Essays in platform economics

In this thesis we present three papers which investigate informative content generated by consumers, aiming to improve the usefulness for matching high quality products at lower prices. Following a general perspective, we explore platform product listing, searchable through a decision making mechanism. In a more specialized perspective, we take into account a dropping price modality service, differentiating the consumer benefit in the case of high or low quality product matching. Chapter 1 Product quality on platform markets. Abstract Many studies have questioned the meaning of \u201cproduct quality\u201d, hanging between a characteristic interpretation of a product for improving consumer satisfaction, and scientific approach to measure its benefits. Starting from the historical quality setting as mirror image of the price, we investigate the adoption of new signals, developed over the years to adjust the original relationship. Recently, bootstrapping by emperor of e-commerce platforms, the rating system has emerged as a reference contribute for product quality informativeness. We study this tendency, to show its failure in the presence of low price market and new brands. For this purpose, we collect User Generated Contents from a well-known online retailing platform. We capture and distill meaningful features in order to adjust the rating assigned by reviewers, and propose a novel quality formula able to increase the accuracy of the information provided to the consumer. We suggest that our formula better captures product quality, and, when adopted by a platform for sorting the products, it increases the products variety and, consequently the satisfaction of the consumer. Our proposal suggests a way to facilitate the consumer search (as we will show in the second chapter). Moreover, it can be used as a measure of market efficiency in the case of voluntary opacity of the platform in exposing product quality signals.Chapter 2 Optimizing Product Quality in Online Search Abstract Exploiting an original definition of product quality, based on the information we can get from the User Generated Content, and driven by a statistical learning algorithm, we propose a new ordering mechanism for product search on platforms. This product quality formula is imported in a decision making mechanism which adopts an optimal Stopping Rule, in order to set the optimal time to terminate the search process and choose a good to purchase. We show how the consumer can benefit from the implementation of such a mechanism, demonstrating an improvement in terms of consumer utility at different levels of price, with respect to other sorting traditionally adopted by platforms. We propose a utility function fitted to a Gumbel distribution, and we demonstrate a stochastic dominance of our model. Experimental evidences on the camera market category put in relevance the efficiency of our quality index for ranking the effective quality compared to the more traditional rating system. This is particularly true for the low-price accessory market segment of products, in which we show higher utility dominance and slightly higher elasticity of demand.Chapter 3 Price Matching and Platform Pricing Abstract In this study we investigate the effects of Price Matching Guarantees (PMG) commercial policies on U.S. online consumer electronics daily prices. By applying a Diff-in-Diff identification strategy we find evidence in favor of price reductions occurring after the PMG policy is repealed. We further investigate if such effect is heterogeneous according to products characteristics, by exploiting User Generated Contents (products popularity and quality) and online search visibility measures (Google Search Rank). Estimates suggest that for high quality (visibility) products PMG policies harms competition by keeping prices high, while for low quality (visibility) products, prices decrease during the policy validity period

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Polymer Reactor Modeling, Design and Monitoring

Polymers range from synthetic plastics, such as polyacrylates, to natural biopolymers, such as proteins and DNA. The large molecular mass of polymers and our ability to manipulate their compositions and molecular structures have allowed for producing synthetic polymers with attractive properties. new polymers with remarkable characteristics are synthesized. Because of the huge production volume of commodity polymers, a little improvement in the operation of commodity-polymer processes can lead to significant economic gains. On the other hand, a little improvement in the quality of specialty polymers can lead to substantial increase in economic profits

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Estimating the price of privacy in liver transplantation

In the United States, patients with end-stage liver disease must join a waiting list to be eligible for cadaveric liver transplantation. However, the details of the composition of this waiting list are only partially available to the patients. Patients currently have the prerogative to reject any offered livers without any penalty. We study the problem of optimally deciding which offers to accept and which to reject. This decision is significantly affected by the patient's health status and progression as well as the composition of the waiting list, as it determines the chances a patient receives offers. We evaluate the value of obtaining the waiting list information through explicitly incorporating this information into the decision making process faced by these patients. We define the concept of the patient's price of privacy, namely the number of expected life days lost due to a lack of perfect waiting list information.We develop Markov decision process models that examine this question. Our first model assumes perfect waiting list information and, when compared to an existing model from the literature, yields upper bounds on the true price of privacy. Our second model relaxes the perfect information assumption and, hence, provides an accurate representation of the partially observable waiting list as in current practice. Comparing the optimal policies associated with these two models provides more accurate estimates for the price of privacy. We derive structural properties of both models, including conditions that guarantee monotone value functions and control-limit policies, and solve both models using clinical data.We also provide an extensive empirical study to test whether patients are actually making their accept/reject decisions so as to maximize their life expectancy, as this is assumed in our previous models. For this purpose, we consider patients transplanted with living-donor livers only, as considering other patients implies a model with enormous data requirements, and compare their actual decisions to the decisions suggested by a nonstationary MDP model that extends an existing model from the literature

Data driven discovery of cyber physical systems

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber- physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance

The Nature of Problem Solving

Solving non-routine problems is a key competence in a world full of changes, uncertainty and surprise where we strive to achieve so many ambitious goals. But the world is also full of solutions because of the extraordinary competences of humans who search for and find them. We must explore the world around us in a thoughtful way, acquire knowledge about unknown situations efficiently, and apply new and existing knowledge creatively. The Nature of Problem Solving presents the background and the main ideas behind the development of the PISA 2012 assessment of problem solving, as well as results from research collaborations that originated within the group of experts who guided the development of this assessment. It illustrates the past, present and future of problem-solving research and how this research is helping educators prepare students to navigate an increasingly uncertain, volatile and ambiguous world

Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries, being widely used not only among enthusiastic consumers but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is full of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. First, the SoA principles for collision avoidance against stationary objects are reviewed. Afterward, a novel image processing approach that uses deep learning and optical flow is presented. This approach is capable of detecting and generating escape trajectories against potential collisions with dynamic objects. Finally, novel models and algorithms combinations were tested, providing a new approach for the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, created from scratch using the framework developed.Os veículos aéreos não tripulados (VANTs), embora dificilmente considerados uma nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias, sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais de alta exigência, sendo expectável um impacto social massivo nos próximos anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados). Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente impossível de resolver com os algoritmos existentes, tornando o uso de VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos do subsistema de prevenção de colisão para atingir níveis aceitáveis de segurança e confiabilidade. Os estudos presentes na literatura para prevenção de colisão contra objectos estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas de aprendizagem profunda e processamento de imagem, para realizar a prevenção de colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura apresentada