2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Reducing Carbon Emissions In Grocery Retail
Ekaterina Astashkina, INSEAD, Boulevard de Constance,
Fontainebleau, 77305, France, ,
Elena Belavina
We build a stylized model for traditional and online grocery retail chains to
understand the drivers of the consumer and retailer carbon footprint, including
emissions that come from food waste and transportation. In our model,
consumers make endogenous choices between different channels and the
associated food-buying policies, while retailers optimally manage their inventory
replenishment. We find that, in most cases, the availability of an online retailer
reduces the emissions associated with the grocery sector in a city. We also
consider the effectiveness of alternate policy instruments including sales and
carbon taxes, and identify actions that improve the behavior of the worst
offenders.
4 - Optimizing Water Pollution Monitoring System: Regulation Policy
Guideline For Curbing Nutrient Pollution
Michael Lim, U of Illinois at Urbana-Champaign, Champaign, IL,
61820, United States,
We examine regulatory guidelines of surface water quality to curb nutrient
pollution resulting from various farming activities. Specifically, we formulate an
optimization model that captures the government’s regulation decision taking
into account farmers’ moral hazard issues: determining the optimal location of
monitor stations along with optimal penalty schemes for each watershed district.
We explore the model using the Illinois State water network to ensure practical
relevance and to obtain further insights on regulation policy.
MB38
206A-MCC
Behavioral Modeling with Social Data
Invited: Social Media Analytics
Invited Session
Chair: Tauhid Zaman, MIT, 77 Mass Ave, Boston, MA, 02139,
United States,
1 - Optimal Policies For Finding Users Hiding In Social Networks
Christopher Marks, MIT,
During 2015 we collected data from approximately 5000 Twitter accounts
belonging to ISIS users, ISIS supporters, and other users that appeared to be
closely connected to the ISIS network. We observe that many of these users are
frequently suspended, only to immediately open new accounts from which they
continue their online activities. We present a dynamic search method for finding
new accounts belonging to previously suspended users that relies on machine
learning methods to generate model inputs. We analyze this search method in the
context of dynamic programming and provide some insights into characteristics of
an optimal search policy.
2 - Optimal Following Policies In Social Networks Using Integer
Programming And Network Centrality
Tauhid Zaman, Massachusetts Institute of Technology,
Krishnan Rajagopalan
We consider the problem of interacting with users in a social network in order to
maximize the number of followers obtained. We formulate the problem as an
integer program (IP). We then show how to dramatically speed the time needed
to solve the IP by modifying the objective using network centrality functions.
Through simulations on real social networks, we find that our modified IP can
increase the number of followers obtained versus random and pure network
centrality based policies.
3 - Bayesian Inference Of User Geolocation Using Social Media
Activity Time Series
Matthew Robert Webb, MIT, Cambridge, MA, United States,
We propose a novel Bayesian classification algorithm to determine the global
location of Muslim extremists from their social media activity based on their
unique pattern of life. The tenants of Islam require five daily prayers; but rather
than being set, prayer times are determined by the location of the Sun in relation
to the Earth’s horizon. By assuming Muslim users will not utilize social media
during prayers, we attempt to infer their longitude and latitude based on their
pattern of inactivity.
4 - The Value Of Social Media To Online Content
Michael Zhao, MIT, Cambridge, MA, United States,
, Sinan Aral
Many believe social media drives online content consumption and vice versa. The
potential of this positive feedback loop is critical to marketers, publishers,
politicians, and beyond. However, this type of relationship induces endogeneity
problems that make casual identification difficult. We overcome this challenge by
constructing a unique article-location panel dataset using proprietary data from a
large online and print media company. We employ a novel IV estimation strategy
by using location-specific weather patterns as instruments for social media sharing
thereby allowing us to identify the degree to which social media effects the
demand for online content.
MB39
207A-MCC
Panel: Future of Applied Probability
Sponsored: Applied Probability
Sponsored Session
Chair: David Goldberg, GA Institue of Technology, Atlanta, GA,
United States,
1 - Future Of Applied Probability
David Goldberg, GA Institue of Technology, 755 Ferst Drive,
Atlanta, GA, 30332-0205, United States,
An opportunity for the entire Applied Probability Community to discuss the
future of the field.
2 - Panelists
Applied Probability Community, Applied Probability Community,
INFORMS, Catonsville, MD, 21228, United States,
MB41
207C-MCC
Advances in Quantitative Finance
Sponsored: Financial Services
Sponsored Session
Chair: Rafael Mendoza, McCombs School of Business,
University of Texas, Austin, TX, 78712, United States,
1 - On Latency And Volatility
Richard Sowers, University of Illinois,
We present a simple model of the effects of latency on the properties of observed
asset prices. In our model, latency is a delay between the observed asset price and
its true, but latent fundamental price. Because of latency, the observed asset price
shadows the true but latent asset price at some deformed time away. Deformation
in a clock gives rise to fluctuations in volatility. We provide an asymptotic result
that links latency to the volatility of volatility.
2 - Energy Production & Games With Stochastic Demand
Ronnie Sircar, Princeton,
The dramatic decline in oil prices, from around $110 per barrel in June 2014 to
around $30 in January 2016 highlights the importance of competition between
different energy sources. Indeed, the price drop has been primarily attributed to
OPEC’s strategic decision not to curb its oil production in the face of increased
supply of shale gas and oil in the US, coupled with reduced demand from China.
We model these phenomena as dynamic Cournot games in a stochastic demand
environment, and illustrate how traditional oil producers may react in counter-
intuitive ways in face of competition from alternative energy sources.
3 - Welfare Analysis Of Dark Pools
Krishnamurthy Iyer, Cornell University, Ithaca, NY, United States,
, Ramesh Johari, Ciamac Cyrus Moallemi
We investigate the welfare implications of operating alternative market structures
known as “dark pools” alongside a “lit” dealer market. Our setting consists of
intrinsic traders and speculators, with heterogeneous private information as to an
asset’s value, who endogenously choose between the two venues. We establish
that while the dark pool attracts relatively uninformed traders, the orders therein
experience adverse selection. Moreover, the informational segmentation created
by a dark pool leads to greater transaction costs in the lit market. From this, we
conclude that there exist reasonable parameter regimes where the introduction of
a dark pool decreases the overall welfare.
MB41