Srivastava / Mock Belief Functions in Business Decisions

Erscheinungsjahr 2013
ISBN: 978-3-7908-1798-0
Verlag: Physica
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 88, 346 Seiten, eBook

Reihe: Studies in Fuzziness and Soft Computing

The book focuses on applications of belief functions to business decisions. Section I introduces the intuitive, conceptual and historical development of belief functions. Three different interpretations (the marginally correct approximation, the qualitative model, and the quantitative model) of belief functions are investigated, and rough set theory and structured query language (SQL) are used to express belief function semantics. Section II presents applications of belief functions in information systems and auditing. Included are discussions on how a belief-function framework provides a more efficient and effective audit methodology and also the appropriateness of belief functions to represent uncertainties in audit evidence. The third section deals with applications of belief functions to mergers and acquisitions; financial analysis of engineering enterprises; forecast demand for mobile satellite services; modeling financial portfolios; and economics.

Zielgruppe

Research

Autoren/Hrsg.

Srivastava, Rajendra P.

Mock, Theodore J.

Inhaltsverzeichnis

Foundations.- to Belief Functions.- Decision Making in a Context where Uncertainty is Represented by Belief Functions.- Empirical Models for the Dempster-Shafer-Theory.- Systems and Auditing Applications.- The Descriptive Ability of Models of Audit Risk.- The Effectiveness and Efficiency of Belief Based Audit Procedures.- Auditors’ Evaluations of Uncertain Audit Evidence: Belief Functions versus Probabilities.- Conflict, Consistency and Consonance in Belief Functions: Coherence and Integrity of Belief Systems.- Operations Management, Finance and Economics Applications.- Evaluating Mergers and Acquisitions: A Belief Function Approach.- Possibilistic Belief Network Constructed by Operators of Composition and its Application to Financial Analysis.- Using Belief Functions to Forecast Demand for Mobile Satellite Services.- Modeling Financial Portfolios Using Belief Functions.- Futures Hedging under Prospect Utility and Knightian Uncertainty.