The evaluation of subjective well-being, and of similar issues related to quality of life, is usually addressed through composite indicators or counting procedures. This leads to inconsistencies and inefficiency in the treatment of ordinal data that, in turn, affect the quality of information provided to scholars and to policy-makers. In this paper we take a different path and prove that the evaluation of multidimensional ordinal well-being can be addressed in an effective and consistent way, using the theory of partially ordered sets. We first show that the proper evaluation space of well-being is the partially ordered set of achievement profiles and that its structure depends upon the importance assigned to well-being attributes. We then describe how evaluation can be performed extracting information out of the evaluation space, respecting the ordinal nature of data and producing synthetic indicators without attribute aggregation. An application to subjective well-being in Italy illustrates the procedure.
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The paper presents the method of Data Envelopment Analysis (DEA) as a tool for compiling rating lists in tasks of ordering elements of assemblages. We describe the differences that result from applying a one-dimensional model and multi-dimensional model, where in the latter a so-called partial order is created for incomparable elements in the assemblage being ordered. The example used to illustrate the issues discussed is the analysis of teaching results in a student group.
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The paper presents the method of Data Envelopment Analysis (DEA) as a tool for compiling rating lists in tasks of ordering elements of assemblages. We describe the differences that result from applying a one-dimensional model and multi-dimensional model, where in the latter a so-called partial order is created for incomparable elements in the assemblage being ordered. The example used to illustrate the issues discussed is the analysis of teaching results in a student group.
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Lehmann and Rojo [8] proposed a concept of invariance of stochastic orders and related probability metrics with respect to increasing transformations of random variables. Bartoszewicz and Benduch [3] and Bartoszewicz and Frąszczak [4] applied a concept of Lehmann and Rojo to new settings. In the paper these results are applied to the problem of robustness in the sense of Zieliński [11], [12]. Metrics related to some stochastic orders are used to study the continuity (robustness) of scale parameter estimators when contaminations of the models are generated by stochastic orders. The exponential model is considered in detail.
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In this paper we show that among the idempotent elements of a generalised inverse semigroup isomorphic to a pseudogroup of transformations on a topological space, is the largest element. We also show how to obtain the smallest element in some inverse semigroups.
Mazurkiewicz traces are a widely used model for describing the languages of concurrent systems computations. The causal structure of atomic actions occurring in a process modeled as a trace generates a partial order. Hasse diagrams of such order are very common structures used for presentation and investigation in the concurrency theory, especially from the behavioural perspective. We present effective algorithms for Hasse diagrams construction and transformation. Later on, we use them for enumeration of all linearisations of the partial order that represents a concurrent process. Additionally, we attach the flexible visual implementation of all considered Algorithms.
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A step trace is an equivalence class of step sequences which can be thought of as different observations of the same underlying concurrent history. Equivalence is determined on basis of a step alphabet that describes the relations between events in terms of potential simultaneity and sequentialisability. Step traces cannot be represented by standard partial orders, but require so-called invariant structures, extended order structures that capture the phenomena of mutual exclusion and weak causality. In this paper, we present an effective way of deciding whether an invariant structure represents a step trace over a given step alphabet. We also describe a method by which one can check whether a given invariant structure can represent a step trace over any step alphabet. Moreover, if the answer is positive, the method provides a suitable step alphabet.
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