09h00 - 09h25
A Dynamic Sales Force Model
Sales agents’ strategic behavior can cause inefficiencies. To understand effort strategies,we propose a dynamic principal-agent model. We characterize the optimal policies using stochastic calculus and provide new managerial insights.
09h25 - 09h50
Mutual Funds Efficiency Measurement Taking into Account Financial and Socially Responsible Criteria
Socially responsible investors have both, financial as well as non-financial goals in investment decision-making. But, while several methods have been developed for investment decision making based on financial criteria, decision making models including also socially responsible criteria are rather underdeveloped. This work proposes a new methodology based on Data Envelopment Analysis (DEA) consistent with second-order stochastic dominance (SSD) efficiency to compute a performance index for equity mutual funds which considers the expected financial return and an environmental responsibility all together. With this aim, a model is proposed which measures social environmental responsible performance at two levels: first, at the level of the companies invested in by the mutual funds (e.g. Corporate Social Performance, CSP) and then, at the level of socially responsible mutual funds’ management. Using Kinder, Lindberg and Domini Inc. (KLD-CSP) data from 1,353 companies invested in by 50 large cap equity U.S. mutual funds (both, socially responsible and conventional mutual funds) and other financial data from Morningstar Ltd., our study presents the first application of a DEA model for mutual funds performance measurement taking into account not only their financial performance but also their social environmental responsibility.
09h50 - 10h15
Demand Forecasting in Revenue Management under Availability Constraints
We investigate the problem of demand forecasting in transportation. Seats are limited therefore;
transportation companies continue to accept reservations in a fare class until the booking limit is reached. We propose an optimization model which takes availability constraints into account in
order to have more accurate insight about demand behavior.
10h15 - 10h40
Decision Making under Uncertainty when Preference Information is Incomplete
We consider the problem of optimal decision making under uncertainty but assume that the decision maker's utility function is not completely known. Instead, we consider all the utilities that meet some criteria, such as preferring certain lotteries over certain other lotteries and being risk averse, s-shaped, or prudent. This extends the notion of stochastic dominance. We then give tractable formulations for such decision making problems. We formulate them as robust utility maximization problems, as optimization problems with stochastic dominance constraints, and as robust certainty equivalent maximization problems. We use a portfolio allocation problem to illustrate our results.