Structural selection in implicit learning of artificial grammars

In the contextual cueing paradigm, Endo and Takeda (in Percept Psychophys 66:293–302, 2004) provided evidence that implicit learning involves selection of the aspect of a structure that is most useful to one’s task. The present study attempted to replicate this finding in artificial grammar learning to investigate whether or not implicit learning commonly involves such a selection. Participants in Experiment 1 were presented with an induction task that could be facilitated by several characteristics of the exemplars. For some participants, those characteristics included a perfectly predictive feature. The results suggested that the aspect of the structure that was most useful to the induction task was selected and learned implicitly. Experiment 2 provided evidence that, although salience affected participants’ awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness

Multinomial Logit Models with Implicit Variable Selection

Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this paper we are developing a boosting technique called multinomBoost that performs variable selection and fits the multinomial logit model also when predictors are high-dimensional. Since in multicategory models the effect of one predictor variable is represented by several parameters one has to distinguish between variable selection and parameter selection. A special feature of the approach is that, in contrast to existing approaches, it selects variables not parameters. The method can distinguish between mandatory predictors and optional predictors. Moreover, it adapts to metric, binary, nominal and ordinal predictors. Regularization within the algorithm allows to include nominal and ordinal variables which have many categories. In the case of ordinal predictors the order information is used. The performance of the boosting technique with respect to mean squared error, prediction error and the identification of relevant variables is investigated in a simulation study. For two real life data sets the results are also compared with the Lasso approach which selects parameters

View Selection in Semantic Web Databases

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201

Implicit Contracts: Two Different Approaches

In this paper, I compare two different approaches to model implicit contracting, the infinite-horizon approach typically used in the literature and afinite-horizon approach building on an adverse-selection model. I demonstrate that even the most convincing result of the infinite-horizon approach, namely that implicit contracting is improved, if the discountrate is lowered, does not carry over to the alternative modeling approach. Predictions of the first approach should therefore be handled with care and subject to athorough reinvestigation

Implicit Contracts: Two Different Approaches

In this paper, I compare two different approaches to model implicit contracting, the infinite-horizon approach typically used in the literature and afinite-horizon approach building on an adverse-selection model. I demonstrate that even the most convincing result of the infinite-horizon approach, namely that implicit contracting is improved, if the discountrate is lowered, does not carry over to the alternative modeling approach. Predictions of the first approach should therefore be handled with care and subject to athorough reinvestigation.Trust; finite horizon; infinite horizon; discounting; implicit contracting

Implicit Collusion in Non-Exclusive Contracting under Adverse Selection

This paper studies how implicit collusion may take place through simple non-exclusive contracting under adverse selection when multiple buyers (e.g., entrepreneurs with risky projects) non-exclusively contract with multiple firms (e.g., banks). It shows that any price schedule can be supported as equilibrium terms of trade in the market if each firm's expected profit is no less than its reservation profit. Firms sustain collusive outcomes through the triggering trading mechanism in which they change their terms of trade contingent only on buyers' reports on the lowest average price that the deviating firm's trading mechanism would induce. It suggests that a good can be overpriced in a competitive market even with fully rational traders and without firms' explicit collusive agreement.collusion, non-exclusive contracting, competing mechanisms

Generalized additive modelling with implicit variable selection by likelihood based boosting

The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple exponential family distributions, including binomial, Poisson and normal response variables. The procedure combines the selection of variables and the determination of the appropriate amount of smoothing. As weak learners penalized regression splines and the newly introduced penalized stumps are considered. Estimates of standard deviations and stopping criteria which are notorious problems in iterative procedures are based on an approximate hat matrix. The method is shown to outperform common procedures for the fitting of generalized additive models. In particular in high dimensional settings it is the only method that works properly

Implicit Logic in Managerial Discourse: A Case Study in Choice of Selection Criteria

Little attention has been paid in mainstream selection theory to how selectors choose to justify criteria and whether there is evidence of any consistency or logic in the manner in which they do so. This paper addresses these questions within a socio-cognitive framework. A newly developed coding system is used to analyse and explain the discourse of 22 managers in justifying selection criteria for technical operators in a European broadcasting company. It was found that, even for a very technical position, managers with experience of the job for which candidates were being selected were more concerned with the values, beliefs and personalities of candidates. It also was found that, independently of their different levels of seniority and experience of selection or interviewing, all managers are more concerned with Person-Organisation Fit for both present and future needs than with immediate Person-Job Fit. The consistency of the findings suggests that there is an ‘implicit logic’ in the manner in which managers as selectors adopt criteria derived from implicit learning and tacit knowledge of both operational and organisational experience.