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In this paper we propose a strategy for investing in new companies for which there is relatively little hard data available. We use fuzzy set theory to represent these new companies as finite fuzzy subsets of established companies for which there is a history of investment data. A fuzzy set is also used to represent the economic environment in which the proposed new investments will be made. From this fuzzy information we construct a fuzzy expected return for each new investment under consideration. These expected returns are then defuzzified, and those proposed investments whose defuzzified expected returns fail to meet some specified criteria are discarded. An investment strategy is then proposed for investing available capital in those new companies that meet the criteria.

In order to allow flexibility in the enforcement of the tax law, the language used is often intentionally vague and ambiguous. This enables the government to implement the intent of the lawmakers in administering that law. However, interpreting these vague and ambiguous laws requires tax professionals to face planning situations that are complex and uncertain. Due to an increase in civil litigation, the importance of tax professionals making defensible decisions has been magnified in recent years. Carnes, et al. (1994) report that tax partners with Big‐Six accounting firms spend about 30 to 45 percent of their time resolving ambiguous tax questions. Therefore, tax professionals could benefit from models or systems (i.e., decision support systems, expert systems, artificial intelligence) that provide decision direction when facing ambiguous tax situations. One such area in which tax professionals must assist their clients is the determination of what levels of compensation are reasonable for owner‐employees of closely‐held corporations (Hagan, et al. 1995).

This paper presents an alternative approach to the usual method of computing expected values of cash flows in capital budgeting situations. The approach is based on the more realistic notion that the cash flows, the probabilities assigned to the cash flows, or both are not always exactly known. Three cases representing different types of uncertainties relating to the cash flow prospects are presented and expected values are derived using the fuzzy set theory. The approach utilized in this paper provides an alternative to the prevalent methodology of estimating cash flows in capital budgeting. This approach offers greater flexibility in dealing with the complex issue of uncertainty than the prevalent probability‐based approach in cases where decisions are complex enough so that neither cash flows nor probability distributions are totally available.

Ramsey‐Boiteaux pricing to services offering limited deferability subject to reliability constraints with a specific application to postal services has been investigated by the first of the above authors in. The general results derived in his paper involve welfare‐optimal pricing for a monopolist faced with stochastic demands which impact both demand and cost. These stochastic demands are modeled by probability distributions with fixed parameters. In this paper we extend this approach to the case where the model parameters are not known with certainty. This uncertainty is dealt with by representing the distribution parameters as fuzzy sets in which the membership of a particular value in the fuzzy set represents the degree of belief associated with this parameter value. This approach requires the solution of a system of nonlinear equations for each possible combination of parameter values, and the result is a fuzzy solution vector. This solution vector is then defuzzified to obtain a specific solution to the problem. As in, we illustrate our approach with an application of welfare‐optimal pricing in the context of postal services. Section I reviews relevant literature and discusses the nature of postal services. The problem statement together with general results are presented in Section II, and applied to a postal service problem in Section III. These results are further illustrated via numerical example in section IV. Conclusions are presented in Section V.

The paper seeks to improve website performance by developing a specific preloading strategy tuned to the needs of a web server. Applying simple preloading strategies, as used in various operating systems' memory management algorithms, does not suffice when managing websites due to the uncertainty of internet users' behaviour.

This paper uses rough, or fuzzy, sets as the framework to introduce a website management strategy based on a user's stated preference. This mathematical approach allows the derivation of preloading strategies from uncertain and partially contradicting information generated from site usage statistics.

A paper example of an application of the algorithm is used to illustrate how this approach can be applied to efficiently manage a website.

Performance is one of the key issues in managing websites, especially as the internet gains popularity and becomes the common access point for information retrieval. This technique has the potential to deliver greater efficiency; faster response times; and reduces the need to hold detailed individual user profiles.

Recent corporate failures such as Enron, WorldCom, Global Crossing and K‐Mart and auditing failures such as Arthur Andersen have sparked great public concern, including the passage of the Sarbanes‐Oxley Act of 2002. This paper aims to address the development of accounting standards.

The approach is to use the fuzzy‐analytical‐hierarchical‐process (FAHP), recently developed by de Korvin and Klyele. Uncertainty in assigning priorities and the use of semantic variables lead naturally to the inclusion of fuzzy sets in the structure of the AHP paradigm. The hierarchy of decisions, constructed sequentially, consists of three levels of attributes.

The paper shows that applying the highly sophisticated mathematical FAHP model is needed to select the optimum mechanism for establishing accounting and auditing standards. The FAHP application results lead to a rational ranking of the four bases to develop accounting standards.

This paper helps to explain the ambiguous and vague nature of the attributes of financial reporting and to apply a recently developed mathematical methodology to help accounting policy makers select the optimum mechanism for developing accounting standards.

Promotions are an important aspect of the careers in managerial accounting. Review of the literature on promotions reveals that the decision‐making process is open to bias due to characteristics of ratees and raters, as well as possible political influences. Such flawed decision making may be due in part to the inherent ambiguity in rating performance of managers. Rough set rules are developed from an decision‐making example in which performance data is used in the decision to promote managers. Implications of incorporating these rules into expert systems used for promotions decisions are presented.

Determining the proper sample size and frequency of sampling such that quality is assured while financial losses are not unnecessarily incurred is critical to an effective quality program. The main purpose of the present work is to design a fuzzy controller to adjust sample sizes and frequency of sampling according to potential fuzzy benefit/loss. A set of fuzzy rules is given where, depending on the antecedents, the sample size and/or sampling frequency may be decreased, remain static or be increased. At any given moment the proportion of defects in the sample determines the firing strength of the rules suggesting an appropriate sample size and sampling frequency. The firing strength is then modified to include an analysis of the decision maker’s belief that as sampling takes place and adjustments are being considered benefit or loss would be incorporated prior to any action or adjustment to sample size and/or frequency.

Accounting, particularly in the area of cost variance analysis, contains a great deal of ambiguity due to imprecise or ill‐defined control terms. Cost accountants must continually incorporate good sense and professional judgment in the accounting process to overcome that ambiguity. Because of the construction of accounting expert systems, no ambiguity is present in the facts or rules, thereby excluding human reasoning and analysis of feedback within those systems. The use of fuzzy sets to build fuzzy control systems provides a method to incorporate ambiguity into expert systems, allowing expert systems to more closely emulate the complex human decision making process.

Determining the proper sample size such that quality is assured while financial losses are not unnecessarily incurred is critical to an effective quality program. The main purpose of the present work is to design a fuzzy controller to adjust sample sizes according to potential fuzzy loss penalties. A set of fuzzy rules is given where, depending on the antecedents, the sample size may be decreased, moderately modified, or increased. At any given moment the proportion of defects in the sample determines the firing strength of the rules suggesting an appropriate sample size. These rules are then modified to include an analysis of the decision maker’s belief that in a particular situation an inappropriate rule is being considered such that an expected loss would be incurred in meeting or falling short of defined quality goals.