Lobbying, Information Transmission, and Unequal Representation

We study the effects of unequal representation in the interest-group system on the degree of information transmission between a lobbyist and a policymaker. Employing a dynamic cheap-talk model in which the lobbyist cares instrumentally about his reputation for truthtelling, we show that the larger is the inequality, the less information can credibly be transmitted to the policymaker. We also investigate the effects of inequality on welfare and discuss the welfare effects of institutions that increase transparency but which as well, as an unintended side-effect, lower the lobbyist’s incentives for truthtelling. ZUSAMMENFASSUNG - (Lobbying, Informationsübertragung und ungleiche Repräsentation) In diesem Papier wird untersucht, welche Wirkung die ungleiche Repräsentation in einem Interessengruppensystem auf den Grad an Informationsübertragung zwischen einem Lobbyisten und einem Politiker hat. Es wird ein dynamisches Modell für 'Cheap-talk' verwendet, in dem angenommen wird, dass der Lobbyist Wert auf seine Reputation als aufrichtiger Informationsvermittler legt. Dabei kann gezeigt werden, dass je größer die Ungleichheit im System, dem Politiker umso weniger Information glaubwürdig übermittelt werden kann. Darüber hinaus wird die Wohlfahrtswirkung der Ungleichheit untersucht und diskutiert, welche Effekte solche Institutionen haben, die zwar einerseits die Transparenz erhöhen, mit unerwünschtem Nebeneffekt aber den Anreiz für Lobbyisten, Informationen wahrheitsgemäß weiterzugeben, verringern.Lobbying, interest groups, reputation, information transmission, representation, inequality, bias

Common Representation of Information Flows for Dynamic Coalitions

We propose a formal foundation for reasoning about access control policies within a Dynamic Coalition, defining an abstraction over existing access control models and providing mechanisms for translation of those models into information-flow domain. The abstracted information-flow domain model, called a Common Representation, can then be used for defining a way to control the evolution of Dynamic Coalitions with respect to information flow

Utilizing Class Information for Deep Network Representation Shaping

Statistical characteristics of deep network representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, such a regularizer aims to manipulate a statistical characteristic over all classes together. For classification tasks, however, it might be advantageous to enforce the desired characteristic per class such that different classes can be better distinguished. Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and class-wise Variance Regularizer (cw-VR). cw-CR targets to reduce the covariance of representations calculated from the same class samples for encouraging feature independence. cw-VR is similar, but variance instead of covariance is targeted to improve feature compactness. For the sake of completeness, their counterparts without using class information, Covariance Regularizer (CR) and Variance Regularizer (VR), are considered together. The four regularizers are conceptually simple and computationally very efficient, and the visualization shows that the regularizers indeed perform distinct representation shaping. In terms of classification performance, significant improvements over the baseline and L1/L2 weight regularization methods were found for 21 out of 22 tasks over popular benchmark datasets. In particular, cw-VR achieved the best performance for 13 tasks including ResNet-32/110.Comment: Published in AAAI 201