The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR’s TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining a ranking, finding ‘scores’ for each obj...

A large body of work in machine learning has focused on the problem of learning a close approximation to an underlying combinatorial function, given a small set of labeled examples. However, for real-valued functions, cardinal labels might not be accessible, or it may be difficult for an expert to consistently assign real-valued labels over the entire set of examples. For instance, it is notori...

Recently, the Markov chain choice model has been introduced by Blanchet et al. to overcome the computational intractability for learning and revenue management for several modern choice models, including the mixed multinomial logit models. However, the known methods for learning the Markov models require almost all items to be offered in the learning stage, which is impractical. To address this...

Making comparative judgments of intangible stimuli or criteria (e.g. the degree of an environmental hazard or pollution factors) involves not only imprecise or inexact knowledge but also inconsistency in our own judgements. The improvement of knowledge elicitation by controlling the inconsistency of experts’ judgments is not only desirable but absolutely necessary. Due to space limitations, the...

Organization of a tender is not easy. Preparation of the relevant specification, taking into account the non-price criteria, implementation of the objective and fair assessment procedure, and last but not least, selecting a satisfactory offer are in practice a considerable challenge. In meeting this challenge appropriate multi-criteria assessment models can help. Models that can cope with diffe...

We study the problem of interactively learning a binary classifier using noisylabeling and pairwise comparison oracles, where the comparison oracle answerswhich one in the given two instances is more likely to be positive. Learning fromsuch oracles has multiple applications where obtaining direct labels is harder butpairwise comparisons are easier, and the algorithm can leverage...

In the problem of ranking from pairwise comparisons, the learner has access to pairwise preferences among n objects and is expected to output a total order of these objects. This problem has a wide range of applications not only in computer science but also in other areas such as social science and economics. In this report, we will give a survey of passive and active learning algorithms for ra...

This paper proposes a new methodology to study environments with unobserved agent heterogeneity. We focus on the settings where the heterogeneous factor takes values from an unknown finite set, and the economic model yields testable implications in the form of pairwise inequalities. The method produces a consistent classification of economic agents according to their unobserved types. The paper...

This study proposes axioms for inconsistency indicators in pairwise comparisons. The new observation (by Szybowski), that “no PC submatrix may have a worse inconsistency indicator than the given PC matrix” is an essential simplification of the axiomatization. The goal of formulating axioms for all future definitions of new inconsistency indicators is difficult and as illusive as the inconsisten...