This paper presents the latest version of the Machinations framework. This framework uses diagrams to represent the flow of tangible and abstract resources through a game. This flow represents the mechanics that make up a game’s interbal economy and has a large impact on the emergent gameplay of most simulation games, strategy games and board games. This paper shows how Machinations diagrams can be used simulate and balance games before they are built.
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In the multi-billion dollar game industry, time to market limits the time developers have for improving games. Game designers and software engineers usually live on opposite sides of the fence, and both lose time when adjustments best understood by designers are implemented by engineers. Designers lack a common vocabulary for expressing gameplay, which hampers specification, communication and agreement. We aim to speed up the game development process by improving designer productivity and design quality. The language Machinations has introduced a graphical notation for expressing the rules of game economies that is close to a designer’s vocabulary. We present the language Micro- Machinations (MM) that details and formalizes the meaning of a significant subset of Machination’s language features and adds several new features most notably modularization. Next we describe MM Analysis in Rascal (MM AiR), a framework for analysis and simulation of MM models using the Rascal meta-programming language and the Spin model checker. Our approach shows that it is feasible to rapidly simulate game economies in early development stages and to separate concerns. Today’s meta-programming technology is a crucial enabler to achieve this.
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Business decisions and business logic are an important part of an organization’s daily activities. In the not so near past they were modelled as integrative part of business processes, however, during the last years, they are managed as a separate entity. Still, decisions and underlying business logic often remain a black box. Therefore, the call for transparency increases. Current theory does not provide a measurable and quantitative way to measure transparency for business decisions. This paper extends the understanding of different views on transparency with regards to business decisions and underlying business logic and presents a framework including Key Transparency Indicators (KTI) to measure the transparency of business decisions and business logic. The framework is validated by means of an experiment using case study data. Results show that the framework and KTI’s are useful to measure transparency. Further research will focus on further refinement of the measurements as well as further validation of the current measurements.
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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
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This paper presents four Destination Stewardship scenarios based on different levels of engagement from the public and private sector. The scenarios serve to support destination stakeholders in assessing their current context and the pathway towards greater stewardship. A Destination Stewardship Governance Diagnostic framework is built on the scenarios to support its stakeholders in considering how to move along that pathway, identifying the key aspects of governance that are either facilitating or frustrating a destination stewardship approach, and the required actions and resources to achieve an improved scenario. Moreover, the scenarios and diagnostic framework support stakeholders to come together to debate and scrutinise how tourism is managed in a way that meets the needs of the destination, casting new light on the barriers and opportunities for greater destination stewardship.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
MULTIFILE
2025 ILC Annual International Conference , 16th & 17 June, 2025, Genoa, Italy, Global Collaboration,Local Action for Fundamentals of Care Innovation. Zie bladzijde 81. An international group of experts has joined forces for the further development of Artificial Intelligence (AI) in relation to the Fundamentals of Care (FoC) framework. AI, or its categories like machine learning and deep learning, offers potential to identify patterns in healthcare data, develop clinical prediction models, and derive insights from large datasets. For example, algorithms can be created to detect the start of the palliative phase based on electronic health records, or to inform nursing decisions based on lifestyle monitoring data for older adults. These AI applications significantly influence nurses' roles, the nurse-client relationship and nurses’ professional identity. Consequently, nurses must take responsibility to ensure that AI applications align with person-centered fundamental care, professional ethics, equity, and social justice. Thus, nursing leadership is essential to lead the development and use of AI applications that support nursing care according to the FoC framework, and enhance patient outcomes. The aim of the current project is to explore nurses’ responsibility for how AI adds value to the FoC framework. Firstly, nurse leaders play a vital role in overseeing the quality and relevance of data collected in daily practice, as these data are foundational for AI algorithms. The elements as articulated in the FoC framework should be the building blocks for any algorithm. These building blocks can be linked to clinical and social conditions, and life stages, building from the basis of the individual's human needs. Secondly, it is crucial for nurses to participate in the interdisciplinary teams that develop AI algorithms. Their participation and expertise ensure that algorithms are co-created with an understanding of the needs of their clients, maximizing the potential for positive outcomes. In addition to education, policy, and regulation, a nurse-led, interdisciplinary research program is needed to investigate the relationship between AI applications, the FoC framework and it’s impact on nurse-client relationships, nurses’ professional identity, and patient outcomes.
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This paper investigates how structures of emergence and progression in games might be integrated. By leveraging the formalism of Machination diagrams the shape of the mechanics and a game’s internal economy that typically control progression in games are exposed. Two strategies to create mechanics that control progression but exhibit more emergent behavior by including feedback loops are presented.
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From the article: The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice. In this position paper we propose a framework for translating the AI-HLEG ethics guidelines into the specific context within which an AI system operates. This approach aligns well with a set of Agile principles commonly employed in software engineering. http://ceur-ws.org/Vol-2659/
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This report describes "a framework that delivers insight into the tangible and intangible effects of a mobile (IT) system, before it is being implemented".
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