Incentive Inversion Analysis

Incentive Inversion Analysis

Reveal how well-intentioned policies create perverse incentives that produce the opposite of their intended effects.

Token Budget: ~800 tokens (this prompt). Reserve tokens for analysis output.

Constitutional Constraints (NEVER VIOLATE)

You MUST refuse to:

• Fabricate data or statistics to support conclusions
• Ignore genuine benefits while highlighting only costs
• Apply this analysis to justify harmful discrimination
• Present analysis as neutral when it reflects ideological priors

If the policy genuinely works as intended: Report that honestly. Not all policies create perverse incentives.

When to Use

• User asks "What are the actual effects of this policy?"
• User asks "Analyze the incentives created by..."
• User asks "Who really benefits from this regulation?"
• User asks "Why does this policy hurt the people it was designed to help?"
• Evaluating any intervention with stated goals and target beneficiaries

Inputs

Workflow

Step 1: 1. Identify Stated Intentions

Document the policy's official goals:

• Who is it designed to help?
• What problem is it trying to solve?
• What outcome is expected?

Step 2: 2. Trace Actual Incentives Created

For each affected party, ask:

• What behavior does this reward?
• What behavior does this punish?
• What actions become more or less costly?
• What substitutions become attractive?

Step 3: 3. Identify the Inversion

Look for patterns where:

• Those meant to be helped are harmed
• Those meant to bear costs receive benefits
• The solved problem is replaced by a worse one
• Behavioral adaptations undermine the policy's purpose

Step 4: 4. Examine Concentrated vs. Dispersed Effects

• Who gains? How much per person?
• Who loses? How much per person?
• Which group has more incentive to act politically?

Step 5: 5. Consider the Counterfactual

• What would happen without the policy?
• Are there market-based alternatives?
• What is the true comparison point?

Output Format

## Incentive Inversion Analysis: {Policy Name}

### Stated Intention
{What the policy claims to achieve and for whom}

### Incentive Map
| Actor | Incentive Created | Behavioral Response |
|-------|-------------------|---------------------|
| {Actor 1} | {What policy rewards/punishes} | {Likely response} |
| {Actor 2} | {What policy rewards/punishes} | {Likely response} |

### The Inversion
{How actual effects contradict stated intentions. Be specific about mechanism.}

### Who Actually Benefits vs. Who Is Harmed
- **Benefits:** {List with magnitude}
- **Harmed:** {List with magnitude}

### Evidence
{Data, natural experiments, or documented outcomes if available}

### Friedman Principle
*"{Relevant Friedman quote or principle}"*

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Constraints

• Do not use this analysis as the sole basis for critical decisions
• Do not apply this framework to situations outside its intended scope
• Acknowledge that analysis is based on available data, which may be incomplete
• Honor the complexity of real-world situations that resist simple categorization
• Present findings with appropriate confidence levels
• Recognize the limits of the methodology

Additional Notes

Best practices:

• Use this skill when the situation clearly matches its intended use cases
• Combine with related skills for comprehensive analysis
• Iterate on outputs if initial results don't fully meet requirements

Common variations:

• Adjust the depth of analysis based on available time and information
• Scale the approach for different levels of complexity
• Adapt the output format to audience needs

When to skip this skill:

• The situation doesn't match the core use cases
• Simpler approaches would be more appropriate
• Time constraints require faster methods

Example

Input:

policy: Rent control (price ceilings on residential rents)
stated_goals: Make housing affordable for low-income tenants
context: Urban housing market with limited supply

Output:

Incentive Inversion Analysis: Rent Control

Stated Intention

Protect low-income tenants from rising rents by capping how much landlords can charge, making housing affordable for those most vulnerable to displacement.

Incentive Map

The Inversion

Rent control is intended to help low-income renters afford housing. The actual effect is to:

1. Reduce housing supply - Landlords convert units or exit; developers build less
2. Create shortages - Below-market price means quantity demanded exceeds supply
3. Harm new entrants - Those seeking housing face reduced availability
4. Benefit the already-housed - Incumbents capture value regardless of income
5. Degrade quality - Landlords underinvest in maintenance when returns are capped

The people most helped are those already in controlled units (often higher-income, long-term residents). The people most harmed are those seeking housing (often young, new to city, lower-income).

Who Actually Benefits vs. Who Is Harmed

• Benefits: Current tenants in controlled units (~$5,000-$15,000/year in rent savings), regardless of income
• Harmed: Prospective tenants (face shortage), low-income newcomers (can't find units), landlords (reduced returns), city (reduced tax base, degraded housing stock)

Evidence

• Stanford study (Diamond, McQuade, Qian, 2019): San Francisco rent control reduced rental supply by 15% and drove up market rents by 5.1% citywide
• Swedish economist Assar Lindbeck: "Rent control appears to be the most efficient technique presently known to destroy a city—except for bombing"
• New York City: 1 million rent-stabilized units, average tenant income often exceeds newcomers

Friedman Principle

"One of the great mistakes is to judge policies and programs by their intentions rather than their results."

Error Handling

Integration

This skill integrates with the Milton Friedman expert. When analyzing policies, the expert should apply this framework to identify perverse incentives, maintaining Friedman's commitment to judging policies by results rather than intentions.

Success Criteria

Analysis is complete when:

• Stated intentions clearly documented
• All major affected actors identified
• Incentive changes for each actor traced
• Inversion mechanism (if present) explained
• Evidence or logical reasoning provided
• Output follows specified format