Informs 2015

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INFORMS Philadelphia – 2015
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TB27
27-Room 404, Marriott
Multiple Criteria Decision Aiding
Sponsor: Multiple Criteria Decision Making
Sponsored Session
Chair: Roman Slowinski, Prof., Poznan University of Technology, Pl.
Marii Sklodowskiej-Curie 5, Poznan, PL, 60-965, Poland,
1 - FITradeoff: Flexible and Interactive Tradeoff Elicitation Procedure
Adiel T. DeAlmeida, Professor, Universidade Federal de
Pernambuco, Caixa Postal 7462, Recife, PE, 50630-971, Brazil,
Adiel De Almeida Filho,
Jonatas Araujo De Almeida, Ana Paula Costa
FITradeoff is a Flexible and Interactive Tradeoff elicitation procedure for
multicriteria additive models in MAVT scope. The classical tradeoff procedure is
one of the approaches with strongest theoretical foundation. However, behavioral
studies have shown inconsistences of DM during elicitation. The FITradeoff
reduces DM’s effort in the process, by using partial information, thereby
contributing for reducing inconsistences. It is implemented in a DSS, which is
illustrated by applications.
2 - An Enhanced “Election Machine” for the Finnish Parliamentary
Elections: Theory and Implementation
Jyrki Wallenius, Professor, Aalto University School of Business,
Runeberginkatu 22-24, Helsinki, Finland,
Tommi Pajala, Akram Dehnokhalaji,
Pekka Korhonen, Pekka Malo, Ankur Sinha
Web-based questionnaires to match candidates’ and voters’ views play an
important role in Finland. We have collaborated with Helsingin Sanomat, who
runs the most influential of such questionnaires, to enhance and further develop
it. Our algorithm was tested in last April’s Parliamentary Elections. We describe
our algorithm and the feedback.
3 - Multicriteria and Multiobjective Models for Risk, Reliability and
Maintenance Context
Rodrigo J P Ferreira, Assistant Professor, Universidade Federal de
Pernambuco, Av. Professor Morais Rego, 1235., Recife, PE,
50670-901, Brazil, , Adiel T De Almeida,
Cristiano A V Cavalcante, Marcelo H Alencar,
Adiel De Almeida Filho, Thalles V Garcez
The use of multiple criteria and multiobjective models in risk, reliability and
maintenance research has increased in recent years. These models may affect the
strategic results of any organization, as well as, human life and the environment.
In such situations, optimal solutions for one objective function cannot be suitable.
These issues are presented according to the reference Multicriteria and
Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis.
4 - Constructive Preference Learning in Value-driven Multiple
Criteria Sorting
Roman Slowinski, Prof., Poznan University of Technology, Pl.
Marii Sklodowskiej-Curie 5, Poznan, PL, 60-965, Poland,
, Milosz Kadzinski,
Krzysztof Ciomek
We present an interactive preference learning technique for multiple criteria
sorting driven by a set of additive value functions compatible with a rich
preference information acquired from the user. This information may include: (1)
imprecise assignment examples, (2) desired class cardinalities, and (3)
assignment-based pairwise comparisons. The output results are necessary and
possible assignments, and extreme class cardinalities.
TB28
28-Room 405, Marriott
Empirical Market Design
Cluster: Auctions
Invited Session
Chair: Peng Shi, MIT Operations Research Center, 1 Amherst Street,
E40-149, Cambridge, MA, 02139, United States of America,
1 - Market Congestion and Application Costs
John Horton, Assistant Professor, NYU Stern School of Business,
44 West Fourth Street, Kaufman Management Center,
New York, NY, 10012, United States of America,
Ramesh Johari, Dana Chandler
We report the results of an experimental intervention that increased the cost of
applying to vacancies in an online labor market by requiring workers to answer
questions about the job. Although the ordeal positively selected candidates, it was
the information in the answers that mattered for match formation. Although the
overall number of matches and speed to fill a vacancy was unchanged, employers
engaged in less recruiting activities and formed higher quality matches.
2 - Experiments as Instruments: Heterogeneous Position Effects in
Sponsored Search Auctions
Justin Rao, Researcher, Microsoft Research, 641 Avenue of
Americas, New York, NY, 10014, United States of America,
, Matthew Goldman
The Generalized Second Price auction has been shown to achieve an efficient
allocation and favorable revenue properties provided the causal impact of ad
position on user click probabilities is a constant the scaling factor for all ads. We
develop a novel method to re-purpose internal business experimentation at a
major search engine and we strongly reject the conventional multiplicatively-
separable model, instead finding substantial heterogeneity of the causal impact of
position on CTR.
3 - Optimal Design of Two-sided Market Platforms: An Empirical
Case Study of Ebay
Brent Hickman, Assistant Professor Of Economics, University of
Chicago, 1226 E 59th St, Chicago, IL, 60637, United States of
America, , Joern Boehnke,
Aaron Bodoh-creed
We investigate design of platform markets that house many auctions over time.
We combine a unique dataset with a model of bidding where the option value of
re-entering the market creates incentive for buyers to shade bids below private
valuations in the current period. We show the model is identified using the
Bellman equation for a representative bidder. We estimate the model and
investigate the degree to which eBay is able to reduce transaction costs and
approach the efficient allocation.
4 - Stability of Demand Models Across Policy Reforms: An Empirical
Study with Boston Public Schools
Peng Shi, MIT Operations Research Center, 1 Amherst Street,
E40-149, Cambridge, MA, 02139, United States of America,
, Parag Pathak
In counterfactual analysis using demand modelling, an important but seldom
checked assumption is that the proposed reform does not affect the demand
model. We validate this assumption across a major school choice reform in Boston
in 2014. To control for post-analysis bias, we precommit to forecasts before the
reform. We find that while our prediction of the number of applicants were off,
the logit and mixed-logit demand models we fit were stable before and after the
reform.
TB29
29-Room 406, Marriott
Applications of Analytics II
Sponsor: Analytics
Sponsored Session
Chair: Tarun Mohan Lal, Mayo Clinic,
1 - Combating Attrition through New Developments in Transaction
Analytics and Customer Dialogue
Gerald Fahner, Analytic Science Senior Director, FICO, 181 Metro
Drive, San Jose, United States of America,
”Silent” attrition remains a costly problem requiring fast detection and insight to
create effective retention offers. Our credit card case study shows how ensemble
models instrumented with low-latency transaction features rapidly detect card-
level and merchant category-level attrition. We explain our models and relate
performance to profitability. We show how to boost persuasiveness of offers by
customer dialogues to learn their preferences. Using a simulation we illustrate the
value of dialogue.
2 - How Bringing Decision Optimization to the Cloud Will
Democratize Optimization
Susara Van Den Heever, IBM France, 1681 Route des Dolines,
France, Xavier Ceugniet, Alain Chabrier,
Stéphane Michel
Even though Decision Optimization has been used effectively across industries for
decades, it remains under-utilized. Complexity and costs are often cited as barriers
to wider adoption. The emergence of cloud computing, as well as the renewed
emphasis on cognitive analytics platforms, breaks down these barriers to bring the
benefits of optimization to a wider audience. We will demonstrate this vision
through a case study involving IBM Decision Optimization on Cloud, and IBM
Watson Analytics.
TB29