【正文】 set of attributes to minimize the probable project cost escalation. To analyze and control the impact of these attributes on the project cost, it is important to collate the past project performance data available with the user firm. Furthermore, these data should be analyzed with respect to the new project characteristics by using an appropriate analytical medium. A puterized decision support system (DSS) would therefore be advantageous to assist the user in developing a suitable project cost control strategy. ATTRIBUTE VERSUS LINE ITEMS The tenn attribute (as used in the present paper) does not refer to the conventional tenn line items. However, it pertains to the factors that might be responsible for generating cost escalation in the line items of a project. The difference is emphasized to delineate the point of departure for this research. In recent years, many researchers have addressed the issue of cost control by using techniques such as Monte Carlo simulation, management exception reporting, and probabilistic estimating. Noheless, their research fothe variance in line items. However, from the cost management perspective, it would be more beneficial to identify the cause of variance in the line items, which, when controlled, would minimize the overall project cost escalation. During the estimating process for a given project, we might assume a certain state for attributes such as management errors, regulatory approval, error/rework, worker morale, and crew balance. The underlying concept of this research is that during the course of the project the assumed state of these attributes might change due to one reason or another. The change in state or loss of equilibrium of an attribute might not only influence certain other attributes but also might influence the line items that were estimated based on the assumed state of the attribute. This, in turn, might cause a percentage escalation in the estimated project cost. An attribute is considered to be in the active state if, over the course of the project, the cost or status of an attribute differs from what was assigned to it at the estimating stage. For example, the labor productivity obtained during the course of the project might differ from what was assumed at the es timating stage. Similarly, at the estimating stage, a nonactive status might be assigned to the attribute, management, or project team. However, there is a possibility that during the course of the project the management or project team might make a decision error, influencing many other attributes. This would change the nonactive status of the attribute, management, or project team, to an active state. The probability and the resulting cost impact of these events cannot be neglected. Attribute state is defined by using a binary mode, where state = 1 implies that the attribute was in active state in that project, whereas state = 0 implies otherwise. The plex in terrelationship between the attributes suggests that even a minor change in the assumed equilibrium state of an attribute has the potential to trigger a domino effect. This effect could not only influence some other attributes but could also influence the project cost. Therefore, the binary mode of representation was considered to be most appropriate for this research, since any intennediate state between active and nonactive would not provide any additional infonnation. THE ATTRIBUTES For the purpose of this research, attributes that have a potential to cause project cost escalation were identified . In the past, several authors have examined the impact of isolated attributes on project cost However, no project management tool is available to account for the collective impact of all possible attributes. The attributes were divided into two groups, quantifiable and nonquantifiable attributes. Attributes that have a cost value associated with them in the project estimate were defined as quantifiable attributes, ., total material cost, total labor cost, total equipment cost, project management cost, and total cost of the project at end of work. Attributes that do not have a cost value associated with them in the project estimate were defined as nonquantifiable attributes. The need to differentiate between quantifiable and nonquantifiable attributes is elaborated later under modeling assumptions. FIG. 1. Example Influence Pattern Refers to the percentage cost escalation over the estimated project cost. To satisfy these requirements, a DSS such as COMPASS would be most suitable. MODELING ASSUMPTIONS The interrelationships between attributes, the resulting influence pattern, and the impact of attributes on the project cost have been structured by defining the five following modeling assumptions: Assumption 1 If an attribute, ., F (refer to Fig. 1) is influenced by a set of attributes, ., C and D, then the individual influence of the attributes in that set on F (., the influence of C on F and the influence of D on F) is considered to be independent, . p[(F n C)I(F n D)] =p(F n C) (Ia) :. p[(F n C) n (F n D)] 。 (2) the interrelationships between attributes established in the influence pattern。 and (3) module 3to develop a project cost control strategy to minimize the expected loss. FRAMEWORK OF COMPASS The accuracy of a system depends to a large extent on the validity