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Tools to think with? Towards understanding the use of computer-based support tools in policy relevant research
Abstract
As environmental science has broadened to address policy concerns, there has been an effort to transfer the perceived benefits of formal modelling to these new areas through the creation of computer-based support tools. However, a number of poorly addressed issues pose barriers to the uptake of such tools. These issues are discussed to argue that the current support tool research agenda is too focussed on hard, technical concerns and that greater emphasis needs to be given to soft, contextual aspects of design and use. To counter these deficiencies we propose a framework for research based upon the concepts of innovation and receptivity. Three different sources of innovation relevant to support tools and end-user receptivity are identified. We contend that new technologies and new techniques for manipulating them have to be translated into the pre-existing knowledge and working practices of user communities before they can be effectively employed. To illustrate the proposed framework, the paper explores the impact of one innovation source on receptivity within the context of a research project developing and applying support tool technology. The need to better understand the dimensions of innovation and how they relate to the processes that determine user receptivity to support tools is emphasised.
... Decision support systems are computer-based information systems designed to support policymakers and practitioners in decision-making processes. They help users access, interpret and understand information from data, analyses and models, and help them in identification of possible actions (Arnott and Pervan, 2005;McIntosh et al., 2007;Van Delden et al., 2011;Santoro et al., 2013;Rose et al., 2016). In the years since research into decision support systems emerged it has developed a number of different sub-fields (see Pervan, 2005, 2008). ...
... Users of SDSS need solutions and certainty, while developers and scientists offer probabilities and multiple scenarios. Moreover, while scientists work towards problemsolving, SDSS users would benefit more from problem exploration, reflection and critical exchange (see also McIntosh et al., 2007;Ramsey, 2009). McIntosh et al. (2007) argue for a shift in focus away from the technical features of SDSS (e.g., system functionalities, coupling opportunities with GIS and integrating models), to the soft contextual aspects of receptivity, design and use. ...
... Moreover, while scientists work towards problemsolving, SDSS users would benefit more from problem exploration, reflection and critical exchange (see also McIntosh et al., 2007;Ramsey, 2009). McIntosh et al. (2007) argue for a shift in focus away from the technical features of SDSS (e.g., system functionalities, coupling opportunities with GIS and integrating models), to the soft contextual aspects of receptivity, design and use. They point to the need to understand what users require, and can utilise, and also how SDSS fit into organisational structures and support users in their tasks, which often involve collaboration with others and integration of the knowledge that others have. ...
Spatial decision support systems (SDSS) represent a step forward in efforts to account for the spatial dimension in environmental decision-making. The aim of SDSS is to help policymakers and practitioners access, interpret and understand information from data, analyses and models, and guide them in identifying possible actions during a decision-making process. Researchers, however, report difficulties in up-take of SDSS by the intended users. Some suggest that this field would benefit from investigation of the social aspects involved in SDSS design, development, testing and use. Borrowing insights from the literature on science-policy interactions, we explore two key social processes: knowledge integration and learning. Using a sample of 36 scientific papers concerning SDSS in relation to environmental issues, we surveyed whether and how the selected papers reported on knowledge integration and learning. We found that while many of the papers mentioned communication and collaboration with prospective user groups or stakeholders, this was seldom underpinned by a coherent methodology for enabling knowledge integration and learning to surface. This appears to have hindered SDSS development and later adoption by intended users.
... Decision support systems are computer-based information systems designed to support policymakers and practitioners in decision-making processes. They help users access, interpret and understand information from data, analyses and models, and help them in identification of possible actions (Arnott and Pervan, 2005;McIntosh et al., 2007;Van Delden et al., 2011;Santoro et al., 2013;Rose et al., 2016). In the years since research into decision support systems emerged it has developed a number of different sub-fields (see Pervan, 2005, 2008). ...
... Users of SDSS need solutions and certainty, while developers and scientists offer probabilities and multiple scenarios. Moreover, while scientists work towards problemsolving, SDSS users would benefit more from problem exploration, reflection and critical exchange (see also McIntosh et al., 2007;Ramsey, 2009). McIntosh et al. (2007) argue for a shift in focus away from the technical features of SDSS (e.g., system functionalities, coupling opportunities with GIS and integrating models), to the soft contextual aspects of receptivity, design and use. ...
... Moreover, while scientists work towards problemsolving, SDSS users would benefit more from problem exploration, reflection and critical exchange (see also McIntosh et al., 2007;Ramsey, 2009). McIntosh et al. (2007) argue for a shift in focus away from the technical features of SDSS (e.g., system functionalities, coupling opportunities with GIS and integrating models), to the soft contextual aspects of receptivity, design and use. They point to the need to understand what users require, and can utilise, and also how SDSS fit into organisational structures and support users in their tasks, which often involve collaboration with others and integration of the knowledge that others have. ...
Spatial decision support systems (SDSS) represent a step forward in efforts to account for the spatial dimension in environmental decision-making. The aim of SDSS is to help policymakers and practitioners access, interpret and understand information from data, analyses and models, and guide them in identifying possible actions during a decision-making process. Researchers, however, report difficulties in up-take of SDSS by the intended users. Some suggest that this field would benefit from investigation of the social aspects involved in SDSS design, development, testing and use. Borrowing insights from the literature on science-policy interactions, we explore two key social processes: knowledge integration and learning. Using a sample of 36 scientific papers concerning SDSS in relation to environmental issues, we surveyed whether and how the selected papers reported on knowledge integration and learning. We found that while many of the papers mentioned communication and collaboration with prospective user groups or stakeholders, this was seldom underpinned by a coherent methodology for enabling knowledge integration and learning to surface. This appears to have hindered SDSS development and later adoption by intended users.
... The use of models in policy has been said to fall short of its potential (Mcintosh et al. 2008;Turnpenny et al. 2009). Moreover, research on the broader subject of model use is argued to be in its infancy (McIntosh et al. 2007) and some contend that this research field has failed to progress over the last few decades (Syme et al. 2011). McIntosh et al. (2007 claim that the research agenda has tended to focus on the technical aspects of models, but has largely ignored other criteria that influence their use. ...
... The use of models in policy has been said to fall short of its potential (Mcintosh et al. 2008;Turnpenny et al. 2009). Moreover, research on the broader subject of model use is argued to be in its infancy (McIntosh et al. 2007) and some contend that this research field has failed to progress over the last few decades (Syme et al. 2011). McIntosh et al. (2007 claim that the research agenda has tended to focus on the technical aspects of models, but has largely ignored other criteria that influence their use. Previous research has suggested a variety of such criteria that can drive or constrain model acceptance, for instance, userfriendliness (Fildes et al. 2006;van Delden et al. 2011), inter ...
... In the context of this paper, such models sit within the third category defined by Nilsson et al. (2008). McIntosh et al. (2007) assert that many support-tool technologies contain models. More specifically, many decision support systems, planning support systems, and management information systems utilize models as part of a more extensive software package. ...
Models are used to inform policymaking and underpin large amounts of government expenditure. Several authors have observed a discrepancy between the actual and potential use of models in government. While there have been several studies investigating model acceptance in government, it remains unclear under what conditions models are accepted. In this paper, we address the question “What criteria affect model acceptance in policymaking?”, the answer to which will contribute to the wider understanding of model use in government. We employ a thematic coding approach to identify the acceptance criteria for the eight models in our sample. Subsequently, we compare our findings with existing literature and use qualitative comparative analysis to explore what configurations of the criteria are observed in instances of model acceptance. We conclude that model acceptance is affected by a combination of the model’s characteristics, the supporting infrastructure and organizational factors.
... The application of models in policymaking is characterised by several challenges from the perspective of modellers and (policy) users [22][23][24]. These problems include the inability of models to answer specific questions that users need answered [22], low transparency of models [25], lack of trust in models by policymakers, inability of models to deliver timely support for decision-making, missing capacities in institutions to make use of complex modelling, the diversity of stakeholder involvement in the decision-making or changes, and uncertainties inherent in the policy environment [26]. ...
... The application of models in policymaking is characterised by several challenges from the perspective of modellers and (policy) users [22][23][24]. These problems include the inability of models to answer specific questions that users need answered [22], low transparency of models [25], lack of trust in models by policymakers, inability of models to deliver timely support for decision-making, missing capacities in institutions to make use of complex modelling, the diversity of stakeholder involvement in the decision-making or changes, and uncertainties inherent in the policy environment [26]. ...
As energy models become more and more powerful, they are increasingly used to support energy policymaking. Although modelling has been used for policy advice for many years, there is little knowledge about how computer-based models actually influence policymaking, and to what extent policymakers influence the modelling process. Here, we empirically investigate (i) whether, how and when models influence the policy-making process, and (ii) whether, how and when policymakers influence the design, use and results of energy modelling. We analysed modelling and policy documents and conducted thirty-two interviews with different stakeholder groups in five different European jurisdictions. We show that models are used and have an impact on policymaking, especially by assessing impacts and supporting target setting, and sometimes by exploring policy options to reach these targets. We also show that policymakers influence models and modellers, especially by affecting data and assumptions, as well as the study scope, and by deciding how the modelling results are used. Hence, energy modelling and policymaking influence each other. In their exploratory mode, models can help investigate policy options and ambitious target setting. However, models can also be instrumentalised to justify already decided policies and targets. Our study implies that greater transparency, including open-source code and open data, and transdisciplinary elements in modelling could increase model legitimacy and impact in policymaking.
... Thus, many models fall short of their potential in policymaking (McIntosh et al., 2008). Several difficulties along the policy process cause the gap between the design of models in research and their use in policy (McIntosh et al., 2008;Van Ittersum and Sterk, 2015;Capros, 2016;Savvidis et al., 2019;Chang et al., 2020), such as: inability of models to fit to end-user needs (McIntosh et al., 2007), missing transparency of models (Pfenninger et al., 2018) and lack of trust, inability of models to deliver timely support for decision-making, missing capacities in institutions to make use of complex modelling, the diversity of stakeholder involved in the decision-making or changes, and uncertainties inherent in the policy environment (Kolkman et al., 2016). ...
... In contrast and in line with previous research (McIntosh et al., 2007;Kolkman et al., 2016;Pfenninger et al., 2018), we find different reasons why models are not used and thus have no influence in policymaking. Modelling processes have been perceived as very complex, timeconsuming, resource-intensive processes, involving many actors. ...
Achieving the EU’s commitment under the Paris Agreement, the Energy Union Strategy, and the European Green Deal, requires a significant transformation of current energy systems. Renewable energy is a major component of this transition, and thus, policymakers face the challenge of making decisions about new renewables-dominated energy systems. Because real world experimentation is in large scale not possible, models can serve as ‘laboratories’ by allowing policymakers to explore different decarbonisation options in a virtual world and generate a better understanding of the policy domain.
While many energy policies are backed by computational models, we do not know exactly how and when policymakers use models, and to what extend policymakers influence modelling performed. We take these gaps as a starting point to empirically investigate the twofold processual interaction between computational energy modelling and energy policymaking. In particular we study: (i) how and when models are used in the policymaking process, and (ii) whether and how policymakers influence the design, use and results of energy modelling. Thus, we investigate how energy modelling and energy policymaking affect each other, so as to advance future model development for sounder policymaking. We conducted analyses of modelling and policy documents and performed 32 interviews with four different stakeholder groups in five different jurisdictions within Europe.
First, we show that models are used and have an impact on policymaking. Depending on countries context, we reveal that models are used to push ambitious climate and energy policy, while in other cases models are not used at all, or model results are used to justify political inaction. Furthermore, we show that modelling tools function as ‘laboratories of sustainable transition’ and support decision-making processes along the whole policy-cycle: from target setting, through policy formulation to evaluation. Models are especially useful when they are set up to directly answer specific questions that policymakers might have, i.e. to explore the implications of options that they are considering. In contrast, they are less useful when they tell policymakers what course of action, from the modeller’s perspective, would be best. We find, however, that model use is also limited, because of the complexity of modelling processes, as well as the lack of open data and open-source models. In the end, models have to compete with other information sources and concerns.
Second, we also show that policymakers influence models and modellers. Especially, ‘in-government’ and government-commissioned modelling allows policymakers to set the framework conditions of modelling performed. Even a higher level of the policymakers’ influence is reflected by deciding over how models and their results are politically used. Overall, the case studies demonstrate, energy modelling and policymaking can influence each other ‘for the good and for the bad’: they can foster radical policy changes and ambitious target setting, or they can be instrumentalised to justify inaction and radical no-change, respectively.
Based on our research, we draw implications for the development and use of models for and in policymaking: first, models should be improved to be applied as ‘sustainable energy transition laboratories’, not delivering exact numbers, but to be used for exploring questions and policy measures policymakers are having in mind. In this regard, they can be applied within stakeholder processes to catalyse the political and societal debate on what are the pros and cons of different possible energy futures. Second, open-source models and an open modelling platform can foster model understanding, trust and use, as well as deliver comparable and credible results for European and national policymaking. Importantly, all interested stakeholders from the energy sphere should have an equal and understandable access to such tools, even if they are not modelling experts and developers, because it could increase model legitimacy and impact in policymaking. To conclude, computational energy models can assist in exploring different energy futures towards Europe’s climate neutrality, but it requires ambitious modelling in line with the Paris Agreement, the Energy Union’s objectives and the European Green Deal.
... These systems were primarily developed to assess water quality improvements coming from government programs and report results to governments and the public (Jones et al., 2018) rather than raise awareness among farmers. A different approach is needed to maximise the impact of this information on farmers' awareness (McIntosh et al., 2007). Specifically, to help farmers understand the link between crop management and nitrogen pollution, water quality information systems need to be real-time, high-frequency and provide contextual information that can assist farmers interpret the data (Glavan et al., 2019;Jones et al., 2018). ...
... In developing the 16222WQ app we instituted a deliberate design methodology to maximise the impact of the software. To maximise the benefit from research investment, new technologies need to be developed in concert with stakeholders, rather than through purely top-down technology transfer approaches where the social and design aspects are left unaccounted for (Glavan et al., 2019;McIntosh et al., 2007). We developed 1622WQ, a web-based application that delivers real-time, high-frequency data on stream nitrate to farmers, as well as contextual information that facilitates the interpretation of the data. ...
Intensive agricultural practices represent a major threat to aquatic ecosystems because they impair water quality. However, this can be ameliorated by farmers improving crop management provided they are aware of their contribution to declining water quality. Water quality information systems can increase farmer awareness, but most were developed to assess water quality targets set in regulations rather than inform farmers. We developed the 1622WQ application using user-centred design principles to provide farmers with real-time information on nitrate and other contextual variables in their local creeks and rivers. The design process identified barriers to uptake of the application such as: (a) limited internet connection; (b) poor data quality; and (c) operational issues. Once these barriers were addressed, there was substantial uptake. Nevertheless, providing real-time information to farmers is only part of the solution due to legacy issues caused by a digital divide between traditional industries and those that are digitally enabled.
... Although still the subject of academic debate, there is a growing consensus the implementation gap is the result of: limited exposure to and experience with PSS, a lack of data availability and quality, low user friendliness, and the simplicity and limited usefulness of outputs (te Brömmelstroet, 2013;Vonk et al., 2005). Despite these insights, there remains a lack of empirical research focussing on practitioner perceptions regarding the causes of this WSUD planning the implementation gap (McIntosh et al., 2007). ...
... Contemporary PSS scholars point to a lack of direct engagement between PSS developers and everyday planning practices and practitioners, as the core of the implementation gap (e.g. Crona and Parker, 2012;McIntosh et al., 2007;Pelzer et al., 2015;Rodela et al., 2017;te Brömmelstroet, 2013;Vonk et al., 2005). Indeed, the failure to directly engage with PSS end-users has led to a range of weaknesses in PSS design, which ultimately act as barriers to uptake, which are summarised in Table 1. ...
The multiple benefits of adopting distributed, green stormwater technologies in the local environment are increasingly
recognised, particularly in relation to water quality, flood mitigation, amenity and aesthetics. To
advance the integration of these systems into everyday decision-making practices, Planning Support Systems
(PSS) are considered vital. Despite several PSS available to support planners and key decision-makers, their
uptake remains constrained; a phenomenon known as the ‘implementation gap’. While scholars have hypothesised
why the adoption of PSS is limited, there remains little empirical investigation regarding the reasons why.
This paper tests the hypotheses underlying the implementation gap in relation to water sensitive urban design
(WSUD) planning. Drawing on the tacit experience of 24 key urban water planning professionals in the frontrunner
city of Melbourne, Australia, in-depth semi-structured interviews were undertaken to unpack the contemporary
planning processes used and reveal characteristics leading to success and failure of PSS application.
Data analysis revealed WSUD planning professionals regard the adoption of PSS as a significant step towards
improving contemporary decision-making practices, which are regarded as opportunistic rather than strategic.
PSS use was widespread, though the type, intensity and sophistication of use varied among interview participants.
Confirming the hypotheses from planning literature, practitioners suggested PSS need to be user-friendly
and align closely to planning practice. Additionally, however, it was found that it is crucial for PSS to meet
industry conventions. Suggested improvements to current PSS included incorporating socio-economic factors
alongside biophysical and planning factors, hence the role for GIS-based suitability analysis tools. Overall, this
study provides current and future PSS-developers with critical insights regarding the type, function and characteristics
of an ‘ideal’ PSS aimed at enhancing the usefulness and uptake of PSS, and thus improve planning that
supports expediting green infrastructure implementation.
... Conceptual models may help researchers, policymakers and decision makers gain a better understanding of the complex issues involved. Models help in various ways, especially by engaging multidisciplinary stakeholders, facilitating dialogue and helping to overcome disciplinary and policy boundaries [30][31][32]. Models can also be used to find or develop actions that are effective and acceptable to those involved, and help to find ways to more accurately evaluate these actions. Predictably, issues like climate change and health inequality are difficult to frame using simple linear representations. ...
... Therefore, conceptual models can be important tools to bring together fragmentary knowledge, promoting an integrated understanding of, and directing action towards, complex issues. Conceptual models can be useful tools to think with [30] and when applied to specific issues, the family of DPSEEA models have been shown to be effective in engaging multidisciplinary stakeholders [31,32]. Conceptual models can also support intersectoral cooperation by facilitating a common understanding and language, promoting dialogue between disciplines and adoption of a shared recognition of roles and responsibilities. ...
The need for analysis and action across the interrelated domains of human behaviors and lifestyles, environmental sustainability, health and inequality is increasingly apparent. Currently, these areas are often not considered in conjunction when developing policies or interventions, introducing the potential for suboptimal or conflicting outcomes. The INHERIT model has been developed within the EU-funded project INHERIT as a tool to guide thinking and intersectoral action towards changing the behaviors and lifestyles that play such an important role in today’s multidisciplinary challenges. The model integrates ecological public health and behavioral change models, emphasizing inequalities and those parts of the causal process that are influenced by human behaviors and lifestyles. The model was developed through web-based and live discussions with experts and policy stakeholders. To test the model’s usability, the model was applied to aspects of food consumption. This paper shows that the INHERIT model can serve as a tool to identify opportunities for change in important −food-related behaviors and lifestyles and to examine how they impact on health, health inequalities, and the environment in Europe and beyond. The INHERIT model helps clarify these interrelated domains, creating new opportunities to improve environmental health and health inequality, while taking our planetary boundaries into consideration.
... There has been a substantial growth in the demand and supply of computer-based systems meant to support professionals in spatial planning and environmental decision-making (McIntosh et al., 2011). This trend can be explained by the broadly accepted recognition that environmental issues are complex, and that decision-makers need to balance between multiple, and often competing, claims (McIntosh, Seaton, & Jeffrey, 2007;Perez-Soba & Maas, 2015;Rodela, Reinecke, Bregt, Kilham, & Lapeyre, 2015). However, research has shown that after spatial decision support systems (SDSS) are made available to the intended end users, these are used little, or not at all (Arnott & Pervan, 2008;Uran & Janssen, 2003). ...
... However, research has shown that after spatial decision support systems (SDSS) are made available to the intended end users, these are used little, or not at all (Arnott & Pervan, 2008;Uran & Janssen, 2003). McIntosh et al. (2007) wrote about this and identified a need to expand the research agenda beyond technical aspects and include questions about usability, user needs and tool performance in explicit decision-making processes. Yet, there are challenges to the study of SDSS in the context of real-world decisionmaking. ...
Note: a pdf of the final and accepted version of this paper is available for download until 1 November 2018 at this link https://authors.elsevier.com/a/1XizX_4XYggJAo from ScienceDirect
This study explores the implications of engaging students, versus professionals/experts, in pilot-testing of SDSS, and discusses likely differences in terms of expected outcomes for the given pilot-test.To this end, we use data collected during two pilot tests of a novel SDSS that was developed by members of our project team. The pilot-tests were done with two different groups; one made of 13 doctoral students, while the other of 12 professionals/stakeholders. The pilot-test served to gather feedback on SDSS usability and other aspects of interest to the development team. On the basis of the outcomes obtained we develop an analytical framework meant to summarise what we come to notice as key aspects distinguishing how different types of testers will engage in an SDSS pilot-test, and the type of feedback these will consequently provide. These key aspects include expertise, stage of life, and institutional context (ESI). This framework could offer some help to other teams in planning, organizing, and delivering pilot-test, and processing the assessments received.
... Several terms have been used to characterize what is missing from the relationship between the producer of information and its intended user: 'mutual understanding' (McCown, 2002), 'receptivity' (McIntosh et al., 2007), or 'systems integration' (Kristjanson et al., 2009). These problems point to a lack of recognition and understanding of the views of stakeholders outside the academic community, as well as a lack of interest or know-how from scientists in building partnerships with relevant stakeholders. ...
... The poor engagement of stakeholders often leads to tools that prescribe action instead of facilitating learning. This dynamic stems partly from power asymmetries, with scientific knowledge often carrying outsized influence compared to local knowledge (Kristjanson et al., 2009), making it a challenge for multiple legitimate views of reality to be included in the decision-making process (McIntosh et al., 2007). ...
A vibrant, resilient and productive agricultural sector is fundamental to achieving the Sustainable Development Goals. Bringing about such a transformation requires optimizing a range of agronomic, environmental and socioeconomic outcomes from agricultural systems – from crop yields, to biodiversity, to human nutrition. However, these outcomes are not independent of each other – they interact in both positive and negative ways, creating the potential for synergies and trade-offs. Consequently, transforming the agricultural sector for the age of sustainable development requires tracking these interactions, assessing if objectives are being achieved and allowing for adaptive management within the diverse agricultural systems that make up global agriculture. This paper reviews the field of agricultural trade-off analysis, which has emerged to better understand these interactions – from field to farm, region to continent. Taking a “cradle-to-grave” approach, we distill agricultural trade-off analysis into four steps: 1) characterizing the decision setting and identifying the context-specific indicators needed to assess agricultural sustainability, 2) selecting the methods for generating indicator values across different scales, 3) deciding on the means of evaluating and communicating the trade-off options with stakeholders and decision-makers, and 4) improving uptake of trade-off analysis outputs by decision-makers. Given the breadth of the Sustainable Development Goals and the importance of agriculture to many of them, we assess notions of human well-being beyond income or direct health concerns (e.g. related to gender, equality, nutrition), as well as diverse environmental indicators ranging from soil health to biodiversity to climate forcing. Looking forward, areas of future work include integrating the four steps into a single modeling platform and connecting tools across scales and disciplines to facilitate trade-off analysis. Likewise, enhancing the policy relevance of agricultural trade-off analysis requires improving scientist-stakeholder engagement in the research process. Only then can this field proactively address trade-off issues that are integral to sustainably intensifying local and global agriculture – a critical step toward successfully implementing the Sustainable Development Goals.
... DSTs have maximum impact and effectiveness when designed in collaboration with stakeholders [9,10], specifically when they are demand-driven rather than science-driven [11], though the design of interactions with stakeholders must be considered otherwise DSTs can still prove to be ineffective [3,12,13]. Many of the UKCR-derived tools have been developed through engagement with stakeholders to ensure the DST meets their needs and can inform decision making processes. ...
The definition of decision support tools in the context of climate change and adaptation is explored, highlighting the variation in approaches to design and form of tools.
Several challenges are identified that have impeded the successful development of decision support tools, including financial restrictions, time constraints and meaningful stakeholder engagement.
We highlight a number of potential areas for future research, including work to address the challenges of scaling up decision support tools and stronger frameworks for guiding stakeholder engagement.
... Nevertheless, there can be barriers to the publishing of open code or datasets due, for example, to issues of data ownership, privacy or security. The right balance between data protection and transparency is still to be determined (Süsser 2021;Acs 2019;McIntosh 2007). ...
This study presents the results of a comprehensive research of qualitative and quantitative modelling approaches to analyse the efficacy of policy instruments. In addition to reviewing existing modelling approaches, this study addresses the opportunities and limitations of applying the findings to the policy field of climate change adaptation. Starting with an introduction to the field of policy modelling, the study is structured as follows:
1. Introduction to the field of policy and behavioural modelling and explanation of the research methodology
2. Examples of qualitative and quantitative modelling for the analysis of the efficacy of policy instruments in the fields of policy advice and the climate change adaptation (Chapter 2),
3. Introduction and overview of the reviewed qualitative (Systems Thinking, Concept Mapping, Causal Loop Diagrams), semi-quantitative (Fuzzy Cognitive Mapping, Social Network Analysis, Scenario Analysis, Decision-oriented Modelling) and quantitative (System Dynamics, Agent-based modelling, Cellular Automata, Empirical Modelling and Bayesian Networks) core modelling methods (Chapter 3),
4. Identifying the opportunities and limitations of modelling for policy instruments' efficacy analysis in the field of policy advice (Chapter 4); and
5. Discussion of the applicability of the identified qualitative and quantitative modelling approaches to the policy field of climate change adaptation (Chapter 5).
... Smallholders are mostly concerned about the cost-effectiveness and ease-of-use of agronomic practices [49] . Furthermore, scientists tend to produce generic recommendations that bypass established decision-making processes, which often results in poor understanding of the science outside the academic community and low interest from smallholders in scientific research [50] . Knowledge of localized best-practices for farmers with varying resource constraints is one of the primary constraints limiting the adaptation rate of recommended technologies [51] . ...
Sustainable food production to feed the growing
population in Africa remains a major challenge. Africa
has 64% of the global arable land but produces less than
10% of its food locally due to its inherently low soil
nutrient concentrations. Poor soil fertility and a lack of
fertilizer use are the major constraints to increasing crop
yields in Africa. On average only about 8.8 kg NPK
fertilizer is applied per hectare by African smallholder
farmers. There is therefore considerable potential for
increasing food production through sustainable intensification
of the cropping systems. The low crop yields in Africa
are also partly due to limited farmer access to modern
agronomic techniques, including improved crop varieties,
a lack of financial resources, and the absence of
mechanisms for dissemination of information to smallholders.
This study analyzed the Science and Technology
Backyards (STBs) model and investigated its use for the
transformation of agriculture in Africa. Some key lessons
for sustainable crop intensification in Africa can be found
from analysis of the STB model which is well established
in China. These include (1) scientist-farmer engagement to
develop adaptive and innovative technology for sustainable
crop production, (2) dissemination of technology by
empowering smallholders, especially leading farmers, and
(3) the development of an open platform for multiple
resource involvement rather than relying on a single
mechanism. This review evaluates the benefits of the STB
model used in China for adoption to increase agricultural
productivity in Africa, with a perspective on sustainable
crop intensification on the continent.
... Smallholders are mostly concerned about the cost-effectiveness and ease-of-use of agronomic practices [49] . Furthermore, scientists tend to produce generic recommendations that bypass established decision-making processes, which often results in poor understanding of the science outside the academic community and low interest from smallholders in scientific research [50] . Knowledge of localized best-practices for farmers with varying resource constraints is one of the primary constraints limiting the adaptation rate of recommended technologies [51] . ...
Sustainable food production to feed the growing population in Africa remains a major challenge. Africa
has 64% of the global arable land but produces less than
10% of its food locally due to its inherently low soil
nutrient concentrations. Poor soil fertility and a lack of
fertilizer use are the major constraints to increasing crop
yields in Africa. On average only about 8.8 kg NPK
fertilizer is applied per hectare by African smallholder
farmers. There is therefore considerable potential for
increasing food production through sustainable intensification of the cropping systems. The low crop yields in Africa
are also partly due to limited farmer access to modern
agronomic techniques, including improved crop varieties,
a lack of financial resources, and the absence of
mechanisms for dissemination of information to smallholders. This study analyzed the Science and Technology
Backyards (STBs) model and investigated its use for the
transformation of agriculture in Africa. Some key lessons
for sustainable crop intensification in Africa can be found
from analysis of the STB model which is well established
in China. These include (1) scientist-farmer engagement to
develop adaptive and innovative technology for sustainable crop production, (2) dissemination of technology by
empowering smallholders, especially leading farmers, and
(3) the development of an open platform for multiple
resource involvement rather than relying on a single
mechanism. This review evaluates the benefits of the STB
model used in China for adoption to increase agricultural
productivity in Africa, with a perspective on sustainable
crop intensification on the continent.
... Smallholders are mostly concerned about the cost-effectiveness and ease-of-use of agronomic practices [49] . Furthermore, scientists tend to produce generic recommendations that bypass established decision-making processes, which often results in poor understanding of the science outside the academic community and low interest from smallholders in scientific research [50] . Knowledge of localized best-practices for farmers with varying resource constraints is one of the primary constraints limiting the adaptation rate of recommended technologies [51] . ...
... Flexibility means that the assessment tool will provide information relevant for a diversity of decision contexts (for different stages of project development) and operating methods (whether the assessment is made collectively or not, to support the design of a project, justify its selection, assess its potential impacts, etc.). Many studies pointed out the low level of use of decision support tools due to the gap between the way designers elaborated the tool and the way users make decisions (Díez and McIntosh 2009;McCown 2002;McIntosh et al. 2007); therefore, our study enriches the understanding of users' assessment practices that should be taken into account for the design of a flexible tool to assess sustainability of PIUA projects. ...
With the rapid growth of professional intra-urban agriculture (PIUA) projects in the Global North, sponsors, projects leaders, and experts developing these projects are seeking to evaluate their sustainability. As existing assessment tools are not adapted to PIUA projects, they establish their own assessment practices. Our study examines these practices to identify their original features, criteria, and indicators used. To this end, we analysed 19 case studies of different PIUA projects. We identified four dimensions underpinning sustainability assessment, namely, internal sustainability, external sustainability, the project leader’s credibility, and the innovative nature of the project. We also shed light on the wide diversity of the 67 assessment criteria identified, as well as the qualitative nature of 78% of indicators used. In addition, our study highlights that assessment practices evolve over time as the project progresses from ideation to implementation, according to the variety of assessment situations. Our study is the first to provide an in-depth exploration of PIUA stakeholders’ sustainability assessment practices and to shed light on their specific features. Our results afford a better understanding of the way the sustainability of PIUA projects is assessed and contribute to reflection on the design of a flexible assessment tool, considering the diverse criteria and practices used by stakeholders to assess the sustainability of PIUA.
... Equally, they may support health impact assessment, gap analysis or the configuration of information or surveillance systems. However, in their simplest terms, conceptual models might best be understood as tools with which to think and communicate (McIntosh et al., 2007;Reis et al., 2015). ...
... When referring to stakeholders, we therefore primarily address decision-makers who work in policy and management. Better integration of models in policy-making has been suggested with a focus on modelling for public policy (Gilbert et al., 2018), model acceptance in policy-making (Kolkman et al., 2016) and models used as decision support tools (McIntosh et al., 2007;van Delden et al., 2011;Zasada et al., 2017). Our paper contributes to this strand of literature targeting the special requirements that SES modelling bears. ...
Dynamic process‐based modelling is often proposed as a powerful tool to understand complex socio‐environmental problems and to provide sustainable solutions as it allows disentangling cause and effect of human behaviour and environmental dynamics. However, the impact of such models in decision‐making and to support policy‐making has so far been very limited.
In this paper, we want to take a critical look at the reasons behind this situation and propose steps that need to be taken to change it. We investigate a number of good practice examples from fields where models have influenced policy‐making and management to identify the main aspects that promote or impede the application of these models.
Specifically, we compare examples that differ in their extent to how explicitly they represent human behaviour as part of the model, ranging from purely environmental systems (including models for river management, honeybee colonies and animal diseases), where modelling techniques have long been established, to coupled socio‐environmental systems (including models for land use, fishery management and sustainable water use).
We use these examples to synthesise four key factors for successful modelling for policy and management support in socio‐environmental systems. They cover (a) the specific requirements caused by modelling the human dimension, (b) the importance of data availability and accessibility, (c) essential elements of the partnership between modellers and decision‐makers and (d) insights related to characteristics of the decision process.
For each of these aspects, we give recommendations specifically to modellers, decision‐makers or both to make the use of models for practice more effective. We argue that if all parties involved in the modelling and decision‐making process take into account these suggestions during their collaboration, the full potential that socio‐environmental modelling bears can increasingly unfold.
A free Plain Language Summary can be found within the Supporting Information of this article.
... However, energy models are often not geared to the needs of the users they are meant to advise. Models that are tailored to users and their needs are more appropriate for the purpose and more likely to be used (e.g., Gilbert et al., 2018;McIntosh et al., 2007;Van Daalen et al., 2002). Thus, an iterative approach with reconcilement between model developers and model users is essential, as " […] it can result in a better correspondence between model analyses and user needs, making the results more relevant to potential users" (Stalpers et al., 2009:966). ...
In this report, we identify the needs of the energy model users and the users of energy model results in policy, industry, civil society, and science, both in the present and future. Based on a comprehensive literature review, qualitative interviews in five European jurisdictions, a survey, and a workshop, we identify what different user groups need from energy models: What types of questions, input, and results are useful to them? We also identify user needs regarding the modeling platform of SENTINEL: How do we need to define such a platform to make it worthwhile for potential users? We find several unmet user needs regarding (I) model content, (ii) model design and data, (iii) modeling process, and (iv) model outreach.
... Smallholders are mostly concerned about the cost-effectiveness and ease-of-use of agronomic practices [49] . Furthermore, scientists tend to produce generic recommendations that bypass established decision-making processes, which often results in poor understanding of the science outside the academic community and low interest from smallholders in scientific research [50] . Knowledge of localized best-practices for farmers with varying resource constraints is one of the primary constraints limiting the adaptation rate of recommended technologies [51] . ...
Sustainable food production to feed the growing population in Africa remains a major challenge. Africa has 64% of the global arable land but produces less than 10% of its food locally due to its inherently low soil nutrient concentrations. Poor soil fertility and a lack of fertilizer use are the major constraints to increasing crop yields in Africa. On average only about 8.8 kg NPK fertilizer is applied per hectare by African smallholder farmers. There is therefore considerable potential for increasing food production through sustainable intensifica-tion of the cropping systems. The low crop yields in Africa are also partly due to limited farmer access to modern agronomic techniques, including improved crop varieties, a lack of financial resources, and the absence of mechanisms for dissemination of information to smallholders. This study analyzed the Science and Technology Backyards (STBs) model and investigated its use for the transformation of agriculture in Africa. Some key lessons for sustainable crop intensification in Africa can be found from analysis of the STB model which is well established in China. These include (1) scientist-farmer engagement to develop adaptive and innovative technology for sustainable crop production, (2) dissemination of technology by empowering smallholders, especially leading farmers, and (3) the development of an open platform for multiple resource involvement rather than relying on a single mechanism. This review evaluates the benefits of the STB model used in China for adoption to increase agricultural productivity in Africa, with a perspective on sustainable crop intensification on the continent.
... Taking the stakeholder needs and wishes as a design's point of departure places emphasis on prospective users and context of use and should therefore lead to game designs that cater to the their actual needs. Failure to take the prospective users into account in the design of serious games may lead to limited adoption in practice, as has happened with decision support systems (Borowski and Hare, 2007;Brugnach et al., 2007;McIntosh et al., 2011;McIntosh et al., 2007;Vonk et al., 2005). ...
Serious games are increasingly developed and applied in environmental management as tools to collaboratively explore complexity. Yet, the research, design and implementation of serious games is still largely driven by technical aspects as a result of advances in computational power and in the gaming industry. Less attention has been given to research the extent to which the design of serious games can cater to the needs and desires of stakeholders with diverse backgrounds. The aim of this thesis is therefore to explore how a human centered design of serious games contributes to foster exploring complexity, facilitating stakeholder participation and stimulating social learning in environmental management.
In this thesis, the design, application and evaluation of the Virtual River Game is described. First, the scope and background of the game was researched by conducting interviews with various stakeholders to gain an understanding of Dutch river management challenges and stakeholder perspectives on these. Second, the state-of-the-art in evaluating social learning outcomes of serious games to collaboratively explore complexity was analyzed and used as both design guidelines and an assessment framework for the game. Third, a novel, hybrid interface design was developed that, based on tangible interaction, has a physical game board with a bidirectional link to computer models that are widely used in practice. Fourth, the final prototype of the Virtual River Game was played in five sessions with both domain experts (river management professionals) and non-experts (non-professionals and laymen) to evaluate to what extent the game: (1) enables these stakeholders with diverse backgrounds to collaboratively explore river management complexity; and (2) stimulates social learning.
The four main contributions of this thesis are: (1) applying a human centered design process to the design of serious games; (2) developing a novel interface design that uses a physical game board linked to environmental computer models to make the models accessible and transparent; (3) adding the ability to experiment with the design of management interventions as a game mechanic over selecting predefined interventions; and (4) creating the interface design as a platform to customize existing or develop new serious games with a spatial component.
... (van Delden et al., 2007)), mais les récentes publications insistent sur les points critiques. Parmi ceux-ci, se dégagent : un déséquilibre entre le temps de développement et le temps d'utilisation des modèles (McIntosh et al., 2007), un déséquilibre entre les considérations environnementales, économiques et sociales (Jakeman and Letcher, 2003), une mauvaise appréciation des effets de transferts de pollutions (Cuttle et al., 2007;Collins and McGonigle, 2008) ou une traduction imprécise d'une question posée à la recherche en scénarios effectivement modélisés . Ces mises en évidence de points critiques ont fait émerger de nombreux travaux sur la formalisation des « bonnes pratiques d'utilisation de la modélisation comme outil d'appui à la gestion» ou des « facteurs déterminants de succès » de tels exercices (Jakeman et al., 2006;McIntosh et al., 2008;Parson, 2008). ...
... Jakeman and Letcher (2003) point out that models provide a way of exploring and explaining trade-offs, a tool for adoption and adaptation by stakeholders, a longer term memory of the project methods and a library of integrated data sets. McIntosh, Seaton, and Jeffrey (2004) recognize the role that models can play in enforcing discipline in debates and their role as vehicles for consensus. More recently Gramelsberger and Mansnerus (2012) and McBurney (2011) suggest that models function as narratives and offer a distinct way of telling the story about a particular issue. ...
Governments increasingly use algorithmic models to inform their policy making process. Many suggest that employing such quantifications will lead to more efficient, more effective or otherwise better quality policy making. Yet, it remains unclear to what extent these benefits materialize and if so, how they are brought about. This paper draws on the sociology and policy science literature to study how algorithmic models, a particular type of quantification, are used in policy analysis. It presents the outcomes of 38 unstructured interviews with data scientists, policy analysts, and policy makers that work with algorithmic models in government. Based on an in-depth analysis of these interviews, I conclude that the usefulness of algorithmic models in policy analysis is best understood in terms of the commensurability of these quantifications. However, these broad communicative and organizational benefits can only be brought about if algorithmic models are handled with care. Otherwise, they may propagate bias, exclude particular social groups, and will entrench existing worldviews.
... Such model-based methods collect data and knowledge from a wide range of scientific disciplines and integrate them to investigate a research question, most often in a "policy oriented context", to analyze system responses to changes and to design sustainable management strategies (Pahl-Wostl et al. 2000;Tol and Vellinga 1998;Ahrweiler and Gilbert 2015). The potential of integrated modeling to address NR management and policy problems is now well-established (Voinov and Bousquet 2010;Sterk et al. 2009;Bots and Daalen 2008;McIntosh et al. 2007;Jakeman et al. 2006;Oxley et al. 2004). Models are cognitive artifacts that are essential for making complex problems intelligible (Sterk et al. 2009;Conklin 2006). ...
The success of Integrated Assessment and Modeling of social-ecological systems (SESs) requires a framework allowing members of this process to share, organize and integrate their knowledge about the system under consideration. To meet this need and ease management of successful modeling processes, we present a conceptual framework for integrated agent-based modeling and simulation of SESs in the form of a formal “entity-process meta-model”, along with a distinction between three levels of models—conceptual, concrete and simulation—and characterization of the research question using indicators and scenarios. We then describe how to represent the structural and dynamic dimensions of SESs into conceptual and concrete models and to derive the simulation model from these two types of models. Finally, we discuss how our framework solves some of the challenges of integrated SES modeling: integration and sharing of heterogeneous knowledge, reliability of simulation results, expressiveness issues, and flexibility of the modeling process.
... Après l'essor de l'utilisation de la modélisation pour l'analyse de scénarios exploratoires (Van Delden et al. 2007), de nombreux auteurs mettent en évidence des points de vigilance. Ainsi, McIntosh et al. (2007) alertent sur les temps de développement versus le temps d'utilisation du modèle. Si l'on prend l'exemple de cette thèse, il est clair que la mise au point du modèle en parallèle de la thèse et la construction des jeux de données ont absorbé l'essentiel du temps de travail aux dépens de l'analyse approfondie des résultats de simulation. ...
Les problèmes environnementaux liés à l’excès d’azote d’origine agricole restent une préoccupation majeure en France malgré une réglementation contraignante. Pour concilier maintien de la production agricole et limitation de cet excès, des stratégies novatrices d’atténuation des flux d’azote ont été identifiées, puis leurs effets ont été simulés à l’échelle de paysages agricoles à l’aide de modèles agro-hydrologiques spatialisés. Pour cela, la méthodologie mise en oeuvre a consisté, à partir d’enquêtes de fermes dans deux contextes contrastés (Bretagne et Gascogne),à simuler des scénarios : i) d’optimisation des pratiques agricoles en accord avec le 5ème programme d’actions de la directive Nitrate et ii) d’aménagements paysagers du territoire et en particulier des zones environnementales (prairies fauchées non-fertilisées), en variant l’emprise et la localisation. Les résultats montrent l’intérêt de placer ces zones en position d’interception (i.e. en zone riparienne humide), plutôt qu’en tête de thalweg, notamment sur le site breton où circulations d’eau sub-superficielles dominent. Cette solution limiterait les pertes nitriques sans augmenter les autres émissions d’azote.
... Such model-based methods collect data and knowledge from a wide range of scientific disciplines and integrate them to investigate a research question, most often in a "policy oriented context", to analyze system responses to changes and to design sustainable management strategies (Pahl-Wostl et al. 2000;Tol and Vellinga 1998;Ahrweiler and Gilbert 2015). The potential of integrated modeling to address NR management and policy problems is now well-established ( Voinov and Bousquet 2010;Sterk et al. 2009;Bots and Daalen 2008;McIntosh et al. 2007;Jakeman et al. 2006;Oxley et al. 2004). Models are cognitive artifacts that are essential for making complex problems intelligible ( Sterk et al. 2009;Conklin 2006). ...
The success of Integrated Assessment and Modeling of social-ecological systems (SESs) requires a framework allowing members of this process to share, organize and integrate their knowledge about the system under consideration. To meet this need and ease management of successful modeling processes, we present a conceptual framework for integrated agent-based modeling and simulation of SESs in the form of a formal “entity-process meta-model”, along with a distinction between three levels of models—conceptual, concrete and simulation—and characterization of the research question using indicators and scenarios. We then describe how to represent the structural and dynamic dimensions of SESs into conceptual and concrete models and to derive the simulation model from these two types of models. Finally, we discuss how our framework solves some of the challenges of integrated SES modeling: integration and sharing of heterogeneous knowledge, reliability of simulation results, expressiveness issues, and flexibility of the modeling process.
... The growth of DSFs across many environmental domains has been matched by increased discussion about potential users and their needs (McIntosh et al. 2007;Romsdahl and Pyke 2009;Romsdahl 2011), concerns about low take-up by target users (Valls-Donderis et al. 2014) and the need for user involvement in testing so that frameworks will be usable and used (O'Dea et al. 2007;Romsdahl 2011;Voinov and Bousquet 2010). Increasingly proposed is that developers and potential users should collaborate in DSF design and content, for three principal reasons: First, so that content meets users' expectations, contains legitimate and relevant information, for example, at appropriate spatial and temporal scales, and can be integrated with existing organisational knowledge systems (Dilling and Lemnos 2011;Valls-Donderis et al. 2014;Meadow et al. 2015); second, so that the process of negotiating the DSF and its content builds trust in the developers and faith in the final product (Lemnos et al. 2012;Valls-Donderis et al. 2014); and finally, so that users become active rather than passive participants, helping to build the DSF community and improve practice through social learning (Jakku and Thorburn 2010;Prokopy et al. 2017). ...
For a decision support framework (DSF) to enable effective decision-making in climate change adaptation, it is important that stakeholders are involved in its development, in order to ensure that it is usable and useful. More specifically, stakeholder involvement may help to ensure that the DSF better meets user needs and expectations, as well as providing legitimate, relevant and trusted information. Involving users also helps to support social learning and build a community of adaptors. This paper describes a case study in Australia of the development of a DSF, called CoastAdapt, for coastal decision makers to adapt to the impacts of climate change, in particular sea-level rise. We use the IAP2 Spectrum to outline how stakeholders were involved. We also describe the specific activities undertaken in developing the DSF, according to how they contribute to conditions of legitimacy, credibility and saliency suggested in ‘boundary’ work. We conclude with some practical suggestions for considering these attributes in development of a DSF, noting that each attribute is important and requires consideration both separately and together.
... Provided that these abilities are accompanied by normal scientific scepticism and the guiding principle of parsimony, there is every reason to believe that new generations of environmental models with sufficient accuracy and generality will continue to provide useful supports to societal decision making. Models are 'tools to think with ' (McIntosh et al. 2007), and if these tools are made accessible to a wider range of scientists and other actors, the benefit of more thinking can go into decisions concerning the environment. ...
Society requires rapid, most-probable predictions for specific and/or multifaceted questions related to environmental and geological science. In principle, models that encapsulate disciplinary knowledge are useful tools for making predictions and testing theory, but academic rewards favour disciplinary specialism and a proliferation of often insufficiently tested models. Decision makers have to assess the quality and robustness of predictions for complex environmental issues, and may prefer a model that performs accurately in a case study to a more parsimonious and generalizable model. Predictive ecosystem models tend to grow, as more processes are considered, even when a simpler model may be more appropriate and give results that are easier to interpret within a policy-relevant timeframe. Model fusion provides a practical way to combine knowledge from different disciplines, but can accelerate model growth. How then can we facilitate the evolution of useful predictive models? Coherent design is essential. When combining models it is often necessary to resolve overlapping scope, so tools need to allow for the disaggregation of model implementations as well as their fusion. Modelling software and integration frameworks can help resolve technical constraints, but to make models useful and used it is essential to involve stakeholders in their design and interpretation.
... Research consortia funded on a 3-5 years project framework often form an environment from which researchers can offer data, products and services to groups concerned with a topic of societal relevance (Schmid et al. 2016;. Classified as intermediary organisations with shared common objectives and a temporary nature , the research consortia are expected to realize the full potential of research data and tools in an application oriented environment (Klerkx et al. 2017;Schut et al. 2016;McIntosh et al. 2007;Arranz and de Arroyabe 2006). Researchers engaging in transdisciplinary cooperation have become increasingly aware of their role as a resource of knowledge that can be tapped not only by industry, but also by government departments, community groups and clients from civil society (Bonaccorsi 2010;. ...
Due to greater financial uncertainty in connection with ecological risks, sustainability-oriented innovations are less likely to be successfully adopted in a market environment. The overall objective of this study is to analyse innovation processes in agriculture, and to assess their ability to integrate market-driven as well as ecosystem-oriented activities across different levels of relationship interaction. To overcome a domain approach in agriculture, a framework for the analysis and management of sustainability-oriented innovation processes in agriculture was developed: the Triple Helix System of Innovation for Sustainability (THIS). It is set to enhance the diffusion and utilisation of new knowledge generated by interaction between agricultural practice, research and policy. The framework looks at three levels of relationship interaction relevant to the negotiation of market versus ecosystem, including 1) technological, 2) organisational, and 3) governance aspects. The negotiation of sustainability goals is conducted by translating the overarching global United Nations Sustainable Development Goals (SDGs) to the substantive focus of innovation in each case. Each level of relationship interaction reveals specific research questions, addressing for example the consideration of sustainability impacts at the beginning of an innovation process (the front end), management functions required to organise and steer an innovation process, and the potential for integrating indicators for sustainability in policy regulation and governance. The study contributes to the debate on viable forms of innovation management for goal-oriented innovation processes. The application of THIS achieves to address additional uncertainty posed particularly by environmental aspects characteristic to the agricultural sector. Finally, this study shows how the requirements of iterative management can be reflected in organisational structures for institutional support. http://edoc.hu-berlin.de/18452/20073
... Research has uncovered intricate interactions between urban form and water infrastructure, which include, for example, the effects of land use planning (Lee et al., 2009), impervious cover (Arnold and Gibbons, 1996), density, street layout and residential neighbourhood design (Stone, 2004) on stormwater runoff, water quality, water supply security and other aspects that affect ecosystem services and the overall liveability of cities (Vlachos and Braga, 2001). Despite evolution of urban and water systems planning disciplines over the last few decades (Klosterman, 1997;Brown et al., 2009;Gurran, 2011) towards becoming more complex and 'wicked problems' (Rittel and Webber, 1973;Campbell, 1996;Gauthiez, 2004), considerable advancements have also been concurrently made in the numerical and computational tools to support this process (Geertman and Stillwell, 2004;McIntosh et al., 2007;Bach et al., 2014). ...
A spatial model is presented, based on urban planning concepts for abstracting urban form characteristics in new and existing areas. Requiring input maps of land use, elevation, population and parameters from planning regulations, the model conceptualises (on a spatial grid) attributes including impervious fraction, allotment geometry and roof areas among other relevant characteristics for integrated urban water management. The model is calibrated to three different Melbourne districts, varying in size (10–60 km²) and land use. Performance was evaluated by comparing modelled outputs with observations of total dwelling count, employment and spatial distribution of impervious fraction and residential roof areas. Results not only highlight reasonably good prediction, particularly with spatially variable indicators such as imperviousness across all case studies, but also logical contrasts and consistency in the chosen planning parameters across the different case study districts. Discrepancies highlight aspects needing improvement and potential for exploring auto-calibration and model sensitivity.
... Finally, the work process of developing the soft-linking methodology has been characterized by integrity, trust, and mutual respect between team members e all three identified by Parker et al. [39] and McIntosh et al. [31] as important factors in interdisciplinary projects to achieve successful integration and communication. By working closely together with the soft-linking method, a mutual understanding of the respective scientific approaches has arisen. ...
This paper proposes and discusses a soft-linking procedure between a Computable General Equilibrium (CGE) model and an energy system model with the aim to improve national energy policy decision-making. Significant positive and negative experiences are communicated. Specifically, the process of soft-linking the EMEC and TIMES-Sweden models is presented, and unlike previous work we rely on the use of multiple direction-specific connection points. Moreover, the proposed soft-linking methodology is applied in the context of a climate policy scenario for Sweden. The results display a partly new description of the Swedish economy, which when soft-linking, generates lower CO2-emissions in the reference scenario due to a decline in industrial energy demand. These findings point at the importance of linking bottom-up and top-down models when assessing national energy and climate policies.
... Umberto NXT modeling software (Umberto, 2015) was chosen to produce the Life Cycle Impact Assessment (Peças et al., 2013;Fthenakis et al., 2009), as well as to organize, calculate, analyze and compare how the involved materials and processes change their behavior as the variables of each scenario are introduced (Mcintosh et al., 2007;Matthews et al., 2011). ...
Optical fibers have become the backbone of long distance telecommunications, thus, reducing the environmental impacts of its production process poses as a crucial step towards its sustainable deployment worldwide. This paper presents and discusses the Life Cycle Impact Assessment of the Modified Chemical Vapor Deposition (MCVD) vitreous optical fiber production process. The environmental impacts of 18 production scenarios were analyzed and compared using the Umberto NXT modeling software, generating cradle-to-gate results in accordance to the criteria of two Project Oriented Environmental Management indicators: IPCC 2007 Global Warming Potential and ReCiPe Hierarchical Average Environmental Impact. Two main results were achieved: (a) the carbon footprint of the MCVD production process (8.02 kgCO2eq per kilometer of optical fiber, business as usual) – which represents a novel contribution to this field of scientific research –, and (b) less environmentally impactful production alternatives – namely metallic, ceramic and chalcogenic raw material combinations, renewable energies and a different catalyst. Secondary results and analyses of the production process were also discussed in order to highlight the importance of decision-making in raw material and energy sourcing strategies as drivers to reduce the environmental impacts of vitreous optical fiber production.
... Simulation models predict the future system state in response to assumed management. To that capability, S-O models add the ability ( Refsgaard and Henriksen, 2004;Scholten et al., 2004;Refsgaard et al., 2005;Henriksen et al., 2007) to design optimal strategies that consider and satisfy constraints (Peralta and Kowalski, 1988;Lemon, 1999;Parker et al., 2002;Oxley et al., 2004;McIntosh et al., 2007). S-O models can address many problems, including conjunctive and integrated water management (Jakeman and Letcher, 2003;Bromley et al., 2005). ...
Computational requirements sometimes discourage using mathematical optimization for groundwater management. To dramatically reduce computation time, the presented hybrid response matrix method (RMM), Coefficient Generation and Use method 4 (CGU4), prepares surrogate simulators used during optimization. CGU4 reduces numerical model simulations needed to populate the surrogates (linearized convolution equations, LCEs). LCEs often represent flow and head constraints within groundwater flow optimization problems. CGU4 reduces computations for problems having: varying time period sizes; eras of sequential time periods of equal duration; and system (non)linearity. For a situation having: linear, piece-wise, and nonlinear groundwater flows; 20 periods of varying and sequentially constant durations; and optimization problems employing (non)linear objective functions and linear head and aquifer-surface seepage constraints, CGU4 requires 22–61% fewer simulations to compute optimal objective function values within 0.001–0.003% of the best alternative RMM. For hypothetical (non)linear dynamic stream-aquifer problems, CGU4 required the same or 63–89% less time than previous RMMs.
... Formal representations of the organization provided a test bed with which to examine the various ways scenario-based interventions affect overall system behavior as an emergent property of interactions. These provide a "tool to think with" (see, for example, McIntosh, Jeffrey, Lemon, & Winder, 2005;McIntosh, Seaton, & Jeffrey, 2007;Torrens, 2003), which may be used as part of wider discussions on the effectiveness of proposed measures. One advantage of ABM approaches in this regard is that it can be used to try out policy options that would be costly, or entail political risks if tried out in the real world. ...
Pro-environmental behaviors have been analyzed in the home, with little attention to other important contexts of everyday life, such as the workplace. The research reported here explored three categories of pro-environmental behavior (consumption of materials and energy, waste generation, and work-related commuting) in a public large-scale organization in Spain, with the aim of identifying the most effective policy options for a sustainable organization. Agent-based modeling was used to design a virtual simulation of the organization. Psychologically informed profiles of employees were defined using data gathered through a questionnaire, measuring knowledge, motivations, and ability. Future scenarios were developed using a participatory backcasting scenario development methodology, and policy tracks were derived. Dynamic simulations indicated that, to be effective, organizational policy should strengthen worker participation and autonomy, be sustained over time, and should combine different measures of medium intensity for behavior change, instead of isolated policies of high intensity.
... Building on these ideas, and consistent with the notion of waste elimination, various perspectives in supportive communication and ubiquitous computing [59][60][61] emphasize the need to provide just-in-time support only when the person is receptive. Here, receptivity is defined as the individual's transient ability and/or willingness to receive, process, and utilize just-in-time support; receptivity is a function of both internal (e.g., mood) and contextual (e.g., location) factors [50]. ...
Background
The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual?s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual?s state can change rapidly, unexpectedly, and in his/her natural environment. PurposeDespite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods
Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusion
As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.
... As conciliating natural science's knowledge with social, economic and governance processes has, in the last decade, been recognized as an important step towards a sustainable society [15], a wide range of software and modeling technologies are being developed as tools for providing science-based information for policy and planning activities [16] [17], in the quest of supporting policymakers globally. Examples of these technologies are available at the Climate Change Knowledge Portal from the World Bank (http://sdwebx.worldbank.org/climateportal/index.cfm), the Ecosystem-Based Management Tools Network (https://www.ebmtoolsdatabase.org/resource/climate-change-vulnerability-assessment-and-adaptation-tools), the United Nations Framework Convention on Climate Change [18] and studies such as [17]. ...
The complexity of multi-dimensional climate, environmental and human health information complicates bringing together scientists, civil society, and decision makers to provide adequate mitigation and adaptation options for the consequences of global environmental change. To provide an effective pathway to conciliate (integrate) these datasets, we present PULSE-Brazil as the Platform for Understanding Long-term Sustainability of Ecosystems and human health. The overarching aim of this paper is to focus on two study cases in the Brazilian state of Acre, 1) analyzing recent socio-environmental disasters such as the impacts of droughts and consequent increases in fire detections on the incidence of respiratory diseases, and 2) evaluating the impacts of temperature increases in combination with rainfall seasonality upon the incidence of Dengue fever. Based on data available in PULSE-Brazil platform, we also aim to provide insights on the consequences of future climate variability on ecosystems and human health. Finally, we provide a discussion on the collaborative process between scientists and policymakers that defined the PULSE-Brazil platform specifications and datasets and propose a conceptual pathway for promoting the interaction between science and policy during the process of producing a tool that allows the understating of climate-relate processes. Our results clearly demonstrated that ecosystems are under increased fire risk in the future that will, in turn, affect the health of human populations. Moreover, humans are also exposed to critical Dengue fever outbreaks with the projected increase in minimum temperatures. Therefore, minimizing the impacts of these potentially ascending socio-environmental problems is the first step for adapting to a changing climate in the Amazon region. We expect that PULSE-Brazil will serve not only as a technical tool for supporting governance, management, mitigation and long-term adaptation plans for climate impacts and natural disasters, but also will take advantage of its graphical capabilities to be an instructive vehicle that facilitates information flow for societal governance.
... As conciliating natural science's knowledge with social, economic and governance processes has, in the last decade, been recognized as an important step towards a sustainable society [15], a wide range of software and modeling technologies are being developed as tools for providing science-based information for policy and planning activities [16] [17], in the quest of supporting policymakers globally. Examples of these technologies are available at the Climate Change Knowledge Portal from the World Bank (http://sdwebx.worldbank.org/climateportal/index.cfm), the Ecosystem-Based Management Tools Network (https://www.ebmtoolsdatabase.org/resource/climate-change-vulnerability-assessment-and-adaptation-tools), the United Nations Framework Convention on Climate Change [18] and studies such as [17]. ...
The complexity of multi -dimensional climate, environmental and human
health information com-plicates bringing together scientists, civil society, and decision makers to provide adequate mitigation and adaptation options for the consequences of global environmental change. To provide an effective pathway to conciliate (integrate) these datasets, we present PULSE -Brazil as the Plaform for Understanding Long -term Sustainability of Ecosystems and human health. The overarching aim of this paper is to focus on two study cases in the Brazilian state of Acre, 1) analyzing recent
socio-environmental disasters such as the impacts of droughts and consequent increases in fire
detections on the incidence of respiratory diseases, and 2) evaluating the impacts of temperature
increases in combination with rainfall seasonality upon the incidence of Dengue fever. Based on
data available in PULSE-Brazil platform, we also aim to provide insights on the consequences of
future climate variability on ecosystems and human health. Finally, we provide a discussion on the
collaborative process between scientists and policymakers that defined the PULSE
-Brazil platform specifications and datasets and propose a conceptual pathway for promoting the interaction between science and policy during the process of producing a tool that allows the understating of climate-relate processes. Our results clearly demonstrated that ecosystems are under increased fire risk in the future that will, in turn, affect the health of human populations. Moreover, humans
are also exposed to critical Dengue fever outbreaks with the projected increase in minimum tem-peratures. Therefore, minimizing the impacts of these potentially ascending socio-environmental
problems is the first step for adapting to a changing climate in the Amazon region. We expect that
PULSE-Brazil will serve not only as a technical tool for supporting governance, management, mitgation and long
-term adaptation plans for climate impacts and natural disasters, but also will take advantage of its graphical capabilities to be an instructive vehicle that facilitates information flow for societal governance.
... However, the implementation of proposed actions is significantly hampered due to insufficient necessary data, and the lack of interactions between hydrological and ecological components (Dimitriou and Zacharias 2006). According to McIntosh et al. (2007), a variety of software and modeling technologies are emerging in the form of "support tools" to better handle the problems of use of scientific knowledge in environmental research and planning activities. These technologies are motivated by legitimate concerns about the inefficiency of conventional research methods and of ensuring that science can be effective and easily transferred to management applications, particularly with regard to water resources. ...
The Guájaro reservoir is the most important water body located at the north of Colombia. It is supplied by an artificial channel (Canal del Dique) through a two floodgates system. As a result of excess nutrients and other pollution loads from the drainage basin in recent decades, the Guájaro reservoir suffers eutrophication and other pollution problems; however it still continues being exploited. For this reason, it is necessary to regulate the hydraulic structures that supply this water body, as they play an important role in managing levels, and these in turn for water supply and environmental purposes. The present work is carried out as a sustainability management alternative of the reservoir. The implementation of a two-dimensional hydrodynamic model and its calibration is achieved using time series of the free surface levels, and comparing the measured velocities and those estimated by the model for two different climatic periods, to assist the operation of the Canal del Dique-Guájaro hydrosystem. The corresponding comparisons showed a good behavior between measured and simulated data. Based on the quantitative results of the Nash-Sutcliffe reliability method, the results are considered quite satisfactory for estimating and predicting the amount of water flowing in and out of the reservoir through the channel reservoir hydrosystem.
... Despite the central role of integration in interdisciplinary research, it remains a major conceptual and methodological challenge. Frameworks, defined here as "tools to think with" [44], are needed to promote and facilitate integration. Although frameworks have been proposed to facilitate interdisciplinary research (many of which have emerged from the field of natural resource management and conservation i.e., [45] ) , frameworks that promote 'broad' interdisciplinarity and integration across the natural, social, and health sciences are rare despite being particularly relevant to the effective application of ecological approaches [46]. ...
Abstract: The shortcomings of public health research informed by reductionist and fragmented
biomedical approaches and the emergence of wicked problems are fueling a renewed interest in
ecological approaches in public health. Despite the central role of interdisciplinarity in the context of
ecological approaches in public health research, inadequate attention has been given to the specific
challenge of doing interdisciplinary research in practice. As a result, important knowledge gaps exist
with regards to the practice of interdisciplinary research. We argue that explicit attention towards the
challenge of doing interdisciplinary research is critical in order to effectively apply ecological
approaches to public health issues. This paper draws on our experiences developing and conducting
an interdisciplinary research project exploring the links among climate change, water, and health to
highlight five specific insights which we see as relevant to building capacity for interdisciplinary
research specifically, and which have particular relevance to addressing the integrative challenges
demanded by ecological approaches to address public health issues. These lessons include: (i) the
need for frameworks that facilitate integration; (ii) emphasize learning-by-doing; (iii) the benefits of
examining issues at multiple scales; (iv) make the implicit, explicit; and (v) the need for reflective
practice. By synthesizing and sharing experiences gained by engaging in interdisciplinary inquiries
using an ecological approach, this paper responds to a growing need to build interdisciplinary
research capacity as a means for advancing the ecological public health agenda more broadly.
Urbanization is putting a lot of pressure on city planners who need to navigate the many needs that different stakeholder groups have in terms of access to services, infrastructure, and other amenities in urban areas. This, combined with ambitions for more sustainable living environments, often puts planners and practitioners in challenging situations where they need to navigate complex combinations of needs and priorities with limited or little information available for them to use. Given the complexity and the push for evidence-based policymaking, we are witnessing a more or less steady growth of Decision Support Tools and Systems - often referred to by the acronym DST and DSS - meant to assist practitioners in planning and designing our cities and living environments. This report serves as a summary of our efforts in inventorying and summarizing currently available Decision Support Tools, providing a valuable resource for those engaged in the dynamic field of urban planning and governance.
Stakeholder engagement has become a watchword for environmental scientists to assert the societal relevance of their projects to funding agencies. In water research based on computer simulation modelling, stakeholder engagement has attracted interest as a means to overcome low uptake of new tools for water management. An increasingly accepted view is that more and better stakeholder involvement in research projects will lead to increased adoption of the modelling tools created by scientists in water management. However, we cast doubt on this view by drawing attention to how the freedom of stakeholder organizations to adopt new scientific modelling tools in their regular practices is circumscribed by the societal context. We use a modified concept of co-production in an analysis of a case of scientific research on drought in the UK to show how relationships between actors in the drought governance space influence the uptake of scientific modelling tools. The analysis suggests an explanation of why stakeholder engagement with one scientific project led to one output (data) getting adopted by stakeholders while another output (modelling tools) attracted no discernible interest. Our main objective is to improve the understanding of the limitations to stakeholder engagement as a means of increasing societal uptake of scientific research outputs.
Simulation models can be valuable tools in supporting development of air pollution policy. However, exploration of future scenarios depends on reliable and robust modelling to provide confidence in outcomes which cannot be tested against measurements. Here we focus on the UK Integrated Assessment Model, a fast reduced-form model with a purpose to support policy development with modelling of multiple alternative future scenarios, and the EMEP4UK model which is a complex Eulerian Atmospheric Chemistry Transport Model requiring significant computing resources. The EMEP4UK model has been used to model selected core scenarios to compare with UKIAM, and to investigate sensitivity studies such as the interannual variability in response to meteorological differences between years. This model intercomparison addresses total PM2.5, primary PM2.5 and Secondary Inorganic Aerosol concentrations for a baseline of 2018 and selected scenarios for projections to 2040. This work has confirmed the robustness of the UK Integrated Assessment Model for assessing alternative futures through a direct comparison with EMEP4UK. Both models have shown good agreement with measurements, and EMEP4UK shows an ability to replicate past trends. These comparisons highlight how a combination of reduced-form modelling (UKIAM) and complex chemical transport modelling (EMEP4UK) can be effectively used in support of air pollution policy development, informing understanding of projected futures in the context of emerging evidence and uncertainties.
This research utilizes an Object-Oriented Bayesian Network (OOBN) to model the relationships between the Sustainable Development Goal (SDGs) and resilience and sustainability at national, regional, and global levels. The ability of the OOBN to learn the parameters, i.e., the conditional probability distributions between the variables included in the network, was exploited to explore the impacts of progress of SDGs on the sustainability and resilience of nations. The resulting OOBN is used to examine different situations pertinent to policy analysis and design at the times of disasters, particularly in the wake of the COVID-19 pandemic. Three case studies are used to illustrate the step by step process of using the proposed OOBN as well as the expected results of its application in policy analysis and evaluation contexts. The proposed is able to provide insight regarding which SDGs will have more significant impacts on both resilience and sustainability as well as their constituent components. The results of this research indicate how data induced OOBNs can be utilised by policy makers to prioritize new policies and evaluate the impacts of existing policies on both the resilience and sustainability of societies.
Sustainable development goals (SDG) represent one of the pillars of the UN’s 2030 agenda aiming to secure a more sustainable future for all nations. The 17 goals comprising the SDG framework form a transformative action plan that is designed to help nations achieve a more sustainable future focusing on their environmental, social, and economic systems. Achieving the goals within the designated timeframe, however, entails overcoming multiple challenges related to policy design, resources management, and harnessing the synergies and trade-offs among the goals themselves. Recognizing the vital role modeling and simulation can play in facilitating the achievement of the SDG, this paper provides a review of the academic literature published between 2015 and 2021 relating to the models developed to tackle SDG related issues. The main purpose of this review is to explore the modeling practices related to SDG and to determine how aligned those modeling practices are with the priorities of sustainability science. We developed a multi-dimension classification scheme to categorize the models retrieved from the literature based on their main characteristics focusing on characteristics such as the modeling approach used, model uncertainty treatment, model temporal and spatial scales, and which science priority the model focuses on. The results of this research show that most of the models focused on studying policy changes needed to achieve the SDG and monitoring the progress of the SDG. The results also highlight missing important characteristics in SDG models such as cross-scale treatment and advanced uncertainty treatment. While this research can be valuable in informing modelers about the gaps need to be filled in SDG modeling and facilitates the communication between modelers and policy makers, it can be further extended by including an evaluation for the modeling approaches used in SDG contexts as well as adding more characteristics to the model categorization scheme.
Energy system models are advancing rapidly. However, it is not clear whether models are becoming better, in the sense that they address the questions that decision-makers need answered to make well-informed decisions. Therefore, we investigate the gap between model improvements relevant from the perspective of modellers compared to what users of model results think models should address. Thus, we ask: What are the differences between energy model improvements as perceived by modellers, and the actual needs of users of model results? To answer this question, we conducted a literature review, 32 interviews, and an online survey. Our results show that user needs and ongoing improvements of energy system models align to a large degree, so that future models are indeed likely to be better than current models. We also find mismatches between the needs of modellers and users, especially in modelling of social, behavioural and political aspects, the trade-off between model complexity and understandability, and the ways that model results should be communicated. Our findings suggest that a better understanding of user needs and closer cooperation between modellers and users is imperative to truly improve models and unlock their full potential to support the transition towards climate neutrality in Europe.
In the context of relentless global urbanisation, and with most of the world’s population already living in cities, the urban environment retains its historic status as a key arena for public health policy and action. The concept of Ecological Public Health emphasises the interconnections between humans and the natural world and the reality that health, wellbeing, equity and indeed humanity’s survival rely on natural systems and processes. This chapter discusses conceptual models to support policy and action in environmental health but, especially, to deliver ecological public health in the urban context. One ‘family’ of conceptual models, the DPSEEA model and its derivatives, is particularly suited to this role as it offers an integrated and policy-relevant conceptualisation of the relationship between human activity, the physical environment, and health. The chapter illustrates a number of conceptual models and emphasises their utility as tools with which to think, communicate and deliver when addressing public health and societal challenges of unprecedented complexity. Only by developing and applying the correct tools can we confront the challenges of the urban context and design policies that create a healthier, more sustainable and more equal world.
The key issue of contemporary planning is that it has to work within and respond to the scarcity of resources. The competition for land is particularly intense in growth areas, especially around cities, where suburbanisation is accelerating and causes fundamental change of the evolved settlement pattern and the structure of land use. Planning in this situation must perform the difficult role of mediator in the competition for land, guiding the growth process and at the same time saving and protecting the open spaces which are indispensable for healthy living and are significant natural heritage. The integrated decision support systems (DSS) are more and more popular among spatial planners and decision makers since 2000. These systems facilitate to incorporate scientific knowledge in the decision making process. During the last years in the unit for spatial planning of the Lechner Knowledge Centre we developed for a country specific spatial decision support, the Hungary Spatial Decision Support model. This study introduces this model and how it can assist territorial planning and decision making.
Natural hazard risk is largely projected to increase in the future, placing growing responsibility on decision makers to proactively reduce risk. Consequently, decision support systems (DSSs) for natural hazard risk reduction (NHRR) are becoming increasingly important. In order to provide directions for future research in this growing area, a comprehensive classification system for the review of NHRR-DSSs is introduced, including scoping, problem formulation, the analysis framework, user and organisational interaction with the system, user engagement, monitoring and evaluation. A review of 101 papers based on this classification system indicates that most effort has been placed on identifying areas of risk and assessing economic consequences resulting from direct losses. However, less effort has been placed on testing risk-reduction options and considering future changes to risk. Furthermore, there was limited evidence within the reviewed papers on the success of DSSs in practice and whether stakeholders participated in DSS development and use.
Whilst the drivers for public participation in water resources management have been firmly established by the Water Framework Directive, guidance on the design, implementation and management of appropriate tools, particularly Information & Communication Technology (ICT) based tools, and processes to support such participation remains sparse. This paper begins by reviewing the motivations for stakeholder participation in water resources planning & governance and highlighting some of the methodological and technical problems associated with the design and deployment of suitable ICT platforms. It then goes on to report the design and field testing of a specific Multi-Media based ICT platform for use in stakeholder debates, drawing on case study material from a catchment in Southern France. Specifically, we focus our comments on the issues of content structuring, information presentation, knowledge quality and tool functionality, seen as the major determinants of the tool's ability to support dialogue among non-scientific stakeholders.
The discipline of modeling has advanced only slowly compared to disciplines concerned with analyzing and solving models once they are brought into being. Structured Modeling is an attempt to redress this imbalance.
Structured Modeling aims to provide a formal mathematical framework and computer-based environment for conceiving, representing, and manipulating a wide variety of models. The framework uses a hierarchically organized, partitioned, and attributed acyclic graph to represent the semantic as well as mathematical structure of a model. The computer-based environment is evolving via experimental prototypes that provide for ad hoc query, immediate expression evaluation, solving simultaneous systems, and optimization.
If successful, Structured Modeling will enable model-based work to be done with greater productivity and acceptance by nonspecialists, will exploit important developments in small computers, and will cross-fertilize management science/operations research, artificial intelligence, database management, programming language design, and software engineering.
This paper is an introduction and status report on a long term project. The presentation is based largely on examples; rigorous development and details are left to a series of technical reports.
A great deal of new knowledge and research material have been generated from research carried out under the auspices of the European Union (EU). However, only a small amount has been made available as practical policy-support tools. In this paper, we describe how EU funded research models and understanding have been integrated into an interactive decision-support system addressing physical, economic and social aspects of land degradation in the Mediterranean. We summarise the 10 constituent models that simulate hydrology, human influences, crops, natural vegetation and climatic conditions. The models operate on very different spatial and temporal scales and utilise different modelling techniques and implementation languages. Many scientific, modelling and technical issues were encountered during the transformation of ‘research’ models into ‘policy’ models. We highlight the differences between each type of model and discuss some of the ontological and technical problems in re-using research models for policy-support, including resolving differences in temporal scale and some of the software engineering aspects of model integration. The involvement of policy-makers, ‘stakeholders’ and other end-users is essential for the specification of relevant decision-making issues and the development of useful interactive support tools. We discuss the problems of identifying both the decision-makers and the issues they perceive as important, their receptivity to such tools, and their roles in the policy-making process. Finally, we note the lessons learned, the resources needed, and the types of end-users, scientists and mediators required to ensure effective communication, technical development and exploitation of spatial modelling tools for integrated environmental decision-making.
Due to mounting human pressure, stakeholders in northern Thailand are facing crucial natural resources management (NRM) issues. Among others, the impact of upstream irrigation management on downstream agricultural viability is a growing source of conflict, which often has both biophysical and social origins. As multiple rural stakeholders are involved, appropriate solutions should only emerge from negotiation. CATCHSCAPE is a Multi-Agent System (MAS) that enables us to simulate the whole catchment features as well as farmer’s individual decisions. The biophysical modules simulate the hydrological system with its distributed water balance, irrigation scheme management and crop and vegetation dynamics. The social dynamics are described as a set of resource management processes (water, land, cash, labour force). Water management is described according to the actual different levels of control (individual, scheme and catchment). Moreover, the model’s architecture is presented in a way that emphasises the transparency of the rules and methods implemented. Finally, one simulated scenario is described along with its main results, according to different viewpoints (economy, landscape, water management).
Sumario: Advances in human-computer interaction have stimulated the development of cognitive ergonomics to model interaction between two different information processing systems. However, predicting human-computer systems performance also involves task, user, and environmental factors. This paper reviews developments relating to all of these factors at the individual human-computer interaction level, and at the socio-technical systems level. Thereafter it examines the problems of improving human-computer interaction in practice, and the need to embed human considerations in all forms and at all stages of computer systems design processes
Environmental processes have been modelled for decades. However, the need for integrated assessment and modeling (IAM) has grown as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that are needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers, stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century.
This report highlights several research areas likely to produce significant results over the next decade. It is the results of a workshop sponsored by the Systems Theory and Operations Research (STOR) Program of the Electrical, Communications, and Systems Engineering Division of the National Science Foundation; 24 operations research professionals participated, representing 15 U.S. universities, 1 foreign university, and 3 U.S. companies. After summarizing some significant OR accomplishments, the report sketches some important opportunities in the field of optimization, stochastic processes, the operations research/artificial intelligent interface, operational and modeling science, and manufacturing and logistics.
Knowledge system theory (KST) seeks to close the rift between hard and soft methods by reimposing the von Bertalanffy definition of a system while allowing that systems are epistemological constructs. KST is located within the broad thrust of western philosophy by association with classical rationalism, modernism and with the 20th century rediscovery of emergence. KST blurs the conventional distinctions of research management from research method and of science from metaphysics. A research team is an appreciative system whose purpose is to create useful new knowledge within time and budgetary constraints. An undisciplined research team can destabilize the human ecosystems it is contracted to serve. There are ethical and pragmatic reasons for accepting certain disciplines. One way of avoiding these ethical problems is to study academic knowledge creation both as a source of practical insights that can be used to manage the research process more effectively and of theoretical insights that provide a wider understanding of cultural ecodynamics. In general, the larger the group, the less it can be said to know. This suggests resolution into small workgroups with relatively simple and infrequent information flow between them. A provisional typology of knowledge communities and research problems is presented and practical advice for the organization of integrative research offered. This advice challenges the popular view that communication is best served by removing the boundaries between epistemic communities. Intellectual diversity is an asset that must be conserved.
The need to better understand how people and communities understand, interact with, and behave in relation to, water and water management systems has long been highlighted by both national and supra-national governmental bodies. Such understandings are of particular relevance to support the design and deployment of water policy instruments. We argue that, in analysing individual, community and organizational interactions with water as both a natural resource and a commodity, there is a need to move away from policy tool design and deployment perspectives which characterize consumer response as an artefact of social or economic status to one which explicitly incorporates the capacity for response as well as the ambition of policy. A framework for evaluating water management policy instruments based on a conceptual model of ‘receptivity’ is presented in detail and three case studies demonstrating its use in a variety of contexts (regional water management, water recycling, and water filters) are reported on. Comments on the relevance and scope of the approach are provided together with conclusions regarding strengths, weaknesses, and suitable application contexts.
Set on its current course thirty years ago by Herbert Simon’s notions of bounded rationality and sequential stages, the research literature of organizational decision making is claimed in this paper to have suffered from three major limitations labeled reification, dehumanization, and isolation. In particular, it has been stuck along a continuum between the cerebral rationality of the stage theories at one end and the apparent irrationality of the theory of organized anarchies at the other. This paper seeks to open up decision making in three respects. First, the concept of “decision” is opened up to the ambiguities that surround the relationship between commitment and action. Second, the decision maker is opened up to history and experience, to affect and inspiration, and especially to the critical role of insight in transcending the bounds of cerebral rationality. Third, the process of decision making is opened up to a host of dynamic linkages, so that isolated traces of single decisions come to be seen as interwoven networks of issues. The paper concludes with a plea to open up research itself to the development of richer theory on these important processes.
An exploratory study of the difficulties of design reuse has been conducted in two design organisations. The study involved building simple causal networks to explain each of 171 cases of problematic reuse described in unstructured interviews with 50 designers and design managers. Most reuse problems concerned transfer that was inhibited in some way, although error and unexpected effort in the reuse process was also a significant outcome. Many of the causes lying behind these problems were local (such as unprecedented client requirements). But many were more general and structural. For instance, reuse was often precluded by differences in preference among designers, or among clients, and these differences appeared to arise from reinforcing feedback effects, which in turn arose from limitations of human inference-like insensitivity to sampling variability and confirmation bias. Reuse was also precluded by engineering problems, such as the combinatorial properties of constraints, which meant, in some instances, component designs could almost never be transferred between applications without extensive modification. In almost all cases, reuse was made problematic by a combination of factors ranging across several categories. Designers' motivations to innovate, for instance, only inhibited reuse because the environment they worked in led them to expect that existing designs would be deficient, and because they had different preferences from the original designer.
Under the sponsorship of the Systems Theory and Operations Research STOR Program of the National Science Foundation, a Committee on the Next Decade in Operations Research CONDOR held a 3-day workshop in mid-1987. The report of this committee appeared a year later in Operations Research pp. 619-637. In view of the importance of this subject, and because CONDOR had, a fortiori, to limit its considerations to a number of important ones of primary interest to its members, a number of other OR analysts of widely different experience were asked to comment on this report. Four of the responses are presented below.
Model formulation is a critical craft skill that deserves more attention from OR practitioners and educators. As a step toward understanding how experts formulate OR models, I conducted an experiment in which twelve skilled analysts generated think-aloud protocols while working model formulation exercises 60 minutes long. The coded transcripts tracked the modelers' attention to five topics corresponding to stages in the usual OR paradigm: problem context and model structure, realization, assessment, and implementation. Analysis of the transcripts yielded these conclusions: topics other than structure attracted a significant proportion of modelers' attention; modelers frequently switched their attention among the topics; the switching was usually an alternation between structure and the other topics, especially assessment; on average, the modelers progressed through the topics in the given order; there was limited support for the notion of modeler-specific and problem-specific effects influencing the attention given to each topic; and there was support for the idea of individual modeling styles.
To meet the challenges of sustainability and catchment management requires an approach that assesses resource usage options and environmental impacts integratively. Assessment must be able to integrate several dimensions: the consideration of multiple issues and stakeholders, the key disciplines within and between the human and natural sciences, multiple scales of system behaviour, cascading effects both spatially and temporally, models of the different system components, and multiple databases. Integrated assessment (IA) is an emerging discipline and process that attempts to address the demands of decision makers for management that has ecological, social and economic values and considerations. This paper summarises the features of IA and argues the role for models and information systems as a prime activity. Given the complex nature of IA problems, the broad objectives of IA modelling should be to understand the directions and magnitudes of change in relation to management interventions so as to be able to differentiate between associated outcome sets. Central to this broad objective is the need for improved techniques of uncer-tainty and sensitivity analysis that can provide a measure of confidence in the ability to differentiate between different decisions. Three examples of problems treated with an IA approach are presented. The variations in the way that the different dimensions are integrated in the modelling are discussed to highlight the sorts of choices that can be made in model construction. The conclusions stress the importance of IA as a process, not just as a set of outcomes, and define some of the deficiencies to be overcome.
The search for scientific bases for confronting problems of social policy is bound to fail, becuase of the nature of these problems. They are wicked problems, whereas science has developed to deal with tame problems. Policy problems cannot be definitively described. Moreover, in a pluralistic society there is nothing like the undisputable public good; there is no objective definition of equity; policies that respond to social problems cannot be meaningfully correct or false; and it makes no sense to talk about optimal solutions to social problems unless severe qualifications are imposed first. Even worse, there are no solutions in the sense of definitive and objective answers.
Better integration in land planning and management can be supported through the use of suitable model-based tools. Vegetation state transition models have been noted as being useful in this context, providing a simple, useful means of capturing available ecological knowledge. We describe a simple ‘proof of concept’ rule-based system developed to contribute methodologically to management-oriented modelling of vegetated landscapes. The system is based upon a clear separation of direction from rate of change and the use of a general temporal reasoning system, a feature that facilitates modelling of situations where environmental change occurs causing an increase or decrease in rate without affecting direction of vegetation change. To ease model development and use the system represents vegetation dynamics in a way that has a close correspondence to the structure of understanding communicated by vegetation ecologists. A test model is described and run under different conditions to demonstrate the system. The results show that although the rule-based system and in particular the temporal reasoning system used operate successfully, there are a number of deficiencies in the modelling system as currently implemented. Future development possibilities are detailed along with a broader discussion regarding the needs of management-oriented modelling and the utility of state transition approaches.
Seaton and Cordey-Hayes [1] have drawn attention to many of the limitations and deficiencies in traditional technology transfer mechanisms. They argued that this was largely due to the dominance of the linear model of innovation on conventional thinking. Furthermore, they presented an alternative model of technology transfer (accessibility-mobility-receptivity) which emphasized the interactive nature of the process and highlighted the absence of any substantial research within the area of receptivity. This paper continues from that 1993 paper and focuses on the concept of ‘receptivity’ in the context of inward technology transfer.A conceptual framework is developed which identifies four major components of the inward technology transfer process. These are: ‘awareness’, ‘association’, ‘assimilation’ and ‘application’. Using this conceptual device, a series of studies is conducted within a number of businesses within ICI Chemicals and Polymers Ltd.The conclusions indicate the importance of non-routine activities and effective communications between credible boundary-spanning individuals. These are key aspects of the assimilation of new knowledge and the inward transfer process. This requires successful organizations not only to be efficient and competitive at their routine business in order to survive in the short term, but also to make room for opportunities for these creative, non-routine assimilation processes, which are more stochastic in nature, if they are to remain competitive in the long term.
In recent years, pressure has increased on environmental scientist/modellers to both undertake good science in an efficient and timely manner, under increasing resource constraints, and also to ensure that the science being performed is immediately relevant to a particular environmental management context. At the same time, environmental management is changing, with increasing requirements for multi-scale and multi-objective assessment and decision making that considers economic and social systems, as well as the ecosystem. Integration of management activities, and also of the modelling undertaken to support management, has become a high priority. To solve the problems of application and integration, knowledge encapsulation in models is being undertaken in a way that both meets the needs for good science, and also provides the conceptual and technical structures required for broader and more integrated application of that knowledge by managers. To support this modelling, tools and technologies from computer science and software engineering are being transferred to applied environmental science fields, and a range of new modelling and software development approaches are being pursued. The papers in this Special Issue provide examples of the integrated modelling concepts and applications that have been, or are being, developed. These include the use of object-oriented concepts, component-based modelling techniques and modelling frameworks, as well as the emerging use of integrated modelling platforms and metadata support for modelling semantics. This paper provides an overview of the science and management imperatives underlying recent developments, discusses the technological and conceptual developments that have taken place, and highlights some of the semantic, operational and process requirements that need to be addressed now that the technological aspects of integrated modelling are well advanced.
The paper begins by offering a process oriented description of industrial technology transfer. This is used as a basis for a brief review of the limitations of recent and current models of industrial technology transfer. In response to these limitations, an interactive model (and evaluative framework) is developed on the basis of three components: accessibility, mobility and receptivity (AMR). The deficiencies of identifying technology largely by its techno-economic attributes are discussed, and a multiple constituency approach is proposed.After definition and discussion of these concepts, the paper illustrates the application of the AMR framework and the development of the multiple-constituency approach in four contexts. Each context is explored through an applied study in which a researcher from the team has spent two or three years working with an industrial partner, a group of companies or a technology transfer intermediary.A final section gives the substantive applied conclusions from the four case studies and presents some general policy implications.
Recent structural and jurisdictional developments in natural resources management have resulted in requirements for more open and inclusive management processes. These processes generally use participatory approaches to involve a wide stakeholder audience in management investigations and policy development. Along with these changes have come requirements for more open approaches to the combined spatial and temporal modelling often used in management, leading to the development of tools that are designed to meet the needs of managers and other stakeholders. This paper presents a modelling shell that has been developed to support the participatory assessment and management of natural resources. The modelling shell concept is based upon experience gained during development of a number of natural resources modelling applications over the past 10 years. The shell supports the basic functions of data manipulation, simulation management and output presentation, and is underlain by a simple, accessible and easily understood system of data storage and manipulation. By providing these common functions, the modelling shell allows the people involved in system model construction to concentrate on the important issues of algorithm selection and development, rather than the often messy and time consuming issues of input–output.
In this article, we identify four typical roles played by computer models in environmental policy-making, and explore the relationship of these roles to different stages of policy development over time. The four different roles are: models as eye-openers, models as arguments in dissent, models as vehicles in creating consensus and models for management. A general environmental policy life cycle is used to assess the different roles models play in the policy process. The relationship between the roles of models and the different stages of the policy life cycle is explored with a selection of published accounts of computer models and their use in environmental policy-making.
With rapid intensification of agricultural catchments in Northern Thailand a suite of environmental issues has surfaced. The Integrated Water Resources Assessment and Management (IWRAM) project was instigated in response to these issues. The project developed a Decision Support System (DSS) for the exploration of biophysical and socio-economic impacts of water resource use options. The IWRAM-DSS is comprised of a ‘Biophysical Toolbox’ that can be implemented alone and an ‘Integrated Toolbox’ that links socio-economic models with the Biophysical Toolbox to explore economic trade-offs and impacts of various scenarios. The Biophysical Toolbox is comprised of three modules—the CATCHCROP crop model, a hydrologic module based upon the IHACRES rainfall-runoff model, and a Universal Soil Loss Equation (USLE) approach modified to suit conditions in Northern Thailand. This paper describes the Biophysical Toolbox and runs forest conversion, land management, and climate scenarios to demonstrate the potential of this tool in exploring the environmental effects of land and water management options.
To study the possible impact on the Lake Malawi/Nyasa/Niassa water quality due to actions performed at the watershed level, a modelling project supported by the World Bank, was conducted by the United Nations University, the University of Waterloo and WL Delft Hydraulics to integrate physical and bio-chemical processes models in the lake and its basin which affect lake and river water quality. The purpose of the integration of different models was to provide a set of tools in order to analyze the impact on the lake due to actions performed in the watershed. In this paper, we present the watershed and lake box models integration and a case application to find how agricultural practices and deforestation may impact on the water quality of rivers and streams which will then lead to changes in the nutrient loading to the lake.
MULINO, an ongoing project financed by the European Commission, has released the prototype of a Decision Support System software (mDSS) for the sustainable management of water resources at the catchment scale. The software integrates socio-economic and environmental modelling, with geo-spatial information and multi-criteria analysis. The policy background refers to the EU Water Framework Directive. The challenging multi-disciplinary context was approached by developing an innovative and dynamic implementation of the DPSIR framework, originally proposed by the European Environmental Agency. In mDSS integrated assessment modelling provides the values of quantitative indicators to be used for transparent and participated decisions, through the application of value functions, weights and decision rules chosen by the end user. Simple routines for the sensitivity analysis and comparison of alternative weight vectors also provides effective decision support by exploring and finding compromises between conflicting interests/perspectives in a multi-stakeholder context.
Organizational decisions of the future may include social, environmental, and economic concerns, and be much more "wicked" [Policy Sciences, 4 (1973) 155], complex and interconnected than those of the past. Organizations and their decision support systems must embrace procedures that can deal with this complexity and go beyond the technical orientation of previous DSS. Singerian inquiring organizations [Australian Journal of Information Systems, 6 (1) (1998) 3; http://www.cba.uh.edu/~parks/fis/fis.htm (1998); Proceedings of 3rd Americas Conference on Information Systems, Indianapolis, August 1997, p. 293; Proceedings of the 1999 Meeting of the America's Conference on Information Systems, Milwaukee, August 1999; Special Issue of Information Systems Frontiers on Philosophical Reasoning in Information Systems Research (in press)], based on Churchman's [The Design of Inquiring Systems: Basic Concepts of Systems and Organization, Basic Books, New York, NY, 1971] inquiring systems and Mitroff and Linstone's [The Unbounded Mind: Breaking the Chains of Traditional Business Thinking, Oxford Univ. Press, New York, 1993] unbounded systems thinking (UST), are designed to deal with wicked decision situations. This paper discusses DSS and knowledge management in Singerian organizations and calls for a new decision-making paradigm for DSS.
This paper presents a vision of a desired future of science. In this vision, the future will bring the reintegration of the study of humans and the rest of nature. The barriers between the traditional disciplines will dissolve and a true 'consilience' of all the sciences and humanities will occur. This consilient transdisciplinary science will emerge from a rebalancing of analysis and synthesis, a recognition of the central role of envisioning in science, a pragmatic philosophy built on complex systems theory and modeling, a multiscale approach, and a consistent theory of cultural and biological co-evolution. It will allow us to build a world that is both sustainable and desirable and that recognizes our fundamental partnership with the rest of nature. It is a world that we must first imagine in order to achieve.
A suite of “desirable features” for Environmental Decision Support Systems (EDSS) is proposed by identifying the general attributes of environmental systems which are of importance to modelling and simulation, and the different categories of users of EDSSs. With these features as a guide, a review and discussion of the approaches to delivering Environmental Decision Support Systems is presented. The two most efficient approaches are: (i) the use of modelling and simulation software tools, and (ii) the use of model integration and re-use techniques. A review of the currently available software tools for environmental modelling and simulation is therefore presented, and an overview of the current research activities in model integration and re-use is provided. Numerous existing EDSS are used as examples of the different approaches throughout the review. The review highlights the advantages and disadvantages of the two main approaches to EDSS delivery, and subsequently discusses the role emerging information technologies may play in the future delivery of EDSS.The paper concludes that while the available software for modelling and simulation is very advanced, some of the desirable features of EDSS (such as representation of spatial data and provision of expert help) cannot be easily delivered using such software. While the research activities in model integration and re-use are making real advances, especially in delivering those features which cannot currently be provided using modelling and simulation software, this is not occurring in any coordinated or synergistic manner. The ultimate goal should be to provide a fully general EDSS development platform which would allow system analysts to develop EDSS for any environmental domain complete with all desirable features. This is still considered to be very much a long-term ambition.
Environmental processes have been modelled for decades. However, the need for integrated assessment and modeling (IAM) has grown as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that are needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers, stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century.
Accomplished authors, Preece, Rogers and Sharp, have written a key new textbook on this core subject area. Interaction Design deals with a broad scope of issues, topics and paradigms that has traditionally been the scope of Human-Computer Interaction (HCI) and Interaction Design (ID). The book covers psychological and social aspects of users, interaction styles, user requirements, design approaches, usability and evaluation, traditional and future interface paradigms and the role of theory in informing design. The topics will be grounded in the design process and the aim is to present relevant issues in an integrated and coherent way, rather than assembling a collection of chapters on individual HCI topics.KEY FEATURES: This truly integrated approach to HCI provides students with background information from psychology, sociology, anthropology, information systems and computer science provides principles and skills for designing any technology through the use of many interesting and state of the art examples. The author supported, highly interactive Web Site provides resources that allow students to collaborate on experiments, participate in design competitions, collaborate on design, find resources and communicate with others. The accompanying Web Site also features examples, step-by-step exercises and templates for questionnaires.
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