Parrondo's Paradox Calculator

Explore how two losing games can combine to create a winning strategy

Simulate Parrondo's Paradox

Choose the game-playing strategy

Games played in each simulation run

Number of independent simulation runs

Initial capital amount

Game Rules

Game A (Losing Game)

• Simple coin flip with slightly unfavorable odds

• Probability of winning: 49.5%

• Win: +$1, Lose: -$1

• Expected value per game: -$0.01 (losing)

Game B (Also Losing Game)

• Two sub-games based on your current capital

Game B1 (when capital is multiple of 3):

→ Probability of winning: 9.5% (very unfavorable)

Game B2 (when capital is not multiple of 3):

→ Probability of winning: 74.5% (very favorable)

• Overall expected value: -$0.009 (losing)

The Paradox

• Combining these two losing games can create a winning strategy!

• Strategies like alternating (ABAB) or AABB patterns result in positive expected value

• The key is that mixing games allows you to spend more time in favorable states

• Even random selection between the two games produces a winning outcome

Strategy Guide

Game A Only

Always losing (-0.5% per game)

Game B Only

Always losing (-0.9% per game)

Alternating (AB)

Winning strategy! (~+1% per game)

AABB Pattern

Winning strategy! (~+0.5% per game)

Random Selection

Winning strategy! (~+0.8% per game)

Game Probabilities

49.5%
Game A Win Rate
9.5%
Game B1 Win Rate
(capital ÷ 3 = 0)
74.5%
Game B2 Win Rate
(capital ÷ 3 ≠ 0)

Real-World Applications

Population dynamics in biology

Financial market strategies

Game theory research

Ratchet mechanisms in physics

Understanding Parrondo's Paradox

What is Parrondo's Paradox?

Parrondo's Paradox demonstrates the counterintuitive result that two losing games can combine to produce a winning game. This paradox was discovered by Spanish physicist Juan Parrondo while studying Brownian ratchets in physics.

Why Does It Work?

  • Game B has state-dependent probabilities based on your capital
  • Mixing strategies allows more time in favorable states
  • The correlation between games creates positive expected value

Mathematical Foundation

Markov Chain Analysis

The system can be modeled using states based on capital mod 3:

State 0: Capital ÷ 3 = 0 (play B1)

State 1: Capital ÷ 3 = 1 (play B2)

State 2: Capital ÷ 3 = 2 (play B2)

Steady State Probabilities

π₀ = 0.384 (unfavorable state)

π₁ = 0.154 (favorable state)

π₂ = 0.462 (favorable state)

Key Insights

Not Applicable to Casinos

Real casino games are designed to be independent, preventing this paradox from working

Requires Correlation

The games must be related through some external parameter (like current capital)

Broader Applications

Found in biology, economics, and physics where systems can switch between states