The Algorithmic Bureaucrat
Dismantling Clientelist Networks in Public Procurement Through Agent-Based Modeling
Bibliographic Data
| ID | 24219012 |
|---|---|
| Authors | Aboud Barsekh-Onji (0009-0004-5440-8092, Instituto Politécnico Nacional Escuela Superior de Comercio y Administración, Santo Tomás), Edgar Oliver Cardoso Espinosa (0000-0001-7588-9439, Instituto Politécnico Nacional Escuela Superior de Comercio y Administración, Santo Tomás) |
| Year | 2026 |
| Volume | 6 |
| Issue | 3 |
| Publication date | 2026-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Urban Governance (JOURNAL) |
| Journal identifiers | ISSN: 2664-3286 • E-ISSN: 2667-3800 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/26643286261493873 |
| OpenAlex | W7214885638 |
| Language | EN |
| References cited | 37 |
The transition from e-Government to Intelligent Governance promises to dismantle systemic corruption by replacing human discretion with algorithmic objectivity, yet empirical validation remains scarce given data opacity. Using Agent-Based Modeling (ABM) calibrated with confidential micro-data from a Mexican public agency, this quasi-experimental study simulates the structural impact of Artificial Intelligence (AI) on clientelist networks. The framework integrates rational choice with behavioral psychology (moral disengagement, Theory of Planned Behavior, inequity aversion), providing a conservative lower bound. We compare three regimes: a discretionary Status Quo, an ex-post AI Auditor, and an ex-ante AI Meritocracy. Under AI Meritocracy, the Herfindahl-Hirschman Index (HHI) collapsed from 1,752 (oligopolistic capture) to 218 (competitive market), dissolving the cartel without legal intervention; cost overruns fell from 19.24% to 10.69% and bribery from 95% to near-zero. Outcomes are invariant to the distribution of bureaucratic integrity: algorithmic allocation removes the human decision point that integrity governs. A sensitivity analysis with up to 20% “gaming” agents (suppliers inflating quality signals to manipulate the score) confirms deconcentration persists (HHI below 275 in all 150 runs) while efficiency erodes moderately (overruns reach 13.3%), making quality verification the binding design constraint. A bounded-memory learning check (Erev–Roth reinforcement with forgetting) leaves structural results unchanged but shows surveillance deterrence decaying as agents forget sanctions. These findings substantiate the “Automated Bureaucratic Model”: the most effective anti-corruption strategy is not surveillance but algorithmic removal of the discretionary market for favors. Findings are Mexico-calibrated; endogenous gaming coordination, cultural moderators, and psychological reactance remain priority extensions.
algorithmic decision-making · artificial intelligence in auditing · artificial intelligence in governance · bribery · Clientelism · Corruption · cost overruns · E-Government · Herfindahl-Hirschman index · public procurement · Auction Theory and Applications · E-Government and Public Services · Public Procurement and Policy
Artificial Intelligence and the Public Sector—Applications and Challenges
Creating public value through smart technologies and strategies
Experience-weighted Attraction Learning in Normal Form Games
The theory of planned behavior
Controlling Corruption
How AI assistance enhances work adaptability in the public sector
Hybrid intelligence for the public sector
A Behavioral Model of Rational Choice
Advancing smart public administration
Clientelism from the Client’s Perspective
Local Governments Facing Turbulence
A Public Management for All Seasons
A Theory of Fairness, Competition, and Cooperation
| Highly cited | No |
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