Lesson
The theory โ Gambler's Ruin
Gambler's ruin is the problem of a player who starts with a chips and bets on independent trials, each won with probability p, until she has nothing or reaches a target N. With one chip a bet, the chance of reaching N is (1 โ ra)/(1 โ rN) with r = q/p, or a/N when p = ยฝ, and the expected number of bets is a/(q โ p) โ (N/(q โ p))ยทP(reach N), or a(N โ a) when p = ยฝ. Whatever the strategy, the expected loss is the edge q โ p times the expected total staked.
What each symbol means
a- the chips you bring
N- the target; your opponent holds N โ a
p- the chance of winning one even-money bet, with q = 1 โ p
T- the number of bets the evening lasts
S- the total staked over the evening, each bet's stake counted once
- Assumes
- Every bet pays even money and is independent of the last; a losing bet loses the whole stake, zero included; stakes are whole chips; and the player stops only at 0 or at N.
Problem solved in full
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One in a million 4 steps
Betting one chip at a time on red, how many chips must you bring before doubling them becomes a one-in-a-million chance? Press Double 100, one chip a bet first: the page gives the chance for 100 chips.
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From the page, for 100 chips on a European wheel:
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When the target is twice the start, N = 2a, the fraction simplifies, because 1 โ r2a = (1 โ ra)(1 + ra). For 100 chips r100 = 222.90:
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Set it equal to one in a million and solve for a:
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Chips come whole, so check the two either side:
Answer
256 chips on a European wheel. With 255 the chance is 1.03 in a million, with 256 it is 0.975 in a million. On an American wheel r = 20/18 and the same steps give 131.1, so 132 chips. Bold play doubles 48.65% of the time on a European wheel and 47.37% on an American one, at any of these sizes; set 256 and 512 on the page and move the stake to see both.
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Learning path
Fair games that ruin you
References (3)
- The gambler's ruin problem and its formula for the chance of ruin, the limit against an "infinitely rich" adversary (ruin with probability tending to 1 in a fair game, which the first insight quotes), and de Moivre's 1711 derivation by giving the counters nominal values (q/p)^k so that the unfair game becomes fair, which the third insight retells: C. M. Grinstead and J. L. Snell, Introduction to Probability, 2nd edition, American Mathematical Society; the CHANCE Project version dated 4 July 2006, freely redistributable under the GNU Free Documentation License. Section 12.2, Gambler's Ruin, pp. 486โ490.
- Timid play (a constant bet until ruin or the target) with its probability of winning f(x) = ((q/p)^x โ 1)/((q/p)^a โ 1), or x/a when p = 1/2, and its expected number of games g(x) = x/(q โ p) โ (a/(q โ p)) f(x), or x(a โ x); that larger constant bets help in an unfair game and make no difference in a fair one; bold play's functional equation F(x) = pF(2x) or p + qF(2x โ 1), its binary-digit sum, the table F(k/8) and G(k/8), and that for p = 1/2 bold play reaches the target with probability x/a, as timid play does. The page's formulas are these, and check:compute holds its bold-play solver to the binary-digit sum and to the table: K. Siegrist, Random: Probability, Mathematical Statistics, Stochastic Processes (randomservices.org). Chapter 12, Games of Chance: ยง9 Red and Black, ยง10 Timid Play, ยง11 Bold Play.
- Bold play is optimal when the trials are unfair (p โค 1/2), proved through the sufficient condition pV(x + y) + qV(x โ y) โค V(x); bold play is not the only optimal strategy; and the chapter's own statement that its exposition 'is based primarily on' Dubins and Savage. The second insight cites both through this page, and check:compute applies the same condition to every whole-chip bet in the page's table: K. Siegrist, Random: Probability, Mathematical Statistics, Stochastic Processes (randomservices.org). Chapter 12, Games of Chance, ยง12 Optimal Strategies; the chapter is based on L. E. Dubins and L. J. Savage, Inequalities for Stochastic Processes (How to Gamble If You Must), 1965.