Download or read book Bayesian Social Learning from Consumer Reviews written by Bar Ifrach and published by . This book was released on 2019 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by the proliferation of user-generated product-review information and its widespreaduse, this note studies a market where consumers are heterogeneous in terms of their willingness-to-pay for a new product. Each consumer observes the binary reviews (like or dislike) of consumers who purchased the product in the past and uses Bayesian updating to infer the product quality. We show that the learning process is successful as long as the price is not prohibitive and therefore at least some consumers, with sufficiently high idiosyncratic willingness-to-pay, will purchase the product irrespective of their posterior quality estimate. We examine some structural properties of the dynamics of the posterior beliefs.Finally, we study the seller's pricing problem, and we show that, if the set of possible prices is finite, then a stationary optimal pricing policy exists. If putting one item on the market involves a constant cost, then under this optimal policy, learning fails with positive probability.
Download or read book Social Learning in a Competitive Market with Consumer Reviews written by Stefano Vaccari and published by . This book was released on 2016 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two products of unknown qualities are simultaneously launched by two different firms in the market. An infinite population of consumers with heterogeneous preferences sequentially decide whether to purchase one of the two products or not to buy at all. Arriving consumers estimate the qualities of products based on the distribution of binary online reviews reported by prior consumers and decide accordingly, obeying a non-Bayesian rule. The goal of our work is to describe how the online review mechanism drives the spread of information in a competitive market and to provide conditions under which consumers' quality estimates converge almost surely to the true qualities of products.
Download or read book The Oxford Handbook of the Economics of Networks written by Yann Bramoullé and published by Oxford University Press. This book was released on 2016-03-01 with total page 857 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective decision making, opinion formation, and diffusion dynamics. From a methodological perspective, the contributors to this volume devote attention to theory, field experiments, laboratory experiments, and econometrics. Theoretical work in network formation, games played on networks, repeated games, and the interaction between linking and behavior is synthesized. A number of chapters are devoted to studying social process mediated by networks. Topics here include opinion formation, diffusion of information and disease, and learning. There are also chapters devoted to financial contagion and systemic risk, motivated in part by the recent financial crises. Another section discusses communities, with applications including social trust, favor exchange, and social collateral; the importance of communities for migration patterns; and the role that networks and communities play in the labor market. A prominent role of networks, from an economic perspective, is that they mediate trade. Several chapters cover bilateral trade in networks, strategic intermediation, and the role of networks in international trade. Contributions discuss as well the role of networks for organizations. On the one hand, one chapter discusses the role of networks for the performance of organizations, while two other chapters discuss managing networks of consumers and pricing in the presence of network-based spillovers. Finally, the authors discuss the internet as a network with attention to the issue of net neutrality.
Download or read book Bayesian Analysis for the Social Sciences written by Simon Jackman and published by John Wiley & Sons. This book was released on 2009-10-27 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.
Download or read book Bayesian Methods written by Jeff Gill and published by CRC Press. This book was released on 2007-11-26 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical models More technical and philosophical details on prior distributions A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.
Download or read book Fast and Slow Learning from Reviews written by Daron Acemoglu and published by . This book was released on 2017 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops a model of Bayesian learning from online reviews, and investigates the conditions for asymptotic learning of the quality of a product and the speed of learning under different rating systems. A rating system provides information about reviews left by previous customers. A sequence of potential customers decide whether to join the platform. After joining and observing the ratings of the product, and conditional on her ex ante valuation, a customer decides whether to purchase or not. If she purchases, the true quality of the product, her ex ante valuation, an ex post idiosyncratic preference term and the price of the product determine her overall satisfaction. Given the rating system of the platform, she decides to leave a review as a function of her overall satisfaction. We study learning dynamics under two classes of rating systems: full history, where customers see the full history of reviews, and summary statistics, where the platform reports some summary statistics of past reviews. In both cases, learning dynamics are complicated by a selection effect -- the types of users who purchase the good and thus their overall satisfaction and reviews depend on the information that they have available at the time of their purchase. We provide conditions for asymptotic learning under both full history and summary statistics, and show how the selection effect becomes more difficult to correct for with summary statistics. Conditional on asymptotic learning, the speed (rate) of learning is always exponential and is governed by similar forces under both types of rating systems, though the exact rates differ. Using this characterization, we provide the rate of learning under several different types of rating systems. We show that providing more information does not always lead to faster learning, but strictly finer rating systems always do. We also illustrate how different rating systems, with the same distribution of preferences, can lead to very fast or very slow speeds of learning. Keywords: Bayesian learning, online reviews, recommendation systems, social learning, speed of learning. JEL classification: C72, D83, L15.
Download or read book Advances in Data Science and Information Engineering written by Robert Stahlbock and published by Springer Nature. This book was released on 2021-10-29 with total page 965 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students.
Download or read book Revenue Management and Pricing Analytics written by Guillermo Gallego and published by Springer. This book was released on 2019-08-14 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: “There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it." Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
Download or read book Bayesian Statistics for the Social Sciences written by David Kaplan and published by Guilford Publications. This book was released on 2014-07-23 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.
Download or read book Alone Together written by Tommaso Bondi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a dynamic model of naïve social learning from consumer reviews. In our model, consumers decide which of two products to buy based on both expected quality and idiosyncratic taste. Products' qualities are initially unknown, and are (mis)learned from reviews. At the heart of the model lies a dynamic feedback loop between reviews, beliefs, and choices: period t reviews influence t + 1 consumers' beliefs, and thus choices; these determine the average of t + 1 reviews, which in turn influences t + 2 beliefs, choices and reviews. We show that in the long-run (t = ∞), reviews are systematically biased, leading some consumers astray. In particular, reviews relatively advantage lower quality and more polarizing products, since these products induce stronger taste-based consumer self-selection. Thus, in stark contrast with the winner-takes-all dynamics of classic observational learning models, in which consumers learn from the choices of their predecessors, social learning from opinions generates excessive choice fragmentation. Our findings have implications for interpreting the variance of reviews; pricing in presence of reviews; the design of crowd-sourced exploration; and the short and long term effectiveness of fake reviews.
Download or read book Introduction to Applied Bayesian Statistics and Estimation for Social Scientists written by Scott M. Lynch and published by Springer Science & Business Media. This book was released on 2007-06-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
Download or read book Advances in Artificial Intelligence and Applied Cognitive Computing written by Hamid R. Arabnia and published by Springer Nature. This book was released on 2021-10-14 with total page 1152 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the proceedings of two conferences: The 22nd International Conference on Artificial Intelligence (ICAI’20) and The 4th International Conference on Applied Cognitive Computing (ACC’20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020, and are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Topics include: deep learning; neural networks; brain models; cognitive science; natural language processing; fuzzy logic and soft computing (ICAI) and novel computationally intelligent algorithms; bio inspired cognitive algorithms; modeling human brain processing systems (ACC); and more. Authors include academics, researchers, and professionals. Presents the proceedings of two conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the tracks: artificial intelligence and applied cognitive computing; Features papers from the 22nd International Conference on AI (ICAI’20) and the 4th International Conference on Applied Cognitive Computing (ACC’20).
Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Download or read book On Information Distortions in Online Ratings written by Omar Besbes and published by . This book was released on 2016 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consumer reviews and ratings of products and services have become ubiquitous on the Internet. This paper analyzes, given the sequential nature of reviews and the limited feedback of such past reviews, the information content they communicate to future customers. We consider a model with heterogeneous customers who buy a product of unknown quality and we focus on two different informational settings. In the first setting customers observe the whole history of past reviews. In the second one they only observe the sample mean of past reviews. We examine under which conditions, in each setting, customers can recover the true quality of the product based on the feedback they observe. In the case of total monitoring, if consumers adopt a fully rational Bayesian updating paradigm, then they asymptotically learn the unknown quality. With access to only the sample mean of past reviews, inference becomes intricate for customers and it is not clear if/when and how social learning can take place. We first analyze the setting when customers interpret the mean as the proxy of quality. We show that in the long run, the sample mean of reviews stabilizes and in general, they overestimate the underlying quality of the product in the long run. We establish properties of the bias, which stems from the selection associated with observing only reviews of customers who purchase. Then, we show the existence of a simple non-Bayesian quality inference rule that leads to social learning when all customers use such a rule. In this framework, when the population of consumers is mixed, we show that a subgroup of customers can learn the true quality and that this subgroup negatively affects the more naive customers.
Download or read book 2022 2nd International Conference on Management Science and Software Engineering ICMSSE 2022 written by Syed Abdul Rehman Khan and published by Springer Nature. This book was released on 2023-02-10 with total page 921 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. Management science and engineering is a systematic discipline that combines modern information technology and digital technology, and then uses some related discipline methods, such as systems science, mathematical science, economics and behavioral science, and engineering methods. After analyzing and researching some problems arising from social economy, engineering, education, finance, etc., and making corresponding countermeasures. The main purpose is to achieve control and planning, decision-making and adjustment in social, economic, education, engineering and other aspects, and then make improvements, and finally organize and coordinate. The relevant departments can be combined to achieve system management, so that the allocation of resources and the Management can be rationally optimized, so that individual functions can play the greatest role, minimize resource consumption, and maximize the optimal allocation of resources. This is also the ultimate research purpose. Liangliang Wang said:" Management is the productive force, which promotes the development of the country, society and enterprise. The relationship between management practice and management science is the relationship between theory and practice. The research on management science helps to improve the level of management, and then promote the development of the country, society and enterprises. On the other hand, management practice changes with the continuous progress of the times. It is necessary to study the current situation and trend of management science in the new era, which will help to clarify the future development direction of the discipline and discover the deficiencies in management scientific research and grasp it. The focus of management science research, thereby promoting research in management science." Therefore, it is necessary to create a space for management science practitioners, engineering practitioners, researchers and related enthusiasts to gather and discuss this current issue. The 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022) aims to accommodate this need, as well as to: 1. provide a platform for experts and scholars, engineers and technicians in the field of management and software engineering to share scientific research achievements and cutting-edge technologies 2. understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote the industrialization cooperation of academic achievements 3. Promote the institutionalization and standardization of management science through modern research The conference will focus on software processing and information systems, combining research directions in the field of management. ICMSSE International Conference on Management Science and Software Engineering welcomes papers dealing with management systems research, software programming, management systems optimization, information systems management, etc. The 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022) will be held in Chongqing on July 15-17, 2022. The conference sincerely invites experts, scholars, business people and other relevant personnel from domestic and foreign universities, research institutions to participate in the exchange.
Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Download or read book Modeling Consumer Learning from Online Product Reviews written by Ying Zhao and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a structural model to study the effect of online product reviews on consumer purchases of experiential products. Such purchases are characterized by limited repeat purchase behavior of the same product item (such as a book title) but significant past usage experience with other products of the same type (such as books of the same genre). To cope with the uncertainty in quality of the product item, we posit that consumers may learn from their experience with the same type of product and others' experiences with the product item. We model the review credibility as the precision with which product reviews reflect the consumer's own product evaluation. The higher the precision, the more credible the information obtained from product reviews for the consumer, and the larger the effect of reviews on the consumer's choice probabilities. We extend the Bayesian learning framework to model consumer learning on both product quality and review credibility. We apply the model to a panel data set of 1,919 book purchases by 243 consumers. We find that consumers learn more from online reviews of book titles than from their own experience with other books of the same genre. In the counterfactual analysis, we illustrate the profit impact of product reviews and how it varies with the number of reviews. We also study the phenomenon of fake reviews. We find that fake reviews increase consumer uncertainty. The effects of more positive reviews and more numerous reviews on consumer choice are smaller on online retailing platforms that have fake product reviews.
