Informs 2015

INFORMS Philadelphia – 2015

154

2 - Using an AHP Approach for Eyewitness Identification

Enrique Mu, Carlow University, Pittsburgh, PA,

United States of America,

emu@carlow.edu,

Tingting Rachel Chung, Lawrence Reed

Eyewitnesses of a crime are usually asked to identify a potential criminal out of a

lineup of suspects. An online experiment using Amazon MT was conducted.

Results show that an AHP approach may offer better eyewitness identification

success and more importantly less false positive identification ratios than

currently sequential lineup approach.

3 - Modeling the Sensitivity and Stability of Preferences Among

Colorectal Cancer Screening Alternatives

Magda Gabriela Sava, PhD Candidate, Joseph M. Katz Graduate

School of Business, University of Pittsburgh, 241 Mervis Hall,

Pittsburgh, PA, 15260, United States of America,

mgsava@katz.pitt.edu,

Luis Vargas, James G. Dolan,

Jerrold H. May

Patients are faced with multiple alternatives when selecting the preferred method

for colorectal cancer screening, and there are multiple criteria to be considered in

the decision process. We model the patient’s choice using an Analytic Network

Model and propose a new approach for characterizing the idiosyncratic preference

regions for each patient. We show how to use that characterization to derive

insights as to the sensitivity and stability of a patient’s individual choice of

alternative.

4 - A Stakeholder-theory Based Employer Health Plan

Selection Model

Mehdi Amini, Professor, The University of Memphis, Department

of Marketing & SCM, Fogelman College of Business & Economics,

Memphis, TN, 38152, United States of America,

mamini@memphis.edu,

Orrin Cooper, Mike Racer

Organizations are called to re-evaluate current plan offerings and potentially, for

the first time, select new healthcare providers and policies to ensure that a

minimum level of coverage required by law. A new stakeholder-theory based

Analytic Network Process (ANP) model is developed to capture a health plan

selection decision with the consideration of multiple stakeholders’ interests.

What-if analysis is used to explore the robustness of the selected plan.

MA28

28-Room 405, Marriott

Matching Markets and Their Applications

Cluster: Auctions

Invited Session

Chair: Thayer Morrill, NC State University, Raleigh, NC, United States

of America,

thayer_morrill@ncsu.edu

1 - Incentives in the Course Allocation Problem

Hoda Atef Yekta, University of Connecticut School of Business,

Storrs, CT, United States of America,

Hoda.AtefYekta@business.uconn.edu

Kominers et al. (2011) introduced a heuristic for comparing incentives among the

course allocation problem (CAP) algorithms. We investigate their method and

adapt it to a more realistic setting with course overlap and a limited number of

courses for each student. We compare algorithms including the bidding-point

mechanism, the draft mechanism, and recently proposed algorithms like the

proxy-agent second-price algorithm in their vulnerability to non-truthful bidding.

2 - Near-optimal Stochastic Matching with Few Queries

John Dickerson, CMU, 9219 Gates-Hillman Center, Pittsburgh,

PA, 15213, United States of America,

dickerson@cs.cmu.edu

Avrim Blum, Nika Haghtalab, Ariel Procaccia, Tuomas Sandholm,

Ankit Sharma

In kidney exchange, patients with kidney failure swap donors. Proposed swaps

often fail before transplantation. We explore this phenomenon through the lens

of stochastic matching, which deals with finding a maximum matching in a graph

with unknown edges that are accessed via queries, and its generalization to k-set

packing. We provide adaptive and non-adaptive algorithms that perform very few

queries, and show that they perform well in theory and on data from the UNOS

nationwide kidney exchange.

3 - The Secure Boston Mechanism

Thayer Morrill, NC State University, Raleigh, NC, United States of

America,

thayer_morrill@ncsu.edu,

Unut Dur, Robert Hammond

We introduce the first mechanism that Pareto dominates the Deferred Acceptance

algorithm (DA) in equilibrium. Our algorithm, the Secure Boston Mechanism

(sBM), is a hybrid between the Boston Mechanism and DA. It protects students

that are initially guaranteed a school but otherwise adjusts priorities based on

student rankings. We demonstrate that sBM always has an equilibrium that

weakly dominates the DA assignment, and that in equilibrium no student

receives worse than a fair assignment.

4 - Mechanism Design for Team Formation

Yevgeniy Vorobeychik, Vanderbilt University, 401 Bowling Ave,

Nashville, TN, United States of America,

eug.vorobey@gmail.com

Mason Wright

We present the first formal mechanism design framework for team formation,

building on recent combinatorial matching market design literature. We exhibit

four mechanisms for this problem, two novel, two simple extensions of known

mechanisms from other domains. We use extensive experiments to show our

second novel mechanism, despite having no theoretical guarantees, empirically

achieves good incentive compatibility, welfare, and fairness.

MA29

29-Room 406, Marriott

Applied Analytics Across Industries

Sponsor: Analytics