Conspiracy of Corporate Networks in Corruption Scandals

J. R. Nicolás-Carlock & I. Luna-Pla

Front. Phys

Corruption in public procurement transforms state institutions into private entities where public resources get diverted for the benefit of a few. On this matter, much of the discussion centers on the legal fulfillment of the procurement process, while there are fewer formal analyses related to the corporate features which are most likely to signal organized crime and corruption. The lack of systematic evidence on this subject has the potential to bias our understanding of corruption, making it overly focused on the public sector. Nevertheless, corruption scandals worldwide tell of the importance of taking a better look at the misuse and abuse of corporations for corrupt purposes. In this context, the research presented here seeks to contribute to the understanding of the criminal conspiracy of companies involved in public procurement corruption scandals under a network and complexity science perspective. To that end, we make use of a unique dataset of the corporate ownership and management information of four important and recently documented cases of corruption in Mexico, where hundreds of companies were used to embezzle billions of dollars. Under a bipartite network approach, we explore the relations between companies and their personnel (shareholders, legal representatives, administrators, and commissioners) in order to characterize their static and dynamic networked structure. In terms of organized crime and using different network properties, we describe how these companies connect with each other due to the existence of shared personnel with role multiplicity, leading to very different conspiracy networks. To best quantify this behavior, we introduce a heuristic network-based conspiracy indicator that together with other network metrics describes the differences and similarities among the networks associated with each corruption case. Finally, we discuss some public policy elements that might be needed to be considered in anti-corruption efforts related to corporate organized crime.

Read the full article at: www.frontiersin.org

Simon DeDeo on How Explanations Work and Why They Sometimes Fail

You observe a phenomenon, and come up with an explanation for it. That’s true for scientists, but also for literally every person. (Why won’t my car start? I bet it’s out of gas.) But there are literally an infinite number of possible explanations for every phenomenon we observe. How do we invent ones we think are promising, and then decide between them once invented? Simon DeDeo (in collaboration with Zachary Wojtowicz) has proposed a way to connect explanatory values (“simplicity,” “fitting the data,” etc) to specific mathematical expressions in Bayesian reasoning. We talk about what makes explanations good, and how they can get out of control, leading to conspiracy theories or general crackpottery, from QAnon to flat earthers.

Listen at: www.preposterousuniverse.com

Is Green Development an Oxymoron?

Ricardo Hausmann

Decarbonization will transform global production and trade patterns so radically that new growth opportunities are bound to arise for the Global South. The goal for them should not be to stop global warming by restricting domestic emissions, but rather to carve out a role for themselves in a rapidly greening world economy.

Read the full article at: www.project-syndicate.org

Too Lazy to Read the Paper: Episode 9 with Marta Sales-Pardo and Roger Guimera

Today on the pod is Marta Sales-Pardo & Roger Guimera.
What a great talk. We could have gone on for hours. Peer review, power-laws, becoming scientists, Bayesian statistics, and much, much more.
Marta and Roger study fundamental problems in all areas of science including natural, social and economic sciences. They have expertise in a broad set of tools from statistical physics, network science, statistics and computer science.
Both were many years at Northwestern before starting a group at URV in Catalonia. They are authors of many classic papers in Network Science, lots of important work, e.g. on community detection. 
We talk about their paper “A Bayesian machine scientist to aid in the solution of challenging scientific problems”

Listen at: toolazy.buzzsprout.com

Uncovering Coordinated Networks on Social Media: Methods and Case Studies

Coordinated campaigns are used to manipulate social media platforms and influence their users, a critical challenge to the free exchange of information. Our paper introduces a general, unsupervised, network-based methodology to uncover groups of accounts that are likely coordinated. The proposed method constructs coordination networks based on arbitrary behavioral traces shared among accounts. We present five case studies of influence campaigns, four of which in the diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war, and cryptocurrency manipulation. In each of these cases, we detect networks of coordinated Twitter accounts by examining their identities, images, hashtag sequences, retweets, or temporal patterns. The proposed approach proves to be broadly applicable to uncover different kinds of coordination across information warfare scenarios.

By Diogo Pacheco, Pik-Mai Hui, Chris Torres, Bao Truong, Sandro Flammini & Fil Menczer

Read the full open-access article from the Proceedings ICWSM2021