2016 INFORMS Annual Meeting Program

253 / 561

INFORMS Nashville – 2016
253
3 - “Hidden Profiles” In Corporate Prediction Markets: The Impact Of
Public Information Precision And Social Interactions
Jingchuan Pu, University of Florida, Gainesville, FL, United States,
, Liangfei Qiu, Hsing K Cheng
Recently, large companies are experimenting with corporate prediction markets
run among their employees. we develop an analytical model to analyze the effects
of information precision and social interactions on prediction market
performance. But increased precision of public information is not always
beneficial to prediction market accuracy because of the “hidden profiles” effect:
participants place a larger than efficient weight on existing public information. A
socially embedded prediction market with information sharing mechanism may
help correct such inefficiency. We also identify conditions under which increased
precision of public information is detrimental in both cases.
4 - Credit Card Companies And Is-integrated Marketing Platforms:
A Comparison Of Social Network Promotions And Targeted
Promotions
Soohyun Cho, University of Florida, Gainesville, FL,
United States, Liangfei Qiu,
Subhajyoti Bandyopadhyay
In this paper, we investigate two types of marketing promotions that credit card
companies have recently been featuring in collaboration with partnered retailers:
public promotions through social networking services and targeted promotions
through companies’ websites. To analyze the strategic impacts on the promotion’s
participants (including companies/retailers and consumers), we develop a game
theoretical model and then determine the best strategies for different participants.
The study is extended by considering the advertising effect of social network
services as well as security issues involving targeted promotions.
TA57
Music Row 5- Omni
Insights from Relaxing Traditional Modeling
Assumptions about Human Behavior in OM Settings
Sponsored: Behavioral Operations Management
Sponsored Session
Chair: Jordan Tong, Wisconsin School of Business, 4293 Grainger Hall,
Madison, WI, 53706, United States,
1 - Utility Based Queueing: Predicting Delay When
Servers Are Strategic
Amy Ward, University of Southern California,
Sherwin Doroudi,
Ragavendran Gopalakrishnan, Adam Wierman, Dongyuan Zhan
Most common queueing models used for service system design assume the
servers work at fixed (possibly heterogeneous) rates. However, real-life service
systems are staffed by people, and people may change their service speed in
response to incentives. To model this, we assume each server selfishly chooses his
service speed in order to maximize his expected utility. Under various
assumptions on the utility function, we characterize the equilibrium service
speed, which can then be used to estimate system performance.
2 - Service Systems With Unknown Quality & Customer
Anecdotal Reasoning
Tingliang Huang, Carroll School of Management, Boston College,
140 Commonwealth Avenue, Chestnut Hill, MA, 02467, United
States, Hang Ren, Kenan Arifoglu
We consider a service system where customers do not know the distribution of
uncertain service quality and cannot estimate it fully rationally; instead, they
form beliefs by taking sample averages of anecdotes. The number of anecdotes
can be used to measure to what extent customers are boundedly rational. We
characterize customers’ joining behavior and the server’s pricing and quality
decisions.
3 - The Effect Of Social Information On Demand In
Quality Competition
Dayoung Kim, Cornell University,
We investigate the impact of different types of social information on the demand
characteristics of firms competing through service quality. We develop a Hidden
Markov model to understand the choice mechanism of a consumer under social
information. We then conduct a lab experiment where a consumer chooses to
visit one of two firms, each with unknown service quality. In the experiment, a
consumer may have access to (1) no information, (2) market “share-based” social
information, or (3) “quality-based” social information. Our results show that
different types of information have dramatically different effects on firms’ market
shares and demand uncertainty.
4 - Selling To Experience-sampling Customers: Quality Conformance,
Pricing, And Promotions
Gregory A DeCroix, University of Wisconsin - Madison,
Jordan Tong
We consider a firm that sells a service, the quality of which is stationary but
stochastic. Customers cannot directly observe mean quality, but instead base their
estimate of quality on their own past purchases. Customers are risk neutral but
boundedly rational - their purchase decisions are described by a logit model based
on customer surplus (estimated quality minus price). We explore several
phenomena that arise in such a setting. For example, poor quality conformance
(high service variability) leads to reduced sales revenues, even though customers
are risk neutral. In addition, under certain circumstances occasional promotions
can help counter this erosion in revenues.
TA58
Music Row 6- Omni
Energy VX
Contributed Session
Chair: Ruediger Schultz, University of Duisburg Essen, Thea-Leymann-
Str. 9, Essen, D-45127, Germany,
1 - Managing Stored Energy In Microgrids Via Multistage
Stochastic Programming
Arnab Bhattacharya, PhD Candidate, University of Pittsburgh,
1031 Benedum Hall, 3700 O’Hara Street, Pittsburgh, PA, 15261,
United States, , Jeffrey P. Kharoufeh,
Bo Zeng
Energy storage systems are used to mitigate adverse effects of renewable sources
in a microgrid where procurement and storage decisions are made under
uncertain demand, renewable supply and prices. A multistage stochastic
programming (SP) model is formulated to minimize the expected total costs in a
microgrid. To improve computational tractability of the SP model, a customized
stochastic dual-dynamic programming (SDDP) algorithm is employed to obtain
high-quality solutions within reasonable time bounds. A numerical study
highlights significant cost reductions and computational benefits.
2 - A Crowdfunding Model For Green Energy Investment
Ying Xu, Assistant Professor, Singapore University of Technology
and Design, Singapore, Singapore, ,
Ronghuo Zheng, Nilanjan. Chakraborty, Katia P. Sycara
Motivated by emerging community solar farms, this paper studies a new
renewable energy investment model through crowdfunding. We develop a
sequential game theory model to capture the interactions among crowdfunders,
the solar farm owner, and an electricity company in a multi-period framework.
We find that under crowdfunding although the farm owner reduces its
investment level, the overall green energy investment level is increased due to
the contribution of crowdfunders. We also find that crowdfunding can increase
the penetration of green energy in consumption. Finally, the numerical results
based on real data indicates crowdfunding is a simple but effective way to boost
green generation.
3 - Incentive-based Coordination Mechanism For Backup Renewable
Energy Investment
Sadra Babaei, Oklahoma State University, Stillwater, OK,
United States, , Chaoyue Zhao
Due to the intermittent nature of renewable energy, the renewable energy
producers are exposed to high risks in delivering what they have already
committed to the energy market. Cooperating with other market participants like
conventional energy producers poses a possibility to mitigate this issue. Using
stochastic modeling formulation, this paper aims to find optimal bidding strategies
that provide incentive for both renewable and conventional power producers to
cooperate with each other. Additionally, the trading volume and price between
the participants are determined by using Nash game framework. Numerical
experiments have been conducted to verify the effectiveness of the model.
4 - Nomination Validation In Gas Grids Under Uncertainty
Ruediger Schultz, University of Duisburg Essen, Thea-Leymann-
Str. 9, Essen, D-45127, Germany,
Gas flows in the pipes and pressures at the nodes, both under uncertainty of gas
withdrawals from the network (loads) at exit (delivery) nodes are studied.
Assuming the uncertainty of withdrawals is stochastic with known distributions,
methods for calculating probabilities for the feasibility of load coverage are
presented. Emphasis is placed on mildly meshed networks.
TA58