Delhi Policy Scam: Prescriptive Analytics - Optimal Policy Design

Design an optimal liquor excise policy using prescriptive analytics. Build a decision framework that recommends policy parameters maximizing government revenue while minimizing corruption risk.

## Background

Having completed diagnostic and predictive analyses, you are now tasked with prescriptive analytics - designing the optimal excise policy that balances revenue maximization with corruption prevention.

## The Challenge

Build a decision framework that recommends optimal liquor policy parameters:
- Margin caps that prevent windfall profits
- License eligibility thresholds that ensure qualified operators
- Revenue sharing that aligns incentives
- Oversight mechanisms that minimize corruption risk

## Your Mission

### 1. Optimization Model
Create an optimization framework:
- Objective: Maximize government revenue
- Constraints:
- Vendor profitability (minimum viable margin)
- Consumer protection (reasonable prices)
- Corruption risk minimization
- Legal/regulatory compliance

### 2. Decision Framework
Build a comprehensive framework:
- Policy parameter recommendations
- Implementation roadmap
- Monitoring mechanisms
- Enforcement protocols

### 3. Simulation & Testing
Test your recommendations:
- Simulate policy outcomes
- Stress-test under various scenarios
- Compare with actual implemented policy
- Quantify improvements

## Analytics Approach

### Phase 1: Objective Definition
- Define revenue maximization function
- Quantify corruption risk factors
- Set acceptable trade-offs
- Identify stakeholder objectives

### Phase 2: Optimization
- Linear/Non-linear programming
- Multi-objective optimization
- Constraint satisfaction
- Pareto optimal solutions

### Phase 3: Policy Design
- Margin cap recommendations
- License structure design
- Fee optimization
- Compliance mechanisms

### Phase 4: Validation
- Expert review
- Stakeholder feedback
- Sensitivity analysis
- Implementation feasibility

## Deliverables

1. Optimization Model (Code + Documentation)
- Mathematical formulation
- Solution algorithm
- Parameter sensitivity
- Validation results

2. Policy Recommendation Report
- Executive summary
- Optimal parameters
- Implementation plan
- Expected outcomes
- Risk mitigation

3. Decision Framework
- Decision trees
- Flow charts
- Monitoring protocols
- Enforcement guidelines

4. Impact Assessment
- Revenue projection
- Corruption risk reduction
- Stakeholder impact
- Comparison with status quo

## Success Criteria

- Revenue improvement over current policy
- Corruption risk reduction
- Practical implementability
- Stakeholder acceptability
- Legal compliance

Data Sources:
- Delhi excise policy documents
- Revenue optimization models
- Corruption risk indicators
- Comparative state policies
- Vendor profitability data
- Consumer price sensitivity

Optimization Parameters:
- Profit margin caps (decision variable)
- License fees (decision variable)
- Number of licenses (decision variable)
- Compliance requirements (constraints)
- Revenue targets (objectives)

Constraints:
- Legal limits on taxation
- Market viability
- Political acceptability
- Administrative capacity

Tools:
- Python (scipy.optimize, PuLP, Gurobi)
- R (optim, lpSolve)
- Excel Solver (for basic models)
- AMPL/GAMS (for complex optimization)

Prescriptive Analytics Framework:

1. Objective Function
```
Maximize: Government_Revenue
Subject to:
- Vendor_Margin ≥ Minimum_Viable
- Consumer_Price ≤ Market_Tolerance
- Corruption_Risk ≤ Acceptable_Threshold
- Legal_Compliance = 100%
```

2. Decision Variables
- Profit margin cap (%)
- License fee structure
- Number of licenses
- Compliance requirements

3. Optimization Methods
- Linear programming
- Integer programming
- Multi-objective optimization
- Genetic algorithms (if non-linear)

4. Decision Framework Components
- Parameter recommendations
- Implementation timeline
- Monitoring mechanisms
- Enforcement protocols
- Review cycles

5. Validation
- Back-testing
- Scenario analysis
- Stakeholder review
- Legal review

Mathematical Formulation:
```
Max R = f(margin_cap, license_fee, volume)
s.t.
margin_cap ≥ 8%
margin_cap ≤ 15%
vendor_ROE ≥ 12%
consumer_price_index ≤ baseline × 1.1
corruption_risk_score ≤ threshold
```

Implementation Roadmap:
1. Policy drafting
2. Stakeholder consultation
3. Legal review
4. Phased implementation
5. Monitoring & adjustment

Monitoring Mechanisms:
- Monthly revenue tracking
- Quarterly compliance audits
- Annual policy review
- Real-time price monitoring