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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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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