2016 INFORMS Annual Meeting Program

All Technology Tutorials will take place in the Music City Center, 5 th Avenue Lobby.

Cornell Tech

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dimensional, unknown domains. Also, while AI is commonly associated with another buzzword, “Big Data”, we wish to prove that AI can be useful for dealing with problems for which we possess little or no data. Here, expert knowledge modeling is critical, and we describe how even a minimal amount of expertise can serve as a basis for sound reasoning aided by AI. Sunday, November 13, 11:45am–12:30pm Technology Tutorial: AArtificial Intelligence in Marketing Science: Marketing Mix Modeling and Optimization with Bayesian Networks & BayesiaLab , “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” Various versions of this quote have been attributed to John Wanamaker, Henry Ford, and Henry Procter, yet 100 years after these marketing pioneers, in this day and age of big data and advanced analytics, the quote still rings true. The current practice is often “more art than science.” The lack of a well-established marketing mix methodology has little to do with the domain itself. Rather, it reflects the fact that marketing is yet another domain that typically has to rely on nonexperimental data for decision support. Marketing mix modeling is a causal problem, which means we are not looking for a prediction of an outcome based on the observation of marketing variables, but attempting to manipulate the marketing variables to optimize the outcome. Thus, we must simulate interventions, not observations, and switch from observational to causal inference. This brings us to deriving causal inference from observational data. We introduce the fundamental concepts of graphical models and how they can help us perform causal identification, i.e., determine whether it is possible to estimate causal effects from observational data, which requires causal assumptions about the domain plus a decision criterion, e.g., the Adjustment Criterion. However, the complexity of the marketing domain limits the practical application of this criterion. We introduce the Disjunctive Cause Criterion, which reduces the number of assumptions required for causal identification and, thus, confounder selection. Proceeding from causal identification to estimation requires an “inference engine.” In the simplest case, we could use a regression, but with dozens of interacting variables, that is not practical. Instead we use Artificial Intelligence by employing BayesiaLab’s machine- learning algorithms, which builds a high-dimensional Bayesian network model that represents the joint probability distribution of all variables. This causal inference engine plus BayesiaLab’s Target Optimization algorithm enable us to search efficiently for the ideal marketing mix.

http://tech.cornell.edu

At Cornell Tech, students across programs learn and work side-by-side, spending one-third of their experience together working on a studio-based core curriculum. They collaborate with the tech industry and postdoc-level researchers to build start-up companies and new products. By bringing these talents together at the start, there is enormous potential for better, more impactful and ultimately more successful companies and products. Programs offered: Master of Engineering in Computer Science, Master of Engineering in Electrical and Computer Engineering, Master of Engineering in Operations Research and

Information Engineering, Technion-Cornell Dual Degree in Connective Media, Technion-Cornell Dual Degree in Health Tech, Johnson Cornell Tech MBA, and Master of Laws in Law, Technology and Entrepreneurship.

Darden Business Publishing http://store.darden.virginia.edu

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Darden Business Publishing markets case-based educational materials written by the renowned faculty at the University of Virginia Darden School of Business. Darden maintains a catalogue of student-centered learning materials that energize classrooms around the world with dynamic interactive simulations and thought-provoking paper cases.

Dynamic Ideas, LLC www.dynamic-ideas.com

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Exhibit Listings & Technology Tutorials

Dynamic Ideas, LLC is a publisher of scientific books that have quality and originality in the areas of Operations Research and Applied Mathematics. The key objective of our titles is to “educate the next generation.” Many of our

books are currently being used as the main textbook in academic courses in some of the finest universities and research institutions in the world.

Elsevier

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www.elsevier.com/decisionsciences

Cambridge University Press www.cambridge.org/academic

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Elsevier publishes leading journals in OR/MS and Decision Sciences, including European Journal of Operational Research , Computers & Operations Research , and Omega- International Journal of Management Science . Elsevier journals occupy 7 of the Top 10 Impact Factor positions in the Thomson Reuters

Cambridge University Press’ publishing in books and journals combines state-of-the-

art content with the highest standards of scholarship, writing and production. Visit our stand to browse new titles, available at 20% discount, and to pick up sample copies of our journals. Visit our website to find out more about what we do.

‘Operations Research & Management Science’ category. Come to the booth to find out more, including how to use Elsevier’s researcher centric tools to develop your research.

Clemson University/COIN-OR Foundation

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FDA/Center for Drug Evaluation and Research

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www.coin-or.org

www.fda.gov/drugs

The Computational Infrastructure for Operations Research publishes high quality, free, open-source tools for OR professionals and students, suitable for commercial,

The Center for Drug Evaluation and Research (CDER) performs an essential public health task by making sure that safe and effective drugs are available to improve the health of people in the United States.

educational, and personal use. COIN-OR is the place to go when you need a “white box” for algorithm research and development. COIN-OR is a strategic partner of the INFORMS Computing Society.

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