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Data-science algorithms and models changed the way we search for information on products and services, make payments, procure and trade, and along the way, changed how firms use individual-level consumer data, and how business transactions are created, documented, regulated, and analyzed. The emergence of data-intensive environments and algorithms also challenges management science research in many ways. It affects how knowledge is organized, produced, and assessed; how we search for answers to management questions, analyze information or validate insights and findings; and it challenges how we discover, design, describe, motivate, and replicate solutions. More importantly, it demands new theory to enable the development of models that exploit the type and volume of data available.

It is a fact that people make decisions and have been making decisions for a very long time. Contrary to what some of us who are interested in decision-making may like to believe, most people do not take seriously the existence of theories which purport to set their thinking and feeling right. They claim to know their own value system and what they want. They may wonder how anyone else can know well enough to tell them how best to organize their thinking in order to make better choices. Yet, research has shown that complex decisions are beyond the capacity of the brain to synthesize intuitively and efficiently. Since decision making is a natural characteristic of people, how do we describe what they do so that an ordinary mortal can understand what we are saying? We do not wish to legislate the method with which people should make decisions, but only to describe it even when it is prescribed by some method. In the process, we may learn things that can help people make better decisions. How? The Analytic Hierarchy Process (AHP) (Forman et al., Harker 1986, Harker and Vargas 1987, Saaty 1986, 1988a, b, Saaty and Vargas 1987, Xu 1988, Golden et al. 1989, Saaty and Alexander 1989) is a theory of measurement. When applied in decision making it assists one to describe the general decision operation by decomposing a complex problem into a multi-level hierarchic structure of objectives, criteria, subcriteria and alternatives. The AHP provides a fundamental scale of relative magnitudes expressed in dominance units to represent judgments in the form of paired comparisons. A ratio scale of relative magnitudes expressed in priority units is then derived from each set of comparisons. An overall rcatio scale of priorities is then synthesized to obtain a ranking of the alternatives. From its axioms to its procedures, the AHP has turned out to be historically and theoretically a different and independent theory of decision making from utility theory. Much as a dialogue evolved in mathematics around the consistency of different geometries and around absolute and relative space and time in physics, both to dispel absolute notions, those who believe that only utility theory can tell us the absolute truth about man's decision-making might take a close look at the AHP. It has found varied and serious applications. It also has a particular way of generating ratio scales and dealing with inconsistency in judgment that have contributed to its effectiveness in resource allocation and in the setting of priorities by a group of decision makers. Utility theory is a normative process. The AHP as a descriptive theory encompasses procedures leading to outcomes as would be ranked by a normative theory. But it must go beyond to deal with outcomes not accounted for by the demanding assumptions of a normative theory. We must briefly describe the AHP to enable the reader to see that a practicable theory based on ratio scales need not dilute itself to satisfy expectations of people who derive their understanding from a theory based on interval scales. This is particularly true if the rival theory, in aspiring for generality, also makes unrealistic assumptions, for example about the transitivity and consistency of preferences and the difficult use of lotteries

A simple linear model is constructed for production, use and recycling of various paper and related products. This model is used to estimate the maximum feasible recycling rate given the current state of pulp and paper technology. Parametric linear programming is used to investigate the effect of the efficiency of collecting, sorting and transporting waste paper on the fraction recycled.

Organizations today have access to enormous data sets, and sophisticated business analytical tools are needed to harness the tremendous potential of these data to improve day-to-day decision making. To encourage further developments in this rapidly growing area of interdisciplinary research, Management Science will publish a special issue dedicated to business analytics. Applications of business analytics extend to nearly all managerial functions in an organization. Using microlevel data, mathematical models can become more realistic, be validated more easily, and match more closely true behavioral phenomena. Through business analytics, historical data can be used to discover relationships among important elements in an organization's environment, make more formal statistical inferences regarding the structure or strength of these relationships, and improve actions taken by the organization. Because these relationships are often not static, the entire cycle of identification, estimation, prediction, and decision making is repeated frequently and on an ongoing basis, leading to real-time and highly targeted data-driven decisions. We envision business analytics applied to many domains, including, but surely not limited to: digital market design and operation; network and social-graph analysis; pricing and revenue management; targeted marketing and customer relationship management; fraud and security; sports and entertainment; retailing to healthcare to financial services to many other industries. We seek novel modeling and empirical work which includes, among others, probability modeling, structural empirical models, and/or optimization methods. Submission: Please submit your manuscript online via http://mc.manuscriptcentral.com/ms You must select "Special Issue" as the Manuscript Type in Step 1 and select one of the issue's four coeditors in Step 5. Manuscripts will generally be assigned to one of the Associate Editors for this issue (see below; please recommend three in Step 4), but you may also recommend guest Associate Editors.

Recently, a growing number of Operations Management researchers have started to investigate the interface between the two fields leveraging the institutional richness of Operations Management research, and proposing ways to operationally hedge operational and competitive exposures not effectively handled through typical financial hedging contracts. It is now timely to compile state-of-theart research at the interface of Finance and Operations Management in a special issue of Management Science. Such research will capitalize on the richness of the two fields while exploiting gaps left in their interfaces, with clear objectives: • contributing to the theory of at least one of the fields, while rigorously representing knowledge of the other. • creating managerial insights, practical knowledge and/or tools that general managers and risk management practitioners can use in their decisions and applications. The goal of this call for papers is to include research that shows conditions under which a tighter integration of both functions within and across enterprises leads to higher value creation, while at the same time advances the knowledge and creates tools for enterprise risk management. We solicit contributions spanning a diverse set of topics and using rigorous methodological approaches as appropriate to the question under study (theoretical modeling, conceptual frameworks, empirical, survey research, experimental etc.). Topics we are interested in, but not limited to: • Joint financing and capacity-investment choices • Integrated financial and operational hedging • Risk issues in commodity procurement • Real-options valuation and hedging in operations management • Global risk-management in manufacturing • Supply-chain contracting • Cross-functional approaches to managing large project risks • Risk measures, performance evaluation and incentives • Managing disruption and catastrophic risks in global supply chains: Building the Resilient Enterprise • Insurance and supply chain risks • Managing global outsourcing risks • Cross-functional approaches to managing new product and technology development risks • Managing operational risks in the service sector (banking, entertainment, travel, rentals etc.) • Operationalizing supply-chain risk management • Risk aversion in supply-chain management • Revenue management: financial implications of capacity-planning and dynamic pricing strategies • Risk considerations in revenue management • Special topics in sectors such as financial services and energy and commodity trading Submitted papers should feature original research contributions, display the rigor emphasized in the editorial statement of Management Science, clearly articulate their managerial relevance, and obey the journal’s author guidelines. The review process will be handled by the guest editors and an ad-hoc set of associate editors representing the fields of Operations and Supply Chain Management, Operations Research, Financial Economics and Applied Finance, and Financial Engineering. Submissions are due by July 31, 2007. Publication of the special issue is expected in early 2009. Paper provisions and submission guidelines are available at http://mansci.pubs.informs.org/.