When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
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The European Commission aims for a full circular economy (CE), an economy that aims to reuse all resources in 2050. CE is a promising way to increase welfare and wellbeing while decreasing environmental footprints. Industrial symbiosis, in which companies exchange residuals for resource efficiency, is essential to the circular transition. However, many companies are hesitant to implement business models for industrial symbiosis because of the various roles, stakes, opinions, and resulting uncertainties for business continuity.This dissertation supports researchers, professionals, and students in understanding and shaping circular business models for industrial symbiosis networks through collaborative modelling and simulation methods. Three theoretical perspectives, design science research, complex adaptive socio-technical systems, and circular business model innovation, shed light on designing business models for industrial symbiosis. A serious game and agent-based models were developed in multiple case studies with researchers, practitioners, and students. These were then used to design circular business models and explore their efficacy under uncertain conditions, such as various behavioural intentions of potential partners in diverse natural and societal contexts.This thesis advances business model design and experimentation by integrated simulation of social and technical aspects of industrial symbiosis. Furthermore, the research shows how simulations facilitate learning processes in designing circular business models. Ultimately, the thesis equips researchers, practitioners, and students with knowledge, tools, and methods to shape a circular economy.
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A case study and method development research of online simulation gaming to enhance youth care knowlegde exchange. Youth care professionals affirm that the application used has enough relevance as an additional tool for knowledge construction about complex cases. They state that the usability of the application is suitable, however some remarks are given to adapt the virtual environment to the special needs of youth care knowledge exchange. The method of online simulation gaming appears to be useful to improve network competences and to explore the hidden professional capacities of the participant as to the construction of situational cognition, discourse participation and the accountability of intervention choices.
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This research contributes to understanding and shaping systems for OFMSW separation at urban Small and Medium Enterprises (SMEs, such as offices, shops and service providers). Separating SMEs’ organic fraction of municipal solid waste (OFMSW) is both an opportunity and a serious challenge for the transition towards circular cities. It is an opportunity because OFMSW represents approximately 40% of the total waste mass generated by these companies. It is challenging because post-collection separation is not feasible for OFMSW. Therefore, SMEs disposing of waste should separate their solid waste so that processing the organic fraction for reuse and recycling is practical and attainable. However, these companies do not experience direct advantages from the extra efforts in separating waste, and much of the OFMSW ends up in landfills, often resulting in unnecessary GHG emissions. Therefore, governments and waste processors are looking for ways to improve the OFMSW separation degree by urban companies disposing of waste through policies for behaviour change.There are multiple types of personnel at companies disposing of waste. These co-workers act according to their values, beliefs and norms. They adapt their behaviour continuously, influenced by the physical environment, events over time and self-evaluation of their actions. Therefore, waste separation at companies can be regarded as a Socio-Technical Complex Adaptive System (STCAS). Agent-based modelling and simulation are powerful methods to help understand STCAS. Consequently, we have created an agent-based model representing the evolution of behaviour regarding waste separation at companies in the urban environment. The model aims to show public and private stakeholders involved in solid waste collection, transport and processing to what extent behaviour change policies can shape the system towards desired waste separation degrees.We have co-created the model with participants utilising literature and empirical data from a case study on the transition of the waste collection system of a business park located at a former harbour area in Amsterdam, The Netherlands. First, a conceptual model of the system and the environment was set up through participatory workshops, surveys and interviews with stakeholders, domain experts and relevant actors. Together with our case participants, five policies that affect waste separation behaviour were included in the model. To model the behaviour of each company worker’s values, beliefs and norms during the separation and disposal of OFMSW, we have used the Value-Belief-Norm (VBN) Theory by Stern et al. (1999). We have collected data on waste collection behaviour and separation rates through interviews, workshops and a literature study to operationalise and validate the model.Simulation results show how combinations of behaviour profiles affect waste separation rates. Furthermore, findings show that single waste separation policies are often limitedly capable of changing the behaviour in the system. Rather, a combination of information and communication policies is needed to improve the separation of OFMSW, i.e., dissemination of a newsletter, providing personal feedback to the co-workers disposing of waste, and sharing information on the (improvement of) recycling rates.This study contributes to a better understanding of how policies can support co-workers’ pro-environmental behaviour for organic waste separation rates at SMEs. Thus, it shows policymakers how to stimulate the circular transition by actively engaging co-workers’ waste separation behaviour at SMEs. Future work will extend the model’s purpose by including households and policies supporting separating multiple waste types aimed at various R-strategies proposed by Potting et al. (2016).
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Introduction F-ACT is a flexible version of Assertive Community Treatment to deliver care in a changing intensity depending on needs of individuals with severe mental illnesses (Van Veldhuizen, 2007). In 2016 a number of the FACT-teams in the Dutch region of Utrecht moved to locations in neighborhoods and started to work as one network team together with neighborhood based facilities in primary care (GP’s) and in the social domain (supported living, social district teams, etc.). This should create better chances on clinical, social and personal recovery of service users. Objectives This study describes the implementation, obstacles and outcomes for service users. The main question is whether this Collaborative Mental Health Care in the Community produces better outcome than regular FACT. Measures include (met/unmet) needs for care, quality of life, clinical, functional and personal recovery, and hospital admission days. Methods Data on care utilization regarding the innovation are compared to regular FACT. Qualitative interviews are conducted to gain insight in the experiences of service users, their family members and mental health care workers. Changes in outcome measures of service users in pilot areas (N=400) were compared to outcomes of users (matched on gender and level of functioning) in regular FACT teams in the period 2015-2018 (total N=800). Results Data-analyses will take place from January to March 2019. Initial analyses point at a greater feeling of holding and safety for service users in the pilot areas and less hospital admission days. Conclusions Preliminary results support the development from FACT to a community based collaborative care service.
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The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an (Formula presented.) -greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.
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Background: Shared decision-making is one key element of interprofessional collaboration. Communication is often considered to be the main reason for inefficient or ineffective collaboration. Little is known about group dynamics in the process of shared decision-making in a team with professionals, including the patient or their parent. This study aimed to evaluate just that. Methods: Simulation-based training was provided for groups of medical and allied health profession students from universities across the globe. In an overt ethnographic research design, passive observations were made to ensure careful observations and accurate reporting. The training offered the context to directly experience the behaviors and interactions of a group of people. Results: Overall, 39 different goals were defined in different orders of prioritizing and with different time frames or intervention ideas. Shared decision-making was lacking, and groups chose to convince the parents when a conflict arose. Group dynamics made parents verbally agree with professionals, although their non-verbal communication was not in congruence with that. Conclusions: The outcome and goalsetting of an interprofessional meeting are highly influenced by group dynamics. The vision, structure, process, and results of the meeting are affected by multiple inter- or intrapersonal factors.
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Currently, various higher education (HE) institutes develop flexible curricula for various reasons, including promoting accessibility of HE, the societal need for more self-regulated professionals who engage in life-long learning, and the desire to increase motivation of students. Increasing flexibility in curricula allows students to choose for example what they learn, when they learn, how they learn, where they learn, and/or with whom. However, HE institutes raise the question of what preferences and needs different stakeholders have with regard to flexibility, so that suitable choices can be made in the design of policies, curricula, and student support programs. In this workshop, we focus on student preferences and share recent insights from research on HE students' preferences regarding flexible education. Moreover, we use participants’ expertise to identify new (research) questions to further explore what students’ needs imply for several domains, namely curriculum-design, student support that is provided by educators/staff, policy, management, and the professional field. Firstly, a conceptual framework on flexible education and student’s preferences will be presented. Secondly, participants reflect in groups on student personas. Then, discussion groups have a Delphi-based discussion to collect new ideas for research. Finally, participants share the outcomes on a ‘willing wall’ and a ‘wailing wall’.
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Social networks and news outlets entrust content curation to specialised algorithms from the broad family of recommender systems. Companies attempt to increase engagement by connecting users with ideas they are more likely to agree with. Eli Pariser, the author of the term filter bubble, suggested that it might come as a price of narrowing users' outlook. Although empirical studies on algorithmic recommendation showed no reduction in diversity, these algorithms are still a source of concern due to the increased societal polarisation of opinions. Diversity has been widely discussed in the literature, but little attention has been paid to the dynamics of user opinions when influenced by algorithmic curation and social network interaction.This paper describes our empirical research using an Agent-based modelling (ABM) approach to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. We address under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect.The simulation confirmed that an environment curated by a recommender system did not reduce diversity. The same outcome was observed in a simple social network with items shared among users. However, opinions were less susceptible to change: The difference between users' current and innate opinions was lower than in an environment with users randomly selecting items. Finally, we propose a modification to the collaborative filtering algorithm by selecting items in the boundary of users' latitude of acceptance, increasing the chances to challenge users' opinions.
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Shared Vision Planning (SVP) is a collaborative approach to water (resource) management that combines three practices: (1) traditional water resources planning; (2) structured participation of stakeholders; (3) (collaborative) computer modeling and simulation. The authors argue that there are ample opportunities for learning and innovation in SVP when we look at it as a form of Policy Analysis (PA) in a multi-actor context. SVP faces three classic PA dilemmas: (1) the role of experts and scientific knowledge in policymaking; (2) The design and management of participatory and interactive planning processes; and (3) the (ab)use of computer models and simulations in (multi actor) policymaking. In dealing with these dilemmas, SVP can benefit from looking at the richness of PA methodology, such as for stakeholder analysis and process management. And it can innovate by incorporating some of the rapid developments now taking place in the field of (serious) gaming and simulation (S&G) for policy analysis. In return, the principles, methods, and case studies of SVP can significantly enhance how we perform PA for multi-actor water (resource) management.
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