The Combinatorial World (of Auctions) According to GARP

Revealed preference techniques are used to test whether a data set is compatible with rational behaviour. They are also incorporated as constraints in mechanism design to encourage truthful behaviour in applications such as combinatorial auctions. In the auction setting, we present an efficient combinatorial algorithm to find a virtual valuation function with the optimal (additive) rationality guarantee. Moreover, we show that there exists such a valuation function that both is individually rational and is minimum (that is, it is component-wise dominated by any other individually rational, virtual valuation function that approximately fits the data). Similarly, given upper bound constraints on the valuation function, we show how to fit the maximum virtual valuation function with the optimal additive rationality guarantee. In practice, revealed preference bidding constraints are very demanding. We explain how approximate rationality can be used to create relaxed revealed preference constraints in an auction. We then show how combinatorial methods can be used to implement these relaxed constraints. Worst/best-case welfare guarantees that result from the use of such mechanisms can be quantified via the minimum/maximum virtual valuation function

Evaluasi Lintasan Pemboran Berarah pada Sumur Z Lapangan Xyy Petrochina International

Pemboran berarah adalah suatu teknik mengarahkan lubang bor mengikuti garis lintasan yangtelah direncanakan untuk mencapai zona target yang ditentukan. Pemboran berarah dilakukanapabila zona target pemboran tidak dapat dicapai dengan pemboran vertikal karena suatu alasantopografis, geologis, maupun ekonomi. Oleh karena itu, maka pemboran berarah memiliki resikooperasi yang lebih tinggi dibandingkan dengan operasi pemboran vertikal.Lintasan sumur berarahZ lapangan XYZ adalah lintasan“J” – type.Dalam membuat perencanaan lintasan pemboranberarah sebagai acuan directional driller, tidak hanya terfokus pada hit target saja, tetapi fokuspada parameter pendukung yang meminimalisasi resiko hole problem selama pengeboranberlangsung. Parameter yang diatur yaitu titik kedalaman KOP dan nilai BUR yangdiaplikasikan.Kondisi kekerasan formasi lapangan yang lunak menjadi salah satu pertimbangandalam menentukan kedalaman KOP. Nilai BUR yang diaplikasikan akan menghasilkan nilaiinklinasi yang akan berpengaruh terhadap hole cleaning dan pemilihan penggunaan bent sub atauAKO motor. Dengan mengevaluasi perencanaan pada beberapa kedalaman KOP dan nilai BURdengan metode Minimum of Curvature, dapat diketahui titik kedalaman KOP dan BUR optimumyang dapat dijadikan acuan pengeboran selanjutnya pada Lapangan Y. KOP dan BUR optimumuntuk sumur Z yaitu 500 ft dan 3°/100ft. Dengan perencanaan tersebut, tidak terjadi masalah pipesticking, dragging, dan hole circulation, tetapi pada actual sumur Z terjadi masalah terhadap faktorformasi yang menambah waktu operasi

Frustrations of fur-farmed mink

Captive animals may suffer if strongly motivated to perform activities that their housing does not allow. We investigated this experimentally for caged mink, and found that they would pay high costs to perform a range of natural behaviours, and release cortisol if their most preferred activity, swimming, was prevented. Investigates the effect of limitations on caged mink. Popularity of fur farming; Research into the possible deprivation of mink, which result in their frustration; Details of the experiment; Impact of an access to water; Results which indicate that fur-farmed mink are still motivated to perform the same activities as their wild counterpart

The invisible power of fairness. How machine learning shapes democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.Comment: 12 pages, 1 figure, preprint version, submitted to The 32nd Canadian Conference on Artificial Intelligence that will take place in Kingston, Ontario, May 28 to May 31, 201

Testing Consumer Rationality using Perfect Graphs and Oriented Discs

Given a consumer data-set, the axioms of revealed preference proffer a binary test for rational behaviour. A natural (non-binary) measure of the degree of rationality exhibited by the consumer is the minimum number of data points whose removal induces a rationalisable data-set.We study the computational complexity of the resultant consumer rationality problem in this paper. This problem is, in the worst case, equivalent (in terms of approximation) to the directed feedback vertex set problem. Our main result is to obtain an exact threshold on the number of commodities that separates easy cases and hard cases. Specifically, for two-commodity markets the consumer rationality problem is polynomial time solvable; we prove this via a reduction to the vertex cover problem on perfect graphs. For three-commodity markets, however, the problem is NP-complete; we prove thisusing a reduction from planar 3-SAT that is based upon oriented-disc drawings

Revealed Preference Dimension via Matrix Sign Rank

Given a data-set of consumer behaviour, the Revealed Preference Graph succinctly encodes inferred relative preferences between observed outcomes as a directed graph. Not all graphs can be constructed as revealed preference graphs when the market dimension is fixed. This paper solves the open problem of determining exactly which graphs are attainable as revealed preference graphs in $d$-dimensional markets. This is achieved via an exact characterization which closely ties the feasibility of the graph to the Matrix Sign Rank of its signed adjacency matrix. The paper also shows that when the preference relations form a partially ordered set with order-dimension $k$, the graph is attainable as a revealed preference graph in a $k$-dimensional market.Comment: Submitted to WINE `1

The Fairness Challenge in Computer Networks

In this paper, the concept of fairness in computer networks is investigated. We motivate the need of examining fairness issues by providing example future application scenarios where fairness support is needed in order to experience sufficient service quality. Fairness definitions from political science and their application to computer networks are described and a state-of-the-art overview of research activities in fairness, from issues such a queue management and tcp-friendliness to issues like fairness in layered multi-rate multicast scenarios, is given. We contribute with this paper to the ongoing research activities by defining the fairness challenge with the purpose of helping direct future investigations to with spots on the map of research in fairness

Autoinflammatory constrictive pericarditis and chronic myelomonocytic leukaemia: when one speciality is not enough

We present a case of constrictive pericarditis with concomitant blood and bone marrow appearances of chronic myelomonocytic leukaemia (CMML). Despite surgical treatment with pericardiectomy, the patient deteriorated into multiorgan failure. Pericardial histology disclosed a typical inflammatory picture with no evidence of monocytic or malignant infiltrate. Following intensive collaboration between cardiologists, haematologists and rheumatologists via daily email exchanges, a diagnosis was reached of autoinflammatory constrictive pericarditis with a non-infiltrative coexisting CMML. The key to achieving a rapid and sustained response was a trial of high-dose steroids followed by intravenous immunoglobulins. This achieved restoration of cardiac function, resolution of symptoms and near normalisation of inflammatory markers. A diagnosis of concurrent CMML was confirmed at 3 months. The patient remains well, taking colchicine and steroids

Nonparametric instrumental regression with non-convex constraints

This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, like integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition