative low calibration needs and high model efficiency, but lack the ability to consider localization effects of land use change. A regionalisation of these models allows a sensitivity analysis of the localization effects. HBV-D, a conceptual hydrological model is used in this study. The regionalization for the German watershed Parthe (∼317 km 2) is coded in the framework of SME (spatial modeling environment) which allows a fast grid based regionalization of the model. Additional complexity at the finer scale is handled by downscaling of calibration parameters fromthe semi-distributedmodel by using auxiliary information (soil, relief). This allows a better representation of the heterogeneity in the watersheds without the need of grappling with hundreds of calibration parameters. A Monte-Carlo analysis is used to simulate the effects of the different spatial pattern of land use changes on discharge. This allows a better forecasting of land use change effects and can be used to generate uncertainty estimates for existing semi-distributed models. We focus here on the following major questions: 1. how can we downscale the calibration parameters from the semi-distributed model to the distributed model, 2. how do downscaling approaches differ, 3. how does land use composition and configuration influence discharge and 4. how do these results depend on catchment characteristics?
McIntosh, BS, Voinov, A, Smith, C & Giupponi, C 2006, 'Bridging the gaps between design and use: Developing appropriate tools for environmental management and policy', Proceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software".
Integrated assessment models, decision support systems (DSS) and Geographic Information Systems (GIS) are examples of a growing number of computer-based tools designed to provide scientific decision and information support to people within environmental management and policy organizations. It is recognized that end-user organizations are often not as receptive to using such tools as desired but that little research has been done to uncover and understand the reasons why. As part of the process to understand what tools are used and why, and conversely what tools are not used and why, this paper presents some views on the issues involved. No claim is made regarding the completeness of the issues covered, rather the purpose of the paper is to instigate discussion about how to improve tool design practices in such a way as to benefit environmental management and policy. Conflict between the aims of tool designers to develop usable and useful tools which also contain some degree of technological innovation is highlighted as a potential cause of problems. A call for clarity of purpose in tool design is made to make it clearer both to the designer and the client organization what the main aim of the design process is as a means of uncovering mismatches in expectation. Further, a call is made for designers to move from a technology-push to a demand-pull perspective as a necessary step towards designing more appropriate tools. A range of social dimensions of relevance to tool design are also discussed including the need to involve clients and stakeholders early in the design process, whether a model should present a simple and engaging story and to what extent good science can be implemented through the use of computer models, and the need to build trust between tool designers and tool users as a necessary part of making tools useful.
Voinov, A, Hood, RR & Daues, JD 2006, 'Building a community modeling and information sharing culture', Proceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software".
By copying information from sources and distributing it to new destinations we do not lose information at the sources. Nevertheless, exchange of information is still restricted by patent law, as well as by institutional, cultural and traditional hurdles that create protective barriers hindering the free flow of this valuable commodity. We believe that one of the greatest challenges we face in creating a new research paradigm will be building the community modeling and information sharing culture. How do we get engineers and scientists to put aside their traditional modes of doing business? How do we provide the incentives that will be required to make these changes happen? How do we get our colleagues to see that the benefits of sharing resources far outweigh the costs? We argue that timely sharing of data and information is not only in the best interest of the research community, but that it is also in the best interest of the scientist who is doing the sharing.
Voinov, AA 2005, 'Understanding and communicating sustainability: Global versus regional', AIChE Annual Meeting, Conference Proceedings, p. 12970.
Sustainability in its present connotation is a Western concept that has emerged in the West and largely epresents the attitudes of the developed world. Systems in the developing countries are in transition that is further promoted by globalization. They are foreign to sustainability because by definition they are apt to change rather than maintenance, they are either in the release or renewal stages that hardly anybody wishes to sustain, or have just entered the growth stage. Sustainability is enticing for the developed economic systems, which have reached the conservation phase, and would rather endure this stage. In communicating the knowledge of sustainability it is essential to adapt to the local specifics and redefine sustainability accordingly. Local sustainability can be ensured only by borrowing energy, resources and adaptive potential from outside of the system, or by decreasing the sustainability of the global system. Sustainability of a subsystem is achieved at the expense of the supersystem or other subsystems. Therefore institutions that are to maintain life support systems on this planet need to emphasize global priorities and test policies and strategies against the sustainability of the biosphere, rather than regional or local sustainability. We illustrate these ideas with our findings in the Kola Peninsula Russia) and in the Mekong watershed.
Voinov, A, Fitz, C, Boumans, R & Costanza, R 2001, 'Modular ecosystem modeling', ENVIRONMENTAL MODELLING & SOFTWARE, 51st Annual Meeting of the Congress-of-Neurological-Surgeons, ELSEVIER SCI LTD, SAN DIEGO, CA, pp. 285-304.View/Download from: Publisher's site
Seppelt, R & Voinov, A 2003, 'Optimization methodology for land use patterns - evaluation based on multiscale habitat pattern comparison', ECOLOGICAL MODELLING, ELSEVIER SCIENCE BV, pp. 217-231.View/Download from: Publisher's site
Parker, P, Letcher, R, Jakeman, A, Beck, MB, Harris, G, Argent, RM, Hare, M, Pahl-Wostl, C, Voinov, A, Janssen, M, Sullivan, P, Scoccimarro, M, Friend, A, Sonnenshein, M, BAker, D, Matejicek, L, Odulaja, D, Deadman, P, Lim, K, Larocque, G, Tarikhi, P, Fletcher, C, Put, A, Maxwell, T, Charles, A, Breeze, H, Nakatani, N, Mudgal, S, Naito, W, Osidele, O, Eriksson, I, Kautsky, U, Kautsky, E, Naeslund, B, Kumblad, L, Park, R, Maltagliati, S, Girardin, P, Rizzoli, A, Mauriello, D, Hoch, R, Pelletier, D, Reilly, J, Olafsdottir, R & Bin, S 2002, 'Progress in integrated assessment and modelling', ENVIRONMENTAL MODELLING & SOFTWARE, ELSEVIER SCI LTD, pp. 209-217.View/Download from: Publisher's site
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