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INFORMS Journal Highlights from November 2017
AUTHOR SPOTLIGHT
NATALIE M. SCALA
“I earned my PhD in Industrial Engineering from the University of Pittsburgh and taught independently towards the end of my study. When I joined Towson [University], I noticed that various aspects of my lectures were resonating differently with business students — some concepts better than the engineers I previously taught, others were more of a challenge. I suspected something was driving this difference, and I wanted to figure it out...”Read Natalie's Latest Article
“Motivation and Analytics: Comparing Business and Engineering Students”
in INFORMS Transactions on Education
More by this Author
Council Member – Communications, Military Applications Society; Vice Chair – Programs, SpORts Section; Executive Advisor, Maryland Chapter
J.G. "Jim" Dai
Mathematics of Operations Research
“Approximate dynamic programming (ADP) and reinforcement learning (RL) provide techniques for making sequential decisions in complex settings, with a wide spectrum of applications in operations research, robotics, artificial intelligence, and beyond. Most ADP and RL algorithms focus on optimizing expected cost or reward — instead, Jiang and Powell introduce a “risk-averse Q-learning” algorithm that operates on problems specified under dynamic measures of risk. When the decision maker observes the true transition dynamics of the problem, it is often challenging to obtain a sufficient number of samples to adequately learn about the risky regions of the environment. This issue is addressed in the paper through a fully adaptive procedure that prioritizes the experience of transitions into these risky states, which serves to both stabilize and accelerate the learning process.”
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures
Daniel R. Jiang, Warren B. Powell
K. Sudhir
Marketing Science
“Mobile money,’ a checking account attached to a mobile phone number, has revolutionized the financial lives of millions of people in many developing countries without access to a banking infrastructure. Using text messages or apps on consumer phones and in partnership with mom-and-pop retailers, who serve as cash-in and cash-out points, telecom companies have created an accessible and cost-effective virtual banking infrastructure in many developing countries that facilitates sending money to others. However, Economides and Jeziorski in their study “Mobile Money in Tanzania” discover another very important source of value for mobile money in developing countries — a form of theft insurance by protecting cash from street robberies and stealing by relatives or burglars at home. The authors examine the the transaction behavior of over 1.4 million customers of Tigo — the second largest Tanzanian mobile service provider — in response to a price change. They find that more than 35% of funds entering the network did not involve transfers or exchanges across people, but fell into two types: short-duration transactions that are cashed in and out within a couple of hours, with a distance between cashing in and out between 5 and 6 km; and longer-duration transactions, usually a few days, that are cashed out pretty close to where they were originally deposited. The authors estimate how much consumers are willing to pay for a kilometer of money transportation or for a day of storage based on the response to a price change, drawing important implications for how mobile money providers should price these services, and providing a big wakeup call to governments and policy makers in developing countries on the hidden costs of crime on an economy.”
Mobile Money in Tanzania
Nicholas Economides, Przemyslaw Jeziorski
43
New Articles
Introduction to the Special Issue on Decision Analysis and Social Media
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Appreciation to Referees, 2017
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INFORMS MEETING CALENDAR
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Editorial Board
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Turner Optimizes the Allocation of Audience Deficiency Units
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A Practitioner’s Guide to Best Practices in Data Visualization
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Near-Optimal Course Scheduling at the Technion
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Book Reviews
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Reviewers 2017
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INFORMS MEETING CALENDAR
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Research Spotlights
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About Our Authors
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194,944
Article Downloads
Applications of Operations Research in the Air Transport Industry
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Making Operations Management Fun: Littlefield Technologies
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Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research
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Using Mathematics to Solve Some Problems in Industry
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Cognition, Technology, and Organizational Limits: Lessons from the Air France 447 Disaster
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Scenario Analysis in Spreadsheets with Excel's Scenario Tool
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JOURNAL SPOTLIGHT
Decision Analysis
Editor-in-Chief: Rakesh Sarin
Impact Factor: 1.242
5-year Impact Factor: 1.180
Impact Factor: 1.242
5-year Impact Factor: 1.180
“Decision Analysis focuses on the practical aspects of decision analysis and decision theory, and covers a wide range of topics aimed at helping enhance real-world decision making. The Special Issue on Decision Analysis and Social Media, published in December 2017, explores the ways in which decision analysis concepts and techniques can be applied to analyses incorporating data from social media platforms.
Given ever-increasing data storage and processing capabilities, many different types of organizations have recognized social media platforms as a valuable source of data. The goal of this special issue is to apply decision analysis approaches to foster the link between social media data sets and improved real-world outcomes.
The special issue consists of four excellent papers.
The first paper is a review of prior work in social media analytics, which serves as an extremely valuable resource for academics and practitioners working in this area.
The second paper develops a method for decision makers to update inputs quickly each time new information is obtained from a social media source, and applies the method to a cybersecurity example.
The third paper explores how college sports recruiters can augment traditional multi-attribute preferences with information revealed via social media, thus obtaining a better estimate of the likelihood of an athlete attending a given school. This leads to improved decisions in the allocation of recruiting resources.
Finally, the fourth paper applies predictive analytics techniques to determine whether a Facebook user satisfies a given characteristic: in this case, whether the user is a soccer player. The results can be used to improve marketing decisions, and the approach can easily be adapted to a wide variety of contexts.”