Axiomatic characterization of committee scoring rules

The paper proposes a general definition of positionalist voting rules. Unlike the commonly-employed scoring rules, our notion of positionalism allows for non-linear criteria to be included in the requisite class. We define a voting rule as positionalist if, for any profile of strict individual orderings, any two alternatives are compared collectively solely on the basis of their positional scores according to the individual rankings. In contrast to the class of scoring rules, however, we do not restrict attention to linear aggregation procedures. Two plausible unanimity properties are examined in the context of our new class of positionalist rules and, moreover, we characterize the lexicographic extensions that refine the plurality rule and its inverse counterpart.

Proper scoring rules provide a theoretically principled framework for the quantitative assessment of the predictive performance of probabilistic forecasts. While a wide selection of such scoring rules for univariate quantities exists, there are only few scoring rules for multivariate quantities, and many of them require that forecasts are given in the form of a probability density function. The energy score, a multivariate generalization of the continuous ranked probability score, is the only commonly used score that is applicable in the important case of ensemble forecasts, where the multivariate predictive distribution is represented by a finite sample. Unfortunately, its ability to detect incorrectly specified correlations between the components of the multivariate quantity is somewhat limited. In this paper the authors present an alternative class of proper scoring rules based on the geostatistical concept of variograms. The sensitivity of these variogram-based scoring rules to incorrectly predicted means, variances, and correlations is studied in a number of examples with simulated observations and forecasts; they are shown to be distinctly more discriminative with respect to the correlation structure. This conclusion is confirmed in a case study with postprocessed wind speed forecasts at five wind park locations in Colorado.

Committee scoring voting rules are multiwinner analogues of positional scoring rules, which constitute an important subclass of single-winner voting rules. We identify several natural subclasses of committee scoring rules, namely, weakly separable, representation-focused, top- k -counting, OWA-based, and decomposable rules. We characterize SNTV, Bloc, and k -Approval Chamberlin--Courant as the only nontrivial rules in pairwise intersections of these classes. We provide some axiomatic characterizations for these classes, where monotonicity properties appear to be especially useful. The class of decomposable rules is new to the literature. We show that it strictly contains the class of OWA-based rules and describe some of the applications of decomposable rules.

PurposeThis study aims to investigate the trending term: “Netiquette” as an important element in the effective digital citizenship. The research suggests a systematic framework of netiquette rules in the field of online education based on the classical core rules of netiquette and according to the digital citizenship scale (DCS). The research also studies the corresponding responsibilities of both educators and students to raise awareness towards using technology in a balanced, safe, smart and ethical way as the shift towards the digital activities increased significantly in the post-corona time.Design/methodology/approachThe research used the qualitative data that were based on the everyday observation and analysis of the online education experience at the university of Jordan in the academic year 2020/2021; the online group discussions of students and teachers; and investigating the guidelines of the online learning netiquette rules in various academic institutes. Comparative analysis was conducted to merge and eliminate redundant rules and to add sub rules, and then to cluster them into groups. The suggested clustered groups were distributed into the classical core rules outline of netiquette. In each core rule, the sub rules were reclassified and recategorized according to the DCS by studying the complexity levels and their corresponding factors. The suggested framework updates and adds DCS levels and factors considering the exceptional experience of online education through the pandemic.FindingsThe research finds that “Netiquette” had been neglected in cyber ethics literature, and so it has to be rediscovered through the lens of digital citizenship that becomes very noticeable issue in the post-COVID era. So, the research presents a systematic framework that outlines more than 150 netiquette sub rules in the field of online education, and that were clustered according to DCS and the classical core rules of netiquette. It also adds a new factor to the bottom level of DCS which is the primarily skills and traits, and also updates the internet and political activism fac-tor by adding the social perspective.Originality/valueA novel classification of the classical core rules of netiquette was proposed in the field of online education to serve as a spectrum of identifying the complexity of digital citizenship levels and factors. This research can be a starting point of more works on netiquette research in online education and on other fields such as online business meetings, social media networking and online gaming.

Proper scoring rules for general decision models

We study the trade-off between two risks for scoring rules used in sports competitions containing multiple contests: (1) the threat of early clinch when the title is clinched before the last contest(s) of the competition take place; and (2) the danger of winning the competition without finishing first in any contest. Four historical points scoring systems of the Formula One World Championship are compared with the family of geometric scoring rules. The current scheme seems to be a reasonable compromise close to the Pareto frontier. Our results contribute to the issue of choosing an optimal set of point values.

This paper discusses and develops “non-welfaristic” arguments on distributive justice a la J. Rawls and A. K. Sen, and formalizes, in cooperative production economies, “non-welfaristic” distribution rules as game form types of resource allocation schemes. First, it conceptualizes Needs Principle which the distribution rule should satisfy if this takes individuals' needs into account. Second, one class of distribution rules which satisfy Needs Principle, a class of J-based Capability Maximum Rules, is proposed. Third, axiomatic characterizations of the class of J-based Capability Maximum Rules are provided.

Approval-based scoring rules require participants to submit ballots that correspond to the candidates they approve of, and each candidate then receives some score from each ballot it appears on. One particularly natural class of approval-based scoring rules is the class of size-approval rules. For rules in this class, each ballot is weighted based on the number of candidates it contains. We provide a deep axiomatic analysis of the class of size-approval rules and related classes of approval-based scoring rules. We organise existing work by shedding light on the exact role played by core axioms involved in previous characterisations of size-approval rules, while also providing multiple new characterisations of the same class and its closest relatives. We furthermore provide an original characterisation of even-and-equal cumulative voting, the most paradigmatic representative of the size-approval voting rules, where each ballot's weight is inversely proportional to its size.

Allocating extra revenues from broadcasting sports leagues

Approval voting and fixed electorate with dichotomous preferences

Proper Scoring Rules That Are Sensitive to Bracketing

Decision makers often face a dilemma when they have to arbitrate between the quantity of support for a decision (i.e., the number of people who back it) and the quality of support (i.e., at which level to go down in voters’ preferences to obtain sufficient level of support). The trade-off between the quality and quantity of support behind alternatives led to numerous suggestions in social choice theory: without being exhaustive we can mention Majoritarian Compromise, Fallback Bargaining, Set of Efficient Compromises, Condorcet Practical Method, Median Voting Rule, Majority Judgement. Our point is that all these concepts share a common feature which enables us to gather them in the same class, the class of compromise rules, which are all based upon elementary scoring rules described extensively by Saari. One can exploit his results to analyze the compromise rules with relative ease, which is a major point of our paper.

Ensembles used for probabilistic weather forecasting tend to be biased and underdispersive. This paper proposes a statistical method for postprocessing ensembles based on quantile regression forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function (PDF) like in ensemble model output statistics (EMOS) but provides an estimation of desired quantiles. This is a nonparametric approach that eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the ensemble but any predictor available including statistics on other variables. The method is applied to the Météo-France 35-member ensemble forecast (PEARP) for surface temperature and wind speed for available lead times from 3 up to 54 h and compared to EMOS. All postprocessed ensembles are much better calibrated than the PEARP raw ensemble and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for human forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.

An axiomatic characterization of the Baker-Thompson rule

In the evaluation of experiments often the problem arises of how to compare the predictive success of competing probabilistic theories. The quadratic scoring rule can be used for this purpose. Originally, this rule was proposed as an incentive compatible elicitation method for probabilistic expert judgments. It is shown that up to a positive linear transformation, the quadratic scoring rule is characterized by four desirable properties.