Achieving MAX-MIN Fair Cross-efficiency scores in Data Envelopment Analysis

Algorithmic decision making is gaining popularity in today's business. The need for fast, accurate, and complex decisions forces decision-makers to take advantage of algorithms. However, algorithms can create unwanted bias or undesired consequences that can be averted. In this paper, we propose a MAX-MIN fair cross-efficiency data envelopment analysis (DEA) model that solves the problem of high variance cross-efficiency scores. The MAX-MIN cross-efficiency procedure is in accordance with John Rawls’s Theory of justice by allowing efficiency and cross-efficiency estimation such that the greatest benefit of the least-advantaged decision making unit is achieved. The proposed mathematical model is tested on a healthcare related dataset. The results suggest that the proposed method solves several issues of cross-efficiency scores. First, it enables full rankings by having the ability to discriminate between the efficiency scores of DMUs. Second, the variance of cross-efficiency scores is reduced, and finally, fairness is introduced through optimization of the minimal efficiency scores

Knowledge management in the public sector – the case of Serbian local government

Knowledge has become an increasingly important determinant of competitiveness for the local governments. The purpose of this paper was to explore the existence of KM in Serbian local government and to identify and analyzes processes and infrastructure that supports KM practices. The paper consists of two parts; the first part covers comprehensive literature review that summarizes the existing research done on the subject of knowledge management in public sector in order to put research in context and to highlight the contribution of this paper to the existing body of knowledge. The second part consists of empirical research conducted to get a preview of the current state of the KM practice in Serbian local government. There were several important findings regarding KM practices in Serbian LGs identified in the study, i.e., public servants have a positive attitude toward KM concept; KM activities are mostly technology supported; KM is perceived as a management priority; there is not any formal attribution of responsibility for knowledge practice to executive staff, human resource unit or IT unit, neither any mention of knowledge capacity development in strategies or plans of LGs. Finally, the results of this research study provide valuable guidance for future research for both academics and practitioners

Formalizing Financial Decision-Making Process: Classical or Fuzzy Approach?

The importance and the complexity of financial decision-making process are the reasons why so much work has been invested over the years in formulating methods that would realistically treat this issue. The requirement for adequate and effective methods and procedures is justified by very high complexity of the real situation, making it more difficult to fit into restrictive hypotheses on which mathematical models are often based. Financial decision-making represents a field where decision support systems can be successfully implemented, especially knowledge based decision support systems and intelligent decision support systems. This paper presents the most important features of two decision support systems, a classical system and a system based on fuzzy logics. The performances of these two models are compared and the advantages achieved through the introduction of fuzzy concepts into the classical decision support systems determined

Serbia

Abstract: Financial decision-making is one of the most current issues of modern financial management. Financial decision-making is an area where decision support systems, knowledge-based decision support systems, and intelligent decision support systems are successfully applied. In consequence of the importance and complexity of this problem area a large number of methods of support to financial decisionmaking was developed. This paper presents the most important features of two decision support systems, a classical system and a system based on fuzzy logic. The performances of these two models are compared and the advantages achieved through the introduction of fuzzy concepts into the classical decision support systems are determined. Key words: decision-making, decision support systems, fuzzy logic. 1

DATA WAREHOUSING AND DATA MINING- A CASE STUDY

Abstract: This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS), University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making