Exploring the quality of social information disclosed in non-financial reports of Croatian companies

By enacting the provisions of Directive 2014/95/EU and the Croatian Accounting Act on disclosing non-financial and diversity information, companies of public interest registering 500 and more employees are required to disclose non-financial information. The purpose of this research is to assess the quality of disclosed social information in non- financial/sustainability reports of Croatian companies. The assessment of the social information was grounded on the framework defined by globally accepted sustainability reporting standards by assessing the quality of social subcategories of human rights, labour practice, community/society and product, measured by attributes of relevance, clarity, verifiability, comparability and clarity. With the overall quality score of 13.16 (out of possible 36), the results prove that Croatian companies do disclose certain social information, but the reliability of this information for benchmarking and competitiveness assessment is questionable, as a consensus on the minimum of information to be disclosed as a fundamental requirement for benchmarking has not yet been reached

Learning Models of Behavior From Demonstration and Through Interaction

This dissertation is concerned with the autonomous learning of behavioral models for sequential decision-making. It addresses both the theoretical aspects of behavioral modeling — like the learning of appropriate task representations — and the practical difficulties regarding algorithmic implementation. The first half of the dissertation deals with the problem of learning from demonstration, which consists in generalizing the behavior of an expert demonstrator based on observation data. Two alternative modeling paradigms are discussed. First, a nonparametric inference framework is developed to capture the behavior of the expert at the policy level. A key challenge in the design of the framework is the objective of making minimal assumptions about the observed behavior type while dealing with a potentially infinite number of system states. Due to the automatic adaptation of the model order to the complexity of the shown behavior, the proposed approach is able to pick up stochastic expert policies of arbitrary structure. Second, a nonparametric inverse reinforcement learning framework based on subgoal modeling is proposed, which allows to efficiently reconstruct the expert behavior at the intentional level. Other than most existing approaches, the proposed methodology naturally handles periodic tasks and situations where the intentions of the expert change over time. By adaptively decomposing the decision-making problem into a series of task-related subproblems, both inference frameworks are suitable for learning compact encodings of the expert behavior. For performance evaluation, the models are compared with existing frameworks on synthetic benchmark scenarios and real-world data recorded on a KUKA lightweight robotic arm. In the second half of the work, the focus shifts to multi-agent modeling, with the aim of analyzing the decision-making process in large-scale homogeneous agent networks. To fill the gap of decentralized system models with explicit agent homogeneity, a new class of agent systems is introduced. For this system class, the problem of inverse reinforcement learning is discussed and a meta learning algorithm is devised that makes explicit use of the system symmetries. As part of the algorithm, a heterogeneous reinforcement learning scheme is proposed for optimizing the collective behavior of the system based on the local state observations made at the agent level. Finally, to scale the simulation of the network to large agent numbers, a continuum version of the model is derived. After discussing the system components and associated optimality criteria, numerical examples of collective tasks are given that demonstrate the capabilities of the continuum approach and show its advantages over large-scale agent-based modeling

Direkte und indirekte Beschaeftigungswirkungen technologischer Innovationen

SIGLEAvailable from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel A 172361 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Correlation Priors for Reinforcement Learning

Many decision-making problems naturally exhibit pronounced structures inherited from the characteristics of the underlying environment. In a Markov decision pro- cess model, for example, two distinct states can have inherently related semantics or encode resembling physical state configurations. This often implies locally cor- related transition dynamics among the states. In order to complete a certain task in such environments, the operating agent usually needs to execute a series of tempo- rally and spatially correlated actions. Though there exists a variety of approaches to capture these correlations in continuous state-action domains, a principled solution for discrete environments is missing. In this work, we present a Bayesian learning framework based on Pólya-Gamma augmentation that enables an analogous reasoning in such cases. We demonstrate the framework on a number of common decision-making related problems, such as imitation learning, subgoal extraction, system identification and Bayesian reinforcement learning. By explicitly modeling the underlying correlation structures of these problems, the proposed approach yields superior predictive performance compared to correlation-agnostic models, even when trained on data sets that are an order of magnitude smaller in size