RESCHEDULED: ORC IAP Seminar 2015
"Social Networks in Operations Research"
Description: Social networks have grown in scale and popularity in recent years, with several social networks boasting more than one billion registered users. The MIT Operations Research Center has organized a day-long seminar on the role of social networks in operations research, exploring how data from these networks can be used to make decisions or to understand social phenomena. Three speakers, including those from both industry and academic settings, will present their work and experience on topics ranging from marketing to studying social response to disease outbreaks.
Speaker: Jiwoong Shin. Professor of Marketing at the School of Management, Yale University.
Title: Managing Buzz
Abstract: We model the incentives of individuals to engage in word of mouth (or buzz) about a product, and how a firm may strategically influence this process through its information release and advertising strategies. In the model individuals are privately motivated by a desire to signal their type to others. Individuals are either a high or a low type, and during social interactions it is valuable for any individual to increase another person's posterior belief that she is a high type. We find that a firm will restrict access to information by low types at the information release stage. We also find that advertising may crowd out the incentives for consumers to engage in word of mouth, and that a firm can benefit from a credible commitment not to engage in advertising. Finally, we find that the ability by the firm to target advertising to well-connected consumers may be detrimental to the signaling value of word of mouth. Our model provides new insights into the tradeoff a firm may face between spreading information quickly versus maximizing the total spread of information about the product.
Bio: Jiwoong Shin is the Professor of Marketing at the School of Management, Yale University. He holds a Ph.D. in Management Science at MIT and MS and BS from Seoul National University, Korea.
Shin's research focuses on analytical modeling of strategic interactions between firms and consumers, and advances our understanding about firms’ strategic actions in the context of social interactions; in particular, consumer search theory, word of mouth, advertising, pricing strategies, and CRM. His current work in communication strategy investigates (i) the role of vague messages and offers novel explanations why and how those vague messages can convey price and quality information to consumers and (ii) the relative roles of consumer search and firm advertising in signaling product quality. Also, his work in customer management strategy addresses a long standing puzzle in practice: Should a firm offer a lower price to its own customers or to competitors' customers? When is it profitable to reward one's own customers? His recent work in communication analyzes why an individual engages in word of mouth about a firm’s product by explicitly incorporating an individual’s social motivation.
For two years in a row (2010, 2011), Shin has been the recipient of the John D. C. Little Best Paper Award, awarded for the best marketing paper published in Marketing Science and Management Science. In 2011, he was also named a Marketing Science Institute Young Scholar, a title awarded to “potential leaders of the next generation of marketing academics.”
Speaker: Natasha Markuzon. Researcher in Information & Decision Systems, Draper Laboratory.
Title: Interplay between Social Response and Disease Spread: Panic, Behavior Changes, and Disease Containment
Abstract: With the globalization of travel and economic trade, disease can spread rapidly across the globe, sometimes causing panic, population flight and other forms of social disorder. These responses often herald a significant change in the epidemiological pattern or etiology of an infectious disease event. It is therefore increasingly important not only to detect outbreaks of infectious disease early, but also to anticipate and describe the social response to the disease. We use social network analysis to model situations in which a society exhibits social strain in connection with a disease. We model negative social response (NSR) by coupling disease spread and opinion diffusion and verify the results against real-world scenarios including H1N1, SARS, and Ebola outbreaks. This model captures the complex interaction between disease and culturally determined social responses, providing insights that may help operational analysts and policy makers better respond to sudden disease outbreaks.
Bio: Dr. Natasha Markuzon is a Principal Member of the Technical Staff at the Draper Laboratory. She has extensive experience in data mining and knowledge discovery using machine-learning, social network analysis and visual analytics methods, and has developed novel pattern recognition algorithms and tools. Dr. Markuzon has applied her expertise to numerous application areas, including earth science, clinical data analysis, biosurveillance, marketing, and anti-terrorism. She is currently the Principal Investigator and Technical Lead for several biosurveillance analytic development projects. As a graduate student at Boston University, she was part of the group that created Fuzzy ARTMAP, a self-organizing neural network that is used in numerous applications. Prior to joining Draper Lab, Dr. Markuzon worked as a data-mining scientist at several start-up companies, including MDInteractive, Anvil Informatics, Inc., and Neovista.
Speaker: Tauhid Zaman. KDD Career Development Professor in Communications and Technology and an Assistant Professor of Operations Management at the MIT Sloan School of Management.
Title: Optimizing the Timing of Content in Online Social Networks
Abstract: In online social networks, such as Twitter, users generate content for which they wish to maximize impressions by other users. We show how one can select the time the content is posted in Twitter in order to maximize its impressions. We present a model of user behavior which combines temporal and network aspects of Twitter. Our model is fairly general and can extend to other social networks.
Bio: Tauhid Zaman is an Assistant Professor of Operations Management at the MIT Sloan School of Management. His research focuses on utilizing large-scale data from online social networks such as Facebook and Twitter to develop predictive models for user behavior and enhance business operations. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT. Before returning to MIT he spent one year as a postdoctoral researcher in the Wharton Statistics Department at the University of Pennsylvania. His work has been featured in Wired, Mashable, the LA Times, and Time Magazine.
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