Knowledge that Transforms

Preference models and utility functions are often assessed by eliciting value trade-offs among attributes. Prior research has shown that trade-off judgments can be biased in systematic ways: for example, the attribute which is used as response receives more relative subjective weight, i.e. the so-called scale compatibility effects (Tversky et al. [Tversky, A., S. Sattath, P. Slovic. 1988. Contingent weighting in judgment and choice. Psych. Rev. 95 371–384.]). This paper proposes a new procedure to elicit value trade-offs called bidimensional matching, or “bi-matching”, designed to alleviate this effect. Bi-matching differs from traditional trade-off judgments, in that both attributes are adjusted simultaneously to reach indifference judgments. Bi-matching is compared with simple matching and choice in four experimental studies, to measure preferences for lotteries and riskless multiattribute alternatives. The main results are: (1) bi-matching produces trade-offs intermediate between those derived from matching on the “more important” attribute and matching on the less important attribute, although closer to the former; (2) the trade-offs derived from choice reflect more relative weight on the more important dimension than those from bi-matching; (3) bi-matching appears to reduce response error compared to standard matching. These results are generally consistent with theoretical predictions. We discuss the normative question of which preference assessment method is preferable. The current results as a whole and the built-in features of the bi-matching procedure already position this elicitation method as a worthwhile alternative to traditional methods for helping decision-makers introspect and construct their value trade-offs.

This research is part of a larger effort to build machine-based tools for developing scientific theories. In analogy with the research process in empirical research, we describe a logical cycle of theory development: (1) starting with an informal version of a theory, (2) then moving to its formal representation, (3) applying formal logic to investigate this representation, and (4) using the results as feedback for the update/revision of the original theory. A central aspect of the logical cycle is the detection of the (hidden) implications of a theory (called “partial deductive closure”). In this paper, we present an algorithm that performs the partial deductive closure for a relevant class of theorems, while filtering out trivial results. The algorithm is applied to an important organization theory, Organizational Ecology, and is shown to generate new theorems of interest.

Fleet sizing and empty equipment redistribution are important issues in managing transportation systems. Most of the mathematical models that have been developed for these problems are complex and computationally demanding, including dynamic linear programming and stochastic/dynamic mathematical programs. Our research takes an alternate approach by building from inventory theory and developing decentralized stock control policies for empty equipment. This approach is applied to hub-and-spoke networks (i.e., center-terminal networks), by first analytically modeling the stochastic processes representing various stock-control variables, and then comparing the analytical results to monte-carlo simulations. A decomposition approach is also developed to determine stock-out probabilities as a function of the fleet size as a whole, and as a function of localized control parameters.

Fueled by its constant technological and price improvements, information technology (IT) is displacing other inputs in the production of goods and services. By 1994, IT accounts for over 15% of fixed investments by the U.S. private sector, and the ratio of new IT investments to labor costs is approaching 5% (1990 dollar basis). The ability to take advantage of improvements in IT is determined in part by the substitutability of IT for other factors of production. This paper builds on the empirical framework of Brynjolfsson and Hitt (Brynjolfsson, E., L. Hitt. 1995. Information technology as a factor of production the role of differences among firms. Econom. Innovations and New Tech. 3 183–199.) and extends it to jointly estimate output and substitution elasticities using the CES-translog production function. Our primary source of IT-related data is the IDG/Computerworld annual survey data on IS spending by large U.S. firms, for the period 1988 to 1992, previously analyzed by Brynjolfsson and Hitt ([Brynjolfsson, E., L. Hitt. 1995. Information technology as a factor of production the role of differences among firms. Econom. Innovations and New Tech. 3 183–199.], [Brynjolfsson, E., L. Hitt. 1996. Paradox lost? Firm-level evidence on the returns to information systems spending. Management Sci. 42(4) 541–558.]) and Lichtenberg (Lichtenberg, F. R. 1995. The output contributions of computer equipment and personnel a firm level analysis. Econom. Innovations and New Tech. 3 201–217.). A key result is that IT capital is a net substitute for both ordinary capital and labor, suggesting that the factor share of IT in production will grow to more significant levels over time. We confirm earlier findings of positive returns to IT investment for this data set. Further, we find excess returns on IT investment relative to labor input and some evidence of excess returns relative to ordinary capital. Taken together, these results shed new light on the productivity paradox of IT and on the growth of information intensity across the economy as firms take advantage of the continuing improvements in IT.

This paper deals with the design of dynamic time-space and calibrated static strategic planning models, along with solution algorithms, for the multilevel rail-car fleet management problem faced by RELOAD ® , a branch of the Association of American Railroads (AAR). We discuss a prevalent fleet sizing management model that is static in nature, and propose an alternative dynamic model based on a time-space network representation. This model accurately represents the problem, and also provides information regarding the issue of storing and retrieving empty cars. A suitable decomposition heuristic, that is based on solving subproblems defined for overlapping time segments, is developed to solve this model. This heuristic is shown to recover an optimal solution for all the test problems with a reasonable effort. We also investigate a procedure for calibrating the static model based on this improved time-space representation. Our results show that for the static model, a calibrated use of available data can yield near-optimal total fleet size requirements. This enables the use of such a simple, calibrated static model for accurately conducting fleet sizing, the determination of fleet size allocations among railroads, as well as for analyzing various “what-if” scenarios. The proposed methodology is being currently implemented at the AAR, and the status of this process as well as some test results are presented.

We model the effects of alternative coordination structures on the performance of a firm that faces uncertain demand in multiple horizontal markets. The firm's coordination structure is jointly determined by its decision-rights structure and by its information structure. We compare the performance of decentralized, centralized and distributed structures and study factors that affect the value of coordination. The results quantify and illustrate the value of co-locating decision rights with specific knowledge.

Information systems can serve as intermediaries between the buyers and the sellers in a market creating an “electronic marketplace” that lowers the buyers' cost to acquire information about seller prices and product offerings. As a result, electronic marketplaces reduce the inefficiencies caused by buyer search costs, in the process reducing the ability of sellers to extract monopolistic profits while increasing the ability of markets to optimally allocate productive resources. This article models the role of buyer search costs in markets with differentiated product offerings. The impact of reducing these search costs is analyzed in the context of an electronic marketplace, and the allocational efficiencies such a reduction can bring to a differentiated market are formalized. The resulting implications for the incentives of buyers, sellers, and independent intermediaries to invest in electronic marketplaces are explored. Finally, the possibility to separate price information from product attribute information is introduced, and the implications of designing markets promoting competition along each of these dimensions are discussed.

This paper illustrates a methodology to evaluate how dissimilar military systems support the accomplishment of the United States' counterproliferation objectives. The key questions in evaluating counterproliferation systems are identified. By using decision analysis, an influence diagram model is developed that represents military activities in the counterproliferation process. A value model is developed that enables systems to be evaluated against common criteria. An analysis of intelligence, defensive, and offensive counterproliferation systems suggests that intelligence system improvements may provide the greatest potential to meet the United States' counterproliferation objectives. Sensitivity analysis is conducted to determine which factors in the model are most important. To demonstrate the model, nine systems from the Air Force Vulcan's Forge 1995 wargame are evaluated. This paper illustrates the value of decision analysis, and influence diagrams in particular, by involving decision makers and subject matter experts in structuring complex problems for analysis.

For an uncertain production system, the rule that controls the flow of material relies on parameters, such as the yield rates and the demand rate. These parameters are estimates, and they are usually inaccurate. In this paper, we analyze a type of “pull” rule called a proportional restoration rule. We show that these rules are stable; they do not lead to erratic behavior even when the estimation error is significant. We also show that these rules localize the effect of mis-estimates; e.g., underestimating the demand rate lowers the level of finished goods inventory, but has scant effect within the line. We show that these rules exhibit other desirable attributes—that they are efficient, that they are easy to interpret, and that they recover from disruptions quickly.

Consider a series of companies in a supply chain, each of whom orders from its immediate upstream member. In this setting, inbound orders from a downstream member serve as a valuable informational input to upstream production and inventory decisions. This paper claims that the information transferred in the form of “orders” tends to be distorted and can misguide upstream members in their inventory and production decisions. In particular, the variance of orders may be larger than that of sales, and the distortion tends to increase as one moves upstream—a phenomenon termed “bullwhip effect.” This paper analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to mitigate the detrimental impact of this distortion are also discussed.