Knowledge that Transforms

In a recent article, Bertsimas and Popescu showed that a tight upper bound on a Europeantype call option price, given the first n moments of the distribution of the underlying security price, can be obtained by solving an associated semidefinite programming problem (SDP). The purpose of this paper is to improve and extend their results. We will show that a tight lower bound can be calculated by solving another SDP. Also, we will show that these problems can be solved very quickly by a newly developed cutting plane algorithm when n is less than six or seven.

We present an Approximate Dynamic Programming (ADP) approach for the multidimensional knapsack problem (MKP). We approximate the value function (a) using parametric and nonparametric methods and (b)using a base-heuristic. We propose a new heuristic which adaptively rounds the solution of the linear programming relaxation. Our computational study suggests: (a)the new heuristic produces high quality solutions fast and robustly, (b)state of the art commercial packages like CPLEX require significantly larger computational time to achieve the same quality of solutions, (c) the ADP approach using the new heuristic competes successfully with alternative heuristic methods such as genetic algorithms, (d)the ADP approach based on parametric and nonparametric approximations, while producing reasonable solutions, is not competitive. Overall, this research illustrates that the base-heuristic approach is a promising computational approach for MKPs worthy of further investigation.

Historically, commercial use of university research has been viewed in terms of spillovers. Recently, there has been a dramatic increase in technology transfer through licensing as universities attempt to appropriate the returns from faculty research. This change has prompted concerns regarding the source of this growth-specifically, whether it suggests a change in the nature of university research. We develop an intermediate input model to examine the extent to which the growth in licensing is due to the productivity of observable inputs or driven by a change in the propensity of faculty and administrators to engage in commercializing university research. We model licensing as a three-stage process, each involving multiple inputs. Nonparametric programming techniques are applied to survey data from 64 universities to calculate total factor productivity (TFP) growth in each stage. To examine the sources of TFP growth, the productivity analysis is augmented by survey evidence from businesses who license-in university inventions. Results suggest that increased licensing is due primarily to an increased willingness of faculty and administrators to license and increased business reliance on external R&D rather than a shift in faculty research.

In a multi-item inventory system, such as an assemble-to-order manufacturing system or an online-retailing system, a customer order typically consists of several different items in different amounts. The average order-based backorders are the average number of customer orders that are not yet completely filled. While this is an important measure of customer satisfaction, it has not been widely studied in the operations management literature. This is largely because its evaluation involves the joint distribution of inventory levels of different items and other intricate relations, which is computationally dreadful. Taking a novel approach, this paper develops a tractable way of evaluating this measure exactly. We also develop easy-to-compute bounds, which require the evaluation of item-based backorders only. Numerical experiments indicate that the average of the lower and upper bounds is very effective. The exact results show surprisingly simple structures, which shed light on how system parameters affect the performance. Using these results, we study several examples to gain managerial insights. Questions addressed include: What are the implications of item-based inventory planning decisions on the order-based performance? What is the impact of introducing common components on inventory and service trade-offs? Would order-delivery performance be improved if we restrict the number of choices in product configurations?

The small-office/home-office (SOHO) professionals comprise the fastest growing segment in the labor force today. Typically being a one-person business based at home, SOHO owners mostly rely on office information technology to single handedly run their entire operation. Despite the segment's ostensibly growing dependence and influence on the information technology (IT) industry, still very little is known about the dynamics between SOHO and IT products. With the purpose of addressing this void, we investigate the SOHO professionals' adoption patterns of multigenerational IT products. Accordingly, we develop and empirically estimate an individual SOHO-level initial- and repeat-purchase logit model that captures the procurement patterns for successive generations of technological products, namely the PC category. Specifically, we find that SOHO professionals' procurement choices are influenced by a number of salient dimensions (i.e., income, performance, price, interpurchase time, network externalities). Furthermore, some SOHO owners are found to have a preference for a future (expected) generation (over a currently available one), which is explained via their business dispositions (i.e., technology orientation, result orientation, search orientation) toward accepting technological incertitude.

In this paper, we examine two information-based supply-chain efforts that are often linked to Vendor-Managed Inventory (VMI) programs. Specifically, we consider a supplier serving multiple retailers located in a close proximity. The first effort uses information on the retailers' inventory positions to coordinate shipments from the supplier to enjoy economies of scale in shipments, such as full truckloads.The second effort uses the same information for eventual unloading of the shipments to the retailers to rebalance their stocking positions. How much benefit do we gain from such initiatives? What are the relative benefits of the two initiatives? What are the drivers of such benefits? This paper seeks answers to these questions.

Selecting the right technologies to incorporate in new products is a particularly challenging aspect of new product definition and development. While newer advanced technologies may offer improved performance, they also make the product development process more risky and challenging. In this paper, we focus on the problem of technology selection and commitment under uncertainty, a major challenge to firms in turbulent environments. We argue that the "pizza-bin" approach of rejecting prospective technologies outright may not serve firms well when the pressure to differentiate products is enormous. After motivating the challenges and decisions facing firms using a real-life application from Dell Computer Corporation, we formulate a mathematical model of a firm that must define its products in the presence of technology uncertainty. Specifically, the firm faces two options: (i) a proven technology that is known to be viable and (ii) a prospective technology that offers superior price to performance results but whose viability is not a fully certain outcome. To minimize the impact of technology uncertainty, we consider two approaches to design flexibility, termed parallel path and sufficient design, which allow the firm to concurrently develop its products while the technology is being validated. Our analysis helps understand appropriateness of the different flexible design approaches. We illustrate our model with the Dell portable computer example and note the managerial implications of our analysis.

We present an integral equation approach for the valuation of American-style derivatives when the underlying asset price follows a general diffusion process and the interest rate is stochastic. Our contribution is fourfold. First, we show that the exercise region is determined by a single exercise boundary under very general conditions on the interest rate and the dividend yield. Second, based on this result, we derive a recursive integral equation for the exercise boundary and provide a parametric representation of the American option price. Third, we apply the results to models with stochastic volatility or stochastic interest rate, and to American bond options in one-factor models. For the cases studied, explicit parametric valuation formulas are obtained. Finally, we extend results on American capped options to general diffusion prices. Numerical schemes based on approximations of the optimal stopping time (such as approximations based on a lower bound, or on a combination of lower and upper bounds) are shown to be valid in this context.

Explaining how entrepreneurs overcome information asymmetry between themselves and potential investors to obtain financing is an important issue for entrepreneurship research. Our premise is that economic explanations for venture finance, which do not consider how social ties influence this process, are undersocialized and incomplete. However, we also argue that organization theoretic arguments, which draw on the concept of social obligation, are oversocialized. Drawing on the organizational theory literature, and in-depth fieldwork with 50 high-technology ventures, we examine the effects of direct and indirect ties between entrepreneurs and 202 seed-stage investors on venture finance decisions. We show that these ties influence the selection of ventures to fund through a process of information transfer.

This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff. A key concept is the adequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure—that is, systems for planning, coordination and incentives, and monitoring.