The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study

Since the seminal article of Bates and Granger (1969), a large number of theoretical and empirical studies have shown that pooling different forecasts of the same event tends to outperform individual forecasts in terms of forecast accuracy. However, the results remain heterogenous regarding the size of gains. As there are numerous sources for the large variation of the resulting gains, it is difficult to estimate the improvement in accuracy based on empirical findings. Consequently, we use Monte Carlo techniques which enable us to identify the gains of pooling from VAR forecasts under lab conditions. In particular, the results are allowed to vary with respect to sample size, forecast horizon, number of pooled forecasts, weighting scheme and structure of the model economy. Given strict lab conditions, our setup of the experiment yields a quantification of the virtues that can be obtained in almost any forecast situation. The analysis shows that pooling leads to a substantial reduction of MSE of about 20%, which is comparable to the elimination of estimation uncertainty. Most notably, this reduction is already obtained with an average of about four different forecasts.Pooling of forecasts, model uncertainty, VAR model, Monte Carlo Study

Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond

We present a graphical and dynamic framework for binding and execution of business) process models. It is tailored to integrate 1) ad hoc processes modeled graphically, 2) third party services discovered in the (Inter)net, and 3) (dynamically) synthesized process chains that solve situation-specific tasks, with the synthesis taking place not only at design time, but also at runtime. Key to our approach is the introduction of type-safe stacked second-order execution contexts that allow for higher-order process modeling. Tamed by our underlying strict service-oriented notion of abstraction, this approach is tailored also to be used by application experts with little technical knowledge: users can select, modify, construct and then pass (component) processes during process execution as if they were data. We illustrate the impact and essence of our framework along a concrete, realistic (business) process modeling scenario: the development of Springer's browser-based Online Conference Service (OCS). The most advanced feature of our new framework allows one to combine online synthesis with the integration of the synthesized process into the running application. This ability leads to a particularly flexible way of implementing self-adaption, and to a particularly concise and powerful way of achieving variability not only at design time, but also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

Neural End-to-End Learning for Computational Argumentation Mining

We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance.Comment: To be published at ACL 201

Bounds on the Average Sensitivity of Nested Canalizing Functions

Nested canalizing Boolean (NCF) functions play an important role in biological motivated regulative networks and in signal processing, in particular describing stack filters. It has been conjectured that NCFs have a stabilizing effect on the network dynamics. It is well known that the average sensitivity plays a central role for the stability of (random) Boolean networks. Here we provide a tight upper bound on the average sensitivity for NCFs as a function of the number of relevant input variables. As conjectured in literature this bound is smaller than 4/3 This shows that a large number of functions appearing in biological networks belong to a class that has very low average sensitivity, which is even close to a tight lower bound.Comment: revised submission to PLOS ON

Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!

Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at \url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201

A Fragment of Dependence Logic Capturing Polynomial Time

In this paper we study the expressive power of Horn-formulae in dependence logic and show that they can express NP-complete problems. Therefore we define an even smaller fragment D-Horn* and show that over finite successor structures it captures the complexity class P of all sets decidable in polynomial time. Furthermore we study the question which of our results can ge generalized to the case of open formulae of D-Horn* and so-called downwards monotone polynomial time properties of teams

Gift Exchange and Workers' Fairness Concerns: When Equality Is Unfair

We study how different payment modes influence the effectiveness of gift exchange as a contract enforcement device. In particular, we analyze how horizontal fairness concerns affect performance and efficiency in an environment characterized by contractual incompleteness. In our experiment, one principal is matched with two agents. The principal pays equal wages in one treatment and can set individual wages in the other. We find that the use of equal wages elicits substantially lower efforts. This is not caused by monetary incentives per se since under both wage schemes it is profit-maximizing for agents to exert high efforts. The treatment difference instead seems to be driven by the fact that the norm of equity is violated far more frequently in the equal wage treatment. After having suffered from violations of the equity principle, agents withdraw effort. These findings hold even after controlling for the role of intentions, as we show in a third treatment. Our results suggest that adherence to the norm of equity is a necessary prerequisite for successful establishment of gift-exchange relations.reciprocity, gift exchange, equity, wage equality, wage setting, incomplete contracts

Reciprocity and Payment Schemes: When Equality Is Unfair

A growing literature stresses the importance of reciprocity, especially for employment relations. In this paper, we study the interaction of different payment modes with reciprocity. In particular,we analyze how equal wages affect performance and effciency in an environment characterized by contractual incompleteness. In our experiment, one principal is matched with two agents. The principal pays equal wages in one treatment and can set individual wages in the other. We find that the use of equal wages elicits substantially lower efforts and effciency. This is not caused by monetary incentives per se since under both wage schemes it is profit-maximizing for agents to exert high efforts. The treatment difference is rather driven by the fact that reciprocity is violated far more frequently in the equal wage treatment. Agents suffering from a violation of reciprocity subsequently withdraw effort. Our results suggest that individual reward and punishment opportunities are crucial for making reciprocity a powerful contract enforcement device.laboratory experiment; wage setting; wage equality; gift exchange; reciprocity; social norms; incomplete contracts; multiple agents