## The paradox

A casino offers a game of chance for a single player in which a fair coin is tossed at each stage. The initial stake begins at 2 dollars and is doubled every time heads appears. The first time tails appears, the game ends and the player wins whatever is in the pot. Thus the player wins 2 dollars if tails appears on the first toss, 4 dollars if heads appears on the first toss and tails on the second, 8 dollars if heads appears on the first two tosses and tails on the third, and so on. Mathematically, the player wins {\displaystyle 2^{k}} dollars, where {\displaystyle k} is a positive integer equal to the number of tosses. What would be a fair price to pay the casino for entering the game?

To answer this, one needs to consider what would be the average payout: with probability 1/2, the player wins 2 dollars; with probability 1/4 the player wins 4 dollars; with probability 1/8 the player wins 8 dollars, and so on. The expected value is thus

Assuming the game can continue as long as the coin toss results in heads and in particular that the casino has unlimited resources, this sum grows without bound, and so the expected win for repeated play is an infinite amount of money. Considering nothing but the expected value of the net change in one’s monetary wealth, one should therefore play the game at any price if offered the opportunity. Yet, in published descriptions of the game, many people expressed disbelief in the result. Martin Robert quotes Ian Hacking as saying “few of us would pay even $25 to enter such a game” and says most commentators would agree.^{[4]} The paradox is the discrepancy between what people seem willing to pay to enter the game and the infinite expected value.

## Solutions

Several approaches have been proposed for solving the paradox.

### Expected utility theory

The classical resolution of the paradox involved the explicit introduction of a utility function, an expected utility hypothesis, and the presumption of diminishing marginal utility of money.

In Daniel Bernoulli’s own words:

The determination of the value of an item must not be based on the price, but rather on the utility it yields … There is no doubt that a gain of one thousand ducats is more significant to the pauper than to a rich man though both gain the same amount.

A common utility model, suggested by Bernoulli himself, is the logarithmic function *U*(*w*) = ln(*w*) (known as *log utility*). It is a function of the gambler’s total wealth *w*, and the concept of diminishing marginal utility of money is built into it. The expected utility hypothesis posits that a utility function exists the sign of whose expected net change from accepting the gamble is a good criterion for real people’s behavior. For each possible event, the change in utility ln(wealth after the event) − ln(wealth before the event) will be weighted by the probability of that event occurring. Let *c* be the cost charged to enter the game. The expected incremental utility of the lottery now converges to a finite value:

This formula gives an implicit relationship between the gambler’s wealth and how much he should be willing to pay to pay (specifically, any *c* that gives a positive change in expected utility). For example, with natural log utility, a millionaire ($1,000,000) should be willing to pay up to $20.88, a person with $1,000 should pay up to $10.95, a person with $2 should borrow $1.35 and pay up to $3.35.

Before Daniel Bernoulli published, in 1728, a mathematician from Geneva, Gabriel Cramer, had already found parts of this idea (also motivated by the St. Petersburg Paradox) in stating that

the mathematicians estimate money in proportion to its quantity, and men of good sense in proportion to the usage that they may make of it.

He demonstrated in a letter to Nicolas Bernoulli^{[5]} that a square root function describing the diminishing marginal benefit of gains can resolve the problem. However, unlike Daniel Bernoulli, he did not consider the total wealth of a person, but only the gain by the lottery.

This solution by Cramer and Bernoulli, however, is not completely satisfying, as the lottery can easily be changed in a way such that the paradox reappears. To this aim, we just need to change the game so that it gives even more rapidly increasing payoffs. For any unbounded utility function, one can find a lottery that allows for a variant of the St. Petersburg paradox, as was first pointed out by Menger (Menger 1934).

Recently, expected utility theory has been extended to arrive at more behavioral decision models. In some of these new theories, as in cumulative prospect theory, the St. Petersburg paradox again appears in certain cases, even when the utility function is concave, but not if it is bounded (Rieger & Wang 2006).

### Probability weighting

Nicolas Bernoulli himself proposed an alternative idea for solving the paradox. He conjectured that people will neglect unlikely events (de Montmort 1713). Since in the St. Petersburg lottery only unlikely events yield the high prizes that lead to an infinite expected value, this could resolve the paradox. The idea of probability weighting resurfaced much later in the work on prospect theory by Daniel Kahneman and Amos Tversky.

Cumulative prospect theory is one popular generalization of expected utility theory that can predict many behavioral regularities (Tversky & Kahneman 1992). However, the overweighting of small probability events introduced in cumulative prospect theory may restore the St. Petersburg paradox. Cumulative prospect theory avoids the St. Petersburg paradox only when the power coefficient of the utility function is lower than the power coefficient of the probability weighting function (Blavatskyy 2005). Intuitively, the utility function must not simply be concave, but it must be concave relative to the probability weighting function to avoid the St. Petersburg paradox. One can argue that the formulas for the prospect theory are obtained in the region of less than $400 (Tversky & Kahneman 1992). This is not applicable for infinitely increasing sums in the St. Petersburg paradox.

### Rejection of mathematical expectation

Various authors, including Jean le Rond d’Alembert and John Maynard Keynes, have rejected maximization of expectation (even of utility) as a proper rule of conduct. Keynes, in particular, insisted that the *relative risk*^{[clarification needed]} of an alternative could be sufficiently high to reject it even if its expectation were enormous.^{[citation needed]} Recently, some researchers have suggested to replace the expected value by the median as the fair value.^{[6]}^{[7]}

## Recent discussions

Although this paradox is three centuries old, new arguments are still being introduced.

### Feller

A solution involving sampling was offered by William Feller.^{[8]} Intuitively Feller’s answer is “to perform this game with a large number of people and calculate the expected value from the sample extraction”. In this method, when the games of infinite number of times are possible, the expected value will be infinity, and in the case of finite, the expected value will be a much smaller value.

### Samuelson

Samuelson resolves the paradox by arguing that, even if an entity had infinite resources, the game would never be offered. If the lottery represents an infinite expected gain to the player, then it also represents an infinite expected loss to the host. No one could be observed paying to play the game because it would never be offered. As Paul Samuelson describes the argument:

Paul will never be willing to give as much as Peter will demand for such a contract; and hence the indicated activity will take place at the equilibrium level of zero intensity.

— Samuelson (1960)

## Further discussions

### Marginal utility and philosophical view

The St. Petersburg paradox and the theory of marginal utility have been highly disputed in the past. For a discussion from the point of view of a philosopher, see Martin (2004).

### Heuristic parameters and risks

Recently some authors suggested using heuristic parameters ^{[9]} (e.g. assessing the possible gains without neglecting the risks of the Saint Petersburg lottery) because of the highly stochastic context of this game (Cappiello 2016). The expected output should therefore be assessed in the limited period where we can likely make our choices and, besides the non-ergodic features (Peters 2011a), considering some inappropriate consequences we could attribute to the expected value (Feller 1968).

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