2016 INFORMS Annual Meeting Program

411 / 561

INFORMS Nashville – 2016
411
2 - Coordination And Social Value Of Information In Networks
Gowtham Tangirala, Columbia Business School, 3022 Broadway,
4th Floor West, New York, NY, 10027, United States,
Alireza Tahbaz-Salehi
This paper studies the social and equilibrium value of information in network
games. We provide a complete characterization of conditions under which
equilibrium is efficient under incomplete information and study the impact of
varying the commonality of information across agents and the network structure,
on equilibrium welfare. In particular, we find that when social conformity is
desirable (undesirable), the more interconnected the network is, the lesser
(greater) its equilibrium welfare.
3 - The Effect Of Information On Traffic Congestion
Ali Makhdoumi, Massachusetts Institute of Technology,
We study the implications of additional information about routes provided to
certain users (e.g., via GPS-based route guidance systems) in a traffic network. We
formulate the question in the form of Informational Braess’ Paradox (IBP), which
extends the classic Braess’ Paradox in traffic equilibria, and asks whether users
receiving additional information can become worse off. We provide a necessary
and sufficient condition for the occurrence of this paradox in terms of network
characteristics.
4 - Optimal Promotion Period Of Products With Network Externality
Ningyuan Chen, HKUST, Hong Kong, Hong Kong,
Saed Alizamir, Vahideh Manshadi
Many products exhibit network externality: a customer who has purchased the
product makes his/her neighbors or friends more likely to buy the same product.
This includes eco-friendly products such as electronic cars and solar panels. The
government subsidizes customers to promote such products. We find that it is
optimal for the government to stop the subsidy when the total externality of the
owners reaches a threshold, which depends on the spectrum of the externality
matrix. The optimal stopping time is not monotone in the strength of the
externality between customers. We investigate how the structure of the network
affects the stopping time and the optimal reward of the government.
WB47
209C-MCC
Applications of RM and Pricing
Sponsored: Revenue Management & Pricing
Sponsored Session
Chair: Maxime Cohen, Google NYC, 110 Bleecker Street Apt 6F,
New York, NY, 10012, United States,
1 - Simple Pricing Schemes For Consumers With Evolving Values
Balasubramanian Sivan, Research Scientist, Google Research, New
York, NY, United States, Shuchi Chawla,
Nikhil R. Devanur, Anna Karlin
We consider a pricing problem where a buyer is interested in purchasing/using a
good, such as an app or music or software, repeatedly over time. The consumer
discovers his value for the good only as he uses it, and the value evolves with
each use as a martingale. We provide a simple pricing scheme and show that its
revenue is a constant fraction of the buyer’s expected cumulative value.
2 - Strategic And Proactive Pricing Optimization In The
Airline Industry
Michael Benborhoum, British Airways, New York, NY, United
States, , Maxime Cohen
Pricing in the airline industry has become increasingly competitive, with a strong
emphasis on reactive fare matching, arguably to the detriment of more strategic
and proactive decision frameworks. Setting the right price in a strategic and
proactive fashion raises at least three questions: (i) when is the right time and
what is the right level for a proactive fare change; (ii) what is the right fare ladder
structure leading to optimal sell-ups and fare rule segmentation; and (iii) how
non-pricing factors should affect pricing decisions. In this talk, we propose an
original approach to the strategic and proactive pricing problem in the airline
industry.
3 - Dynamic Pricing With Heterogeneous Patience Levels
Ilan Lobel, NYU Stern,
We consider the problem of dynamic pricing in the presence of patient
consumers. We call a consumer patient if he is willing to wait a certain number of
periods for a lower price, but will purchase as soon as the price is equal to or
below her valuation. We allow for arbitrary joint distributions of patience levels
and valuations. We propose a dynamic-programming-based polynomial-time
algorithm for finding optimal pricing policies. Our findings suggest that pricing for
patient consumers is a more challenging problem than pricing for strategic
consumers, in the sense that the dynamic program requires a larger state-space.
4 - Overcommitment In Cloud Services – Bin-packing With
Chance Constraints
Maxime Cohen, NYU Stern, New York, NY, 10012, United States,
Phil Keller, Vahab Mirrokni,
Morteza Zadimoghaddam
A cloud provider needs to decide how many physical machines to purchase in
order to accommodate the incoming virtual jobs efficiently. This is typically
modeled as a bin-packing optimization problem. Overcommitting servers clearly
improves the bin-packing objective, but induces a risk for the provider. In this
work, we show that the bin-packing with chance constraints can be solved using
a class of simple online algorithms that guarantee a constant factor from optimal.
We explicitly model job size uncertainty to motivate new algorithms and evaluate
them on realistic workloads.
WB48
210-MCC
Business Applications in Social Media Analytics
Invited: Social Media Analytics
Invited Session
Chair: Michel Ballings, University of Tennessee, 255 Stokely
Management Center, Knoxville, TN, 37996, United States,
1 - Identifying New Product Ideas: Waiting For The Wisdom Of The
Crowd Or Screening Them In Real-time
Steven Hoornaert, Ghent University, Ghent, Belgium,
, Michel Ballings, Edward C Malthouse,
Dirk Van den Poel
This article studies idea ranking in innovation communities using the
contributor’s history of submitting ideas and comments, the Content of the idea
suggestion, and the Crowd’s feedback on the idea.
Results show that contributor and content variables improve ranking between
22.6% and 25.8% over exhaustive idea selection across classifiers.
2 - Evaluating The Importance Of Different Communication Types In
Tie Strength Prediction On Social Media
Matthias Bogaert, Ghent University, ,
Michel Ballings, Dirk Van den Poel
The purpose of this paper is to evaluate which communication types on social
media are most indicative of tie strength. To ensure that we have the best possible
model we benchmark several classifiers. The results indicate that we can predict
tie strength with very high accuracy. The top performing classification algorithm
is adaboost with an AUC of 0.976. The top five communication predictors are the
recency of commenting on links, posts, videos, the frequency of liking post
comments and the recency of commenting on albums. To the best of our
knowledge, this study is the first to provide such an extensive analysis of tie
strength in social media.
3 - Evaluating Prediction Models For Targeting Product Reviewers
Michel Ballings, Assistant Professor, University of Tennessee, 249
Stokely Management Center, Knoxville, TN, 37996, United States,
, Rachel Van Deventer, Ryan Erwin,
Miller Moore, Dirk Van den Poel
As customers increasingly rely on product reviews while making their purchases,
businesses must take action and make obtaining high review volume a priority.
The purpose of this study is to develop a predictive model that identifies if an
online reviewer is likely to write a review for a selected product. To develop our
model, we extracted product reviewer data from We find that the
model accurately predicts if an individual will review the focal product.
Businesses can target that population and obtain high review volume for their
investment. While a large body of research has been published on product
reviews we have focused on the individuals behind the reviews.
4 - Behavioral Engagement In Social Media: Measurement, Drivers
And Impact On Purchase Behavior
Welf H. Weiger, University of Goettingen, Platz der Goettinger
Sieben 3, Goettingen, D-37073, Germany, -
Wendy W Moe, Hauke A Wetzel,
Maik Hammerschmidt
In this study, we focus on understanding and measuring behavioral consumer
engagement in social media. Our research combines three sources of individual-
level user data (i.e., matched survey, social media behavior and purchase behavior
data) collected in the context of an online fashion retailer’s social media site. We
develop a composite engagement measure and we identify its motivational drivers
and consequences for purchase behavior. Our results reveal different drivers for
the incidence (i.e., the “whether”) and the nature (i.e., the “how”) of
engagement. As a counterintuitive finding, our results further show that
complaining users buy more than complimenting users.
WB48