Browsing by Subject "Collusion"

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    A Computational Approach to Identify Covertness and Collusion in Social Networks
    (2020-10) Mohanty, Pronab
    Mathematical and computational interventions in the field of social networks have a fairly recent history. Social networks analysis exists at the intersection of several fields, including social sciences, psychology, organizational behavior, business studies, mathematics, physics, and biology. Studies were often manually facilitated in the last century as the social networks’ sizes were typically small. But, the recent emergence of the internet, the world wide web, big data, and numerous platforms of social media have triggered a period of intense academic activities in this field, which is also true in the field of criminology where advances in social network analytics have engendered a flourishing sub-culture that has influenced enforcement techniques spawning new fields such as predictive policing, investigation techniques specifically based on network analytics, and even studies of criminal behavior patterns. Interest in studying criminal and terrorist networks, generally called covert networks, has peaked after recent attacks by terror organizations. There is a felt necessity of presaging criminal or covert activities well before they erupt into public consciousness. However, recent research has been reactive rather than proactive and has essentially focused on analyzing illegal networks unearthed, and the accent is on disrupting such networks. Relatively little focus has centered on the question of why some networks are termed covert or, indeed, if covertness is innate to all networks., which further leads to the related issues of identifying metrics to measure the characteristics that typify covertness and to detect the presence of covert communities in social networks leveraging the metric so developed. A further challenge is an increasing emphasis on privacy rights, data protection measures, and exponential growth in encryption measures, which has placed a ceiling limit on the information obtained on communications. Added to this aspect is the vast volumes of data that need to be processed, requiring commensurately vast use of computational resources, often with very little time. These aspects have been comprehensively addressed by the dissertation, which has used the ENRON email corpus to identify the employees who had been connected with the financial fraud in some manner. The research seeks to identify covertness within networks without any intrusive analysis or content-based measures, which is necessary given the increasing legal and policy constraints based around privacy, encryption, and general exclusion of personal data from the public domain, and also by reducing the size of the problem. The dissertation also develops specific metrics to define covertness in communications among network entities and defines a separate metric to identify covert entities' clusters with common aims. In the process of defining metrics, the dissertation also seeks to solve the problem of resource-constraints common in law-enforcement agencies by reducing the volume of information to be processed.
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    Cutthroat or cartel? an analysis of price competition in farmers markets.
    (2012-05) Horwich, Jeffrey Lloyd
    While the civic and nutritional implications of farmers markets have captured researchers' attention, few have focused on how the "markets" in farmers markets actually work. This paper opens a crucial but largely unexplored field of economic inquiry: how are the prices consumers pay at the farmers market determined? An original dataset of farmers market prices, gathered across five cities over a full calendar year, allows a quantitative look at two specific questions: first, how do prices move as more vendors enter and compete to sell a product? Second, what relation do farmers market prices have to prices in conventional grocery outlets? Using a set of simple regressions and a novel meta-analysis technique, I find meaningful and statistically significant relationships between vendor numbers and price for some products, but not for others. More perishable products seem to display the effect much more powerfully, a result which agrees with theory on search costs and product differentiation. Another important finding is that even where median prices do not decline with vendor count, minimum prices often do, suggesting the diligent consumer can benefit. I also find evidence of price collusion in some markets and products. Finally, I find no discernible, consistent relationship between farmers market prices and supermarket prices. In addition to better informing consumers, these results suggest that policy-makers who wish to expand farmers markets as an option for the general public - and especially lower-income shoppers - have some options for fostering a more competitive environment. But even at the farmers market there is no free lunch, as there are likely trade-offs between consumer welfare and economic rents we may value for local agriculture.