Exclusion in the all-pay auction: An experimental investigation

Contest or auction designers who want to maximize the overall revenue are frequently concerned with a trade-off between contest homogeneity and inclusion of contestants with high valuations. In our experimental study, we find that it is not profitable to exclude the most able contestant in favor of greater homogeneity among the remaining contestants, even if the theoretical exclusion principle predicts otherwise. This is because the strongest contestants considerably overexert. A possible explanation is that these contestants are afraid they will regret a low but risky bid if they lose and thus prefer a strategy which gives them a low but secure pay-off. -- Steht bei einer Auktion oder einem Turnier die Maximierung der Gesamtanstrengung aller Teilnehmer im Vordergrund, besteht ein Zielkonflikt zwischen der Homogenität der Teilnehmer und der Teilnahme ausgesprochen starker Wettbewerber. Aus der theoretischen Literatur ist das sogenannte Ausschlussprinzip bekannt, das besagt, dass der leistungsstärkste Agent aus einer Gruppe von Teilnehmern ausgeschlossen werden sollte, wenn das Leistungsgefälle zu groß ist. Dieses Prinzip wird einem experimentellen Test unterzogen. Es zeigt sich, dass sich der Ausschluss des stärksten Teilnehmers nie lohnt, da sich dieser weit über die Maßen anstrengt, sofern er an der Auktion teilnimmt. Die übermäßige Anstrengung ist umso prominenter, je überlegener der stärkste Teilnehmer gegenüber dem zweitstärksten ist. Dieses Verhalten kann mit einer Aversion gegenüber dem Gefühl des Bedauerns erklärt werden, das die stärksten Teilnehmer spüren, wenn sie sich weniger anstrengen und in Folge den Wettbewerb verlieren.experiments,contests,all-pay auction,heterogeneity,regret aversion

Do individuals recognize cascade behavior of others? An Experimental Study

In an information cascade experiment participants are confronted with artificial predecessors predicting in line with the BHW model (Bikchandani et al., 1992). Using the BDM (Becker et al., 1964) mechanism we study participants' probability perceptions based on maximum prices for participating in the prediction game. We find increasing maximum prices the more coinciding predictions of predecessors are observed, regardless of whether additional information is revealed by these predictions. Individual price patterns of more than two thirds of the participants indicate that cascade behavior of predecessors is not recognized

Do individuals recognize cascade behavior of others? An Experimental Study

In an information cascade experiment participants are confronted with artificial predecessors predicting in line with the BHW model (Bikchandani et al., 1992). Using the BDM (Becker et al., 1964) mechanism we study participants' probability perceptions based on maximum prices for participating in the prediction game. We find increasing maximum prices the more coinciding predictions of predecessors are observed, regardless of whether additional information is revealed by these predictions. Individual price patterns of more than two thirds of the participants indicate that cascade behavior of predecessors is not recognized.information cascades; Bayes' Rule; decision under risk and uncertainty; experimental economics

Do Individuals Recognize Cascade Behavior of Others? - An Experimental Study -

In an information cascade experiment participants are confronted with artificial predecessors predicting in line with the BHW model (Bikchandani et al., 1992). Using the BDM (Becker et al., 1964) mechanism we study participants' probability perceptions based on maximum prices for participating in the prediction game. We find increasing maximum prices the more coinciding predictions of predecessors are observed, regardless of whether additional information is revealed by these predictions. Individual price patterns of more than two thirds of the participants indicate that cascade behavior of predecessors is not recognized.Information Cascades, Bayes' Rule, Decision Under Risk and Uncertainty, Experimental Economics.

Weakly-supervised learning of visual relations

This paper introduces a novel approach for modeling visual relations between pairs of objects. We call relation a triplet of the form (subject, predicate, object) where the predicate is typically a preposition (eg. 'under', 'in front of') or a verb ('hold', 'ride') that links a pair of objects (subject, object). Learning such relations is challenging as the objects have different spatial configurations and appearances depending on the relation in which they occur. Another major challenge comes from the difficulty to get annotations, especially at box-level, for all possible triplets, which makes both learning and evaluation difficult. The contributions of this paper are threefold. First, we design strong yet flexible visual features that encode the appearance and spatial configuration for pairs of objects. Second, we propose a weakly-supervised discriminative clustering model to learn relations from image-level labels only. Third we introduce a new challenging dataset of unusual relations (UnRel) together with an exhaustive annotation, that enables accurate evaluation of visual relation retrieval. We show experimentally that our model results in state-of-the-art results on the visual relationship dataset significantly improving performance on previously unseen relations (zero-shot learning), and confirm this observation on our newly introduced UnRel dataset

Spatially regularized estimation for the analysis of DCE-MRI data

Competing compartment models of different complexities have been used for the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. We present a spatial Elastic Net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial Elastic Net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in-vivo data set

Analysis of DCE-MRI Data using a Nonnegative Elastic Net

We present a nonnegative Elastic Net approach for the analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. A multi-compartment approach is considered, which is translated into a (restricted) least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of some compartment. Using the Elastic Net estimator, we chose clusters of basis functions, and hence, rate constants of compartments. As further challenge, the estimator has to be restricted to positive regression parameters, which correspond to transfer rates of the compartments. The proposed estimation method is applied to an in-vivo data set

Weakly-supervised learning of visual relations

This paper introduces a novel approach for modeling visual relations between pairs of objects. We call relation a triplet of the form (subject, predicate, object) where the predicate is typically a preposition (eg. 'under', 'in front of') or a verb ('hold', 'ride') that links a pair of objects (subject, object). Learning such relations is challenging as the objects have different spatial configurations and appearances depending on the relation in which they occur. Another major challenge comes from the difficulty to get annotations, especially at box-level, for all possible triplets, which makes both learning and evaluation difficult. The contributions of this paper are threefold. First, we design strong yet flexible visual features that encode the appearance and spatial configuration for pairs of objects. Second, we propose a weakly-supervised discriminative clustering model to learn relations from image-level labels only. Third we introduce a new challenging dataset of unusual relations (UnRel) together with an exhaustive annotation, that enables accurate evaluation of visual relation retrieval. We show experimentally that our model results in state-of-the-art results on the visual relationship dataset significantly improving performance on previously unseen relations (zero-shot learning), and confirm this observation on our newly introduced UnRel dataset