Evaluating epistemic uncertainty under incomplete assessments

The thesis of this study is to propose an extended methodology for laboratory based Information Retrieval evaluation under incomplete relevance assessments. This new methodology aims to identify potential uncertainty during system comparison that may result from incompleteness. The adoption of this methodology is advantageous, because the detection of epistemic uncertainty - the amount of knowledge (or ignorance) we have about the estimate of a system's performance - during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections. Across a series of experiments we demonstrate how this methodology can lead towards a finer grained analysis of systems. In particular, we show through experimentation how the current practice in Information Retrieval evaluation of using a measurement depth larger than the pooling depth increases uncertainty during system comparison

A retrieval evaluation methodology for incomplete relevance assessments

In this paper we a propose an extended methodology for laboratory based Information Retrieval evaluation under in complete relevance assessments. This new protocol aims to identify potential uncertainty during system comparison that may result from incompleteness. We demonstrate how this methodology can lead towards a finer grained analysis of systems. This is advantageous, because the detection of uncertainty during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections

A Behavioral Account of the Labor Market: The Role of Fairness Concerns

In this paper, we argue that important labor market phenomena can be better understood if one takes (i) the inherent incompleteness and relational nature of most employment contracts and (ii) the existence of reference-dependent fairness concerns among a substantial share of the population into account. Theory shows and experiments confirm, that even if fairness concerns were only to exert weak effects in one-shot interactions, repeated interactions greatly magnify the relevance of such concerns on economic outcomes. We also review evidence from laboratory and field experiments examining the role of wages and fairness on effort, derive predictions from our approach for entry-level wages and incumbent workers' wages, confront these predictions with the evidence, and show that reference-dependent fairness concerns may have important consequences for the effects of economic policies such as minimum wage laws.fairness, contracts, wages, effort, experiments

A Data-Oriented Model of Literary Language

We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 % of the variation in literary ratings.Comment: To be published in EACL 2017, 11 page

Anticipating Information Needs Based on Check-in Activity

In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM '17), 201

Confidence Interval Estimation Tasks and the Economics of Overconfidence

Experiments in psychology, where subjects estimate confidence intervals to a series of factual questions, have shown that individuals report far too narrow intervals. This has been interpreted as evidence of overconfidence in the preciseness of knowledge, a potentially serious violation of the rationality assumption in economics. Following these results a growing literature in economics has incorporated overconfidence in models of, for instance, financial markets. In this paper we investigate the robustness of results from confidence interval estimation tasks with respect to a number of manipulations: frequency assessments, peer frequency assessments, iteration, and monetary incentives. Our results suggest that a large share of the overconfidence in interval estimation tasks is an artifact of the response format. Using frequencies and monetary incentives reduces the measured overconfidence in the confidence interval method by about 65%. The results are consistent with the notion that subjects have a deep aversion to setting broad confidence intervals, a reluctance that we attribute to a socially rational trade-off between informativeness and accuracy.overconfidence; uncertainty; monetary incentives; experiments