Model Use in Sustainability Negotiations and Decisions

Informations Systems and Operations Management

Speaker: Ellen Czaika
Post Doctoral Researcher , Institute for Data, Systems and Society, Massachusetts Institute of Technology (MIT)

1 December 2015 - HEC Paris - Jouy en Josas Campus - Bulding V - Room Bernard Ramanantsoa - From 11:30 am to 12:30 pm

Sustainability negotiations and decisions require the integration of scientific information with stakeholder interests. Mathematical models help elucidate the physical world and therefore may orient the negotiators in a shared understanding of the physical world. Many researchers suggest collaborative modeling to facilitate integrating scientific information and stakeholder interests. In this thesis, I use methods that enable repeated instances of the same decision; the exploration of alternatives to model use (e.g. learning of a model’s logic, relevant information, or irrelevant information); and the exploration of alternatives to collaborative modeling (e.g. using an expert model or not using a model). This thesis comprises two studies that use serious game role-play simulations. The first study is a computer-driven role-play simulation of governmental policy creation and the second is a five-party role-play simulation to negotiate a more sustainable end-of-life for used paper coffee cups. In the first study, model users reached the Pareto Frontier—the set of non-dominated points—more readily (13%) than non-model-users (2.5%) and model users discovered the win-win nature of electricity access with higher frequency (63%) than non-model users (9%). Participants who learned of the model’s logic through presentation performed nearly as well as model users. In the second study, model use shortened the (mean) duration of the negotiation from 55 minutes to 45 minutes. Negotiating tables that co-created a model had a higher likelihood of reaching favorable agreements (44% compared to 25%). Model use did not significantly alter the value distribution among parties. Tables of negotiators used the model in two predominant manners: to test alternatives as they generated potential agreements and to verify a tentative agreement. The former resulted in higher mean table values than the latter. Together, these studies demonstrate: that mathematical models can be used in sustainability negotiations and decisions with good effect; that learning about the insights of a model is beneficial in decision making—but using a model is more beneficial; and that collaborative model building can provide better negotiation outcomes than using an expert model and can be faster than not using a model.

Pricing and Capacity Allocation for Shared Services

Informations Systems and Operations Management

Speaker: Vasiliki Kostami
Assistant Professor , London Business School

12 November 2015 - HEC Paris - Jouy en Josas Campus - Bulding V - Room Bernard Ramanantsoa - From 11:30 am to 12:30 pm

We study the pricing and capacity allocation problem of a service provider who serves two distinct customer classes. Customers within each class are inherently heterogeneous in their willingness to pay for service, but their utilities are also affected by the presence of other customers in the system. Specifically, customer utilities depend on how many customers are in the system at the time of service as well as who these other customers are. If the service provider can price discriminate between customer classes, pricing out a class, i.e., operating an exclusive system, can sometimes be optimal and that depends only on classes’ perceptions about each other. If the provider must charge a single price, an exclusive system is even more likely. We extend our analysis to a service provider who can prevent class interaction by allocating separate capacity segments to the two customer classes. Under price discrimination, allocating capacity is optimal if our measure of net appreciation between classes is negative. However, under a single–price policy, allocating capacity can be optimal even if this measure is positive. In fact, we show that the nature of asymmetry eventually determines the optimal strategy.

Joint work with Dimitris Kostamis and Serhan Ziya

Short Bio:

Vasiliki Kostami joined the faculty of LBS as an Assistant Professor at the Management Science and Operations Department in 2010 after completing her PhD in Operations Management at Marshall School of Business, USC. Her research interests mainly focus on the management of service operations. She works on the modelling of service systems, such as entertainment facilities, call centers and health care facilities under uncertainty. Specifically, she has looked at queue management problems for amusement parks such as Disneyland, quality management problems for healthcare and optimal inventory management in manufacturing sector. Her research articles have appeared in leading academic journals like M&SOM. She teaches on the full time and executive MBA programmes as well as the PhD programme.

Readmission analytics - Care transformation through information technology

Informations Systems and Operations Management

Speaker: Mohan Tanniru, Ph.D
Professor , Oakland University

16 June 2015 - HEC Jouy-en-Josas Campus - Building V Council room - From 2:30 pm to 4:00 pm

Health care providers are facing multiple challenges such as improving patient satisfaction, operating with reduced reimbursements, and reducing frequent readmissions. Care providers who address these challenges independently often miss out on opportunities that surface when patient care is viewed within a system, influenced by two environments: clinical environment within the hospital and social environment of patients post-discharge. While hospitals strive for greater efficiencies within the clinical environment, they often find coordination post-discharge to reduce readmissions a major challenge. By viewing the system of patient care through the readmission lens and applying some of the templates discussed under Systematic Inventive Thinking: SIT2 (Inside the Box), this presentation looks at several innovative approaches that can help address patient care both inside and outside the hospital walls by leveraging advances in information technology. Several on-going research projects of care transformation through IT will be highlighted including on-going work of patient care at St Joseph Mercy Hospital in Pontiac, MI and peri-operative care in Stanford Medical School (inside the hospitals), and patient empowerment studies at dialysis centers (DaVita) and medication reconciliation/patient follow-up at nursing homes (outside the hospital).

Corn or Soybean: Dynamic Farmland Allocation under Uncertainty

Informations Systems and Operations Management

Speaker: Onur BOYABATLI
Assistant Professor , Singapore Management University

5 June 2015 - HEC Paris - Jouy en Josas Campus - Building S - Room 227 - From 10:30 am to 12:00 pm

This paper studies the farmland allocation decision of a farmer between two crops in a multi-period framework. In each growing period, the farmer chooses the allocation in the presence of revenue uncertainty, and crop rotation benefits across periods, i.e. revenue is stochastically larger when a crop is planted in a rotated land (where the other crop was planted in the previous period). We identify two strategies, monoculture, i.e. fully allocate the farmland to one of the crops, and rotate, i.e. plant each crop in the rotated farmland, which characterize the optimal allocation decision in each period. Our analysis provides rules of thumb for the impact of revenue uncertainty: The farmer benefits from a lower revenue correlation between the two crops. Interestingly, the farmer benefits from a higher revenue volatility only when this volatility is sufficiently high; otherwise, a lower revenue volatility increases the profitability. We propose a heuristic allocation policy which we characterize in closed form. Using a calibration based on a representative farmer planting corn and soybean in Iowa, we show that the proposed policy is near-optimal, and significantly outperforms the commonly used heuristic allocation policies in practice (such as the myopic policy, always-rotate policy and monoculture policy).

Measuring the effectiveness of mobile marketing

Informations Systems and Operations Management

Speaker: Anindya Ghose
Professor of Information, Operations and Management Sciences and Professor of Marketing , New York University's Leonard N. Stern School of Business

3 June 2015 - HEC - Jouy-en-Josas Campus - Building V - Council Room - From 11:00 am to 12:00 pm

The explosive growth of smartphone and location-based services (LBS) has contributed to the rise of mobile advertising. In this talk, we will present results from multiple studies in Europe and Asia that are designed to measure the effectiveness of mobile marketing promotions. In the first randomized field experiment, using data from a location-based app for smartphones, we measure the effectiveness of mobile coupons. The aim is to analyze the causal impact of geographical distance between a user and retail store, the display rank, and coupon discounts on consumers’ response to mobile coupons. In a second large scale field study where we exploit a quasi-natural experiment we examine the role of contextual crowdedness on the redemption rates of mobile coupons. We find that people become increasingly engaged with their mobile devices as trains get more crowded, and in turn become more likely to respond to targeted mobile messages. The study results were consistent across peak and off-peak times, and on weekdays and weekends. The change in behavior can be accounted for by the phenomenon of “mobile immersion”: to psychologically cope with the loss of personal space in crowded trains and to avoid accidental gazes, commuters can escape into their personal mobile space. In turn, they become more involved with targeted mobile messages they receive, and, consequently, are more likely to make a purchase in crowded trains. These studies causally show that mobile advertisements based on real-time static geographical location and contextual information can significantly increase consumers’ likelihood of redeeming a geo-targeted mobile coupon. However, beyond the location and contextual information, the overall mobile trajectory of each individual consumer can provide even richer information about consumer preferences. In the third study, we propose a new mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To examine the effectiveness of this new mobile trajectory-based advertising strategy, we designed a large-scale randomized field experiment in one of the largest shopping malls in the world. We find that mobile trajectory-based advertising can lead to highest redemption probability, fastest redemption behavior, and highest transaction amount from customers at the focal advertising store as well as in the shopping mall. Our studies help firms better understand the question of which kinds of mobile advertising are most effective and how machine learning techniques can be combined with statistical models and field experiments to offer the right product to the right audience at the right time on the right channel.