673,173 research outputs found

    Quantum Information Dynamics and Open World Science

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    One of the fundamental insights of quantum mechanics is that complete knowledge of the state of a quantum system is not possible. Such incomplete knowledge of a physical system is the norm rather than the exception. This is becoming increasingly apparent as we apply scientific methods to increasingly complex situations. Empirically intensive disciplines in the biological, human, and geosciences all operate in situations where valid conclusions must be drawn, but deductive completeness is impossible. This paper argues that such situations are emerging examples of {it Open World} Science. In this paradigm, scientific models are known to be acting with incomplete information. Open World models acknowledge their incompleteness, and respond positively when new information becomes available. Many methods for creating Open World models have been explored analytically in quantitative disciplines such as statistics, and the increasingly mature area of machine learning. This paper examines the role of quantum theory and quantum logic in the underpinnings of Open World models, examining the importance of structural features of such as non-commutativity, degrees of similarity, induction, and the impact of observation. Quantum mechanics is not a problem around the edges of classical theory, but is rather a secure bridgehead in the world of science to come

    Management Accounting for Service: A Research Agenda

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    Purpose – The purpose of the paper is to point out a research agenda for Management Accounting under the emergent Service-Dominant (S-D) Logic. S-D Logic is widely discussed in the field of Marketing, the paper tries to extend S-D Logic in the Management Accounting context and develops some related considerations. Methodology/approach – Service related change in economy and firms raises new challenging issues in management accounting topics such as cost classification, cost structure, cost object, the role of “traditional” accounting tools and models, price-cost relations for pricing decisions. In this paper, we identify several critical research questions that address a tentative research agenda in the field of management accounting to better explore its role within service science. Throughout the paper many different examples are provided in order to support what is sustained. Findings – The conclusions of the paper trace some aspects addressed as core in the distinction between Goods-Dominant Accounting and Service-Dominant Accounting. Considering the new changing service environment, the role of management accounting in providing information to support managerial decision making and control can be widely renewed. Research implications – The paper opens many underexplored topics on Management accounting in the interface with service and traces a research agenda for further research. Originality/value – This is the first paper, after the brief overview on accounting and Service Science provided by Kerr (2008), aiming at understanding the role of Management accounting in the context of S-D Logic.Service-Dominant Logic, Management Accounting, Costing, Measurement, Value.

    A logical analysis of soft systems modelling: implications for information system design and knowledge based system design

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    The thesis undertakes an analysis of the modelling methods used in the Soft Systems Methodology (SSM) developed by Peter Checkland and Brian Wilson. The analysis is undertaken using formal logic and work drawn from modern Anglo-American analytical philosophy especially work in the area of philosophical logic, the theory of meaning, epistemology and the philosophy of science. The ability of SSM models to represent causation is found to be deficient and improved modelling techniques suitable for cause and effect analysis are developed. The notional status of SSM models is explained in terms of Wittgenstein's language game theory. Modal predicate logic is used to solve the problem of mapping notional models on to the real world. The thesis presents a method for extending SSM modelling in to a system for the design of a knowledge based system. This six stage method comprises: systems analysis, using SSM models; language creation, using logico-linguistic models; knowledge elicitation, using empirical models; knowledge representation, using modal predicate logic; codification, using Prolog; and verification using a type of non-monotonic logic. The resulting system is constructed in such a way that built in inductive hypotheses can be falsified, as in Karl Popper's philosophy of science, by particular facts. As the system can learn what is false it has some artificial intelligence capability. A variant of the method can be used for the design of other types of information system such as a relational database

    Aggregation of probabilisitic logically related judgments

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    Information aggregation is at the core of many problems in computer science. Judgement aggregation models multi-agent decision making by aggregating individual opinions from various sources. It does however assume that the sources are certain in their opinions and also subject to the same logical constraints. We relax both of these assumptions and build a more general framework with uncertain information sources that we model in probabilistic logic. We also propose aggregation functions for this new framework
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