Contractual Execution, Strategic Incompleteness and Venture Capital

Contractual execution generates hard information, available to the contracting parties, even when contracts are secretly executed. Building on this simple observation, the paper shows that incomplete contracts can be preferred to complete contracts. This is because (i) execution of incomplete contracts reveals less information to outside parties, giving rise to strategic gains; (ii) secretly executed complete contracts could not do better, given the possible strategic uses of the hard information generated by execution of the contract. The key effects at work are explored in the case of financial contracts for innovative start-up companies, providing a rationale for the observed differences in the extent to which venture capital contracts include a variety of contingencies, and for how this varies across industries and geographically.

Work for Image and Work for Pay

Standard economic models with complete information predict a positive, monotonic relationship between pay and performance. This prediction does not always hold in experimental tests: offering a small payment may result in lower performance than not offering any payment. We test experimentally two main explanations that have been put forward for this result: the "incomplete contract" hypothesis views the payment rule as a signal given to subjects on purpose of the activity. The "informed principal" hypothesis views it as a signal concerning the characteristics of the agent or of the task. The incomplete contract view appears to offer the best overall explanation for our results. We also find that high-powered monetary incentives do not "crowd out" intrinsic motivation, but may elicit "too much" effort when intrinsic motivation is very high.

Diving into Knowledge Graphs for Patents: Open Challenges and Benefits

Textual documents are the means of sharing information and preserving knowledge for a large variety of domains. The patent domain is also using such a paradigm which is becoming difficult to maintain and is limiting the potentialities of using advanced AI systems for domain analysis. To overcome this issue, it is more and more frequent to find approaches to transform textual representations into Knowledge Graphs (KGs). In this position paper, we discuss KGs within the patent domain, present its challenges, and envision the benefits of such technologies for this domain. In addition, this paper provides insights of such KGs by reproducing an existing pipeline to create KGs and applying it to patents in the computer science domain

A normal form analysis in a finite neighborhood of a hopf bifurcation: on the center manifold dimension

The problem of determining the bounds of applicability of perturbation expansions in terms both of the system parameters and the state-space variable amplitude is a key point in the perturbation analysis of nonlinear systems. In the present paper an analysis in a finite neighborhood of a Hopf bifurcation is presented in order to analyze the conditions under which a Normal Form zero-divisors-based approach fails to describe the local dynamics and, therefore, a small divisor approach is required. The condition of “smallness” referred to the divisors is analyzed from both a qualitative and a quantitative point of view. Finally, a simple but effective analytical and numerical example is introduced to illustrate the theoretical issues along with an interpretation within a codimension-two framework

Enhancing random forests performance in microarray data classification

Random forests are receiving increasing attention for classification of microarray datasets. We evaluate the effects of a feature selection process on the performance of a random forest classifier as well as on the choice of two critical parameters, i.e. the forest size and the number of features chosen at each split in growing trees. Results of our experiments suggest that parameters lower than popular default values can lead to effective and more parsimonious classification models. Growing few trees on small subsets of selected features, while randomly choosing a single variable at each split, results in classification performance that compares well with state-of-art studies

Assessing similarity of feature selection techniques in high-dimensional domains

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement