A new knockout tournament seeding method and its axiomatic justification
A new set of axioms and new method (equal gap seeding) are designed. The equal gap seeding is the
unique seeding that, under the deterministic domain assumption, satisfies the delayed confrontation,
fairness, increasing competitive intensity and equal rank differences axioms. The equal gap seeding is the
unique seeding that, under the linear domain assumption, maximizes the probability that the strongest
participant is the winner, the strongest two participants are the finalists, the strongest four participants
are the quarterfinalists, etc.
This paper reports the results of a series of competitive labour market experiments in whichsubjects have the possibility to reciprocate favours. In the high stake condition subjectsearned between two and three times their monthly income during the experiment. In thenormal stake condition the stake level was reduced by a factor of ten. We observe that bothin the high and the normal stake condition fairness concerns are strong enough to outweighcompetitive forces and give rise to non-competitive wages. There is also no evidence thateffort behaviour becomes generally more selfish at higher stake levels. Therefore, our resultssuggest that fairness concerns may play an important role even at relatively high stakelevels.
Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a job interview based on application materials that may include photographs. One promising direction to achieve fairness is by learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair outcomes. All available models however learn latent embeddings which comes at the cost of being uninterpretable. We propose to cast this problem as data-to-data translation, i.e. learning a mapping from an input domain to a fair target domain, where a fairness definition is being enforced. Here the data domain can be images, or any tabular data representation. This task would be straightforward if we had fair target data available, but this is not the case. To overcome this, we learn a highly unconstrained mapping by exploiting statistics of residuals – the difference between input data and its translated version – and the protected characteristics. When applied to the CelebA dataset of face images with gender attribute as the protected characteristic, our model enforces equality of opportunity by adjusting the eyes and lips regions. Intriguingly, on the same dataset we arrive at similar conclusions when using semantic attribute representations of images for translation. On face images of the recent DiF dataset, with the same gender attribute, our method adjusts nose regions. In the Adult income dataset, also with protected gender attribute, our model achieves equality of opportunity by, among others, obfuscating the wife and husband relationship. Analyzing those systematic changes will allow us to scrutinize the interplay of fairness criterion, chosen protected characteristics, and prediction performance.
In this article, the fairdivision problem for two participants in the presence of both divisible and indivisibleitems is considered. Three interrelated modifications of the notion of fairdivision–profitably, uniformly and equitably fairdivisions–were introduced. Computationally efficient algorithm for finding all of them was designed. The algorithm includes repetitive solutions of integer knapsack-type problems as its essential steps. The necessary and sufficient conditions of the existence of proportional and equitable division were found. The statements of the article are illustrated by various examples.
Licensed assisted access (LAA) enables the coexistence of long-term evolution (LTE) and WiFi in unlicensed bands, while potentially offering improved coverage and data rates. However, cooperation with the conventional random-access protocols that employ listen-before-talk (LBT) considerations makes meeting the LTE performance requirements difficult, since delay and throughput guarantees should be delivered. In this paper, we propose a novel channel sharing mechanism for the LAA system that is capable of simultaneously providing the fairness of resource allocation across the competing LTE and Wi-Fi sessions as well as satisfying the quality-of-service guarantees of the LTE sessions in terms of their upper delay bound and throughput. Our proposal is based on two key mechanisms: 1) LAA connection admission control for the LTE sessions and 2) adaptive duty cycle resource division. The only external information necessary for the intended operation is the current number of active Wi-Fi sessions inferred by monitoring the shared channel. In the proposed scheme, LAA-enabled LTE base station fully controls the shared environment by dynamically adjusting the time allocations for both Wi-Fi and LTE technologies, while only admitting those LTE connections that should not interfere with Wi-Fi more than another Wi-Fi access point operating on the same channel would. To characterize the key performance trade-offs pertaining to the proposed operation, we develop a new analytical model. We then comprehensively investigate the performance of the developed channel sharing mechanism by confirming that it allows to achieve a high degree of fairness between the LTE and Wi-Fi connections as well as provides guarantees in terms of upper delay bound and throughput for the admitted LTE sessions. We also demonstrate that our scheme outperforms a typical LBT-based LAA implementation
Конференция Computer Science уровня A* по рейтингу CORE
Equipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself is evolving and multi-faceted. We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future. To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions. We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness. Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space. We show several existing models are special cases of ours. Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions. This will facilitate decision makers to select an operating point and to be accountable for it.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Let k be a field of characteristic zero, let G be a connected reductive algebraic group over k and let g be its Lie algebra. Let k(G), respectively, k(g), be the field of k- rational functions on G, respectively, g. The conjugation action of G on itself induces the adjoint action of G on g. We investigate the question whether or not the field extensions k(G)/k(G)^G and k(g)/k(g)^G are purely transcendental. We show that the answer is the same for k(G)/k(G)^G and k(g)/k(g)^G, and reduce the problem to the case where G is simple. For simple groups we show that the answer is positive if G is split of type A_n or C_n, and negative for groups of other types, except possibly G_2. A key ingredient in the proof of the negative result is a recent formula for the unramified Brauer group of a homogeneous space with connected stabilizers. As a byproduct of our investigation we give an affirmative answer to a question of Grothendieck about the existence of a rational section of the categorical quotient morphism for the conjugating action of G on itself.
Let G be a connected semisimple algebraic group over an algebraically closed field k. In 1965 Steinberg proved that if G is simply connected, then in G there exists a closed irreducible cross-section of the set of closures of regular conjugacy classes. We prove that in arbitrary G such a cross-section exists if and only if the universal covering isogeny Ĝ → G is bijective; this answers Grothendieck's question cited in the epigraph. In particular, for char k = 0, the converse to Steinberg's theorem holds. The existence of a cross-section in G implies, at least for char k = 0, that the algebra k[G]G of class functions on G is generated by rk G elements. We describe, for arbitrary G, a minimal generating set of k[G]G and that of the representation ring of G and answer two Grothendieck's questions on constructing generating sets of k[G]G. We prove the existence of a rational (i.e., local) section of the quotient morphism for arbitrary G and the existence of a rational cross-section in G (for char k = 0, this has been proved earlier); this answers the other question cited in the epigraph. We also prove that the existence of a rational section is equivalent to the existence of a rational W-equivariant map T- - - >G/T where T is a maximal torus of G and W the Weyl group.