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WDLIC Examples

Comprehensive examples demonstrating WDLIC's capabilities across different domains.

Psychology Examples

Example 1: Decision Making Under Uncertainty

Input:

A person decides whether to speak up in a meeting despite fear of judgment

What WDLIC generates:

  • Psychological variables: confidence, fear_of_judgment, desire_to_contribute
  • Decision model: motivation vs. inhibition threshold
  • Outcome updates: confidence adjustment based on success/failure

Run it:

python main.py "A person decides whether to speak up in a meeting despite fear of judgment"

Example 2: Social Validation

Input:

Someone gains confidence after receiving positive feedback from peers

Generated code includes:

  • Confidence tracking over time
  • Feedback processing mechanism
  • Self-esteem updates based on validation

Physics Examples

Example 1: Projectile Motion

Input:

A ball is thrown at 25 m/s at a 60 degree angle

What WDLIC generates:

  • Kinematic equations for projectile motion
  • Time-stepped simulation
  • Calculation of max height, range, and flight time
  • Energy conservation checks

Run it:

python main.py "A ball is thrown at 25 m/s at a 60 degree angle" --category physics

Example 2: Free Fall

Input:

An object falls from 100 meters under gravity

Generated code:

  • Velocity and position updates
  • Acceleration due to gravity (9.81 m/s²)
  • Impact velocity calculation

Mathematics Examples

Example 1: Function Optimization

Input:

Find the minimum of f(x) = x² - 4x + 3

What WDLIC generates:

  • Symbolic differentiation using SymPy
  • Critical point calculation
  • Second derivative test for classification
  • Numerical evaluation

Run it:

python main.py "Find the minimum of a quadratic function" --category math

Example 2: Probability

Input:

Calculate the probability of getting exactly 6 heads in 10 coin flips

Generated code:

  • Binomial probability formula
  • Combination calculation
  • Monte Carlo simulation option

Social Dynamics Examples

Example 1: Opinion Convergence

Input:

Five people with different opinions discuss and influence each other

What WDLIC generates:

  • Agent-based social network model
  • Conformity and peer influence parameters
  • Opinion update dynamics
  • Consensus detection

Run it:

python main.py "Group members influence each other's opinions" --category social

Example 2: Information Spread

Input:

A rumor spreads through a social network with varying trust levels

Generated code:

  • Network propagation model
  • Trust-weighted influence
  • Cascade detection

Mixed Domain Examples

Example 1: Psychology + Social

Input:

A shy person gradually builds confidence through repeated social interactions

What WDLIC generates:

  • Combines psychological state tracking
  • Social feedback loops
  • Confidence evolution over multiple interactions

Example 2: Physics + Optimization

Input:

Find the optimal angle to maximize the range of a projectile

What WDLIC generates:

  • Physics simulation for different angles
  • Optimization loop to find maximum
  • Trade-off analysis

Advanced Examples

Example 1: Multi-Agent Interaction

Input:

Three agents compete for resources while trying to cooperate

Generated features:

  • Game-theoretic decision making
  • Cooperation vs. competition dynamics
  • Nash equilibrium approximation

Example 2: Time-Series Evolution

Input:

A population's average belief changes over time due to media influence

Generated features:

  • Temporal dynamics
  • External influence modeling
  • Equilibrium analysis

Business/Optimization Examples

Example 1: Profit Maximization

Input:

Maximize profit given production costs and market demand constraints

What WDLIC generates:

  • Objective function definition
  • Constraint modeling
  • Optimization using calculus or numerical methods

Example 2: Resource Allocation

Input:

Allocate limited budget across multiple projects to maximize ROI

Generated code:

  • Portfolio optimization
  • Risk-return tradeoff
  • Constraint satisfaction

Using Generated Code

All generated Python code is executable. Here's how to use it:

1. Save and Run Directly

# Generate and save
python main.py "Your scenario" --format python > my_simulation.py

# Run it
python my_simulation.py

2. Modify and Experiment

# The generated code is well-structured and documented
# You can easily modify parameters:

if __name__ == "__main__":
    # Original
    agent = PsychologicalAgent("Person")
    
    # Your modification
    agent = PsychologicalAgent("Alice")
    agent.confidence = 0.9  # Start with high confidence
    agent.anxiety = 0.2     # Lower anxiety

3. Integrate into Projects

# Import generated classes
from my_simulation import PhysicsSimulator

# Use in your code
sim = PhysicsSimulator()
result = sim.projectile_trajectory(v0=30, angle_deg=45)

Format Options

Get Only Pseudo-Code

python main.py "Your scenario" --format pseudo

Shows conceptual logic without implementation details.

Get Only Python Code

python main.py "Your scenario" --format python

Shows only executable code, no IR or pseudo-code.

Get Everything (Default)

python main.py "Your scenario" --format all

Shows category, assumptions, IR preview, pseudo-code, and Python code.

Category Override

Force a specific interpretation:

# Force psychology interpretation
python main.py "Calculate optimal strategy" --category psychology

# Force physics interpretation  
python main.py "Person moves through space" --category physics

# Force math interpretation
python main.py "Find the solution" --category math

Tips for Best Results

  1. Be Specific: Include numbers, relationships, and context

    • Good: "A ball thrown at 20 m/s at 45 degrees"
    • Less good: "Something moves"

  2. Use Domain Keywords: Help the router identify category

    • Psychology: "confidence", "fear", "desire", "motivation"
    • Physics: "velocity", "force", "energy", "acceleration"
    • Math: "optimize", "calculate", "derive", "prove"

  3. Describe Dynamics: Mention how things change

    • "Person gains confidence over time"
    • "Velocity increases due to acceleration"
    • "Opinions converge through discussion"

  4. Set Context: Explain the scenario's purpose

    • "In a job interview, someone decides whether to negotiate"
    • "To win the game, a player must optimize their strategy"

Combining with Other Tools

With Data Science

import pandas as pd
from my_simulation import SocialNetwork

# Run multiple simulations
results = []
for trial in range(100):
    net = SocialNetwork(n_agents=10)
    net.simulate(iterations=20)
    results.append(net.get_final_variance())

df = pd.DataFrame(results)
df.describe()

With Visualization

import matplotlib.pyplot as plt
from my_simulation import PhysicsSimulator

sim = PhysicsSimulator()
positions, velocities, time = sim.projectile_trajectory(v0=25, angle_deg=45)

plt.plot(positions[:, 0], positions[:, 1])
plt.xlabel("Distance (m)")
plt.ylabel("Height (m)")
plt.title("Projectile Trajectory")
plt.show()

Educational Use

WDLIC is great for:

  1. Teaching computational thinking

    • Shows how natural language maps to logic
    • Demonstrates domain-specific modeling

  2. Learning by experimentation

    • Modify generated code to see effects
    • Compare different scenarios

  3. Rapid prototyping

    • Quickly test ideas in code
    • Iterate on models without starting from scratch

More Examples to Try

# Psychology
python main.py "Overcoming procrastination through small wins"
python main.py "Building trust in a new relationship"
python main.py "Managing anxiety before a presentation"

# Physics
python main.py "A pendulum swings with decreasing amplitude"
python main.py "Two objects collide elastically"
python main.py "Circular motion with centripetal force"

# Mathematics
python main.py "Solve the equation x² - 5x + 6 = 0"
python main.py "Find the area under a curve"
python main.py "Calculate compound interest over 10 years"

# Social
python main.py "Innovation spreads through a community"
python main.py "Establishing group norms through interaction"
python main.py "Resolving conflict through compromise"

# Business
python main.py "Pricing strategy for maximum revenue"
python main.py "Balancing risk and return in investments"
python main.py "Supply chain optimization"

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