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The Electronic Oracle: Computer Models and Social Decisions.

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... However, the model choice is an assumption in itself. Each model has a distinct logic for describing the causal mechanisms of a system, and a limited system boundary for what phenomena are considered, held exogenous, or disregarded (Forrester, 1968;Meadows & Robinson, 1985) (see Section 4.2). Using any given model implies assuming that the future evolves according to that model's logic and that phenomena outside the model scope can be reasonably neglected, although such assumptions are often made implicitly (Sterman, 2002). ...
... However, as this means leaving some aspects outside the scope, scoping should be a careful and deliberate choice, and the results' interpretation should consider the chosen scope. Meadows and Robinson (1985) underscore the significance of recognizing system boundaries to ensure impactful interventions. Furthermore, they warn of the inherent limitations and biases and possible unintended outcomes when drawing system boundaries; and that these can also have noteworthy ethical implications. ...
... To structure the scoping, one can categorize how various mechanisms and assumptions affect the system, for example, as shown in Figure 4b. Adapting from Meadows and Robinson (1985), one can distinguish between factors considered within the model, either endogenously or exogenously; or deliberately omitted from the model. We add to this "unknown unknowns"-factors that are neither known nor predictable, and which are thus not deliberately considered nor omitted. ...
Article
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In an increasingly complex world, futures thinking can be used to understand the conditions that define future events' realization. This paper presents a novel approach to explore under what conditions some chosen future event would occur. The approach can be seen as a fusion of exploratory scenarios with the backward-looking perspective of backcasting while resembling cross-impact methods in how future events' realization arises from the interaction of several uncertain assumptions. The approach maps the causal mechanisms and assumptions that lead to the investigated event's realization, and results in a subset of assumption combinations that lead to the event being realized or not, and scenario sets where the event is either realized or not. This analysis provides insights beyond “Is the event likely to occur?”, revealing the various circumstances under which it can occur. We also provide ways for considering probabilities and deliberate decisions within the approach. We illustrate the approach with three example cases from different problem domains, such as reaching long-term climate targets, and employing various methods, such as causal mapping, influence diagrams, and optimization. We conclude with a discussion on the approach's potential to enhance foresight practice, emphasizing its synergistic relation to existing methods and its contribution to a richer, more nuanced anticipation of the future.
... As Meadows said: 'The modellers keep asking: "Why don't you use our model?" The policymakers respond: "Why don't you make models we can use?"' (Meadows and Robinson;1985, p. 7). Fenech, A., Dillon, C., Ntanos, K., Bell, N., Barrett, M. and Strlič, M. (2013). ...
... Degradation risks determined from temperature and relative humidity data, PhD thesis, TU Eindhoven. Meadows, D. H. and Robinson, J. M. (1985). Sterman 2000, p. 28 In the previous chapter, we stated that one of the key elements of the Archival Preservation Management (APM) model is that preservation measures will not be modelled in isolation but in the broader context of other archival and library functions. ...
... Furthermore, the literature abounds with examples of systems that can be modelled with either SD or ABM, taking advantage of each approach's capabilities (Macal;Cimler et al.;. But the most profound difference is that 'different modelling paradigms cause their practitioners to define different problems, follow different procedures, and use different criteria to evaluate the results' (Meadows and Robinson;1985, p. 20). ...
Thesis
As cultural heritage institutions, libraries and archives are responsible for managing collections in order to ensure access for present and future generations, and sustainable preservation. In pursuing these two goals, institutions face the challenge of determining to what extent preservation actions are beneficial in the context of their own collections. This project contributes to the complex decision-making processes of collections management by developing a mathematical model that shows, quantitatively, the effects of different preservation decisions during a collection’s lifetime. The novelty of this research lies in its approach to preservation management not as single, independent measures, but as a process that is part of a complex system: preservation management is not seen in isolation, but in relation to the other archival and library functions in the broader context of collections management. To meet this aim, complex systems modelling and simulation paradigms, such as system dynamics (SD) and agentbased modelling (ABM), are applied. Applying simulation to model preservation management decisions has the potential to develop into an integrated approach for evaluating and comparing the potential benefits of different preservation measures, which, so far, is lacking. This model will support collection keepers in the complex decision-making process of collection management by comparing different strategies, and therefore finding potential synergies as well as counter-intuitive decision outcomes which otherwise might not have been identified.
... Systems and resilience thinking embrace change, uncertainty, unknowability, and normativity, enabling the examination of socio-ecological systems under the influence of a range of different intertwined driving factors and conditions [24][25][26]. They counteract bounded rationality that drives conclusions and actions that are not reasonable within broader contexts and time horizons [2]. ...
... They counteract bounded rationality that drives conclusions and actions that are not reasonable within broader contexts and time horizons [2]. They also reclaim the natural synergy between intuition and rationality, enhancing abilities to acknowledge complexity, be imaginative, and artfully inquire "why" and "what if" [2,24]. Particularly, why does human society continue to exhibit so little achievement in addressing the essential issues of global sustainability? ...
... Although models are designed to improve understanding of complex relationships and manifest a strong congruence with the experienced world, they fall far short of representing it fully [2,3]. The realism of a model is inherently dependent not on its realistic representativeness, but on its faculty to respond with a coherent pattern of behavior over time [24]. Additionally, isolated systems with well-defined boundaries are conceptual constructs (based on perceptions, logical constructs, and social agreements) and do not exist in the intertwined tissues of the observed world, in which everything comes from somewhere and goes somewhere, and continuously evolves [2]. ...
Article
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The main objective of this work is to foster long-term and regenerative perspectives on global sustainability. In pursuing this goal, this study encompasses a critical analysis and synthesis of insights from the fields of systems and resilience thinking, a conceptual framework for modeling and envisioning socio-ecological systems, and the proposition of the “flourishing within boundaries” archetype, which is conceived to provide meaningful insights regarding the essential conditions that would enable global society to flourish not just safely but also fairly. Through systems dynamics modeling, portrayed scenarios emphasize that the higher the regenerative societal change achieved, the greater the exponential behavior of the system’s speed towards promising socio-ecological outcomes. Especially for longer time horizons, the overall results reveal that the pace of the transitions associated with the societal action is inherently higher than those associated with the limiting social and ecological factors. Actions taken by individuals in the local realm and short-term time horizon may not only have immediate local effects but also a variety of innumerous ones that radiate out for years, decades, and even centuries to come. Finally, the pursuit of flourishing within boundaries relies on thinking rationally, globally, and above all, through a long-term and regenerative perspective.
... The compass of a system dynamics model, such as the one of Brooks' law discussed here, means that the rules by which it is validated will be slightly different from other modelling techniques. For example, the output of a system dynamics model is meant to be read, not for particular time-point predictions, but for qualitative behavioural patterns such as growth, decline, oscillation, stability, and instability 29 . This goal of understanding general dynamic tendencies means that the model's parameters are less reliant on highly precise numerical data. ...
... 31 The calibration of soft variables may also seem an arbitrary process in which the model is 'made' to respond in a certain manner. However, the way in which the soft (and hard) variables react must be internally consistent, that is, they must generate behaviour that matches what is observed in the actual system 23,29,31 . ...
... To avoid bias, the SEER should provide all possible futures accommodating multiple and potentially divergent worldviews to the user given the available data and models. Therefore, the SEER must acknowledge that a model is constructed with its own (often implicit) worldview [51]. Model integration requires combination of the views, which can be challenging or even impossible if they contradict. ...
... The research challenge arising from this is not an unrealistic attempt at consolidating all existing worldviews. Instead, what we need are modeling concepts and mechanisms that allow us to contrast different worldviews to illustrate and explore conflicts between the assumptions and implications of two or more worldviews [51]. One option would be to use System Dynamics to reach a group consensus and enhance systems thinking [68]. ...
Article
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Sustainability—the capacity to endure—has emerged as a concern of central relevance for society. However, the nature of sustainability is distinct from other concerns addressed by computing research, such as automation, self-adaptation, or intelligent systems. It demands the consideration of environmental resources, economic prosperity, individual well being, social welfare, and the evolvability of technical systems. Thus, it requires a focus not just on productivity, effectiveness, and efficiency, but also the consideration of longer-term, cumulative, and systemic effects of technology interventions, as well as lateral side effects not foreseen at the time of implementation. Furthermore, sustainability includes normative elements and encompasses multi-disciplinary aspects and potentially diverging views. As a wicked problem (see the sidebar “Wicked Problems”), it challenges business-as-usual in many areas of engineering and computing research.
... Since Meadows and Robinson, (1985) made a strong argument for model building in social decision-making, advances in computing capabilities have opened possibilities for facilitated, computer-assisted processes in group settings. MM is one such participatory approach and uses computer modelling as a consensus building tool. ...
... ‗Management models' such as SDSS have broader application and are developed more to quantitatively explore a range of questions and get an understanding of how addressing one issue can impact on other areas. These models More than 25 years after Meadows and Robinson (1985) published -the electronic oracle: computer models and social decisions‖ the gap between the providers of modelling capacity and end-users persists. The -implementation gap‖ well known from management in the '60s (Ackoff, 1960;Churchman and Schainblatt, 1965) is still very prevalent when it comes to computer modelling (Te Brommelstroet, 2010). ...
Technical Report
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This report looks at some of the integrated computer-based modelling tools available for use by regional level government in New Zealand. These types of modelling tools have the scope to assist end-users by providing the adaptive capacity essential to accelerate the transition toward sustainability. An assessment of eight different modelling tools gives an insight into their use by Regional Authorities (RAs) in New Zealand. As it was of interest to the researchers, the models were evaluated for their degree of integration, spatial capability, and whether they were static or dynamic. It is acknowledged that when making decisions on model use a number of additional criteria need to be considered such as: fitness for purpose, the outputs provided, input data requirements, compatibility with other tools/models, reliability and transparency, user-friendliness, time required for implementation, and times and monetary cost. This study is regarded as a starting point towards a better understanding of the integrated modelling frameworks available for use in planning and further work is anticipated.1 The eight tools covered in this report are: Geographic Information Systems (GIS), Mediated Modelling (MM), Spatially Dynamic Systems Support Modelling (SDSS), Computable General Equilibrium Modelling (CGE), Multi-Criteria Analysis (MCA), Agent-Based Modelling (ABM), Input-Output Modelling (IO), and Bayesian Belief Networks (BBN). These models are assessed according to the degree to which they are integrated, dynamic and spatial. These techniques can be used together assimilating data to populate specific models in order to emphasize different aspects of the questions that each model aims to answer. Responses to a survey undertaken on current model use by RAs indicate they predominantly utilize externally provided models. These models are generally issue specific and in most cases produce a decision-making recommendation. While current model use is issue-based the need for more integrated modelling tools with the capability of demonstrating spatial and temporal change was recognized as important. The three most common barriers to the use of this type of model are: 1) an inability to assess if and how the model adds value; 2) monetary cost; 3) time cost. A theoretical foundation to develop the methodology and tools to build adaptive capacity among stakeholders and decision-makers is provided by the Multi-scale Integrated Modelling for Sustainable Adaptive Systems (MIMSAS) (van den Belt, 2009).
... To avoid bias, the SEER should provide all possible futures accommodating multiple and potentially divergent worldviews to the user given the available data and models. Therefore, the SEER must acknowledge that a model is constructed with its own (often implicit) worldview [51]. Model integration requires combination of the views, which can be challenging or even impossible if they contradict. ...
... The research challenge arising from this is not an unrealistic attempt at consolidating all existing worldviews. Instead, what we need are modeling concepts and mechanisms that allow us to contrast different worldviews to illustrate and explore conflicts between the assumptions and implications of two or more worldviews [51]. One option would be to use System Dynamics to reach a group consensus and enhance systems thinking [68]. ...
... SD models) but also on the complexities of the process itself (Freebairn et al., 2019;Hovmand et al., 2012;Vennix, 2000), as well as on the transformative social outputs it that may derive (Hovmand, 2014;Luna-Reyes et al., 2018;Rouwette et al., 2010). The quantitative SD stream focuses on using models to make sense of complex policy questions through numerical simulation (Meadows and Robinson, 1985;Sterman, 2002). Quantitative SD models are flexible in accommodating quantitative uncertainties in the form of parameter variations, and scenario and sensitivity analyses to make sense of possible futures in a complex and rapidly changing world (Kwakkel and Pruyt, 2013b;Moallemi, Kwakkel et al., 2020). ...
Article
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Problems manifested within social-ecological systems (SES) exhibit dynamic complexity and hold implications for current and future human well-being and environmental sustainability. The complexity of these issues, the ever-present uncertainty inherent to SES, and the multi-stakeholder settings in which they are discussed call for participatory modelling to support decision-making on socio-environmental issues. Yet, this challenging endeavour requires a structured approach-a modelling cycle-to facilitate engagement with the implications of participation and uncertainty as focal points for Good Modelling Practice (GMP). Here we propose an integrated policy analysis framework for SES modelling using System Dynamics (SD). This framework stems from integrating two existing modelling cycles that individually consider participation and uncertainty in SD modelling. Three global modelling phases and a set of tools to address the participation and uncertainty features in SES modelling are distinguished. The framework contributes to mainstreaming GMP, offering a structured model-based approach to enhance the robustness and social acceptance of policies on critical socio-environmental issues.
... However, the literature on ethics in model creation or modeler behavior in system dynamics computer simulation is light. In the field's grounding works, neither Forrester [3] nor Meadows [4] [5] mentioned ethics outside of the context of modeling an ethical case. When referenced in standard textbooks, the term is usually in the context of the modeler's duty to act in a certain way. ...
Conference Paper
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Students, researchers, and practitioners face ethical dilemmas in modeling & simulation (M&S). Motivated over a set of D-Memos on humanitarian aid, we proposed a case method to conduct ethical analysis for M&S efforts. We tested the method against Forrester's 1968 Urban Dynamics and Forrester & Runge's 1971-1974 D-Memos on humanitarian aid, arose the original concerns. Our findings indicated the erasure of Black populations from Urban Dynamics raised more ethical concerns than the humanitarian aid works. We suspect Runge's efforts to systematize ethical perspectives in his D-Memos may have been a contemporary reaction to perceived ethical criticisms of Forrester's Urban Dynamics. Our method revealed many surprises: the novel contributions of Runge decades ahead of his time and forced reevaluation of incorrect beliefs on adoption of Urban Dynamics beyond our field. Rather than being isolated to the historical cases they arose in, the ethical dilemmas identified in these cases remain relevant for study today mirroring dilemmas in current simulation efforts such as climate change. We encourage the adoption of our method as a regular practice for students and to integrate ethical considerations into confidence-building measures on significant M&S efforts. This method will help enhance the holistic view in M&S practices.
... However, none of these have been successful at transforming our economic system (Smith et al. 2010). One of the problems is that most existing approaches are based on the largely monodisciplinary science of the 1950s, '60s and '70s, which were not designed to address the current complex environmental problems (De Greene 1993; Gunderson et al. 1995;Lee 1993;Meadows and Robinson 1985). In the mid-20 th century, sustainability issues were considered to be largely local, reversible, and direct; today, we know that impacts are dynamic, interconnected and occur across broad geographical and economic scales (Daily 2000;Lambin et al. 2001). ...
Preprint
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At the Rio Conference in 1992, the sustainable development agenda promised a new era for natural resource management, where the well-being of human society would be enhanced through the sustainable use of natural capital. Several decades on, economic growth continues unabated at the expense of natural capital, as evidenced by biodiversity loss, climate change and further environmental issues. Why is this happening and what can be done about it? In this research, we present three Agent-Based Models that explore the social, economic and governance factors driving (un)sustainability in complex social-ecological systems. Our modelling results reinforce the idea that the current economic system does not protect the natural capital on which it depends. This is due to a disjunction between the economic and environmental elements upon which the sustainable development paradigm is founded. Additionally, various factors appear to enhance social-ecological system unsustainability: the role of financial entities and monetary debt; economic speculation; technological development and efficiency; lack of long-term views and late government interventions; inefficient tipping point management; and the absence of strong top-down and bottom-up conservation forces. Interestingly, alternative scenarios showed that these same factors could be redirected to enhance sustainable development. The current economic system may, therefore, not be inherently unsustainable, but rather specific economic mechanisms, agents’ decision-making, and the kinds of links between economic and natural systems could be at the root of the problem. We argue that short- and medium-term sustainability can be enhanced by implementing mechanisms that shift capitalist forces to support environmental conservation. Long-term sustainability, however, requires further paradigm change: where the economy integrates, and fully accounts for, externalities and recognises the actual value of natural capital.
... La lecture par la dynamique des systèmes offre une meilleure appréhension des systèmes sociaux et facilitent les processus décisionnels (Sterman, 2000). Aussi, un système est un ensemble de facteurs interdépendants (Meadows & Robinson, 1985 (Sterman, 2000). ...
Thesis
La recherche s’inscrit dans une démarche scientifique et sociologique visant à révéler le facteur principal poussant un ressortissant ouest-africain à quitter son pays et à analyser ses interactions avec d’autres déterminants migratoires. Un examen dans le temps et dans l’espace des mouvements de population, volontaires ou forcés, de la sous-région montre que la mobilité ouest-africaine est en profonde mutation et qu’elle impacte largement les pays extra-régionaux et notamment les Etats de l’Union européenne.Une présentation des théories migratoires démontre que l’analyse des flux ne peut s’effectuer que dans un cadre multidisciplinaire et transversal. Une enquête de terrain forte de 18 entretiens, sa confrontation avec d’autres données et son examen permettent de révéler que le facteur prédominant déclenchant le processus migratoire porte sur une raison économique. Partant de ce facteur, une analyse minutieuse de son interaction avec les autres facteurs établis lors de l’enquête souligne que le « milieu » influe sur le facteur principal déclenchant la migration. Un examen détaillé des principaux déterminants autorise ensuite la modélisation, sous forme d’un graphe d’influence, du processus décisionnel propre à la migration ouest-africaine. Cette action met en évidence l’importance de trois dynamiques sous-jacentes portant sur les sources d’information, la précarité économique et les relations entre l’Etat et les filières d’immigration clandestine, dont l’Etat représente le point commun unique faisant de celui-ci un pivot dans le processus décisionnel.
... Previous studies on modelling did not offer a precise classification that is mutually exclusive but rather provided some distinctions from multiple perspectives. Therefore, we chose to classify models according to four dimensions based on the Association for Computing Machinery's (ACM, 2012) computing classification system in combination with several classic works (Batty, 1976;Meadows & Robinson, 1985;Porgo et al., 2019). These four dimensions are "method," "goal," "motivation," and "focus." ...
Chapter
The COVID-19 pandemic emphasised the need for decision-support tools to assist urban designers in building resilient and smart cities. Therefore, a multi-disciplinary systematic review was conducted following the PRISMA guideline to identify papers relevant for selecting appropriate methodologies that can be applied to build decision-support tools for resilient cities. This paper presents a list of 109 key references, selected from 8,737 records found from the searches, and identified major research themes, fundamental design interventions, and computer modelling techniques. We extracted six groups of interventions categorised by different scales of action: from an individual, crowds (social distancing and travel-related interventions), to a building, a neighbourhood/district, and a city. In addition, there are three sorts of computational modelling approaches, i.e., computer simulation, statistical models, and AI algorithms. Most of the studies developed models for predictive purposes, and 28% of the modelling studies built models for descriptive purposes. This work intends to empower urban designers and planners to overcome and get prepared for unpredictable disasters in pursuit of resilient and smart cities, particularly in the post-pandemic world. This review enables them to quickly find relevant papers as well as suitable methodologies and tools for a particular research purpose.KeywordsUrban designComputer modelDecision-support toolResilience
... Previous studies on modelling did not offer a precise classification that is mutually exclusive but rather provided some distinctions from multiple perspectives. Therefore, we chose to classify models according to four dimensions based on the Association for Computing Machinery's (ACM, 2012) computing classification system in combination with several classic works (Batty, 1976;Meadows & Robinson, 1985;Porgo et al., 2019). These four dimensions are "method," "goal," "motivation," and "focus." ...
Article
The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, nudging behaviour change and facilitating lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, were used to support responses in the current pandemic as well as to the previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. From 8,737 records returned from databases, 109 records were selected and analysed based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design as well as computational modelling supports, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches to evaluate and consider design decisions depending on the disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for COVID-19 or be incorporated into broader frameworks to help cities become more resilient to future disasters.
... 34-44. Meadows describes a model for understanding as useful in exploring new social problems where "old theories and old social structures are called into question [42], p. 24", which describes the context of our work well. System dynamics supports developing models for understanding by making the hypothesis and assumptions explicitly clear in structure [43]. ...
Article
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This paper builds confidence in the terror contagion hypothesis that violent radicalization leading to predatory mass violence operates as a system. Within this system, the contingent values of key root causes create channels within which violent ideologies and terrorism emerge. We built a system dynamics simulation model capable of replicating historical reference modes and sophisticated enough to test the contingent values of these propositions. Of 16 propositions, we identified six root-cause propositions that must simultaneously exist, act in concert and explain the dynamics of their interaction which generate a terror contagion. Other propositions can strengthen or weaken an existing contagion but not eliminate it. We use an experiment to demonstrate how changing the contingent values of these propositions creates downward channels. This experiment helps reconcile the swarm vs. fishermen debate over the true root causes of violent radicalization. Within these channels, the contingent values can favor swarm or fishermen manifestations. The simulation and experimentation results enable the future development of the terror contagion hypothesis, provide a testing environment for research on violent radicalization, and provide a pathway to policy development in the combating of terrorism that arises from violent radicalization.
... In PSS literature, the term 'flexibility' refers both to the extent to which the information model or tool can be influenced by the user or to limitations placed by facilitation on when a PSS is used and what data or visualization method will be used (Pelzer et al., 2015b). The process of model building is to a large extent about making choices (Meadows & Robinson, 2002;Vennix & Gubbels, 1992) and in most PSS many of these choices are made long before the PSS is applied in practice (Vonk & Ligtenberg, 2010;Petch & Reeve, 1999). Newer, more flexible information models solicit user involvement to build queries, select from options and to work with individual characteristics (for examples, see Geertman et al., 2013). ...
Thesis
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Rapid advancements in computer technologies have had a significant impact on the field of spatial planning. However, their added value during the strategic stages of this process remains limited. This thesis takes spatial strategy making under the loupe to examine the dynamics involved in these highly complex and communicative stages. Planning support in the form of serious games is designed together with planning actors as a means of facilitating inter-actor communication and of involving actors in the model building process. The main conclusion of the thesis is that skilled facilitation is needed that structures group processes involving more flexible support, with dedicated support given to individual work.
... Some of the tools that have gained relevance in recent years as methodologies for the analysis of complex problems are the systemic approach and system dynamics. System dynamics is a methodological paradigm for the investigation of complex systems and is considered a subset of the broader paradigm of simulation models (Meadows and Robinson 1985). In the process of analyzing the sustainability of the coffee supply chain, many elements can be ignored. ...
Book
The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.* The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online.
... System dynamics is a methodological paradigm for the investigation of complex systems and is considered a subset of the broader paradigm of simulation models (Meadows and Robinson 1985). In the process of analyzing the sustainability of the coffee supply chain, many elements can be ignored. ...
Chapter
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Given the problems generated in the coffee supply chain and identifying the central problem as its low sustainability, especially in Valle del Cauca (Colombia), it is imperative to propose solutions to the current development of this chain, especially about producers, which are the least benefited. These solutions should allow for balanced growth and be aimed at making it possible to overcome the current socioeconomic system based on predatory and competitive growth at the service of private interests. Thus, sustainability should trigger a profound rethinking about human groups relationships, among themselves and with the environment, betting on cooperation and defense of the general interest. The purpose should not only be the economic growth of companies, many times increasing the poverty of the inhabitants of the region or the environmental risks, but it should also be the wealth of all the involved parties. In our ongoing research, we plan to use dynamic simulation to evaluate alternatives that consider the sustainability of coffee farms, including elements of profitability, environmental impact, and social development. Preliminary simulation results are shown. This approach should ultimately allow us to find applicable and quantifiable solutions for a sustainable balance in the fundamental links of the coffee supply chain in the northern region of Valle del Cauca, Colombia.
... Studies on case and model-based reasoning in simulation projects Hernes & Maitlis, 2010Develaki, 2017a, 2017bGilbert & Justi, 2016Gonz alez et al., 2013Lawson, 2003, 2009Kwon et al., 2006Parnafes & Disessa, 2004Graham et al., 2004Schwartz & Black, 1996a, 1996bZhang & Alem, 1996Schwartz & Black, 1996bMeadows & Robinson, 1985 Induction, deduction, abduction Case-and model-based reasoning Cognitive transition happens during the iteration of inductive (case-based) and deductive (model-based) reasoning, for the formation of hypotheses, experimentation tests and analogues ...
Article
Full-text available
Simulation models, in particular System Dynamics (SD) models, can be used in a group modelling setting to communicate, integrate, learn, collaborate, organize knowledge and derive new insights. Such models can play the roles of conceptual integrators, representations, learning or predictive tools. In this ethnographic study of two in-depth SD group modelling projects we discovered that SD models can be active agents in the group-model building process by initiating cognitive transition on participants’ (model and case based) modes of reasoning. We found that the cognitive transition was achieved through a series of surprises or shocks that refuted participants’ prior conceptions and forced them to switch between case-based and model-based reasoning during the model-building process. Based on these insights, we present a framework that describes how simulation models change the mode of reasoning in group modelling project and explains the model’s agency role. The study addresses the calls from earlier OR articles to contribute with more case studies using an ethnographic method looking into simulation artefact agency
... Studies on case and model-based reasoning in simulation projects Hernes & Maitlis, 2010Develaki, 2017a, 2017bGilbert & Justi, 2016Gonz alez et al., 2013Lawson, 2003, 2009Kwon et al., 2006Parnafes & Disessa, 2004Graham et al., 2004Schwartz & Black, 1996a, 1996bZhang & Alem, 1996Schwartz & Black, 1996bMeadows & Robinson, 1985 Induction, deduction, abduction Case-and model-based reasoning Cognitive transition happens during the iteration of inductive (case-based) and deductive (model-based) reasoning, for the formation of hypotheses, experimentation tests and analogues ...
Article
Full-text available
Simulation models, in particular System Dynamics (SD) models, can be used in a group modelling setting to communicate, integrate, learn, collaborate, organize knowledge and derive new insights. Such models can play the roles of conceptual integrators, representations, learning or predictive tools. In this ethnographic study of two in-depth SD group modelling projects we discovered that SD models can be active agents in the group-model building process by initiating cognitive transition on participants’ (model and case based) modes of reasoning. We found that the cognitive transition was achieved through a series of surprises or shocks that refuted participants’ prior conceptions and forced them to switch between case-based and model-based reasoning during the model-building process. Based on these insights, we present a framework that describes how simulation models change the mode of reasoning in group modelling project and explains the model’s agency role. The study addresses the calls from earlier OR articles to contribute with more case studies using an ethnographic method looking into simulation artefact agency.
... This has the advantage over traditional statistical approaches in that one can explore the robustness of a policy to system states beyond what has been historically observed or collected as part of an experimentsomething that is highly relevant when we consider structural changes in environmental health triggered by global trends such as climate change, pandemics, and forced displacement of populations due to conflict and environmental disasters. However, this potential to be more transparent is often lost when computational models become overly complicated, lack adequate documentation, or require computational resources with limited access (Meadows and Robinson 1985;Pilkey and Pilkey-Jarvis 2007). A number of standards have, therefore, emerged for reporting guidelines (e.g., Caro et al. 2012;Rahmandad and Sterman 2012). ...
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BACKGROUND: Two of the most important causes of global disease fall in the realm of environmental health: household air pollution (HAP) and poor water, sanitation, and hygiene (WASH) conditions. Interventions, such as clean cookstoves, household water treatment, and improved sanitation facilities, have great potential to yield reductions in disease burden. However, in recent trials and implementation efforts, interventions to improve HAP and WASH conditions have shown few of the desired health gains, raising fundamental questions about current approaches. OBJECTIVES: We describe how the failure to consider the complex systems that characterize diverse real-world conditions may doom promising new approaches prematurely. We provide examples of the application of systems approaches, including system dynamics, network analysis, and agent-based modeling, to the global environmental health priorities of HAP and WASH research and programs. Finally, we offer suggestions on how to approach systems science. METHODS: Systems science applied to environmental health can address major challenges by a) enhancing understanding of existing system structures and behaviors that accelerate or impede aims; b) developing understanding and agreement on a problem among stakeholders; and c) guiding intervention and policy formulation. When employed in participatory processes that engage study populations, policy makers, and implementers, systems science helps ensure that research is responsive to local priorities and reflect real-world conditions. Systems approaches also help interpret unexpected outcomes by revealing emergent properties of the system due to interactions among variables, yielding complex behaviors and sometimes counterin-tuitive results. DISCUSSION: Systems science offers powerful and underused tools to accelerate our ability to identify barriers and facilitators to success in environmental health interventions. This approach is especially useful in the context of implementation research because it explicitly accounts for the interaction of processes occurring at multiple scales, across social and environmental dimensions, with a particular emphasis on linkages and feedback among these processes. https://doi.
... This has the advantage over traditional statistical approaches in that one can explore the robustness of a policy to system states beyond what has been historically observed or collected as part of an experimentsomething that is highly relevant when we consider structural changes in environmental health triggered by global trends such as climate change, pandemics, and forced displacement of populations due to conflict and environmental disasters. However, this potential to be more transparent is often lost when computational models become overly complicated, lack adequate documentation, or require computational resources with limited access (Meadows and Robinson 1985;Pilkey and Pilkey-Jarvis 2007). A number of standards have, therefore, emerged for reporting guidelines (e.g., Caro et al. 2012;Rahmandad and Sterman 2012). ...
Article
Background: Two of the most important causes of global disease fall in the realm of environmental health: household air pollution (HAP) and poor water, sanitation, and hygiene (WASH) conditions. Interventions, such as clean cookstoves, household water treatment, and improved sanitation facilities, have great potential to yield reductions in disease burden. However, in recent trials and implementation efforts, interventions to improve HAP and WASH conditions have shown few of the desired health gains, raising fundamental questions about current approaches. Objectives: We describe how the failure to consider the complex systems that characterize diverse real-world conditions may doom promising new approaches prematurely. We provide examples of the application of systems approaches, including system dynamics, network analysis, and agent-based modeling, to the global environmental health priorities of HAP and WASH research and programs. Finally, we offer suggestions on how to approach systems science. Methods: Systems science applied to environmental health can address major challenges by a) enhancing understanding of existing system structures and behaviors that accelerate or impede aims; b) developing understanding and agreement on a problem among stakeholders; and c) guiding intervention and policy formulation. When employed in participatory processes that engage study populations, policy makers, and implementers, systems science helps ensure that research is responsive to local priorities and reflect real-world conditions. Systems approaches also help interpret unexpected outcomes by revealing emergent properties of the system due to interactions among variables, yielding complex behaviors and sometimes counterintuitive results. Discussion: Systems science offers powerful and underused tools to accelerate our ability to identify barriers and facilitators to success in environmental health interventions. This approach is especially useful in the context of implementation research because it explicitly accounts for the interaction of processes occurring at multiple scales, across social and environmental dimensions, with a particular emphasis on linkages and feedback among these processes. https://doi.org/10.1289/EHP7010.
... Mathematical models, particularly dynamic systems models, are quantitative descriptions of the natural and social processes underlying the functions and patterns observed in the real world. Models have become increasingly useful for scientists, managers, and policy-makers due to their ability to capture complex natural and socio-economic processes (and the couplings between them) and present them in a way that inspires scientific creativity, improves management decisionmaking, informs policy-making processes, and critiques or enlightens prevailing mental models (Meadows and Robinson 1985;Sterman 1994;Sterman 2002). Despite this growing interest in and use of dynamic modeling approaches, most scientific training in the life sciences only includes modeling and simulation of such systems as elective or minor courses at a beginners-level or are applied to problems with a narrow model boundary or scope (e.g., single-to a few system processes rather than interactions between ecologic, environmental, agricultural, and economic elements), which may limit accumulation of diverse modeling expertise in such fields. ...
Article
The use of dynamic systems models by scientists, managers, and policy-makers is becoming more common due to the increasingly complex nature of ecological and socioeconomic problems. Unfortunately, most scientific training in the life sciences only includes dynamic modeling as elective, supplementary courses at a beginners-level, which is not conducive to generating the expertise needed to properly develop, test, and learn from dynamic modeling approaches and risks utilization of poor quality models and adoption of unreliable recommendations. The objective of this paper is to fill part of that gap, particularly regarding model experimentation , by summarizing key concepts in experimental design for simulation experiments and illustrating hands-on examples of experiments needed for developing a deeper understanding of complex, dynamic systems. The experiments include extreme conditions testing, sensitivity analyses of model behaviors given variation in both parameter values and graphical (table) functions, and "what-if?" experiments (e.g., counterfactual trajec-tories, boundary-adequacy tests, and intervention threshold experiments). Each experimental example describes the theoretical foundation of the test, illustrates its application using an ecological systems model, and increases in degree of difficulty from novice to advanced skill levels. By doing so, we demonstrate consistent, scientific means to glean valuable insights about the model's structure-behavior link, uncover any unforeseen model flaws or incorrect formulations, and enhance the confidence (validity) of the model for its intended use.
... There is a time delay between the direct cause and the effect, that is, between land clearing and the resulting inundation and soil salinity. Temporal separation between cause and effect has been reported to contribute to the intractable nature of natural resource problems (Meadows and Robinson 1985). Recently, some land-use change from cropland to commercial forestry has occurred, in part as an attempt to combat the hydrological imbalance, although this represents only a very small proportion of the total land area and is mostly in areas with annual rainfall greater than 600 mm. ...
... They can provide insights into the operation of a system, be used to develop "operating or resource policies to improve system performance", test concepts and systems before implementation and for "obtaining information without disturbing the actual system" (FOR-LEARN 2005. Bell lists four different schools of computer simulation and modeling which are relevant for futures studies: input-output analysis, econometrics, optimization, and system dynamics Glossary: frequently used foresight methods CCXIX which "are applied to relatively long-term time horizons and more" (Bell 2003) (see also Meadows and Robinson 2002). ...
Thesis
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In this work, I examine the potential of social epistemology to serve as a foundation for foresight. I firstly describe the history of ideas that led to foresight and clarify the specific characteristics of foresight in contrast to other futures research activities. To develop an epistemological classification of foresight, or rather to show the impossibility of such a classification, I sketch the development of main strands of philosophy of science and the most prominent approaches of futures research and foresight theory. I argue that foresight is best grasped on the basis of socio-epistemic approaches that recognize the importance of values in science. On this basis I propose a socio-epistemic foresight framework that includes rules for scientific criticism, operates in close connection with scientific practice, and accommodates both the role of values and forms of objectivity. The work shows that Longino’s claim that “science is social knowledge” (Longino 1990) can be adapted to foresight as “foresight is science as social knowledge”. https://publikationen.bibliothek.kit.edu/1000084920
... And, like the ancient oracles, Google often provides only ambiguous signs. Our digital oracles also include massive software engines used to predict the future by simulating it (Meadows & Robinson, 1985). These digital simulators generate prophetic descriptions of future economic, climatic, and other conditions. ...
Article
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Theurgy was a system of magical practices in the late Roman Empire. It was applied Neoplatonism. The theurgists aimed to enable human bodies to assume divine attributes, that is, to become deities. I aim to show that much of the structure of theurgical Neoplatonism appears in transhumanism. Theurgists and transhumanists share a core Platonic-Pythagorean metaphysics. They share goals and methods. The theurgists practiced astrology, the reading of entrails, the consultation of oracles, channeling deities, magic, and the animation of statues. The transhumanist counterparts of those practices are genetics, self-tracking with biosensors, artificial intellects like Google and Siri, brain-computer interfaces, programming, and robotics. Transhumanist techno-theurgy shows how Neoplatonism can be a modern philosophical way of life.
... As a result, developers produce planning tools based on abstract ideas far removed from actual practicerather than a clear, shared understanding of the needs and demands of specific planning contexts. Planners, on the other hand, often hold unrealistic expectations of what the tool can offer, where the inevitable disappointment with the provided support leads to antagonistic attitudes towards new knowledge technologies (Meadows and Robinsons, 2002;Te Brömmelstroet, 2010b;Vonk et al., 2005) . Bringing these two worlds together could help bridge the implementation gap and address some of the most pressing urban mobility dilemmas. ...
... As a result, developers produce planning tools based on abstract ideas far removed from actual practicerather than a clear, shared understanding of the needs and demands of specific planning contexts. Planners, on the other hand, often hold unrealistic expectations of what the tool can offer, where the inevitable disappointment with the provided support leads to antagonistic attitudes towards new knowledge technologies (Meadows and Robinsons, 2002;Te Brömmelstroet, 2010b;Vonk et al., 2005) . Bringing these two worlds together could help bridge the implementation gap and address some of the most pressing urban mobility dilemmas. ...
Chapter
Although in the last decades there has been significant attention and investment into Planning Support Systems, very few have actually made it into practice. This phenomenon is mirrored within the domain of accessibility instruments, a specific subset of the Planning Support Systems (PSS) family. Literature suggests that a fundamental dichotomy between supply and demand of PSS could be the main reason for this. On the one hand, planning practitioners – potential users of instruments – are generally unaware of and inexperienced in the use of them. On the other hand, developers of instruments have little awareness of demand requirements for instruments in the complex planning context in which the instruments have to be applied.
... As a result, developers produce planning tools based on abstract ideas far removed from actual practicerather than a clear, shared understanding of the needs and demands of specific planning contexts. Planners, on the other hand, often hold unrealistic expectations of what the tool can offer, where the inevitable disappointment with the provided support leads to antagonistic attitudes towards new knowledge technologies (Meadows and Robinsons, 2002;Te Brömmelstroet, 2010b;Vonk et al., 2005) . Bringing these two worlds together could help bridge the implementation gap and address some of the most pressing urban mobility dilemmas. ...
Chapter
As was described in Chapter 3, the methodological logic of the experiential case study was applied in 17 experiential planning contexts across Europe and Australia. To harvest the experiences of the professionals that participated in these cases, a set of surveys and data gathering methods was developed. Thirteen cases, comprising 80 participants in total, managed to complete all of these instruments. Because of the importance of triangulation possibilities between these measurements, this chapter only uses the findings of these 13 cases to map the hands-on experiences with the perceived quality of the process and the perceived usability of the different accessibility instruments.
... As a result, developers produce planning tools based on abstract ideas far removed from actual practicerather than a clear, shared understanding of the needs and demands of specific planning contexts. Planners, on the other hand, often hold unrealistic expectations of what the tool can offer, where the inevitable disappointment with the provided support leads to antagonistic attitudes towards new knowledge technologies (Meadows and Robinsons, 2002;Te Brömmelstroet, 2010b;Vonk et al., 2005) . Bringing these two worlds together could help bridge the implementation gap and address some of the most pressing urban mobility dilemmas. ...
Chapter
The concept of accessibility offers a valuable professional language to bring stakeholders together in urban strategy-making settings. Most of the recently developed accessibility instruments that aim to do this suffer from low usability. One reason for this is the persisting disconnect between instrument developers and the potential users. Building on the concept of reflection in action, this chapter develops a framework to bridge these two worlds in one ‘experiential learning cycle’. This cycle is used in a wide variety of contexts to improve the usability of accessibility instruments and develop academic insight at the same time.
... SD modelers believe that behaviors of a system arise from the structure of the system itself, not from external exogenous shocks. According to Meadows and Robinson (1985), "The persistent dynamic tendencies of any complex system arise from its internal causal structure-from the pattern of physical constraints and social goals, rewards, and pressures that cause people to behave the way they do…" Emphasis on Feedback. SD modelers view 2-way causation or feedback processes as essential to understanding dynamic behaviors. ...
Article
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Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System Dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. SD methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using two examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction.
... No such review of implementation methodology seems to exist, although attempts at summing up the situation has appeared as chapters in sporadic books in the past (Randers, 1980;Meadows and Robinson, 1985;Senge, 1990;Sterman, 2000, Martinez-Moyano andRichardson, 2013). The paucity may be because implementation is inherently less tangible than modeling, and difficult to discuss in ways that survive into peer-reviewed journal articles. ...
... SD modelers believe that behaviors of a system arise from the structure of the system itself, not from external exogenous shocks. According to Meadows and Robinson (1985), "The persistent dynamic tendencies of any complex system arise from its internal causal structure-from the pattern of physical constraints and social goals, rewards, and pressures that cause people to behave the way they do…" Emphasis on Feedback. SD modelers view 2-way causation or feedback processes as essential to understanding dynamic behaviors. ...
Article
Full-text available
Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System Dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. SD methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity and focuses on feedback processes arising from endogenous system elements. A variety of data sources may be used in SD model development, and parametric sensitivity analysis with SD models can determine priority information needs in feedback-rich systems when data are lacking. Although numerous applications of SD modeling to agriculture exist, the approach is underutilized as a useful tool for research, instruction and programmatic development. This presentation highlights key elements of SD modeling using two examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model, and illustrates the benefits of the SD approach in animal nutrition research and for farm-level nutritional management decisions. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The presentation concludes with recommendations to increase awareness and training is SD methods to enhance its appropriate use in research and instruction.
... A number of potentially important dynamics were not included in this model, in order to focus on the most critical dynamics identified by our development partners (see the discussion of building a system map, in Section 3.1). Figure 4 provides a model-boundary diagram [30], which highlights which dynamics are endogenous, exogenous, and omitted from the model. Many of the omitted dynamics represent long-term changes, such as farmer population trends, market prices, and technology. ...
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Designing international development projects is challenging because the complexity of the systems on which they act makes it difficult to identify the best leverage points for intervention. This paper seeks to identify the best combinations of interventions to increase the availability of and demand for quality seeds in Uganda and similar markets. A system dynamics model simulates the current dynamics in Ugandan seed markets based on data gathered by ongoing development projects. The findings show that one intervention is critical to enabling growth—investing in a system for verifying the quality of seeds—and that a combination of quality verification with education-oriented interventions is more powerful than quality verification alone. The results have implications for systems approaches to development: they suggest that a combination of interventions in different parts of the value chain enables larger changes than any single intervention alone.
... However, these activities usually involve developments on a microscale (selected fragments of streets and squares, individual solutions in new and adapted buildings, lighting), and a complete realization of the concept of a universally designed, healthy city requires a comprehensive public space management plan. A variety of planning support tools have been described in the publications dealing with the issues of land use and transport infrastructure [5][6][7][8][9][10][11][12][13][14]. The foregoing studies concerning the assets and drawbacks of accessibility instruments reveal that despite the numerous theoretical investigations, the application of new tools in the planning practice still remains a big challenge. ...
Article
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The aim of the research was to analyze the "design for all" concept as a key strategy for creating social sustainability. The paper attempts to answer the question: how can universal design contribute to the rational development of the city space? The author has taken part in participatory experiments. The research took into account various criteria, including the level of the city space's adaptation to the needs and capabilities of persons with different disabilities. Analyses included qualitative studies concerning the possibilities of developing the social capital as well as creating and preserving a cohesive social structure. The analytic process allowed determining the means of raising the quality of urban planning. Finding effective and reliable analytical tools enabling the development of healthy cities which are compatible with the principles of sustainability could become both a great chance and a great challenge for urban planners. Transition from the microplanning to the macroplanning scale and following the principles of universal design at the stage of the formation of urban concepts using spatiotemporal modelling methods will lead to the creation of harmonious accessible spaces adjusted to the needs of present and future users, which will generate sustainable development and lead to the healing of a city.
... In PSS literature, the term 'flexibility' refers both to the extent to which the information model or tool can be influenced by the user (Geertman et al. 2013) or to limitations placed by facilitation on when a PSS is used and what data or visualization method will be used (Pelzer et al. 2015). The process of model building is to a large extent about making choices (Meadows and Robinson 2002;Vennix and Gubbels 1992) and in most PSS many of these choices are made long before the PSS is applied in practice (Petch and Reeve 1999;Vonk and Ligtenberg 2010). Newer, more flexible information models solicit user involvement to build queries, select from options and to work with individual characteristics (for examples, see Geertman et al. 2013). ...
Article
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There is a widely shared view that planning actors require planning support systems (PSS) that can be easily adapted to changing project demands packaged in easy-to-understand formats. Recent studies confirm this and show that PSS are increasingly user-friendly. Still, little is known about under what conditions they add value in practice. This paper tests three hypotheses about PSS performance and usability in an experimental study. 133 students were exposed to different conditions of PSS facilitation flexibility and visualization hardware (tablets versus maptable). They performed identical strategy-making tasks consisting of divergence and convergence. In addition to measuring the quantity and quality of ideas, we assessed perceived process quality and usability of the PSS. Tablet groups performed better on idea generation and evaluated their solution to the planning problem more positively. In contrast, maptable groups performed better on ideational quality and evaluated their experiences in terms of collaboration, more positively. Groups under indicator flexibility performed best in idea generation, while groups under no flexibility received the highest score for ideational quality. Process quality scores were highest under no flexibility followed by indicator only flexibility. Findings suggest tablet use may be more effective for idea generation, an outcome of divergence, while maptables better support group communication, a key aspect of convergence. The study confirms the need for tools and methods that fit both individual and group work. Findings also indicate that identifying structured ways of applying adaptive PSS to the complex world of planning practice may be key to contextualizing such tools.
Article
Whether artificial intelligence might benefit human well-being in every sense is an open question. I consider it in the following essay, first putting to one side standard accounts of ‘official AI’, then deriving an ‘unofficial’ counterpart from the evidence of newspaper accounts and magazine features from ca. 1945–1965. Unsurprisingly, these demonstrate a bending of artificial intelligence to military and industrial purposes, hence the enormity of the impediment to therapeutic applications, but at the same time the evidence leaves no doubt as to the imaginative power of smart machines. Contemporary commentary is brought to bear to move from intimations of a dark future to possibilities for constructing a healthy practice. I conclude with two quite different 21st century examples of a way towards it.
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During the 1992 Rio Conference, the sustainable development agenda envisioned a transformative change for the management of natural resources, where the well-being of human society would be enhanced through the sustainable use of natural capital. Several decades on, relentless economic growth persists at the expense of natural capital, as demonstrated by biodiversity decline, climate change and other environmental challenges. Why is this happening and what can be done about it? We present three agent-based models that explore the social, economic and governance factors driving (un)sustainability in complex social–ecological systems. Our modelling results reinforce the idea that the current economic system fails to safeguard the natural capital upon which it relies, leading to the prevailing decoupling between the economic and natural systems. In attempting to find solutions for such disjunction, our research shows that social–ecological systems are complex, dynamic and non-linear. Interestingly, results also reveal that there are common factors to most social–ecological systems that have the potential to improve or diminish sustainability: the role of financial entities and monetary debt; economic speculation; technological development and efficiency; long-term views, tipping point management and government interventions; and top-down and bottom-up conservation forces. These factors can play a dual role, as they can either undermine or enhance sustainability depending on their specific context and particular conditions. Therefore, the current economic system may not be inherently unsustainable, but rather specific economic mechanisms, decision-making processes and the complex links between economic and natural systems could be at the root of the problem. We argue that short- and medium-term sustainability can be achieved by implementing mechanisms that shift capitalist forces to support environmental conservation. Long-term sustainability, in contrast, requires a more profound paradigm shift: the full integration and accounting of externalities and natural capital into the economy.
Conference Paper
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Changes in urban development trajectories towards renewable energy sources and compact, energy-efficient urban agglomerations will have major impacts on ecosystem services, which cities are dependent on but tend to overlook. Such ecosystem services can be provisioning, regulating and cultural ecosystem services, around which competition over land and water resources will increase with energy system shifts. Much of the land and water use conflicts can be foreseen to take place within urbanising regions, which simultaneously is the living environment of a major part of the human population today. In order to inform critical policy decisions, integrated assessment of urban energy system options and ecosystem services is necessary. For this purpose, the model integration platform Land Evolution and impact Assessment Model (LEAM) is built and empowered with models representing urban form, energy supply and use, transportation, and ecological processes and services, all related to the land and water use evolution. These types of analyses of interacting sub- systems require an advanced model integration platform, yet open for learning and for further development, with high visualisation capacity. Case studies are performed for the cities of Stockholm, Chicago and Shanghai, where urbanisation scenarios are under development. In the case study LEAM Stockholm, scenarios for urban compaction and urban sprawl with different energy and water system solutions are being developed, in order to explore the sustainability of urban policy options. This will enable integrated policy assessment of complex urban systems, with the goal to increase their sustainability.
Chapter
This chapter revisits the macroeconomic modelling and medium-term scenarios undertaken at the New Zealand (NZ) Planning Council (now disbanded) in the mid 1980s. A system dynamics macroeconomic model (SDMACRO) was developed using the DYNAMO simulation package, to create macroeconomic scenarios and forecasts of the NZ economy over the period from 1985 to 1995. This period involved considerable economic reform in NZ, shifting it from a highly regulated economy to one of the most deregulated economies in the world. Reviews of completed economics and social science modelling studies are typically not undertaken. It is argued here that a formal ‘Review and Reflections’ stage be added to the SD modelling process. This chapter provides a brief overview of SDMACRO and its recent conversion into the icon based Vensim simulation package. Simulations are provided to demonstrate that the Vensim version of the model corresponds to the behaviour of the original DYNAMO model. The reforms of the New Zealand economy from 1984 to 1995 are briefly summarised and comparisons are provided with the original model scenarios. Some suggestions are offered about possible future model modifications. Online supplementary files include the original Haywood and Cavana 1986 report and the current Vensim electronic version of SDMACRO.
Chapter
In this chapter, we try to disentangle the theoretical background for assessing the macroeconomic impacts of raw material efficiency strategies. We concentrate our discussion on a case study of copper for Germany and show the reasoning behind the implementation in a macroeconomic assessment framework using a system dynamics model. Resource decoupling is one of the vital strategies for limiting environmental impacts of resource extraction and processing while preserving economic growth. It is therefore important to understand the dynamic complexities related to the possible side effects of resource efficiency strategies. The contribution addresses the concern that the sum of the effects may occur on different scales and accrue to different actors in the system, rendering the policy process increasingly complex. We try to show how an effective policy support system can be constructed for resource efficiency measures, which help to compare the outcome of policy modelling exercises.
Chapter
System dynamics and econometrics have traditionally been seen as contrasting and sometimes conflicting approaches for deriving parameters for economic models. System dynamics as a simulation method uses repeated runs to achieve calibration through optimisation, whereas simulation as an estimation procedure is much less common in econometrics . A comparison of system dynamics calibration with econometric estimation is undertaken applied to technology adoption data. Values of the same order of magnitude were achieved for parameters for the same model, with numerical differences and because models are not well identified. However, different model specifications using the same method, which represent various behavioural factors, exhibit wider differences. The comparison also revealed some additional process factors, such as whether to select population (stock) or derived sales (rate) variables to calibrate against data. System dynamics takes an operational approach to economic modelling, which may lead to overspecified models from an econometrics perspective, where the typical criterion is parsimony. However, there is recognition in econometrics teaching and practice that models need to have a more behavioural basis, to be functionally richer to address the ‘non-stationarity revolution’ and to permit better identification and selection of causal factors.
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zet: Tarihi süreç içinde karşılaşılan ulusal ve uluslararası önemli ve acil krizlerin ço ğu, taraflar arasında yetersiz ve etkin olmayan iletişim ve enformasyon eksikliği nede niyle ortaya çıkmıştır. Kontrol teorisi perspektifinden bakıldığında enformasyon akışı en önemli role sahiptir. Konvansiyonel enformasyon sistemleri kriz durumlarında yeter siz kalmaktadırlar. Birçok krizin dinamik yapısı belirsizlik problemlerini daha da art tırmaktadır. Uluslararası ilişkilerde hem uzun dönemde hem de kısa dönemde önemli değişimler meydana gelmektedir. Bu nedenle alan, manipülasyon için yüksek potansiyele sahiptir. Geniş bir zaman aralığında uluslararası ilişkilerin karmaşık öngörüleri için gelecek orientasyonlu araştırmaların uygun metotlarına gereksinme vardır. Uygulayıcılar, poli tikaların yerine getirilmesinin pragmatik yönleri üzerinde yoğunlaşırlar; onlar planla ma orientasyonludurlar, dolayısıyla geleceğin planlanmasıyla ilgilidirler. Sosyal bilim lerin konvansiyonel metotları bu alanda alternatif politika sonuçlarının etkin bir biçim de öngörüleri için uygun değildirler. Bu nedenle, üst düzey karar vericiler için yeterli olmaktan uzaktırlar. Dolayısıyla, daha çok fiziki bilim dallarında uygulanan gelecek orientasyonlu, disiplinlerarası ve bütünleşik bazı operasyonel metotlar sosyal bilim dal larına transfer edilerek bazı uygulamalar başlatılmıştır. Örneğin, bazı matematiksel modeller, simülasyona dayalı planlama teknikleri, uzman görüşlerine dayanan sistema tik yaklaşımlar ve çapraz-etki teknikleri (mesela, sistem dinamikleri) gibi operasyonel metotlar bu gereksinimleri karşılayabilmektedirler. Gelecek oryantasyonlu araştırma metotlarının potansiyelolarak uygulanabilirliği nin zengin olduğu önemli bir alan uluslararası ilişki/erdir. Bu alan, karmaşık sistemle rin bir bütün leşik yaklaşımını ve planlama faaliyetlerini içermektedir. Sistem analizleri ne dayanan yaklaşımlar (sistem dinamikleri simülasyonu gibi) bu gereksinimleri, tanım sal olarak sağlamaktadırlar. Anahtar Sözcükler: Uluslararası ilişkiler, sistem dinamikleri, önkestirim-öngörü, plan lama, enformasyon. • Vi"d. Doç. Dr., Uıuda~ Üniversitesi, itSF Ö~etim Üyesi. Amme İdaresi Dergisi, Cül 38 Sayı 3 Eylül 2005, s. 19-40.
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The Sustainable Development Goals (SDGs) of the UN 2030 Agenda are today's global roadmap to sustainable development. Adopted in 2015, the SDGs are the culmination of 50 years of debate and consensus building on the imperatives of sustainable development. The 2030 Agenda explicitly calls for integrated methods for SDG achievement. Two multisector modeling frameworks have emerged to address integration in SDG policy: the system dynamics based Integrated Sustainable Development Goal (iSDG) model and the multimethod International Futures (IFs) model. Both are feedback rich and thoroughly integrated, and we term them as Integrated Systems Models (ISMs). ISMs enable quantification of policy impacts across SDG sectors, helping identify policies that benefit numerous SDGs as well as potential trade‐offs. These benefits have been witnessed in countries where these ISMs have been put to task on SDG policy. As the sustainable development paradigm becomes increasingly integrated, a central role is being created for further development of ISMs.
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Starting from an example of biosphere modelling, the paper considers the contribution of system dynamics to global modelling. The different purposes of modelling approaches are first considered. These are then related to a specific example—which is then seen to derive from the founding works in this area: “World Dynamics” and “The Limits To Growth.” The response to those publications is considered, both the contemporary reactions to them and the myths that have grown up around them. The paper discusses the general cultural acceptance of the perspective offered by these books and the continuing—and decidedly mixed—significance of these works to the field of system dynamics.
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در سال های اخیر در جامعه سیستم داینامیک توجه به رویکردهای سیستم داینامیک کیفی افزایش یافته است. یکی از روش های استخراج مدل های سیستم داینامیک که در رویکردهای کیفی نیز استفاده فراوانی از آن شده است روش مدلسازی گروهی است که در آن مشتریان و صاحب نظران در فرایند مدلسازی مشارکت داده شده و مبتنی بر مدل های ذهنی افراد مشارکت کننده به استخراج متغیرها و ساختارهای کلیدی و نهفته در سیستم می پردازند. در این میان بررسی و مقایسه مدل های حاصل از جلسات گروهی با مدل های مرجع حاصل از ادبیات موضوع می تواند روشی برای تست و ارزیابی مدل گروهی باشد و بینش های جدیدی را ایجاد کند. در این مقاله به ارزیابی و مقایسه مدل های استخراج شده از جلسات مدلسازی گروهی پروژه "طراحی پارک فناوری هوایی" با مدل مرجع حاصل از مطالعات ادبیات موضوع؛ با استفاده از رویکرد نسبت فاصله در سه سطح عناصر، سطح حلقه های علت و معلولی و سطح مدل کامل به منظور اعتبار سنجی مدل حاصل از مدلسازی گروهی می‌پردازیم. همانطور که از نتایج حاصل از رویکرد نسبت فاصله مشخص شد مقدار 84/6 MDR= بود که بیانگر این است که مدل حاصل از مدلسازی گروهی با مدل مرجع مطابق و همانند بودند. مقادیر EDR و LDR نیز بیانگر تشابه مدل در سطح عناصر و حلقه ها بودند. در نتیجه می توان گفت مدل حاصل از مدلسازی گروهی دارای اعتبار و با مطالعات ادبیات موضوع و مدل حاصل از آن تطابق دارد.
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The system dynamics (SD) approach was used as both the methodology and tool for modelling and simulation in this book. This chapter first gives an historical overview of SD by describing how the SD approach came into being. Following on, the chapter discusses how the SD approach is grounded both in theory and philosophical foundations in terms of its epistemological and ontological underpinnings. The chapter further puts the SD in context as a multidisciplinary modelling approach and then describes the procedures for using it. Additionally, the chapter discusses the array of software under which the SD modelling and simulation can be implemented. The chapter is concluded by discussing the ways in which the SD algorithms can be developed.
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