Two research seminars - 23 June 2015 (Tuesday), 10:00 - 12:00 - Room P3.10

16 junho 2015, 11:57 • Maria do Rosário De Oliveira Silva

1.          Optional-Contingent-Product Pricing in Marketing Channels

Peter Kort

(Tilburg University)

This paper studies the pricing strategies of firms belonging to a vertical channel structure where a base and an optional contingent products are sold. Optional contingent products are characterized by unilateral demand interdependencies. That is, the base product can be used independently of a contingent product. On the other hand, the contingent product’s purchase is conditional on the possession of the base product.

We find that the retailer decreases the price of the base product to stimulate demand on the contingent-product market. Even a loss-leader strategy could be optimal, which happens when reducing the base product’s price has a large positive effect on its demand, and thus on the number of potential consumers of the contingent product. The price reduction of the base product either mitigates the double-marginalization problem, or leads to an opposite inefficiency in the form of a too low price compared to the price maximizing vertically integrated channel profits. The latter happens when the marginal impact of both products’ demands on the base product’s price is low, and almost equal in absolute terms. 

Joint work with Sihem Taboubi and Georges Zaccour

2.          Transmission and Power Generation Investment under Uncertainty

Verena Hagspiel

(NTNU)

The challenge of deregulated electricity markets and ambitious renewable energy targets have contributed to an increased need of understanding how market participants will respond to a transmission planner’s investment decision. We study the optimal transmission investment decision of a transmission system operator (TSO) that anticipates a power company’s (PC) potential capacity expansion. The proposed model captures both the investment decisions of a TSO and PC and accounts for the conflicting objectives and game-theoretic interactions of the distinct agents. Taking a real options approach allows to study the effect of uncertainty on the investment decisions and taking into account timing as well as sizing flexibility.

We find that disregarding the power company’s optimal investment decision can have a large negative impact on social welfare for a TSO. The corresponding welfare loss increases with uncertainty. The TSO in most cases wants to invest in a higher capacity than is optimal for the power company. The exception is in case the TSO has no timing flexibility and faces a relatively low demand level at investment. This implies that the TSO would overinvest if it would disregard the PC’s optimal capacity decision. On the contrary, we find that if the TSO only considers the power companies sizing flexibility, it risks installing a too small capacity. We furthermore conclude that a linear subsidy in the power company's investment cost could increase its optimal capacity and therewith, could serve as an incentive for power companies to invest in larger capacities.

Joint work with Nora S. Midttun, Afzal S. Siddiqui, and Jannicke S. Sletten

Comparison of Statistic and Deterministic Frameworks of Uncertainty Quantification

16 maio 2015, 10:51 • Maria do Rosário De Oliveira Silva

Rui Paulo

(ISEG and CEMAPRE, Universidade de Lisboa)

21 May 2015 (Thursday), 11:30

Room P3.10

(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

Two different approaches to the prediction problem are compared employing a realistic example, combustion of natural gas, with 102 uncertain parameters and 76 quantities of interests. One approach, termed Bound-to-Bound Data Collaboration (abbreviated to B2B) deploys semi-definite programming algorithms where the initial bounds on unknowns are combined with the initial bound of experimental data to produce new uncertainty bounds for the unknowns that are consistent with the data and, finally, deterministic uncertainty bounds for prediction in new settings. The other approach is statistical and Bayesian, referred to as BCP (for Bayesian Calibration and Prediction). It places prior distributions on the unknown parameters and on the parameters of the measurement error distributions and produces posterior distributions for model parameters and posterior distributions for model predictions in new settings.  The predictions from the two approaches are consistent:  B2B bounds and the support of the BCP predictive distribution overlap a very large part of each other. The BCP predictive distribution is more nuanced than the B2B bounds but depends on stronger assumptions. Interpretation and comparison of the results is closely connected with assumptions made about the model and experimental data and how they are used in both settings. The principal conclusion is that use of both methods protects against possible violations of assumptions in the BCP approach and conservative specifications and predictions using B2B.

Joint work with Michael Frenklach, Andrew Packard (UC Berkeley), Jerome Sacks (National Institute of Statistical Sciences) and Gonzalo Garcia-Donato (Universidad de Castilla-La Mancha)

The importance of Statistics in Bioinformatics

20 abril 2015, 17:09 • Maria do Rosário De Oliveira Silva

Lisete Sousa

(Faculdade de Ciências da Universidade de Lisboa, CEAUL)

7 May 2015 (Thursday), 11:30

Room P3.10

(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

Statistics acts in several areas of knowledge, being Bioinformatics one of the most recent application fields. In reality, the role of Statistics in Bioinformatics goes beyond a mere intervention. It is an integral pillar of Bioinformatics. Statistics has been gaining its space in this area, becoming an essential component of recognized merit. In this seminar the speaker intends to show the importance of Statistics in addressing systems as diverse as protein structure or microarray and NGS data. A set of specific studies in Molecular Biology, will be the basis for the presentation of some of the most common statistical methodologies in Bioinformatics. It is also shown the importance of the available software, including some R packages.

Investment Decisions under Multi-uncertainty and Exogenous Shocks

13 abril 2015, 00:56 • Maria do Rosário De Oliveira Silva

Cláudia Nunes

(CEMAT e Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa)

16 April 2015 (Thursday), 11:30

Room P3.10

(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

In this presentation we study the investment problem when both the demand and the investment costs are stochastic. We assume that the processes are independent, and both are modeled using geometric Brownian motion with exogenous jumps driven by independent Poisson processes. We use a real options approach, leading to an optimal stopping problem. Due to the multi-uncertainty, we propose a method to solve explicitly the problem, and we prove that this method leads exactly to the solution of the optimization problem.

Joint work with Rita Pimentel.

Statistical Learning for Natural Language Processing

23 março 2015, 09:19 • Maria do Rosário De Oliveira Silva

Miguel Almeida

(Priberam Labs)

26 March 2015 (Thursday), 11:30

Room P3.10

(Math. Building, IST, Av. Rovisco Pais, 1049-001 Lisboa)

The field of Natural Language Processing (NLP) deals with automatic processing of large corpora of text such as newswire articles (from online newspaper websites), social media (such as Facebook or Twitter) and user-created content (such as Wikipedia). It has experienced large growth in academia as well as in the industry, with large corporations such as Microsoft, Google, Facebook, Apple, Twitter, Amazon, among others, investing strongly in these technologies.

One of the most successful approaches to NLP is statistical learning (also known as machine learning), which uses the statistical properties of corpora of text to infer new knowledge.

In this talk I will present multiple NLP problems and provide a brief overview of how they can be solved with statistical learning. I will also present one of these problems (language detection) in more detail to illustrate how basic properties of Probability Theory are at the core of these techniques.