Law as Hypothesis Testing

🌈 Abstract

The article discusses the need to apply a more rigorous, scientific approach to the legislative process, treating laws as testable hypotheses that can be iteratively improved through data collection, experimentation, and evidence-based policymaking. It highlights the disconnect between the intent and execution of government programs, and proposes a framework called "law as hypothesis testing" to address this issue.

🙋 Q&A

[01] Law as Hypothesis Testing

1. What are the key reasons for the disconnect between the intent and execution of government programs?

• Lack of clear definition of the hypothesis behind a law
• Compromises and trade-offs during the legislative process that dilute the law's focus and effectiveness
• Inadequate data collection and experimentation to measure the impact of laws
• Absence of parsimony, with laws becoming encumbered with extraneous elements
• Inconsistent implementation of laws across different jurisdictions

2. What are the core elements of the "law as hypothesis testing" framework?

• Policies should articulate clear and testable hypotheses with measurable objectives
• Policies should be parsimoniously atomic, addressing target issues without unnecessary complexity
• Policies should incorporate an experimental framework for systematic data collection and analysis
• Policies should provide a clear path for iterative improvement based on empirical findings

3. How does this framework aim to improve the legislative process?

• It forces advocates and opponents to think critically about the outcomes and design the best way to prove their hypotheses
• It aligns legislators and administrators around designing the correct study rather than fixating on a policy they agree with
• It weeds out bad actors and special interests by requiring them to propose hypotheses they believe to be supportable
• It creates a shared incentive for all parties to ensure good experimental design, as the data will ultimately validate or invalidate their positions

[02] Adaptive Policy Trials

1. What is the parallel drawn between adaptive clinical trials in medicine and the proposed adaptive policy-making framework?

• Adaptive clinical trials allow for real-time adjustments to trials based on interim results, enhancing efficiency and benefiting patients
• Similarly, an adaptive policy-making framework can lead to more responsive, effective, and beneficial policy changes by allowing legislation to swiftly adapt to new findings and evolving societal needs

2. What is the role of robust data collection and analysis in this adaptive policy-making framework?

• Comprehensive data collection and sophisticated analysis provide the empirical basis for initial policy formulation and enable continuous monitoring and iterative refinement of policies
• This data infrastructure empowers policymakers to make informed decisions, validate the effectiveness of interventions, and adapt strategies proactively

3. How can the reversibility of policy decisions be incorporated into this framework?

• The concept of "two-way door" and "one-way door" decisions is introduced, where two-way door decisions are more modular and easily reversible
• For one-way door decisions, the focus should be on refining the approach to make the hypothesis more testable and iterative, such as examining a more limited hypothesis or testing through random sampling

[03] Conclusion

1. What are the key benefits of the "law as hypothesis testing" framework?

• It is scale-invariant, adaptable to policies of any scale, from local to national
• It is compatible with existing legislative processes, encouraging the incorporation of scientific rigor without overhauling the system
• It provides political cover and an escape from gridlock by centering politics on empirical measurement

2. What are the potential incentives for policymakers to adopt this framework?

• Genuine desire to improve legislative outcomes and increase flexibility and responsiveness
• Ability to double down on successful initiatives or pivot from failing ones in a rapid, data-driven manner
• Opportunity to incorporate measuring pet causes into the experimental framework without the excesses of traditional pork-barrel politics