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OACRA | Ch. 6 — Consequence Maps: Radical Transparency of Legislative Trade-offs
If every law is a choice about who to benefit and who to sacrifice, why do legislatures keep voting without knowing what they are choosing?
Between 2010 and 2012, Spain passed two successive labor reforms that promised to simultaneously reduce youth unemployment, protect the rights of permanent workers, and improve business competitiveness. By 2013, youth unemployment had climbed past 55% and job precariousness stayed above 25%. The three goals were incompatible under the macroeconomic conditions of the time, but the opacity of the legislative process let the contradiction slip through unnoticed until the damage was already done.
A Consequence Map would have shown, before each vote, that the three objectives formed a trilemma: two could advance together, but not all three. Lawmakers would have been forced to choose explicitly which one to prioritize. Citizens would have known what was being chosen on their behalf.
This chapter builds the full architecture of Consequence Maps — the third generation of regulatory evaluation:
1. Institutional genealogy: from the OECD's Regulatory Impact Assessments (1997) to the EU's Integrated Impact Assessments (2002) to the OACRA Maps.
2. The principle of no forced reconciliation: when models disagree, the Map presents both projections without artificial convergence.
3. The Aggregate Uncertainty Index (AUI): quantifies the degree of agreement or disagreement between models without hiding divergence.
4. Five analytical components: distribution of impacts by group, temporal trajectory, axiological tensions, implementation risks, and inter-legal coherence.
5. Layered architecture: Layer 1 for citizens (one page), Layer 2 for specialized legislators (five to ten pages), Layer 3 as a technical appendix for auditors and researchers.
The extended illustrative case —a proposed 50% increase to the minimum wage— shows how the four models of the Parliament produce divergent evaluations that the Map displays without artificial resolution, forcing lawmakers to choose openly between protecting employed workers and protecting potentially unemployed ones.
The chapter is also candid about the limits of the instrument: bounded predictive capacity, infrastructural bias in the underlying data, the risk of flawed assumptions, the performative effect of projections, and the excess-transparency dilemma Bernstein (2012) documented —where exposing every political concession could erode the legislative cooperation democracy depends on.
As Tufte (2001) put it: integrity in quantitative presentation requires showing uncertainty, not hiding it under a figure with the appearance of precision. The Map does not predict the future. It reveals the present that legislators prefer to keep hidden: that to legislate is, inevitably, to decide what kind of society we want to be.
—
🔹 OACRA — Algorithmic Office for Enhanced Regulatory Quality
Jesús Bernal Allende | Escuela del Deber-Optimizar y la Soberanía de la Evidencia
🌐 https://edo-os.com 🔗 https://www.linkedin.com/in/jesus-bernal-allende-030b2795
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By Jesús Bernal AllendeIf every law is a choice about who to benefit and who to sacrifice, why do legislatures keep voting without knowing what they are choosing?
Between 2010 and 2012, Spain passed two successive labor reforms that promised to simultaneously reduce youth unemployment, protect the rights of permanent workers, and improve business competitiveness. By 2013, youth unemployment had climbed past 55% and job precariousness stayed above 25%. The three goals were incompatible under the macroeconomic conditions of the time, but the opacity of the legislative process let the contradiction slip through unnoticed until the damage was already done.
A Consequence Map would have shown, before each vote, that the three objectives formed a trilemma: two could advance together, but not all three. Lawmakers would have been forced to choose explicitly which one to prioritize. Citizens would have known what was being chosen on their behalf.
This chapter builds the full architecture of Consequence Maps — the third generation of regulatory evaluation:
1. Institutional genealogy: from the OECD's Regulatory Impact Assessments (1997) to the EU's Integrated Impact Assessments (2002) to the OACRA Maps.
2. The principle of no forced reconciliation: when models disagree, the Map presents both projections without artificial convergence.
3. The Aggregate Uncertainty Index (AUI): quantifies the degree of agreement or disagreement between models without hiding divergence.
4. Five analytical components: distribution of impacts by group, temporal trajectory, axiological tensions, implementation risks, and inter-legal coherence.
5. Layered architecture: Layer 1 for citizens (one page), Layer 2 for specialized legislators (five to ten pages), Layer 3 as a technical appendix for auditors and researchers.
The extended illustrative case —a proposed 50% increase to the minimum wage— shows how the four models of the Parliament produce divergent evaluations that the Map displays without artificial resolution, forcing lawmakers to choose openly between protecting employed workers and protecting potentially unemployed ones.
The chapter is also candid about the limits of the instrument: bounded predictive capacity, infrastructural bias in the underlying data, the risk of flawed assumptions, the performative effect of projections, and the excess-transparency dilemma Bernstein (2012) documented —where exposing every political concession could erode the legislative cooperation democracy depends on.
As Tufte (2001) put it: integrity in quantitative presentation requires showing uncertainty, not hiding it under a figure with the appearance of precision. The Map does not predict the future. It reveals the present that legislators prefer to keep hidden: that to legislate is, inevitably, to decide what kind of society we want to be.
—
🔹 OACRA — Algorithmic Office for Enhanced Regulatory Quality
Jesús Bernal Allende | Escuela del Deber-Optimizar y la Soberanía de la Evidencia
🌐 https://edo-os.com 🔗 https://www.linkedin.com/in/jesus-bernal-allende-030b2795