1,720,989 research outputs found
Policy-making and policy assessments with partially ordered alternatives
No full textThe present work collects three essays on social choice and decision-making in the presence of multiple objectives and severe informational limitations. When feasible alternatives must be ordered according to their performance under various criteria, it is typically necessary to make use of a specific functional relation and assume the implied rates of substitution between scores in different criteria. In the special case of collective choice and voting, rather than having proper rates of substitution, each individually preferred ordering of the alternatives is usually weighted according to its frequency in the population. Both decision frameworks imply the availability of extensive information about such functional relation and the proper weights of each criterion or must acknowledge a vast and arbitrary discretion to those in charge of resolving the decision process. The alternative approach herein discussed consists in applying the Pareto criterion to identify Pareto-superior alternatives in each pairwise comparison, a procedure that easily produces an incomplete ordering. Then, applying a tool of Order Theory, a complete ordering is identified from the linear extensions of the partially ordered set derived from the Pareto criterion. The claim is that this method highlights conflicts in value judgements and in incomparable criteria, allowing to search for a conflict-mitigating solution that doesn’t make assumptions on the reciprocal importance of criteria or judgements. The method is actually a combination of existing but unrelated approaches in Social Choice Theory and in Order Theory and provides outcomes with interesting properties. The essays present, respectively, an axiomatic discussion of the properties of this approach and two applications to policy issues
A reassessment of graduation modeling for policy design
No full textA vast and diverse literature estimates graduation chances using logistic models set in an arbitrary timeframe, where a graduation indicator is checked at a conventional point in time and associated with covariates measured at some date. Survival models emerged over time as a robust alternative, for being able to estimate time-to-degree and time-varying effects of predictors. This paper reconsiders the effectiveness of both modeling approaches in addressing policy-relevant questions, particularly in light of the increasingly automated and algorithm-based educational policies. We find that both methods exhibit blind spots and limitations, but that adopting a simple pragmatic approach logistic models can achieve a comparable level of effectiveness at depicting graduation dynamics while also being capable of answering questions that are problematic for survival models. We exploit a unique dataset and the nature of discrete-time survival models as combinations of logistic regressions run at different times to illustrate how arbitrary timeframes impact the estimates of a logistic model of graduation. Conversely, we illustrate how separately running and analyzing all the distinct logistic regressions provides insights that are unlikely to come from a survival model
Ride comfort assessment of high-speed rail vehicles: influence of yaw dampers installation angle
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By diversion rate alone: The inconsistency and inequity of waste management evaluation in a single-indicator system
No full textLocal waste management is commonly evaluated according to performance indicators that have been shown to be weakly or even negatively correlated. If the metrics of waste management are not treated as a multi-indicator system, evaluations will reflect indicator selection rather than the actual sustainability of waste management. We discuss the drawbacks of single-indicator evaluations through the case of Italy, where recent national and regional legislation on waste taxation is largely based on municipal diversion rate. We show that the main assumption for making diversion rate the discriminant between good and bad environmental performances is untenable. Furthermore, we provide evidence that the use of a single parameter has a sizeable regressive effect in the distribution of the tax burden between municipalities
POSET Analysis of Panel Data with POSAC
No full textIn the last two decades, data-driven policymaking has gained more and more importance due to the larger availability of data (and, more recently, Big Data) for designing proper and timely economic and social policies. This larger availability of data has let decision makers have a deeper insight of complex socio-economic phenomena (e.g. unemployment, deprivation, crime, social care, healthcare) but, at the same time, it has drastically increased the number of indicators that can be used to monitor these phenomena. Decision makers are now often in the condition of taking decisions with large batteries of indicators whose interpretation is not always easy or concordant. In order to simplify the decisional process, a large body of literature suggests to use synthetic indicators to produce single measures of vast, latent phenomena underlying groups of indicators. Unfortunately, although simple, this solution has a number of drawbacks (e.g. compensation between components of synthetic indicators could be undesirable; subjective weighting of the components could lead to arbitrary results; mixing information about different phenomena could make interpretation harder and decisionmaking opaque). Moreover, with operational decisions, it is necessary to distinguish between those situations when decisions can be embedded in automated processes, and those that require human intervention. Under certain conditions, the use of synthetic indicators may bring to a misleading interpretation of the real world and to wrong policy decisions. In order to overcome all these limitations and drawbacks of synthetic indicators, the use of multi-indicator systems is becoming more and more important to describe and characterize many phenomena in every field of
science, as they keep the valuable information, inherent to each indicator, distinct (see, for a review: Brüggemann and Patil 2011)
Smart Security: data analysis and inference for evidence-based security policies in the “Smart City”
No full textThe Smart City is defined, among other things, by its ability “to measure and recognize potential problems just as they start to arise [...] and act to make the necessary corrections.” [1] This concept is borrowed from feedback control theory and reflects the centrality of timely, accurate and explicative information in the process of urban governance. Postulating that information is accurate and explicative, though, requires a rather exceptional assumption: that available data and methodologies justify inferences, thus making policy decisions “evidence-based”.
In this work, we define a three-step process to provide decision-makers with information that is sufficient to justify evidence-based local policy decisions on urban crime. Of the three steps, the first one contains a number of methodological innovations and therefore is discussed in greater detail, whereas the other two, while depending on the outcome of the former, are straightforward applications of existing methods. This first step consists of an analytical methodology to explore and model the supposed influence of socio-economic, demographic and spatial factors on crime. The main contribution on the actual literature is the integration of data originating from different databases and with different territorial levels. In particular, we propose a variation of the most widely used techniques which analyze urban structures in terms of networks and graphs, an innovation which gives space a more relevant position in statistical models for evidence based decision making rather than a mere spatial distance matrix. When implemented as an ongoing and evolving process, such methodology produces an extensive knowledge of the covariates of crime, which is the input of the second step, a set of quasi-experimental designs [2] of small scale pilot policy actions on plausible crime determinants. Finally, in the third step, the evaluation of the pilot policy actions and their outcomes provides the necessary evidence to infer on the plausible determinants of crime, therefore supporting (or not) large scale policy decisions. Smart Security as a process implies three main elements of innovation: the introduction of spatial configuration as a component of urban crime models because of its influence on pedestrian and vehicular movement patterns [3]; the collection and analysis of data from different sources and at different scales; the identification of a finite number of urban environment types with different performances in terms of security. To illustrate these points, we make use of data from the 2011 UK Census in London and from the UK Police records during 18 months in the period 2013 – 2014
Wellbeing and Sustainable Development: A Multi-indicator Approach
No full textAbstractWe propose an innovative approach to monitor progress in wellbeing and sustainable development in the context of a multi-indicator situation. We analyze improvement trajectories over two time periods for nine European countries, showing the differences between consistent and unambiguous improvement and non comparable changes. We find that improvement has been widespread in the socio-economic domain and much less so in the environmental domain
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