INVITED SPEAKERS
Ali Emrouznejad
Professor and Chair in Business Analytics, Aston Business School, Aston University Birmingham, UKBig Data Performance Measurement
Big Data is a major source of change in today’s world. It is without doubt a source of immense economic and social value with the potential to impact individuals, organizations, and society alike in ways that are yet to be fully explored. Organizations are constantly collecting a variety data of unprecedented sizes (millions or billions of records / variables) and from various sources, with the aim to use such data to improve their performance. In this talk we first introduce performance measurement with Data Envelopment Analysis (DEA) and further discuss the notion of big data in DEA.
Gustav Feichtinger
Professor Emeritus, ORCOS, Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, AustriaThe Mathematics of Ageing
Age is a crucial variable in social sciences and particularly in population dynamics. In the first part of this paper, a two-state optimal control model is proposed to explain the substantial variations of scientific production over the life cycle of researchers. We identify conditions under which typical hump-shaped age-specific´patterns of scientific production turn out to be optimal for individual researchers. The second part of the paper deals with the ageing of learned societies. In a nutshell, the dilemma of a learned society is that keeping young, i.e. electing young entrants, has the drawback of reducing the replacement rate of members. It turns out that electing a mix of young and old members delivers the optimal solution of the problem, i.e. guaranteeing a young age structure, while ensuring a high recruitment rate.
Zoran Obradović
Member of the Academia Europaea, The Academy of Europe, Foreign Member of the Serbian Academy of Sciences and Arts, L.H. Carnell Professor of Data Analytics, Temple University Director, Center for Data Analytics and Biomedical Informatics, Professor, Computer and Information Sciences Department, Professor, Statistical Science Department, Fox School of BusinessMachine Learning for Decision Making in Complex Systems
An overview of our ongoing projects aimed to facilitate predictive analytics in complex systems will be presented in this talk. Challenges and the proposed solutions will be discussed related to modeling temporal dynamics, network embedding, and structure-aware intrinsic representation learning big networks. The algorithms will be evaluated in the context of applications related to improving quality of service in healthcare and power systems.
Marija Kuzmanović
University of Belgrade, Faculty of Organizational Sciences, BelgradeBounded rationality in decision making models
Traditional models of decision-making are based on the rationality of all actors, including decision-makers and modelers, among the others. Nevertheless, the practice has shown that the behavior and choices of actors are influenced by many factors such as motives, beliefs, opinions, personal and social preferences, as well as cognitive biases. Moreover, it has already been proven that people have limitations in their ability to collect and respond to relevant information, i.e. they are bounded rational. All this has led to the development of behavioral models in many areas including the operations research. In this paper, concept of bounded rationality will be illustrated using p-beauty contest game. The concepts of behavioral operations research will be introduced, followed by a thorough literature review regarding progress in this area.