Informs 2015

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INFORMS Philadelphia – 2015
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3 - Engineering Sustainable Complex Coevolutionary
Agricultural Systems
Dr Alejandro N. Martinez-Garcia, Professor, Instituto Tecnologico
del Valle de Morelia-Tecnologico Nacional de Mexico, km 6.5
Carretera Morelia-Salamanca s/n, Col. Los Angeles, Morelia,
58100, Mexico,
Achieving sustainability (including food security) under the dynamic conditions
of climatic change, increasing human population, and poverty reduction, while
preserving the ability of ecosystems to provide the services on which humanity
depends, implies the need for solving multi-objective optimization problems,
under a new paradigm: sustainable complex coevolutionary systems engineering.
4 - A Multi-objective Mathematical Programming Analysis of Forest
Carbon Management
Midhun Mohan (Mickey), Graduate Student Researcher, North
Carolina State University, 15404 Bragaw Hall, Raleigh, NC,
27607, United States of America,
Henrique Scolforo, Jean Chung, Juan Posse, Tiantian Shen,
Bruno Kanieski, Joseph Roise, Glenn Catts, Kevin Harnish
This study analyzes the valuation and production possibilities on a working forest
using Multi-objective programming, Woodstock, Timber NPV, and Carbon Storage
and Sequestration, and present a forest management model for optimizing Net
Present Value (NPV) and carbon sequestration at Hofmann forest.
SD28
28-Room 405, Marriott
Advances in Auction Theory
Cluster: Auctions
Invited Session
Chair: Benjamin Lubin, Asst. Professor, Boston University,
595 Commonwealth Ave, 621A, Boston, Ma, 02215,
United States of America,
1 - Are you going to do that? Contingent-payment Mechanisms to
Improve Coordination
David C. Parkes, Professor, Harvard University, Cambridge, MA,
United States of America, Hongyao Ma,
Reshef Meir, James Zou
We consider coordination problems, such as allocating the right to use a shared
sports facility or picking the time of a meeting. Outcomes are designated as either
good or bad (is the facility used, will people show up?), and the goal is to attain
good outcomes. Reports in period zero about agents’ uncertain values are used to
design a choice set for agents in period one, defining also payments that depend
on agents’ actions (e.g., using the facility.)
2 - Efficient Interdependent Value Combinatorial Auctions with Single
Minded Bidders
Valentin Robu, Assistant Professor, Heriot-Watt University,
Edinburgh, School Eng. & Physical Sciences, EM3.15,
Riccarton Campus, Edinburgh, EH144AS, United Kingdom,
, David C. Parkes, Takayuki Ito,
Nicholas R. Jennings
We study the design of efficient auctions where bidders have interdependent
values, that depend on signals of other bidders. In particular, we consider a
contingent bid model in which bidders may explicitly condition the value of their
bids on the bids submitted by others. We derive constraints which allows the
efficient second price, fixed point auction to be implemented in single-minded
CAs, and present an alternative mechanism for cases in which the required single
crossing condition fails.
3 - New Core-selecting Payment Rules with Better Fairness and
Incentive Properties
Sven Seuken, Assistant Professor Of Computation And
Economics, University of Zurich, Binzmuhlestrasse 14, Zurich,
ZH, 8050, Switzerland, , Benjamin Lubin,
Benedikt Bönz
We introduce four “Small” rules, which are new core-selecting payment rules for
combinatorial auctions. Via a Bayes-Nash equilibrium analysis, we first show for a
domain with 2 goods and 3 bidders, that one of our rules outperforms the state-
of-the-art Quadratic rule along all dimensions (efficiency, incentives, fairness, and
revenue). We then use a computational approach to evaluate 85 different rules in
a setting with 25 goods and 10 bidders, and show that our new rules still perform
best.
4 - A Faster Algorithm for Computing Prices in Core-selecting
Combinatorial Auctions
Benjamin Lubin, Asst Professor, Boston University, 595
Commonwealth Ave, 621A, Boston, Ma, 02215, United States of
America, , Benedikt Bunz, Sven Seuken
We present a new, faster algorithm for the computationally hard problem of
pricing core-selecting combinatorial auctions. First, we provide an alternative
definition of the core using weakly stronger constraints. Using these, we offer two
new algorithmic techniques that 1) exploit separabililty in allocative conflicts
between bidders, and 2) leverage non-optimal solutions. Using large auction
instances we show that our algorithm is between 2 and 4 times faster than the
current state of the art.
SD29
29-Room 406, Marriott
Baseball Analytics
Sponsor: Analytics
Sponsored Session
Chair: Sean Barnes, University of Maryland, 4352 Van Munching Hall,
University of Maryland, College Park, MD, 20742, United States of
America,
Co-Chair: Margret Bjarnadottir, Assistant Professor of Management
Science and Statistics, Robert H. Smith School of Business, University of
Maryland, 4324 Van Munching Hall, College Park, MD, 20742,
United States of America,
1 - The Effectiveness of Dynamic Pricing Strategies on Single-game
Ticket Revenue in Baseball
Joseph (Jiaqi) Xu, The Wharton School, University of
Pennsylvania, 3730 Walnut Street, Suite 500, Philadelphia, PA,
United States of America,
Peter Fader, Senthil Veeraraghavan
We develop a comprehensive demand model for single-game tickets that can be
used to predict revenue associated with a particular pricing strategy over the
course of sport season. We apply the model to actual sales and pricing data from
an anonymous MLB franchise during a recent baseball season and evaluate the
effectiveness of the dynamic pricing policy. We propose pricing heuristics and find
that optimized dynamic pricing policy can improve revenue by 14.3% compared
to a flat pricing policy.
2 - Pitch Sequence Complexity and Long-Term Pitcher Performance
Joel Bock, Booz Allen Hamilton, 901 15th Street NW,
Washington, DC, United States of America,
Patterns of a baseball pitcher’s pitch type sequencing can be learned by machine
learning models trained on historical data. Individual pitch-wise predictability is
connected with broader performance statistics (ERA, FIP) by a regression model
that may be used to forecast player performance. Less complexity correlates with
higher values of ERA and FIP. This talk outlines the analytical approach and
presents results from a study of Major League Baseball pitchers covering three
recent seasons.
3 - The Value of Positional Flexibility
Timothy Chan, University of Toronto, 5 King’s College Road,
Toronto, ON, M5S 3G8, Canada, ,
Douglas Fearing
Drawing from the theory of production flexibility in manufacturing networks, we
provide the first optimization-based analysis of the value of positional flexibility
(the ability of a player to play multiple positions) for a major league baseball team
in the presence of injury risk. Using publicly available data on baseball player
performance, we derive novel baseball-related insights that can be generalized to
the manufacturing context.
4 - A Bayesian Hierarchical Model for Modeling Called Strikes in
Major League Baseball
Abraham Wyner, Professor, University of Pennsylvania, The
Wharton School, University of Pennsy, 400 JMHH, Philadelphia,
Pe, 19104, United States of America, ,
Sameer Deshpande
We measure a catcher’s ability to “frame” a pitch. The effect exists, but there
remains debate on the effect size. We introduce a systematically constructed,
parametric Bayesian hierarchical model for the probability of a called strike. Our
model adjusts, accounts borrows strength from data on all participants. By sharing
information across all participants we are able to accurately quantify the player’s
framing effect on a pitch and translate that effect into runs added across a season.
SD28