Shadow markets and hierarchies: comparing and modeling networks in the Dark Net

Abstract

This paper analyzes the determinants of network structure, as measured by hierarchy and monopolization, by examining various black market networks. We examine structures of networks on the Internet Dark Net (Virtual) and compare it to network structures of traditional black markets (Ground), using agent-based modeling. The purpose of modeling these two different types of illicit markets is to understand the network structure that emerges from the interactions of the agents in each environment. Traditional black markets are relatively hierarchical, with high degree and high betweenness. We compare the density and average length of the shortest path of the simulated Ground black market networks with our simulated Virtual network. We find that hierarchy and monopolization tendencies in networks are products of different transaction costs and information asymmetries. The Internet is an effective way to lower multiple aspects of network structure. We observe that the network structure surrounding the interactions in the Virtual black market is less hierarchical and slightly more monopolistic than the network structure of the Ground market.

Hybrid Governance in Online Drug Distribution

“…Buyers can peruse sellers and choose the one with the best price, product, and more prior transactions (Van Hout & Bingham, 2014). Hardy and Norgaard (2016, p. 19) consequently described cryptomarkets as “an untaxed and unregulated marketplace; that exists as a completely unfettered free-market.” They concluded that the price mechanism stabilizes cryptomarkets because it “allow sellers to make more money and sellers are incentivized to provide quality service to their customers so that they increase their reputation and thus, make higher profits.” In a subsequent study, Norgaard and colleagues (2018, p. 878) characterized cryptomarkets as an “organization with different levels of authority where groups are ranked in order of authority and central control,” that is, some vendors exert more authority than others do.…”

Give us a little social credit: to design or to discover personal ratings in the era of Big Data

“…More recently, distributed and decentralized online interactions, which exhibit the informational asymmetry of anonymity, have given rise to many new kinds of seals of approval like product and provider star-ratings, seller feedback, and mutual provider/user ratings in the sharing economy. Distributed and decentralized ledger technology, exemplified by the blockchain and related applications like smart contracts, is greatly reducing the cost of enforcing contracts in large and anonymous markets (Norgaard et al, 2018).…”

“…Mutual ratings in the sharing economy and star ratings for traditional products and services alert users to untrustworthy producers, unsafe business practices, and other salient characteristics, albeit imperfectly. Deep Web entrepreneurs of dark e-commerce sites are solving the problem of extreme social extendedness and anonymity by employing blockchain-ledger smart contracts that vet and punish defectors on either side of a sales contract, essentially symmetrizing risk under the conditions of extreme information asymmetry (Norgaard et al, 2018).…”

Shedding Light on the Dark: The Impact of Legal Enforcement on Darknet Transactions

“…For instance, Dolliver and Kenney (2016) examines the characteristics of drug vendors and contrast them across different darknet sites. Norgaard et al (2018) studied the network structure of darknet markets, and find that their interaction networks structure is less hierarchical and slightly more monopolistic than that of the traditional black market. In a related vein of work, Broséus et al (2017) examined trafficking flow of illicit products across countries transacted on darknet markets.…”