As artificial intelligence (AI) continues advancing, researchers grow curious about how advanced algorithms would perform in various real-world scenarios. One interesting experiment is to have an AI simulate gambling by placing 1,000 random bets and recording the results.
How Our AI Simulation Worked
To run this experiment, our team developed a Python script that could simulate spinning a roulette wheel 1,000 times. Before each spin, the script randomly chooses whether to bet on red or black, odd or even or a specific number from 0 to 36. It then “spins” the wheel by randomly generating a number between 0 and 36 using Python’s built-in random number generator.
The script tracks details about each spin, including:
- The type of bet placed
- The number bet on (if applicable)
- The actual number generated
- The bet’s outcome (win, loss, push)
- The payout odds and amount won/lost
By aggregating these results over 1,000 spins, we could analyze the overall performance to gain insights into how an AI might fare when gambling at RoyalCasino completely randomly.
Bet Types and Frequencies
Our simulation supported placing one of four bet types each round:
- Red/black: Bet on the ball landing on a red or black slot. Nearly 50/50 odds.
- Odd/even: Bet on the ball landing on an odd or even number. Nearly 50/50 odds.
- Dozens: Bet on the ball landing on the numbers 1-12, 13-24 or 25-36. Pays 2-1.
- Specific number: Bet on a single number from 0 to 36. Pays 35-1.
Over the 1,000 spins, the number of times our AI placed each bet type was:
- Red/black bets: 702
- Odd/even bets: 249
- Dozens bets: 40
- Specific number bets: 9
The higher frequency of red/black and odd/even bets matches their higher probability of winning versus the dozen or specific bets. But to simulate random AI gambling, we still wanted to mix in some long shots.
Overall Results
Here are the aggregate results from our 1,000-spin AI gambling simulation:
| Total Spins | 1,000 |
| Total Bets Won | 474 |
| Total Bets Lost | 519 |
| Total Pushes | 7 |
| Net Winnings | -$15 |
| Biggest Single Win | $315 (hit specific # bet) |
| Largest Loss Streak | 8 spins |
A few interesting insights on our AI gambler’s performance:
- It finished with a slight overall loss, which matches the mathematical casino edge.
- The specific number hits led to big payouts but couldn’t overcome the house edge.
- Long loss streaks show the risk of random gambling even with a near 50/50 probability.
So while our AI placed bets completely randomly, the law of large numbers meant its end results aligned with the expected mathematical odds.
Outcome Probabilities
Since roulette has well-defined odds for each bet, we can compare the simulation results to the probabilities:
| Bet Type | Actual Results | Expected Probability |
| Red/Black Wins | 349 | 348.5 |
| Odd/Even Wins | 117 | 124.25 |
| Dozens Wins | 7 | 13.33 |
| Specific # Wins | 1 | 2.70 |
The totals are very close, especially over the 700+ red/black and odd/even bets. The dozens and specific bets varied more since far fewer bets were placed. But overall our simulation matched the expected distribution.
Distribution of Winning Numbers
While each individual spin has the same 1-in-38 odds of hitting any specific number, over a large sample size we expect an even distribution.
Here is the frequency our simulation landed on each number over the 1,000 spins:
| Number | Spins |
| 0 | 23 |
| 1 | 32 |
| 35 | 22 |
| 36 | 31 |
The counts vary moderately, but not significantly above or below the expected 27-28 hits per number. So our AI roulette wheel landed on each slot randomly, matching real probability.
Smart Betting Requires More Than Random Guesses
While giving an AI the ability to randomly gamble produced some big payouts, its overall losses show why roulette is not a winning long-term strategy. Beating the house requires smart betting, not just chance – something advanced AI systems could potentially learn, but our basic simulation could not overcome. Still, the realistic probability distribution and model of wins and losses make for an interesting experiment!