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This paper investigates the impacts of two environmental policies: pollution abatement subsidy and emission tax, on a three-tier supply chain, where the manufacturer distributes via multiple competitive retailers and invests in a pollution abatement technology in manufacturing. The government pursues social welfare maximization, while the manufacturer and retailers are profit driven. We find that the subsidy policy offers the manufacturer greater incentives to abate pollution and yields higher profits for channel members.

Thursday, October 4, 2018 9:30 am - 11:00 am EDT (GMT -04:00)

Design research seminar: Dr. John S. Gero

Dr. John S. Gero

Dr. John S. Gero

Research Professor
Department of Computer Science and School of Architecture
University of North Carolina at Charlotte

Research Professor
Krasnow Institute for Advanced Study George Mason University

Is communicating via Skype or other video media equivalent to a face-to-face meeting? We have known for some time that after interacting face-to-face, people can predict the cooperative behaviour of strangers with better-than-chance accuracy. But is this ability affected when communications are mediated by video technology? This study reports four laboratory experiments examining how different communication conditions affect cooperation prediction efficacy.

Bed shortages in hospitals usually have a negative impact on patient satisfaction and medical outcomes. In practice, healthcare managers often use bed occupancy rates (BOR) as a metric to understand bed utilization, which is insufficient in capturing the risk of bed shortages. Based on the riskiness index of Aumann and Serrano (2008), we propose the entropic bed shortage metric, which captures more facets of bed shortage risk than traditional metrics such as the occupancy rate, the probability of shortages and expected shortages.

We compare two models of a multi-server queueing system with state-dependent service rates and return probabilities. In both models, upon completing service, customers are delayed prior to possibly returning to service. In one model, the determination of whether a customer will return occurs immediately upon service completion, at the beginning of the delay. In the other, that determination is made at the end of the delay, capturing the idea that it takes time for the customer’s condition and needs to evolve or assess, before it becomes known whether a return to service is needed.