Pompeu Fabra University

About: Pompeu Fabra University is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 8093 authors who have published 23570 publications receiving 858431 citations. The organization is also known as: Universitat Pompeu Fabra & UPF.

Probabilistic plan recognition using off-the-shelf classical planners

Abstract: Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach to the more general problem of probabilistic plan recognition where a probability distribution over the set of goals is sought under the assumptions that actions have deterministic effects and both agent and observer have complete information about the initial state. We show that this problem can be solved efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal probabilities, are computed by means of two calls to a classical planner that no longer has to be modified in any way. A number of examples is considered to illustrate the quality, flexibility, and scalability of the approach.

Red tape and delayed entry

Abstract: Does cutting red tape foster entrepreneurship in industries with the potential to expand? We address this question by combining the time needed to comply with government entry procedures in 45 countries with industry-level data on employment growth and growth in the number of establishments during the 1980s. Our main empirical finding is that countries where it takes less time to register new businesses have seen more entry in industries that experienced expansionary global demand and technology shifts. Our estimates take into account that proxying global industry shifts using data from only one country?or group of countries with similar entry regulations?will in general yield biased results.

Unemployment vs. mismatch of talents: Reconsidering unemployment benefits

Abstract: We develop an equilibrium search-matching model with risk-neutral agents and two-sided ex-ante heterogeneity. Unemployment insurance has the standard effect of reducing employment, but also helps workers to get a suitable job. The predictions of our simple model are consistent with the contrasting performance of the labor market in Europe and US in terms of unemployment, productivity growth and wage inequality. To show this, we construct two fictitious economies with calibrated parameters which only differ by the degree of unemployment insurance and assume that they are hit by a common technological shock which enhances the importance of mismatch. This shock reduces the proportion of jobs which workers regards as acceptable in the economy with unemployment insurance (Europe). As a result, unemployment doubles in this economy. In the laissez-faire economy (US), unemployment remains constant, but wage inequality increases more and productivity grows less due to larger mismatch. The model can be used to address a number of normative issues.

FA*IR: A Fair Top-k Ranking Algorithm

Abstract: In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria. Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-$k$ should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above. An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.