Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework

Interference Effects in Quantum Belief Networks

Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making. Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision. In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart

Female labor force participation and the big five

This paper investigates the relationship between personality traits and female labor force participation. While research on the role of cognitive skills for individual labor market success has a long tradition in economics, comparatively little is known about the channels through which non-cognitive skills affect individual labor market behavior. There is striking evidence that personality traits play a major role in explaining individual differences in school attendance and school performance. However, comparatively little is known about how and which personality traits effect labor supply decisions. In this paper, we relate personality traits to preference parameters using a conventional structural framework of labor force participation. This allows us to separate the direct effects of personality traits affecting the individual participation decision through different individual preferences from the indirect effects through wages. We can show that personality traits play an important role in the female labor force participation decision. The channels through which personality traits effect labor force participation are manifold and depend on the specific trait. Aggregation of traits to a single index is therefore a suboptimal strategy. --personality traits,female labor supply,wages

Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis

We consider an extension of conventional univariate Kaplan-Meier type estimators for the hazard rate and the survivor function to multivariate censored data with a censored random regressor. It is an Akritas (1994) type estimator which adapts the nonparametric conditional hazard rate estimator of Beran (1981) to more typical data situations in applied analysis. We show with simulations that the estimator has nice finite sample properties and our implementation appears to be fast. As an application we estimate nonparametric conditional quantile functions with German administrative unemployment duration data. --nonparametric estimation,censoring,unemployment duration

Simple nonparametric estimators for unemployment duration analysis

"We consider an extension of conventional univariate Kaplan-Meier type estimators for the hazard rate and the survivor function to multivariate censored data with a censored random regressor. It is an Akritas (1994) type estimator which adapts the nonparametric conditional hazard rate estimator of Beran (1981) to more typical data situations in applied analysis. We show with simulations that the estimator has nice finite sample properties and our implementation appears to be fast. As an application we estimate nonparametric conditional quantile functions with German administrative unemployment duration data." (Author's abstract, IAB-Doku) ((en)) Additional Information Appendix for the FDZ-Methodenreport No. 09/2007: Programme.zipArbeitslosigkeitsdauer, Schätzung - Methode, IAB-Beschäftigtenstichprobe

Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis

The paper analyses the potential impact of stock market developments on lending behaviour from different perspectives. First we scrutinize the impact of stock market movements on the banks? and on the borrowers? balance sheets. Subsequently we estimate aggregate credit supply and demand functions including a stock market indicator as explanatory variable. The analysis reveals no major importance of the bank balance sheet channel for the relationship between stock market volatility and corporate financing possibilities of non-financial companies. A possible impact of stock market movements on banks´ lending behaviour might be rooted in their impact on the balance sheets of corporate borrowers. The empirical results of the credit market analysis yield some confirming evidence for an impact of stock market developments. However, the results are not very stable and depend on the specification of the model and on the time period under observation. --