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1. The Asymmetry of Embeddedness: Illegal Trade Networks and Drug Purchasing Diversity on an Online Illegal Drug Market

   https://doi.org/10.1093/sf/soad134  

   Duxbury, Scott_W; Haynie, Dana_L (October 2023, Social Forces)

   Abstract While economic sociology research and theory argue that excessive network embeddedness depresses competition in illegal markets, prior research does not examine how distinct types of embeddedness may have asymmetric effects on the diversity of purchasing behavior—the range of illegal goods that buyers typically purchase. This study considers how network embeddedness can positively or negatively affect drug purchasing diversity in online drug markets by referring buyers to new vendors or “locking” buyers into recurrent trade for the same products. We analyze novel network data on 16,847 illegal drug exchanges between 7205 actors on one online illegal drug market. Consistent with hypothesized network asymmetry, buyers are more likely to purchase a new type of drug when the transaction is part of an indirect network referral. Although histories of exchange increase the overall frequency of drug purchasing, they are associated with decreases in new drug-type purchases. In the aggregate, these processes either contribute to an integrated market where buyers purchase multiple drugs from multiple vendors (in the case of referrals) or a fragmented market characterized by recurrent trade from the same vendors for the same substances (in the case of repeated trade). We discuss the implications of these findings for research on embeddedness, illegal markets, risky exchange, and drug policy. 

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2. Network embeddedness in illegal online markets: endogenous sources of prices and profit in anonymous criminal drug trade

   https://doi.org/10.1093/ser/mwab027  

   Duxbury, Scott W; Haynie, Dana L (January 2023, Socio-Economic Review)

   Abstract Although economic sociology emphasizes the role of social networks for shaping economic action, little research has examined how network governance structures affect prices in the unregulated and high-risk social context of online criminal trade. We consider how overembeddedness—a state of excessive interconnectedness among market actors—arises from endogenous trade relations to shape prices in illegal online markets with aggregate consequences for short-term gross illegal revenue. Drawing on transaction-level data on 16 847 illegal drug transactions over 14 months of trade in a ‘darknet’ drug market, we assess how repeated exchanges and closure in buyer–vendor trade networks nonlinearly influence prices and short-term gross revenue from illegal drug trade. Using a series of panel models, we find that increases in closure and repeated exchange raise prices until a threshold is reached upon which prices and gross monthly revenue begin to decline as networks become overembedded. Findings provide insight into the network determinants of prices and gross monthly revenue in illegal online drug trade and illustrate how network structure shapes prices in criminal markets, even in anonymous trade environments. 

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3. Open Secrecy: How Police Crackdowns and Creative Problem-Solving Brought Illegal Markets out of the Shadows

   https://doi.org/10.1093/sf/soz140  

   Ladegaard, Isak (March 2020, Social Forces)

   null (Ed.)

   Abstract Can organized illegal activities grow stronger and more advanced in response to legal pressure? In October 2013, the FBI shut down Silk Road, a thriving e-commerce market for illegal drugs. After the shock, market actors adopted a new identity verification method that enabled mass-migration to other markets, and created websites for information distribution that reduced post-shock uncertainties. The outcome was a decentralized market in which actors could operate in “open secrecy” across multiple websites. With verifiable pseudonyms and securely obfuscated real-world identities, actors could publicly discuss, plan, and participate in illegal activities. Threats from police and opportunistic criminals persisted but were no longer crippling concerns as buyers and sellers could reasonably expect that their exchange partners would be available for future business; the illegal market could operate more like a legal one. Drawing on quantitative and qualitative data, the author argues that advances in information technology have expanded the opportunity structure for cooperation and creative problem-solving in the underworld, and therefore that shocks did not hinder but rather stimulate development in digital drug markets. Data, collected in 2013–2017, include nearly one million transactions from three illicit e-commerce markets, three million messages from eight discussion forums, and website traffic from two market-independent websites. 

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4. Protecting Data Markets from Strategic Buyers

   https://doi.org/10.1145/3514221.3517855  

   Raul Castro Fernandez (January 2023, Proceedings ACMSIGMOD International Conference on Management of Data)

   The growing adoption of data analytics platforms and machine learning-based solutions for decision-makers creates a significant demand for datasets, which explains the appearance of data markets. In a well-functioning data market, sellers share data in exchange for money, and buyers pay for datasets that help them solve problems. The market raises sufficient money to compensate sellers and incentivize them to keep sharing datasets. This low-friction matching of sellers and buyers distributes the value of data among participants. But designing online data markets is challenging because they must account for the strategic behavior of participants. In this paper, we introduce techniques to protect data markets from strategic participants, even when the asset traded is data. We combine those techniques into a pricing algorithm specifically designed to trade data. The evaluation includes a user study and extensive simulations. Together, the evaluation demonstrates how participants strategize and the effectiveness of our techniques. 

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5. Optimizing Relevance and Diversity in Online Matching Markets: A Time-Adaptive Attenuation Approach

   https://doi.org/10.1613/jair.1.16635  

   Xu, Evan Yifan; Xu, Pan (June 2025, Journal of Artificial Intelligence Research)

   Real-world online matching markets (OMMs) often involve multiple objectives, such as maximizing relevance and diversity in online recommendation and crowdsourcing systems. In this paper, we propose a generic bi-objective maximization model for OMMs with the following features: (1) there are two types of agents—offline and online—with online agents arriving dynamically and stochastically; (2) upon each online agent’s arrival, an immediate and irrevocable decision must be made regarding which subset of relevant offline agents to assign; and (3) each offline and online agent has a specific matching capacity, i.e., an upper bound on the number of allowable matchings. Our model supports two general linear objective functions defined over all possible assignments to online agents. We formulate a bi-objective linear program (LP) and design an LP-based parameterized algorithm. Departing from prevalent non-adaptive attenuation methods, we introduce a time-adaptive attenuation framework that achieves an almost tight competitive ratio for each objective. To complement our theoretical analysis, we implement the proposed algorithm and evaluate it against several heuristics using two real-world datasets. Extensive experimental results demonstrate the flexibility and effectiveness of our approach, validating our theoretical predictions. 

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