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Il Padrino’s dilemma: a simple model of Mafia decision making

2018· article· en· W2795517714 on OpenAlexaff
Ronald Wintrobe

Bibliographic record

VenueJournal of Public Finance and Public Choice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsDistrustDilemmaBusinessLaw and economicsReputationComputer securityLawEconomicsPolitical science

Abstract

fetched live from OpenAlex

The major threat to security in the 21st century appears to come from ‘ungoverned spaces’ – areas where governmental control is weak or contested by clans, gangs or terrorist organizations. In a situation where trust in government is weak, the Mafia provides the service of enforcing transactions and protection. The paper builds a model to explain why, even though the Mafia appears to fulfill a valuable function, namely to provide contract enforcement when state support for that is weak, it is nevertheless so destructive. The reason, I suggest, is not simply that the Mafia is a private supplier of a public good (trust) but that it also supplies a public bad (distrust). The Mafia supplies trust by enforcing contracts but it also has to prevent that trust from spreading to those who do not pay. To stop this, the Mafia also supplies distrust. So the Mafia leader always has a choice between two ways of making profits: to supply trust, or to supply distrust. This is Il Padrino’s dilemma. The next and crucial point is that trust tends to get undervalued and distrust overvalued by Mafia leaders. Among the reasons for this are that trust is fragile, while distrust is durable, combined with the peculiar ‘ownership’ structure of the Mafia, which is that the reputation of the organization cannot be capitalized and sold. So the Mafia leader oversupplies distrust and neglects the damages this does to the society in which it operates. This distortion in Il Padrino’s thinking—the undervaluation of trust and overvaluation of distrust relative to their social values—partly explains why the Mafia is so destructive, and why areas where their control is strong tend to remain poor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.330
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2018
Admission routes1
Has abstractyes

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