Learning to Price with Reference Effects

As a firm varies the price of a product, consumers exhibit reference effects, making purchase decisions based not only on the prevailing price but also the product's price history. We consider the problem of learning such behavioral patterns as a monopolist releases, markets, and prices products. This context calls for pricing decisions that intelligently trade off between maximizing revenue generated by a current product and probing to gain information for future benefit. Due to dependence on price history, realized demand can reflect delayed consequences of earlier pricing decisions. As such, inference entails attribution of outcomes to prior decisions and effective exploration requires planning price sequences that yield informative future outcomes. Despite the considerable complexity of this problem, we offer a tractable systematic approach. In particular, we frame the problem as one of reinforcement learning and leverage Thompson sampling. We also establish a regret bound that provides graceful guarantees on how performance improves as data is gathered and how this depends on the complexity of the demand model. We illustrate merits of the approach through simulations

Failure prediction models: performance, disagreements, and internal rating systems. NBB Working Papers. No. 123, 13 December 2007

We address a number of comparative issues relating to the performance of failure prediction models for small, private firms. We use two models provided by vendors, a model developed by the National Bank of Belgium, and the Altman Z-score model to investigate model power, the extent of disagreement between models in the ranking of firms, and the design of internal rating systems. We also examine the potential gains from combining the output of multiple models. We find that the power of all four models in predicting bankruptcies is very good at the one-year horizon, even though not all of the models were developed using bankruptcy data and the models use different statistical methodologies. Disagreements in firm rankings are nevertheless significant across models, and model choice will have an impact on loan pricing and origination decisions. We find that it is possible to realize important gains from combining models with similar power. In addition, we show that it can also be beneficial to combine a weaker model with a stronger one if disagreements across models with respect to failing firms are high enough. Finally, the number of classes in an internal rating system appears to be more important than the distribution of borrowers across classes

Provision of metro ethernet services using a reconfigurable photonic access network

The paper proposes a design for traffic engineering to provide Ethernet services using an extended access network. Ethernet has remained the dominant technology for Local Area and Enterprise Networks, the use of Ethernet in metro networks has seen significant interest of late to provide for end to end Ethernet services to the user. The Broadband Photonic (BBP) access network is viewed as a quasi independent stack of EPONs in which geographically spread customer-VLANs (C-VLANs) can be implemented. The use of such a network for providing metro Ethernet like services in addition to traditional access services is presented

Manipulation Robustness of Collaborative Filtering Systems

A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and asymptotically linear are relatively robust. These results provide guidance for the design of future collaborative filtering systems

Model-based Reinforcement Learning and the Eluder Dimension

We consider the problem of learning to optimize an unknown Markov decision process (MDP). We show that, if the MDP can be parameterized within some known function class, we can obtain regret bounds that scale with the dimensionality, rather than cardinality, of the system. We characterize this dependence explicitly as $\tilde{O}(\sqrt{d_K d_E T})$ where $T$ is time elapsed, $d_K$ is the Kolmogorov dimension and $d_E$ is the \emph{eluder dimension}. These represent the first unified regret bounds for model-based reinforcement learning and provide state of the art guarantees in several important settings. Moreover, we present a simple and computationally efficient algorithm \emph{posterior sampling for reinforcement learning} (PSRL) that satisfies these bounds

Functions of p120ctn isoforms in cell-cell adhesion and intracellular signaling

The functions of many organs depend on the generation of an epithelium. The transition from a set of loosely connected nonpolarized cells to organized sheets of closely associated polarized epithelial cells requires the assembly of specialized cell junctions. In vertebrates, three major types of junctions are responsible for epithelial integrity: adherens junctions, tight junctions, and desmosomes. p120 catenin (p120ctn) is an Armadillo family member and a component of the cadherin-catenin complex in the adherens junction. It fulfils pleiotropic functions according to its subcellular localization: modulating the turnover rate of membrane-bound cadherins, regulating the activation of small RhoGTPases in the cytoplasm, and modulating nuclear transcription. Over the last two decades, knowledge of p120ctn obtained from in vitro experiments has been confirmed and extended by using different animal models. It has become clear that p120ctn is essential for normal development and homeostasis, at least in frog and mammals. p120ctn is a Src substrate that can be phosphorylated at different tyrosine, serine and threonine residues and can dock various kinases and phosphatases. Thereby, p120ctn regulates the phosphorylation status and the junctional stability of the cadherin-catenin complex. Multiple p120ctn isoforms are generated by alternative splicing, which allows the translation to be initiated from four start codons and enables the inclusion of four alternatively used exons. We will discuss the effects of different p120ctn isoforms on cadherin turnover and intracellular signaling, in particular RhoGTPase activity and phosphorylation events

Functions of p120ctn in development and disease

p120 catenin (p120ctn), a component of the cadherin-catenin complex, was the first member to be identified in a most interesting subfamily of the Armadillo family. Several p120ctn isoforms are generated by alternative splicing. These isoforms fulfill pleiotropic functions according to their subcellular localization: modulating the turnover rate of membrane-bound cadherins, regulating the activation of small Rho GTPases in the cytoplasm, and modulating nuclear transcription. Over the last two decades, knowledge of p120ctn has grown remarkably, and this has been achieved in part by using different animal models. At least in frog and mammals, p120ctn is essential for normal development and homeostasis. Here we will discuss the effects of different p120ctn isoforms on cadherin turnover and on signaling in the cytoplasm and the nucleus. We will also elaborate on the structure and function of other members of the p120ctn subfamily: ARVCF, p0071 and delta-catenin. Finally, we will overview the respective roles of p120ctn family members in pathological processes, and particularly in cancer as p120ctn is frequently downregulated or mislocalized in various human tumors