Abstract: Choice problems as a class of decision problems have attracted great attention for the last couple of decades. Among the frameworks and supporting theories used in their study, two have had the greatest impact: bounded rationality and cost–benefit. Both theories could find support from past empirical studies under different conditions or problem environments. In the past studies, problem size has been shown to play an important role in decision‐making. As problem size increases, a decision process may be detoured and the decision outcome may be different. In this paper we investigate the impact of problem size on three important aspects of the computer‐aided decision process – strategy selection, decision time/effort, and decision quality – through very large choice problems.

Safeguarding confidential information is of paramount concern to government agencies in publishing statistical data. Given a set of sensitive cells, the problem is to identify a set of complementary cells to suppress so as to mask the values of the sensitive cells. All of the existing cell suppression methods fail to consider the relationships among cell values and the representation of these relationships in marginal totals. That marginal totals may contain potent information has not been appreciated. The paper employs the theory of nominal data analysis to demonstrate that the disclosure of marginal totals can be very risky. It recommends adding a front end test to the existing methods. The goal is to identify a list of sensitive marginal totals that have to be suppressed. This increases the sophistication of cell suppression methodology by providing an extra layer of protection.

Access path deployment is a critical issue in physical database design. Access paths typically include a clustered index as the primary access path and a set of secondary indexes as auxiliary access paths. To deploy the right access paths requires an effective algorithm and accurate estimation of the parameters used by the algorithm. One parameter central to any index-selection algorithm is the block selectivity of a query. Existing methods for estimating block selectivities are limited by restrictive assumptions. Furthermore, most existing methods produce estimates useful for aiding the selection of secondary indexes only. Little research has been done in the area of estimating block selectivities for supporting the selection of the clustered index. The paper presents a set of methods that do not depend on any specific assumption, produce accurate estimates, and can be used to aid in selecting the clustered index as well as secondary indexes.>

An approach to estimating record selectivity rooted in the theory of fitting a hierarchy of models in discrete data analysis is presented. In contrast to parametric methods, this approach does not presuppose a distribution pattern to which the actual data conform; it searches for one that fits the actual data. This approach makes use of parsimonious models wherever appropriate in order to minimize the storage requirement without sacrificing accuracy. Two-dimensional cases are used as examples to illustrate the proposed method. It is demonstrated that the technique of identifying a good-fitting and parsimonious model can drastically reduce storage space and that the implementation of this technique requires little extra processing effort. The case of perfect or near-perfect association and the idea of keeping information about salient cells of a table are discussed. A strategy to reduce storage requirement in cases in which a good-fitting and parsimonious model is not available is proposed. Hierarchical models for three-dimensional cases are presented, along with a description of the W.E. Deming and F.F. Stephan (1940) iterative proportional fitting algorithm which fits hierarchical models of any dimensions.>

Decision support systems (DSSs) built to expand human decision capabilities can simultaneously restrain decision-making. This characteristic is referred to as system restrictiveness. The existing conceptualization of system restrictiveness is reviewed, and the concept of induced restrictiveness as a subtle force that restricts decision-making by inducing decision-makers to take a particular decision process is introduced. The validity of this concept has been demonstrated by a controlled laboratory experiment that employed a protocol analysis technique. The experiment evaluated the decision process induced by Lotus 1-2-3 for a particular type of task. The results indicated that Lotus tended to induce the incremental decision process as opposed to the synoptic decision process favored by the control group. The incremental decision process generates alternatives by making marginal changes to a previous solution, whereas the synoptic decision process is characterized by unbiased search for solutions. These results are discussed in the context of induced restrictiveness.>