We show that a two-sided matching platform can successfully compete by limiting the number of choices it offers to its customers, while charging higher prices than platforms with unrestricted choice. We develop a stylized model of online dating where agents with different outside options match based on how much they like each other. Starting from these micro-foundations, we derive the strength and direction of indirect network effects, and show that increasing the number of potential matches has a positive effect due to larger choice, but also a negative effect due to competition between agents on the same side. Agents resolve the trade-off between these competing effects differently, depending on their outside options. For agents with high outside options, the choice effect is stronger than the competition effect, leading them to prefer an unrestricted-choice platform. The opposite is the case for agents with low outside options, who then have higher willingness to pay for a platform restricting choice, as it also restricts the choice set of their potential matches. Moreover, since only agents with low outside options self-select into the restricted choice platform, the competition effect is mitigated further. This allows multiple platforms offering different number of choices to coexist without the market tipping.
Motivated by the growing practice of using social network data in credit scoring, this study analyzes the impact of using network based measures on customer score accuracy and on tie formation among customers. We develop a series of models to compare the accuracy of customer scores obtained with and without network data. We also investigate how the accuracy of social network based scores changes when individuals can strategically construct their social networks to attain higher credit scores. We find that, if individuals are motivated to improve their scores, they may form fewer ties and focus them on more similar partners. The impact of such endogenous tie formation on the accuracy of consumer credit scores is ambiguous. Scores can become more accurate as a result of modifications in social networks, but this accuracy improvement may come with greater network fragmentation. The threat of social exclusion in such endogenously formed networks provides incentives to low type members to exert effort that improves everyone's creditworthiness. We discuss implications for both managers and public policy.
Abstract-Recently, large amount of data is widely available in information systems and data mining has attracted a big attention to researchers to turn such data into useful knowledge. This implies the existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing knowledge and useful pattern. Therefore, researchers need relevant data from huge records using feature selection methods. Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this study, a comparison between filter based feature selection methods based on a well-known dataset (i.e., hepatitis dataset) was carried out and four classification algorithms were used to evaluate the performance of the algorithms. Among the algorithms, Naï ve Bayes and Decision I. INTRODUCTIONRecently, thanks to innovations of computer and information technologies, huge amounts of data can be obtained and stored in both scientific and business transactions. This amount of data implies low quality, unreliable, redundant and noisy data to observe useful patterns [1]. Therefore, researchers need relevant and highquality data from huge records using feature selection methods.Feature selection methods reduce the dimensionality of feature space, remove redundant, irrelevant or noisy data. It brings the immediate effects for application: speeding up a data mining algorithm, improving the data quality and the performance of data mining and increasing the comprehensibility of the mining results [2].In this study, the great interest of hepatitis disease was considered which is a serious health problem in the world and a comparative analysis of several filter based selection algorithms was carried out based on the performance of four classification algorithms for the prediction of disease risks [3]. The main aim of this study is to make contributions in the prediction of hepatitis disease for medical research and introduce a detailed and comprehensive comparison of Manuscript received November 5, 2014; revised February 20, 2015. Pinar Yildirim is with the Okan University, Istanbul, Turkey (e-mail: pinar.yildirim@ okan.edu.tr). popular filter based feature selection methods. II. FEATURE SELECTION METHODSSeveral feature selection methods have been introduced in the machine learning domain. The main aim of these techniques is to remove irrelevant or redundant features from the dataset. Feature selection methods have two categories: wrapper and filter. The wrapper evaluates and selects attributes based on accuracy estimates by the target learning algorithm. Using a certain learning algorithm, wrapper basically searches the feature space by omitting some features and testing the impact of feature omission on the prediction metrics. The feature that make significant difference in learning process implies it does matter and should be considered as a high quality feature. On the other hand, filter uses the general characteristics of data itself and work separatel...
Motivated by the growing practice of using social network data in credit scoring, we analyze the impact of using network-based measures on customer score accuracy and on tie formation among customers. We develop a series of models to compare the accuracy of customer scores obtained with and without network data. We also investigate how the accuracy of social network-based scores changes when consumers can strategically construct their social networks to attain higher scores. We find that those who are motivated to improve their scores may form fewer ties and focus more on similar partners. The impact of such endogenous tie formation on the accuracy of consumer score is ambiguous. Scores can become more accurate as a result of modifications in social networks, but this accuracy improvement may come with greater network fragmentation. The threat of social exclusion in such endogenously formed networks provides incentives to low-type members to exert effort that improves everyone's creditworthiness. We discuss implications for managers and public policy.
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