2,809 research outputs found

    The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation

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    An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end users. All of these actors are stakeholders affected by the behavior of the autonomous system. We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of the autonomous system. We propose an ethical recommendation component, which we call Jiminy, that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. Jiminy represents the ethical views of each stakeholder by using normative systems, and has three ways of resolving moral dilemmas involving the opinions of the stakeholders. First, Jiminy considers how the arguments of the stakeholders relate to one another, which may already resolve the dilemma. Secondly, Jiminy combines the normative systems of the stakeholders such that the combined expertise of the stakeholders may resolve the dilemma. Thirdly, and only if these two other methods have failed, Jiminy uses context-sensitive rules to decide which of the stakeholders take preference. At the abstract level, these three methods are characterized by the addition of arguments, the addition of attacks among arguments, and the removal of attacks among arguments. We show how Jiminy can be used not only for ethical reasoning and collaborative decision making, but also for providing explanations about ethical behavior

    Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

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    The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

    Multifaceted modelling of complex business enterprises

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    We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control

    Linking model design and application for transdisciplinary approaches in social-ecological systems

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    This work was supported by the US National Science Foundation through the Mountain Sentinels Research Coordination Network (NSF #1414106), the Swiss National Science Foundation through MtnPaths – Pathways for global change adaptation of mountain socio-ecological systems (#20521L_169916), and the Center for Collaborative Conservation at Colorado State University.As global environmental change continues to accelerate and intensify, science and society are turning to trans- disciplinary approaches to facilitate transitions to sustainability. Modeling is increasingly used as a technological tool to improve our understanding of social-ecological systems (SES), encourage collaboration and learning, and facilitate decision-making. This study improves our understanding of how SES models are designed and applied to address the rising challenges of global environmental change, using mountains as a representative system. We analyzed 74 peer-reviewed papers describing dynamic models of mountain SES, evaluating them according to characteristics such as the model purpose, data and model type, level of stakeholder involvement, and spatial extent/resolution. Slightly more than half the models in our analysis were participatory, yet only 21.6% of papers demonstrated any direct outreach to decision makers. We found that SES models tend to under-represent social datasets, with ethnographic data rarely incorporated. Modeling efforts in conditions of higher stakeholder diversity tend to have higher rates of decision support compared to situations where stakeholder diversity is absent or not addressed. We discuss our results through the lens of appropriate technology, drawing on the concepts of boundary objects and scalar devices from Science and Technology Studies. We propose four guiding principles to facilitate the development of SES models as appropriate technology for transdisciplinary applications: (1) increase diversity of stakeholders in SES model design and application for improved collaboration; (2) balance power dynamics among stakeholders by incorporating diverse knowledge and data types; (3) promote flexibility in model design; and (4) bridge gaps in decision support, learning, and communication. Creating SES models that are appropriate tech- nology for transdisciplinary applications will require advanced planning, increased funding for and attention to the role of diverse data and knowledge, and stronger partnerships across disciplinary divides. Highly contextualized participatory modeling that embraces diversity in both data and actors appears poised to make strong contributions to the world’s most pressing environmental challenges.PostprintPeer reviewe

    The Art of the Possible: Toward a Cognitive Model for Political Action Choice

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    The consideration of political action by individuals is constrained by what those people consider possible, in terms of not only their immediate actions but also in terms of what changes in the world those individuals believe could occur because of their, or others’, actions. However, there are two major complications to this picture: (a) people are heavily influenced by others they are in social contact with in terms of both goals and assessment of possibility, and (b) since what people do is influenced by what world-changes they consider possible, and people know this, what can change is influenced also by what others think others around them think is possible. Politics thus involves complex processes at several levels, including: the spread of goals and possibility assessments throughout a local social network, and adjustments in what people think about others’ possibility judgments. An implementable cognitive model suitable for building into an agent-based model is proposed, drawing on existing cognitive structures to simulate social contact, goal-directed action choice, negotiation and social norms

    Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

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    Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

    Integration of decision support systems to improve decision support performance

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    Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

    Optimization of Agro-Socio-Hydrological Networks under Water Scarcity Conditions: Inter- and Trans-disciplinary Approaches for Sustainable Water Resources Management

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    Sustainable agriculture is one of the greatest challenges of our time. The pathways to sustainable agriculture consist of successive decisions for optimization that are often a matter of negotiation as resources are shared at all levels. This work essentially comprises three research projects with novel inter- and transdisciplinary methods to better understand and optimize agricultural water management under water scarcity conditions. In the first project, climate variability in the US Corn Belt was analyzed with a focus on deficit irrigation to find the optimal irrigation strategies for possible future changes. Two optimization methods for deficit irrigation showed positive water savings and yield increases in the predicted water scarcity scenarios. In the second project, a serious board game was developed and game sessions were carried out to simulate the complex decision space of actors in irrigated agriculture under climate and groundwater variability. The aim of the game was to understand how decisions are made by actors by observing the course of the game and linking these results to common behavioral theories implemented in socio-ecological models. In the third project, two frameworks based on innovation theories and agro-social-hydrological networks were developed and tested using agent-based models. In the first framework, centralized and decentralized irrigation management in Kansas US was compared to observe the development of collective action and the innovation diffusion of sustainable irrigation strategies. The second framework analyzed different decision processes to perform a sensitivity analysis of innovation implementation, groundwater abstraction and saline water intrusion in the Al Batinah region in Oman. Both frameworks allowed the evaluation of diverse behavior theories and decision-making parameters to find the optimal irrigation management and the impact of diverse socio-ecological policies. Inter- and Trans-disciplinary simulations of the interactions between human decisions and water systems, like the ones presented in here, improve the understanding of irrigation systems as anthropogenic landscapes in socio-economic and ecological contexts. The joint application of statistical and participatory approaches enables different but complementary perspectives that allow for a multidimensional analysis of irrigation strategies and water resources management.:Contents Declaration of Independent Work i Declaration of Conformity iii List of Publications v Acknowledgments ix Abstract xi Zusammenfassung xiii Contents xv List of Figures xvii List of Tables xix List of Abbreviations xxi 1. Introduction 3 1.1 Complex Networks Approach 3 1.2 Research Objectives 4 1.3 Thesis Outline 5 2. Literature Review 9 2.1 Agro-Hydrological Systems 9 2.1.1 Necessary Disciplinary Convergence 9 2.1.2 Multi-Objective Optimization Approaches 10 2.2 Optimization of Crop-Water Productivity 11 2.2.1 Irrigation Strategies 11 2.3 Sustainable Management of A-S-H Networks 12 2.3.1 Socio-Hydrology 13 2.3.2 Representation of Decision-Making Processes 14 2.3.3 Influence of Social Network 16 2.4 Socio-Hydrological Modeling Approaches 17 2.4.1 Game Theory Approach 17 2.4.2 Agent-Based Modeling 18 2.4.3 Participatory Modeling 20 2.5 Education for Sustainability 21 2.5.1 Experiential Learning 21 2.5.2 Serious Games 22 2.6 Summary of Research Gaps 24 3. Irrigation Optimization in The US Corn Belt 27 3.1 Agriculture in The Corn Belt 27 3.2 Historical and Prospective Climatic Variability 29 3.3 Simulated Irrigation Strategies 29 3.4 Optimal Irrigation Strategies Throughout the Corn Belt 30 3.5 Summary 31 4. Participatory Analysis of A-S-H Dynamics 35 4.1 Decision-Making Processes in A-S-H Networks 36 4.1.1 Collaborative and Participatory Data Collection Approaches 37 4.2 MAHIZ 38 4.2.1 Serious Game Development 38 4.2.2 Implementation of Serious Game Sessions 39 4.4 Evaluation of The Learning Process in Serious Games 40 4.5 Evaluation of Behavior Theories and Social Parameters 42 4.6 Summary 43 5 Robust Evaluation of Decision-Making Processes In A-S-H Networks 47 5.1 Innovation in A-S-H Networks 47 5.1.1 Multilevel Social Networks 48 5.1.2 Theoretical Framework of Developed ABMs 49 5.2 DInKA Model: Irrigation Expansion in Kansas, US 50 5.2.1 Robust Analysis of Innovation Diffusion 53 5.3 SAHIO Implementation: Coastal Agriculture in Oman 54 5.3.1 SAHIO Sensitivity analysis 58 5.4 Summary 60 6 Conclusions and Outlook 63 6.1 Limitations 64 6.2 Outlook 64 Bibliography 69 Appendix A. Implementation Code 79 A.1 DInKA 79 A.2 SAHIO 82 Appendix B. SAHIO’s Decision-Making Process for Each MoHuB Theory 91 Appendix C. SAHIO A-S-H Innovation Results 97 Appendix D. Selected Publications 101 D.1 Evaluation of Hydroclimatic Variability and Prospective Irrigation Strategies in the U.S. Corn Belt. 103 D.2 A Serious Board Game to Analyze Socio-Ecological Dynamics towards Collaboration in Agriculture. 121 D.2.1 MAHIZ Rulebook 140 D.2.2 MAHIZ Feedback Form 15

    Governing multi-actor decision processes in Dutch industrial area redevelopment

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    In the first part of the thesis, a literature review is presented. In this literature review, industrial areas in the Netherlands are discussed, leading to the conclusion that industrial areas are important for realizing sustainable economic growth in the Netherlands. Industrial areas play an important role in accommodating employment, in stimulating local and regional economies, and in creating a high value added. Furthermore, I conclude that process features have a significant influence on the outcome of industrial area (re)development projects. Subsequently, the most important problem aspects of the current industrial area planning approach are discussed, together with several causes of these problems. It is argued that most of these problematic failures can be traced back to one main problem: the rapid obsolescence of the existing stock of industrial areas. The dimensions of the Dutch industrial area redevelopment task affirm this. This creates a large necessity for redevelopment. However, based upon the disappointing figures on yearly realized redevelopment projects and on the low spatial yields of actually realized redevelopment projects, it is concluded that the execution of industrial area redevelopment projects stagnates. When starting up a redevelopment project in the current increasingly complex and rapidly changing environment, interdependent negotiation processes within and among organizations appear to be problematic, consuming substantial time and effort. Focus within this research lies therefore on studying, supporting and accommodating the consensus-building process within redevelopment projects. The point of departure in this research is the postulate that the main cause of the occurring stagnation in industrial area redevelopment is the absence of a well-functioning process governance system. Several authors support this statement; they posit that the regional arena is the appropriate level for executing such governance. Because of a lack of insight into effective ways to implement a governance system, and because of the presumed advantages related to the acceleration of industrial area redevelopment processes when gaining this insight, the general research objective is as follows: ‘To explore ways to effectively support the governance of involved stakeholders’ choice behavior, in order to stimulate the current decision-making processes in industrial area redevelopment projects’. Thus, governance – and especially meta-governance – is a promising approach for application to complex industrial area redevelopment projects. Several best-practice industrial area redevelopment projects reveal that centrally governing such alliances contributes to project success. The aim of meta-governance within industrial area redevelopment projects is to establish cooperation between relevant parties, in order to realize a number of functions and purposes from a public, social importance, through the establishment of one central governing agency, responsible for the management of the decision-making process. In Dutch industrial area redevelopment, regional development companies seem most appropriate for executing this central governance role. Assuming that meta-governance can be a solution to the occurring problems in the Dutch industrial area redevelopment market, it is essential to analyze the consensusfinding processes, as well as causes of tension and conflict, in order to theoretically support governing agencies in managing decision-making processes. Therefore, the specific goal of the research is to better understand how individual and interactive decision-making of the most important actors in industrial area redevelopment processes can be modeled, in order to analyze and predict the occurrence of cooperation or conflict, and how this decision-making can be influenced by a regional governing agency. A better understanding of these processes is a key requirement for the development of a decision support tool for this regional governing agency, in order to support the acceleration of industrial area redevelopment projects. A formal model of the collaborative decision process has not been developed for this domain, incorporating a governance approach. Therefore, several available techniques for analyzing both individual and interactive decision-making are explored in the second part of the thesis. From this, it is concluded that the discrete choice approach seems applicable for modeling individual choice behavior of actors. Furthermore, the application of game theory seems very interesting for modeling interactive and interdependent choice behavior. In order to make a game-theoretic model that is suitable for studying strategic interactions in industrial area redevelopment, a relatively new approach is advocated in which game theory is combined with a multiattribute trade-off technique. Eventually, the application of game theory leads to an insight in the occurrence of conflicts, and in the causes of these conflicts. The 2x2 game is regarded as most appropriate for application in this research because this game type has been used very often in conflict modeling and conflict management, and it suits the real world negotiation processes in which two players are involved, each roughly having two strategies. Within 2x2 games, three tools are most utilizable for solving conflicts: (1) changing the information of the involved players; (2) changing the payoffs of the players; and (3) changing the rules of the game, focusing on the sequence of decisionmaking and the possible allowance of communication in the game. Because the research focuses on static 2x2 games of complete information, it is concluded that gaining insight in the second tool is most feasible and interesting. In the third part, the results are represented. Firstly, the individual choice behavior of involved actors is modeled, thereby giving a better and more systematical insight in stakeholders’ preferences when accepting or rejecting a development plan, in the (dis)similarities between both stakeholder groups’ preferences in making that choice, and in the most important points of interest when composing a development plan proposal. Resulting data analysis showed that the plan attributes ‘technical quality’ and ‘cost coverage’ are important for both stakeholder groups when choosing a plan proposal. A high level of these attributes in a plan results in a high probability of being chosen, while a low level results in a low choice probability. Besides this, companies find the attribute ‘development speed’ very important when choosing a plan, and municipalities value ‘architectural quality’ highly. Furthermore, municipalities proved to be less demanding in accepting industrial area redevelopment plan proposals. Secondly, the interaction between involved stakeholders is modeled as an interdependent process, using a relative novel approach in which conjoint analysis and game theory are combined, in order to explain the occurrence of cooperation or conflict within Dutch industrial areas redevelopment negotiations. Data analysis reveals that there is one major source of conflicts; stakeholders choosing not to cooperate based upon the presented negotiation setting. A more in-depth analysis of negotiation settings ending up in mutual cooperation demonstrated that the appraisal of both stakeholders for the proposed development plan is the most influential factor, together with an eventual absolute difference between both players’ appraisals. This leads to the conclusion that the content of proposed plans is very important in such negotiations; factors like power and risks play a secondary role. Thirdly, a model is created that supports the decision-making of a central governing agency. This model is based upon the results of the individual and interactive choice models, giving recommendations on how to put meta-governance into practice in industrial area redevelopment. The model consists of three major steps: (1) giving assistance in assessing the initial state of the negotiation; (2) calculating whether the possible conflict occurrence can be prevented by marginally changing the payoffs of both players; and (3) indicating how the equalizing of appraisals can be put into practice. This final step gives insight in the contribution of specific changes in plan proposals to solving the conflicts that are discovered in the first part. After testing the model, it is concluded that altering payoffs in games in order to avoid conflicts is very effective in these games. Furthermore, these payoffs can often be altered through the use of minimal resources. In cases in which the municipality values the proposed plan lower than the company – raising the levels of the attributes technical quality, architectural quality, and value development results most often in an equaling of both players’ appraisals. Furthermore, the attribute architectural quality functions most often as the only solution. Reciprocally, in cases in which the company has a lower plan proposal appraisal, raising the levels of the attributes development speed, technical quality, and cost coverage most often results in an equal plan proposal appraisal. In general, tools are already available for executing interventions on above-mentioned attributes. Thus, focus should be on actual execution of the governance task, not on adding tools to the existing instrumental palette. Concluding, a model is created with which it is possible to give recommendations concerning the decision-making of a central governing agency in different possible industrial area redevelopment negotiations. It entails a new, structured way of solving conflicts, which is empirically testable, and delivers some real world recommendations
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