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INFORMS Nashville – 2016

273

2 - New Core-Selecting Payment Rules With Better Fairness And

Incentive Properties

Benedikt Buenz, Stanford,

buenz@stanford.edu

Most of the recent large-scale combinatorial auctions, e.g. for spectrum rights,

have used core-selecting payment rules. Such rules ensure that no subset of

players is willing to outbid the total payments charged the winning players.

However, while the particular rule used in practice, the Quadratic rule, is a core-

selecting rule, there are many alternatives. We examine several hundred

alternative core-selecting rules in Bayes-Nash equilibrium via a novel numerical

solver to identify better rules. We show that Quadratic is not the optimal rule in

terms of efficiency, incentives, revenue or fairness, and that we can design rules

that outperform Quadratic in all of these dimensions simultaneously.

3 - Linear Item Pricing In Combinatorial Auctions

Robert Day, University of Connecticut, Storrs, CT,

Bob.Day@business.uconn.edu

I will present new results regarding the use of linear item prices in combinatorial

auctions. Prices for items form a solution to an altered dual of WDP, are core-

selecting, and constitute a combinatorial winning-level equilibrium.

TB27

201A-MCC

Social Networks and Learning

Sponsored: Manufacturing & Service Oper Mgmt

Sponsored Session

Chair: Elena Belavina, University of Chicago Booth School of Business,

Chicago, IL, United States,

elena.belavina@chicagobooth.edu

1 - The Use And Value Of Social Network Information In

Selective Selling

Ruslan Momot, INSEAD, Fontainebleau, France,

Ruslan.Momot@insead.edu

, Elena Belavina, Karan Girotra

We consider the use and value of social network information in selectively selling

goods and services whose value derives from exclusive ownership among

network connections. Our model accommodates customers who are

heterogeneous in their number of friends (degree) and proclivity for social

comparisons (conspicuity). We show how the firm with information on either (or

both) of these traits can use it to increase profits making a product selectively

available to the firm’s best targets - high-conspicuity customers within

intermediate-degree segments. We find that information about degree is more

valuable than information about conspicuity and that the two are substitutes.

2 - The Sharing Newsboys

Ming Hu, Rotman School at University of Toronto,

Ming.Hu@Rotman.Utoronto.Ca

We study resource sharing or demand referral behavior among a network of

connected newsboys. Each newsboy only locally knows the number of his

neighbors but does not know the number of his neighbor’s neighbors. Our focus

is to investigate the change of the degree distribution on the newsboy decisions

and social welfare. Surprisingly, we show that more connections may not lead to

a higher social welfare.

3 - Information Externalities In Crowdfunding Projects

Senthil Veeraraghavan, Wharton School,

senthilv@wharton.upenn.edu

, Jiding Zhang

We study the information externalities associated with crowdfunding projects.

Crowdfunding projects suffer from the tragedy of the commons. To raise capital

for successful funds requires overcoming the “startup problem”. We study and

compare mechanisms to improve the project success — lotteries, seeking altruistic

investments, up-front payments and quick dissemination of information.

4 - Managing Service Systems In Presence Of Social Networks

Gad Allon, Wharton School, Philadelphia, PA, 19010, United

States,

gadallon@wharton.upenn.edu

, Dennis Zhang

We study a service system with the presence of a social network. In our model,

firms can differentiate resource allocations among customers, and customers learn

the service qualities from the social network. We study the interplay among

network structure, customer characteristics, and information structure, and

characterize the optimal policy. We further calibrate our model with data from

Yelp.com

and quantify the value of social network knowledge empirically.

TB29

202A-MCC

Incentives Issues in Sustainable Operations

Sponsored: Manufacturing & Service Oper Mgmt,

Sustainable Operations

Sponsored Session

Chair: Luyi Gui, The Paul Merage School of Business, UC - Irvine,

Irvine, CA, United States,

luyig@uci.edu

1 - Green Sourcing-the Role Of Premium Sharing And

Consulting Services

Xi Chen, University of Michigan - Dearborn,

xichenxi@umich.edu

Certified sustainable products often times enjoy a significant green premium in

the retail market. In this paper, we study a retailer’s use of a sourcing contract as a

tool of incentivizing suppliers to exert greening efforts which improves the

chances of receiving certification, and in turn capturing the green premium. We

also explore the rationale for retailer to involve in suppliers’ greening efforts.

2 - Inducing Prompt Disclosure In The Presence Of Evasive Effort

Shouqiang Wang, The University of Texas at Dallas, Richardson,

TX, United States,

Shouqiang.Wang@utdallas.edu,

Peng Sun,

Francis E De Vericourt

In supply chains, firms are typically exposed to negative impacts resulting from

random adverse events that occur at and are privately observable to their

suppliers. The firm can use fiscal instrument as well as inspections to uncover the

adverse event. The supplier, however, prefers to conceal and even deliberately

hide such adverse event so as to evade its responsibility. The goal of this paper is

to devise optimal strategies for firms to induce the supplier’s prompt disclosure in

the presence of such evasive behavior.

3 - The Adoption Of Smart Home Appliance For Energy Shifting

Wenbin Wang, Shanghai University of Finance and Economics,

Shanghai, China,

wang.wenbin@shufe.edu.cn,

Yannan Jin

Smart home appliances can shift energy consumption in response to energy price

and thus hold great potential for reducing the energy cost. This paper uses a game

theoretical approach to analyze the consumers’ decisions on adopting smart home

appliances. We study how the adoption decisions are affected by the pricing

decisions of the appliance manufacturer and the utility company, as well as the

government subsidy. We find the appliance manufacturer or the utility company

alone may offer sufficient incentives to adopt smart home appliances. However, to

increase the social welfare the government may need to interfere with these

incentive programs.

4 - Incentives For Joint Product And Process Improvement Under

Collective Extended Producer Responsibility

Luyi Gui, UC Irvine,

luyig@uci.edu

Extended producer responsibility legislation mandates producers’ financial

responsibility of proper post-use treatment of their products. This study

investigates how the widely-adopted collective implementation of EPR legislation

can promote more environmentally friendly product design and more efficient

recycling technology. In particular, we analyze the impact of cost allocation

choices on the joint design-technology advancement.

TB30

202B-MCC

Predictive Modeling in Healthcare

Sponsored: Manufacturing & Service Oper Mgmt,

Healthcare Operations

Sponsored Session

Chair: Anita L Tucker, Brandeis, 415 South Street, MS 032,

Waltham, MA, 02453-2728, United States,

atucker@brandeis.edu

Co-Chair: Hummy Song, Harvard University, Soldiers Field, Boston,

MA, 02163, United States,

hsong@hbs.edu

1 - Accurate Emergency Department Wait Time Prediction

Mohsen Bayati, Stanford University,

bayati@stanford.edu

,

Erjie Ang, Sara Kwasnick, Erica Plambeck, Michael Aratow

In this talk we discuss Q-Lasso method for wait time prediction, which combines

statistical learning with fluid model estimators. In historical data from four

remarkably different hospitals, Q-Lasso predicts the emergency department (ED)

wait time for low-acuity patients with greater accuracy than existing methods. Q-

Lasso achieves greater accuracy largely by correcting errors of underestimation in

which a patient waits for longer than predicted. Implemented on the external

website and in the triage room of the San Mateo Medical Center (SMMC), Q-

Lasso achieves over 30% lower mean squared prediction error than would occur

with the best rolling average method.

TB30