Data Envelopment Analysis

DEA is a well-established performance benchmarking instrument; and many applications as well as theoretical developments have emerged since the famous contribution of Charnes et al. (1978). There are substantially two reasons for such notable interest. First, DEA is useful for calculating the relative efficiency of multiple input-output activities of a certain set of decision-making units. Second, and most interestingly, it is a non-parametric tool that can be applied without knowing the exact production function - this stands in sharp contrast to classical mathematical-statistical approaches.

Entropy-driven Social Network
Analysis

Since
the foundation of online social media services, the development of sophisticated methods for analyzing social networks becomes more and more important. Classical instruments here
are merely based on graph theory, where actors, groups of actors or social events are typically represented by nodes and their
relationships by edges. In addition to pure visualizations of such social fabrics, economists and sociologists are very ambitious in uncovering social patterns. Even here,
graph-based algorithms are commonly applied only. However, an up-and-coming approach is based on information rather than graph theory. In this new framework, all relationships between vertices are reinterpreted as conditionals and stored in a
so-called knowledge base. Then for studying a certain social network, the principle of minimum cross-entropy is applied to the respective knowledge base.