Salwa Ammar

Using Fuzzy Rule–Based Systems to Evaluate Overall Financial Performance of Governments: An Enhancement to the Bond Rating Process

Credit ratings remain a key feature of municipal debt management. The primary objective of this article is to develop a new methodology for evaluating the financial performance and creditworthiness of governments and to illustrate this approach for a sample of large American cities. Specifically, we develop a fuzzy rule–based system (FRBS) that uses economic, debt, and other financial information as well as a measure of financial management to produce rankings of city financial performance. The FRBS credit ratings are highly correlated with actual Moody’s ratings for these cities. FRBS have the potential of enhancing the rating process by standardizing the information used and encouraging consistent rules about what combinations of inputs result in good, fair, or poor performance.

Applying fuzzy-set theory to performance evaluation

Abstract Increasing emphasis on performance evaluation creates the need to develop consistent, fair, and robust models. The emerging methodology of fuzzy-set theory provides the tools necessary to address many of the issues relevant to performance assessment. This paper includes three applications of fuzzy-rule-based systems in performance measurement. All the examples require processing surveys and other forms of imprecise information. The evaluations rely on modeling the judgments experts make as they integrate multiple criteria. The paper includes a description of and motivation for the methodology as well as results of the proposed models. Comparisons with other evaluation practices are also included.

Constructing a fuzzy-knowledge-based-system: an application for assessing the financial condition of public schools

Abstract Financial evaluation in the public sector can utilize some of the tools developed for evaluation in the private sector. However, the emphasis on the bottom line characterized by productivity measures does not adequately address all of the issues faced by public institutions. Recent collaborations by researchers in management science and public administration have led to the successful development of an analytical approach that combines fuzzy set theory and knowledge based systems to produce a tool for evaluating the general performance of public institutions. Successful implementations have included evaluations of the management of state governments, the financial administration of large cities, and the credit worthiness of public institutions. This paper describes a recent collaborative project, funded by the State of New York, to develop a system to evaluate the financial condition of the states nearly 700 school districts.

Ranking State Financial Management: A Multilevel Fuzzy Rule-based System

In 1996 the Alan K. Campbell Institute in the Maxwell School of Citizenship and Public Affairs at Syracuse University was awarded a grant to rate management performance of state and local governments and selected federal agencies. The project includes several parallel initiatives to evaluate government performance. This article contains a description of a multilevel fuzzy rule-based system developed to evaluate state government performance. The objective is to measure effectiveness in state financial management and produce a relative ranking of performance. The system incorporates evaluation criteria and expert judgment. It utilizes survey and other publicly available information relevant to state financial management. Fuzzy set theory is used to represent imprecision in evaluated information and judgments. The results of this evaluation are compared to a parallel journalistic effort to rank state performance. The article highlights the differences between the two approaches and outlines the advantages of the fuzzy rule-based system.

Evaluating City Financial Management Using Fuzzy Rule—Based Systems

Financial management is one of the most important management tasks of government. The complexity of financial management systems has discouraged overall evaluations of financial management. The objective of this article is to develop a method for evaluating government financial management that can handle this complexity. A fuzzy rule–based system (FRBS) is a methodology that can use many different types of data, produce robust input measures and can capture the complex contextual judgment of experts. Using data collected as part of the Government Performance Project survey of large cities, we develop an FRBS for city financial management.

Evaluating Capital Management: A New Approach

The objective of this article is to develop a methodology for evaluating capital management performance. We have employed a new methodology, fuzzy rule–based systems (FRBS), that allows evaluators to break down complex systems into manageable components. FRBS can also use a variety of data types by converting data into ordinal input measures that are not overly sensitive to small measurement errors. In developing an overall performance measure, the evaluator under an FRBS creates explicit rules for combining inputs that can reflect the complex contextual judgments commonly made by evaluators. Using survey data on 35 large American cities from the Government Performance Project, we develop an FRBS for city capital management.

A New Approach to Assessing Performance of State Human Resource Management Systems A Multi-Level Fuzzy Rule-Based System

A growing body of research has focused on evaluating the effectiveness of human resource management systems, yet few efforts have been made to develop a set of criteria and a method for evaluating human resource management systems in the public sector As part of the Government Performance Project, the present study offers a framework and methodology to fill this gap The Government Performance Project worked with a panel of experts to identify relevant criteria to evaluate pubhc sector human resource systems and to construct a survey instrument aligned with the identified criteria After discussing the criteria and survey construction, the article introduces a method of evaluating human resource management systems, fuzzy logic, that models expert judgments and takes into account measurement imprecision

Fuzzy Logic: a Case Study in Performance Measurement

Client satisfaction surveys are a growing part of performance measurement for corporate units. Even the most carefully developed survey yields data that is imprecise in nature. Processing and interpretation of the data introduces additional sources of imprecision. This chapter describes applications of fuzzy logic as a tool to analyze this inherently imprecise data. It also includes a case study implementation in the utility industry in which a corporate information systems unit was attempting to interpret the relationship between client satisfaction and project importance.

Identifying low-performance public schools

‐ The growing emphasis on performance in public organizations has reached public schools. In contrast to past attempts to reform process and government, present efforts to reform American schools have focused more directly on performance. Key features of these performance‐based reforms in schools as well as other public organizations include: establishing clear, measurable performance standards; granting local actors the autonomy to find the best means of achieving these standards; providing rewards for local actors that achieve performance goals; and developing remedies for cases when goals are not met (King & Mathers, 1997). A majority of states now prepare report cards on individual schools which include test scores and other outcome measures, and a growing number of states use this information to rank, reward or sanction schools and districts (Jerald, 2000). While receiving less media attention, an important element in performance‐based reforms is the identification and improvement of organizations with the lowest performance. In the case of education, at least 19 states have established procedures for identifying low‐ performance schools and districts (Jerald & Boser, 1999). The 1994 reauthorization of the federal Title I program encouraged more programs of this sort by requiring states and districts that receive Title I money to establish systems, based primarily on student achievement on state‐wide assessments, for identifying and improving low‐performance schools (Advisory Council to New York State Board of Regents, 1994). Programs to rank schools on student performance and identify those with low performance face two controversial and difficult sets of issues. The first set of issues concerns how to identify which schools should be classified as having low performance. Although not without critics, the idea that low‐performance schools should be identified primarily on the basis of educational outcomes, such as achievement test scores is generally accepted by states with such programs (King & Mathers, 1997). However, several important issues concerning

A fuzzy logic approach to performance evaluation

Client satisfaction surveys are a growing part of performance evaluation for both individuals and corporate units. At one Northeast public utility, when these surveys became a major part of performance evaluation, concerns were raised about the validity of current procedures for analyzing this data. A research and development effort was begun to see how well fuzzy logic could be used to analyze this inherently imprecise data. In this paper, we describe the current procedures and their obvious defects, a preliminary model which corrects the worst deficiencies, and finally the R&D supported model which was developed using a fuzzy rule-based system.