How to build a probability-free casino

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1 How to build a probability-free casino Adam Chalcraft CCR La Jolla Chris Freiling Cal State San Bernardino Randall Dougherty CCR La Jolla Jason Teutsch Ruprecht-Karls-Universität Heidelberg January 13, 2012 Abstract Casinos operate by generating sequences of outcomes which appear unpredictable, or random, to effective gamblers. We investigate relative notions of randomness for gamblers whose wagers are restricted to a finite set. Some sequences which appear unpredictable to gamblers using wager amounts in one set permit unbounded profits for gamblers using different wager values. In particular, we show that for non-empty finite sets A and B, every A-valued random is B-valued random if and only if there exists a k 0 such that B A k. You enter a casino and there is an idiot betting on a sequence of coin flips with wagers of the following values: \$2, \$3, and \$7. He always bets on heads and cycles through these values: \$2, \$3, \$7, \$2, \$3, \$7,... You get a normal stack of values: \$1, \$5, and \$10 with which to bet. The idiot becomes rich and moves to Beverly Hills, and you move into a cardboard box. What happened? Research supported by the Deutsche Forschungsgemeinschaft grant ME 1806/3-1. 1

2 Most casinos extract money from gamblers by exploiting the law of large numbers from probability theory which says that almost surely the number of heads approaches one half the total coin flips in the limit. The title of probability-free casino indicates that we examine an alternate principle under which a casino might operate profitably. In our paradigm, a naïve idiot, as in the example above, seduces the gambler into believing he can duplicate the idiot s sure win. Our main result identifies a sequence in which betting with wager values from one set one can gain arbitrary wealth whereas gambling using another set of wager values results in guaranteed bankruptcy. A sequence is a denumerable list of coin flip outcomes, with h for heads and t for tails. In the following discussion, {h, t} denotes all finite strings of coin flips, and {h, t} ω is the class of infinite sequences of coin flips. A computable function M from {h, t} to non-negative reals which satisfies the fairness condition M(σ) = M(σh) + M(σt) 2 for all σ {h, t} is called a martingale [2]. Informally, a martingale is a gambling strategy, and the M represents the gambler s capital at each position. For any sequence X {h, t} ω, X n denotes the first n coin flips of X, and denotes the length of a string, size of a set, or absolute value of a real number. A martingale M succeeds on X {h, t} ω if M achieves arbitrarily large sums of money over X, that is, lim sup n M(X n) = 1. Otherwise X defeats M. For a martingale M and σ {h, t}, M(σh) M(σ) is called the wager at σ. If the wager at σ is positive, then M predicts heads at σ; if the wager at σ is negative, then M predicts tails at σ. Given a set V of reals, we say that a martingale is V -valued if for all σ the wager of M at σ belongs to V, unless M does not have enough capital in which case the wager at σ is 0. Definition (Bienvenu, Stephan, Teutsch [1]). A sequence X {h, t} ω is V -valued random if no V -valued martingale succeeds on X. 1 Equivalently lim n M(X n) = since we only consider martingales whose nonzero wagers are bounded by some ɛ > 0. If some martingale succeeded lim sup but not lim, then there is some least interval [kɛ, (k + 1)ɛ), k a nonnegative integer, which the martingale s capital visits infinitely often, and after all but finitely many such visits the next positive wager of the martingale must be correct. Thus some other martingale can succeed lim by agreeing with the successful lim sup martingale at these positions of minimal capital. 2

3 For A R, let A k denote the set {x k : x A}. The purpose of this article is to prove the following theorem which answers a question posed by Bienvenu, Stephan, and Teutsch [1]. Theorem. Let A and B be non-empty finite sets of computable real numbers. Then every A-valued random is B-valued random if and only if there exists a k 0 such that B A k. Proof. Assume that B A k for some k 0, and suppose some B-valued martingale S succeeds on a sequence X. Let M be a martingale which bets k times whatever S bets at each position (and starts with k times S s capital). Then M s capital equals k times S s capital everywhere, so M succeeds on X. Conversely, suppose that there is no k such that B A k. We shall exhibit a B-valued martingale S and a sequence X such that S succeeds on X but no A-valued martingale succeeds on X. The martingale S represents the capital of a stooge who works in collusion with the casino X. M 0, M 1, M 2,... is a list of the A-valued martingales, or customers, who bet on the casino s coin flips alongside the stooge. We shall build our casino so as to destroy all customers while allowing the stooge to win arbitrarily large amounts of money. Our casino sequence will be noncomputable since its construction depends on access to a list of all A-valued martingales. The overall heuristic is that each customer must try to copy the bets of the casino s stooge, otherwise the casino can just outright destroy the customer while simultaneously helping the stooge to win. But by the hypothesis that B A k, no customer can exactly copy the stooge s bets over an interval of positions containing all possible wagers for the stooge. In particular, any customer must overbet or underbet somewhere along this interval, and then the casino can exploit the customer s misstep. Assume B = {b 1, b 2,..., b n }. We fix S s strategy now so as to be independent of the choice of X: S(σh) = S(σ) + b i where σ i mod n, unless S(σ) < b i in which case S(σh) = S(σ). In other words, S simply cycles through all possible wager values in B. The initial capital of S is chosen to be at least max i b i. It suffices to build X so as to meet for all e the following requirements: R e : Ruin M e (or reach a stage where M e stops betting). S e : The stooge s capital reaches \$e at some position. 3

4 If all requirements are satisfied, then all M e s are obliterated and S succeeds. Our method for satisfying these requirements is based on the following heuristics: M e always tries to copy S s bets. Otherwise X can hurt M e without harming S s capital. Once S is sufficiently richer than M e, X just hurts both of them until M e dies. The order of priority for satisfying the requirements above is: S 0 > R 0 > S 1 > R 1 > S 2 > R 2 > Higher priority requirements may injure lower priority R e requirements that have already been satisfied, but any particular requirement will only be injured finitely often. Basic module for R e. Our method to defeat a single customer is as follows. We build finite extensions whose limit is X. We shall assume that at the current position the customer and the stooge always both bet on heads (or both bet on tails), since the strategy for the casino is otherwise straightforward: help the stooge while hurting the customer. Every B positions, we compute the ratio k of the stooge s capital to the customer s capital. At those positions where k times the magnitude of the customer s wager exceeds the magnitude of the stooge s wager, we choose a value for X which hurts both the customer and stooge, and otherwise we help them both. Intuitively, the casino hurts the customer if he bets riskier than the stooge; otherwise the casino helps the stooge get ahead. By assumption the customer cannot match any multiple of the stooge s B wager values, hence B consecutive positions will suffice to commit a slight increase in the ratio of stooge-to-customer capital. We shall show that the sequence of k s is strictly increasing and tends to infinity, at least until the customer runs out of money. The latter condition must eventually occur because, due to the assumption that wagers are restricted to a finite set of values, for sufficiently large k the casino always hurts the customer. We now give the full construction. Assume that X has been defined at all positions less than p. We describe how to make the next proper extension of X. Let e be the least index such that either: 4

5 1. S s capital has not reached \$e at any position before p, or 2. M e makes a non-zero wager at position p. If e satisfies the first case, then we set X to agree with S s wager at position p, and we say that requirement S e acts at this position. Otherwise M e made a non-zero wager at position p, and we say that e is the index which receives attention for the next B positions (unless some higher priority requirement interrupts). In this case, we try to satisfy R e. In order to ensure that the ratio of capitals between S and M e looks no less favorable to S than the last time M e made a non-zero wager, we introduce the following function 2 : { e-savings(x p) = max S(X u + 1) : ( j < e) [u was the latest u<p position < p for which M j made a non-zero wager on X u] } {0}. For each index j with priority higher than e, we look at the position of the most recent bet of M j and define e-savings to be the maximum capital of the stooge over all such positions. The casino will prevent the stooge s capital from ever dropping below e-savings so long as R j does not force this to happen for some j < e. The idea is that no requirement with priority less than R e can harm X s progress in defeating M e. Now define the ratio: k(p) = S(X p) e-savings(x p). M e (X p) For each u with p u < p + B where M e makes a nonzero wager, define the predicate [ ] S s wager at X u P (u) = k(p) >. M e s wager at X u P (u) holds when M e commits a greater fraction of capital (relative to position p) than S does. We now enter a loop which lasts for up to B positions. We define p according to the loop, and then successively p + 1, p + 2,..., p + B 1 in the same way unless for some j < e the martingale M j makes a non-zero wager at one of these positions. In this case, the loop breaks and instead j 2 For the first reading of this proof, consider the simplified problem where we diagonalize against only a single strategy M 0. In this case we can just take e-savings to be the constant zero function. 5

6 receives attention: we define a new function k and predicate P based on j and the current position and initiate another length B loop analogously. Eventually one of these loops will complete B repetitions, and at this point we will have defined our next extension of X. For the remainder of the algorithm description, we shall assume that the loop below lasts for the full B repetitions. Assume that X has been defined for all positions less than u. Now define X(u) according to the first of the following cases which is satisfied: S s prediction at u if M e bets opposite from S at u or M e bets 0, X(u) = (1) M e s prediction at u if P (u) holds, M e s prediction at u if P (u) fails, so that M e gets hurt iff she wagers a greater percentage of capital than S (and matches S s prediction) or makes the opposite prediction of S. This completes the construction of X. We now verify that X defeats every A-valued martingale. position u at which M e receives attention and k(p) > At every max{ b : b B} min{ a : a A}, (2) M e must lose money by (1). Thus it suffices to show that k(p) exceeds this bound (2) for all sufficiently large p. Let us examine what happens to k when M e receives attention at position p and the loop lasts for the full n = B repetitions. That is, we assume no higher priority requirement receives attention during these n steps. Let s 1, s 2,..., s n be the dollars won (possibly negative) by S at positions p, p + 1,..., p + n 1, and let m 1, m 2,..., m n be the dollars won by M e at these positions. Let q be the next position at which M e receives attention. For clarity in the calculation (3), let s = s 1 + s s n and m = m 1 + m m n. If we further assume that p is sufficiently large that after position p no index less than e receives attention and no S j with j < e ever acts, then e-savings does not change between p and q and thus k(q) [M e (X p) + m] = [S(X p) e-savings(x p)] + s = k(p) M e (X p) + s = [k(p) M e (X p) + k(p) m] + [ k(p) m + s], 6

7 and so Let c = B max{ a : a A}. Then k(q) = k(p) + k(p) m + s M e (X p) + m. (3) M e (X p) + m M e (X p) + c. (4) In particular, the denominator in (3) increases by at most a constant each time M e receives attention. Now let us inspect the numerator in (3). Define the non-negative constant [ ] ɛ = inf k>0 max min k a j b i i j where the a j s are the elements of A. Recall that B A k by assumption, so by continuity ɛ is nonzero. We claim: k(p) m i s i for all i n, and k(p) m i s i ɛ for at least one such i. We obtain from this claim the following bound for the numerator: k(p) m = k(p) n m i ɛ + i=1 n s i = s ɛ. (5) Combining our observations on the numerator and denominator (4),(5) with (3), we see that ɛ k(q) k(p) + M e (X p) + c. (6) The first part of the claim follows by inspecting each of the cases in (1). If M e and S predict opposite outcomes or M e bets 0, the result is immediate because X hurts M e while helping S at the same time. If P (p+i) fails, then X helps both S and M e at this position and in particular k(p) m i s i. If on the other hand P (p+i) is true, then X hurts both S and M e at this position, and in particular k(p) m i s i because m i and s i are both negative. It remains to establish the second part of the claim. Appealing to our original hypothesis, we find that B is not a subset of A k(p). Since S cycles through all possible bets for B over the n positions p, p + 1,..., p + n 1, there exists some r n such that k(p) m r s r ɛ. Let p 0, p 1, p 2,... be the positions where M e receives attention, with p 0 large enough that no lesser index than e ever requires attention again and i=1 7

8 S j has been satisfied for all j < e. These positions exist by induction on the following argument. As noted in (4), M e (X p i+1 ) M e (X p i ) + c. So by induction on (6) and divergence of the harmonic series we have: k(p r ) k(p 0 ) + r i=1 It follows that either: ɛ M e (X p 0 ) + c i r+1 ɛ max{m e (X p 0 ), c} 1 i. i=2 For all sufficiently large r, k(p r ) satisfies the desired inequality (2), or else M e stops betting at some stage. Either way, M e does not succeed on X. Since each M j (j e) eventually stops betting, there is some stage at at which point S e becomes satisfied (if it was not already) because the algorithm chooses an index to focus on according to case 1. Hence S succeeds. The fact that there is no effective enumeration of M 0, M 1, M 2,... prevents the sequence X from being computable. In a real-world online environment, however, a casino may force gamblers to place their wagers within a fixed number of seconds. In this online case, the analogous diagonal sequence X depends only on a list of time-bounded martingales, which can be effectively enumerated, and therefore X is computable. Question. Does the theorem above generalize to the case where A and B are infinite? As a particular instance, does betting with multiples of some minimum value, i.e. the set of integers, have as much power as gambling with nonzero wagers merely bounded away from zero, i.e. {x Q : x 1} [1]? References [1] Laurent Bienvenu, Frank Stephan, and Jason Teutsch. How powerful are integer-valued martingales? In Fernando Ferreira, Benedikt Löwe, Elvira Mayordomo, and Luís Mendes Gomes, editors, Programs, Proofs, Processes (CiE 2010), volume 6158 of Lecture Notes in Computer Science, pages Springer-Verlag, Berlin, Heidelberg, [2] Rodney G. Downey and Denis R. Hirschfeldt. Algorithmic randomness and complexity. Theory and Applications of Computability. Springer, New York,

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