St. Petersburg paradox

In economics, the St. Petersburg paradox is a paradox related to probability theory and decision theory. It is based on a particular (theoretical) lottery game (sometimes called "St. Petersburg Lottery") that leads to a random variable with infinite expected value, i.e. infinite expected payoff, but would nevertheless be considered to be worth only a very small amount of money. The St. Petersburg paradox is a classical situation where a naïve decision criterion (which takes only the expected value into account) would recommend a course of action that no (real) rational person would be willing to take. The paradox can be resolved when the decision model is refined via the notion of marginal utility, by taking into account the finite resources of the participants, or by noting that one simply cannot buy that which is not sold (and that sellers would not produce a lottery whose expected loss to them were unacceptable).

The paradox is named from Daniel Bernoulli's presentation of the problem and his solution, published in 1738 in the "Commentaries of the Imperial Academy of Science of Saint Petersburg" ref_harvard|Bern|Bernoulli 1738|none. However, the problem was invented by Daniel's cousin Nicolas Bernoulli who first stated it in a letter to Pierre Raymond de Montmort of 9 September 1713. [http://www.cs.xu.edu/math/Sources/Montmort/stpetersburg.pdf#search=%22Nicolas%20Bernoulli%22]

Paradox

In a game of chance, you pay a fixed fee to enter, and then a fair coin will be tossed repeatedly until a tail first appears, ending the game. The pot starts at 1 dollar and is doubled every time a head appears. You win whatever is in the pot after the game ends. Thus you win 1 dollar if a tail appears on the first toss, 2 dollars if on the second, 4 dollars if on the third, 8 dollars if on the fourth, etc. In short, you win 2^"k"−1 dollars if the coin is tossed "k" times until the first tail appears.

What would be a fair price to pay for entering the game? To answer this we need to consider what would be the average payout: With probability 1/2, you win 1 dollar; with probability 1/4 you win 2 dollars; with probability 1/8 you win 4 dollars etc. The expected value is thus

:$E=frac\{1\}\{2\}cdot\; 1+frac\{1\}\{4\}cdot\; 2\; +\; frac\{1\}\{8\}cdot\; 4\; +\; frac\{1\}\{16\}cdot\; 8\; +\; cdots$

::$=frac\{1\}\{2\}\; +\; frac\{1\}\{2\}\; +\; frac\{1\}\{2\}\; +\; frac\{1\}\{2\}\; +\; cdots$

::$=sum\_\{k=1\}^infty\; \{1\; over\; 2\}=infty\; ,.$

This sum diverges to infinity, and so the expected win for the player of this game, at least in its idealized form, in which the casino has unlimited resources, is an infinite amount of money. This means that the player should almost surely come out ahead in the long run, no matter how much they pay to enter; while a large payoff comes along very rarely, when it eventually does it will typically far more than repay however much money they have already paid to play. According to the usual treatment of deciding when it is advantageous and therefore rational to play, you should therefore play the game at any price if offered the opportunity. Yet, in published descriptions of the paradox, e.g. ref_harvard|Martin|Martin, 2004|none, many people expressed disbelief in the result.Martin quotes Ian Hacking as saying "few of us would pay even $25 to enter such a game" and says most commentators would agree.

Solutions of the paradox

There are different approaches 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” [http://www.econterms.com/glossary.cgi?query=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. By the expected utility hypothesis, expected utilities can be calculated the same way expected values are. 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 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 play (specifically, any "c" that gives a positive expected utility). For example, with log utility a millionaire should be willing to pay up to $10.94, a person with $1000 should pay up to $5.94, a person with $2 should pay up to $2, and a person with $0.60 should borrow $0.87 and pay up to $1.47.

Before Daniel Bernoulli published, in 1728, another Swiss mathematician, 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 [http://www.cs.xu.edu/math/Sources/Montmort/stpetersburg.pdf#search=%22Nicolas%20Bernoulli%22] 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.

The solution by Cramer and Bernoulli, however, is not yet completely satisfying, since 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 the (even larger) payoff $e^\{2^k\}$. Again, the game should be worth an infinite amount. More generally, one can find a lottery that allows for a variant of the St. Petersburg paradox for every unbounded utility function, as was first pointed out by ref_harvard|Menger|Menger, 1934|none.

There are basically two ways of solving this generalized paradox, which is sometimes called the "Super St. Petersburg paradox":

*We can take into account that a casino would only offer lotteries with a finite expected value. Under this restriction, it has been proved that the St. Petersburg paradox disappears as long as the utility function is concave, which translates into the assumption that people are (at least for high stakes) risk averse [Compare ref_harvard|Arrow|Arrow, 1974|none] .

*We can assume that the utility function has an upper bound. This does not mean that the utility function needs to be constant after some point. For example the function $u(x)=x/(x+1)$ is bounded above by 1, yet strictly increasing.

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 ref_harvard|Rieger|Rieger and Wang, 2006|none.

Probability weighting

Nicolas Bernoulli himself proposed an alternative idea for solving the paradox. He conjectured that people will neglect unlikely events [http://www.cs.xu.edu/math/Sources/Montmort/stpetersburg.pdf#search=%22Nicolas%20Bernoulli%22] . 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. However, their experiments indicated that, very much to the contrary, people tend to overweight small probability events. Therefore the proposed solution by Nicolas Bernoulli is nowadays not considered to be satisfactory.

One can't buy what isn't sold

Some economists resolve 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. ref_harvard|Samuelson|Samuelson, 1960|none

Finite St. Petersburg lotteries

The classical St. Petersburg lottery assumes that the casino has infinite resources. This assumption is often criticized as unrealistic, particularly in connection with the paradox, which involves the reactions of ordinary people to the lottery. Of course, the resources of an actual casino (or any other potential backer of the lottery) are finite. More importantly, the expected value of the lottery only grows logarithmically with the resources of the casino. As a result, the expected value of the lottery, even when played against a casino with the largest resources realistically conceivable, is quite modest. If the total resources (or total maximum jackpot) of the casino are "W" dollars, then "L" = 1 + floor(log₂("W")) is the maximum number of times the casino can play before it no longer covers the next bet. The expected value "E" of the lottery then becomes:::

The following table shows the expected value "E" of the game with various potential backers and their bankroll "W" (with the assumption that if you win more than the bankroll you will be paid what the bank has):

Notes:The estimated net worth of Bill Gates is from Forbes. The GDP data are as estimated for 2007 by the International Monetary Fund, where one trillion dollars equals $10¹² (one million times a million dollars. A “googolaire” is a hypothetical person worth a googol dollars ($10¹⁰⁰).

A rational person might not find the lottery worth even the modest amounts in the above table, suggesting that the naive decision model of the expected return causes essentially the same problems as for the infinite lottery. However, even so the possible discrepancy between theory and reality is far less dramatic.

The assumption of infinite resources can produce other apparent paradoxes in economics. See martingale (roulette system) and gambler's ruin.

Iterated St. Petersburg lottery

Players may assign a higher value to the game when the lottery is repeatedly played. This can be seen by simulating a typical series of lotteries and accumulating the returns; compare the illustration (right).

If the expected payout from playing the game once is "E₁", the expected average per-game payout from playing the game "n" times is:

:$E\_n\; =\; E\_1\; +\; frac\{1\}\{2\}\; left(log\_2\; n\; ight),.$

Since "E₁" is infinite, "E_n" is infinite as well. Nevertheless, expressing "E_n" in this way shows that "n", the number of times the game is played, makes a "finite contribution" to the average per-game payout. The actual average per-game payout obtained in a series of "n" games is unlikely to fall short of this finite contribution by a significant amount.

To see where the (1/2) log₂ "n" contribution comes from, consider the case of "n" = 1,024. On average:

* 512 games will pay $1
* 256 games will pay $2
* 128 games will pay $4
* 64 games will pay $8
* 32 games will pay $16
* 16 games will pay $32
* 8 games will pay $64
* 4 games will pay $128
* 2 games will pay $256
* 1 game will pay $512
* (1/2) game will pay $1,024; (1/4) game will pay $2,048, etc. Equivalently: "1 game will pay out 1,024 x E₁"

The collective average payout is therefore $5,120 + 1,024 x "E₁", and the per-game average payout is:

:$frac\{\$\; ext\{5,120\}\; +\; ext\{1,024\}\; cdot\; E\_1\}\{\; ext\{1,024\; =\; \$5\; +\; E\_1\; ,.$

Because the finite contribution from "n" games is proportional to log₂ "n", doubling the number of games played leads to a $0.50 increase in the finite contribution. For example, if 2,048 games are played, the finite contribution is $5.50 rather than $5.

It follows that, in order to be reasonably confident of achieving target average per-game winnings of approximately "W" (where "W" > $1), we should play approximately 4^"W" games. This will yield a finite contribution equal to "W". Unfortunately, the number of games required to be confident of meeting even modest targets is astronomically high. $7 requires approximately 16,000 games, $10 requires approximately 1 million games, and $20 requires approximately 1 trillion games.

Further discussions

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 ref_harvard|Martin|Martin, 2004|none.

See also

*Exponential growth
*Gambler's ruin
*Martingale (roulette system)

References

Works cited

note_label|Arrow|Arrow 1974|nonecite journal
last = Arrow
first = Kenneth J.
authorlink = Kenneth Arrow
year = 1974
month = February
title = The use of unbounded utility functions in expected-utility maximization: Response
journal = Quarterly Journal of Economics
volume = 88
issue = 1
pages = 136–138
id = Handle: RePEc:tpr:qjecon:v:88:y:1974:i:1:p:136-38
url = http://ideas.repec.org/a/tpr/qjecon/v88y1974i1p136-38.html
format = PDF
doi = 10.2307/1881800

note_label|Bern|Bernoulli 1738|nonecite journal
last = Bernoulli
first = Daniel
authorlink = Daniel Bernoulli
coauthors = Originally published in 1738; translated by Dr. Lousie Sommer.
year = 1954
month = January
title = Exposition of a New Theory on the Measurement of Risk
journal = Econometrica
volume = 22
issue = 1
pages = 22–36
url = http://www.math.fau.edu/richman/Ideas/daniel.htm
accessdate = 2006-05-30
doi = 10.2307/1909829

note_label|Martin|Martin, 2004|nonecite encyclopedia
last = Martin
first = Robert
editor = Edward N. Zalta
encyclopedia = The Stanford Encyclopedia of Philosophy
title = The St. Petersburg Paradox
url = http://plato.stanford.edu/archives/fall2004/entries/paradox-stpetersburg/
accessdate = 2006-05-30
edition = Fall 2004 Edition
date = July 26, 2004
publisher = Stanford University
location = Stanford, California
id = ISSN|1095-5054

note_label|Menger| [Menger, 1934] |nonecite journal
last = Menger
first = Karl
authorlink = Karl Menger
year = 1934
month = August
title = Das Unsicherheitsmoment in der Wertlehre Betrachtungen im Anschluß an das sogenannte Petersburger Spiel
journal = Zeitschrift für Nationalökonomie
volume = 5
issue = 4
pages = 459–485
doi = 10.1007/BF01311578
id = ISSN|0931-8658 (Paper) ISSN|1617-7134 (Online)

note_label|Rieger|Rieger and Wang, 2006|nonecite journal
last = Rieger
first = Marc Oliver
coauthors = Mei Wang
year = 2006
month = August
title = Cumulative prospect theory and the St. Petersburg paradox
journal = Economic Theory
volume = 28
issue = 3
pages = 665–679
doi = 10.1007/s00199-005-0641-6
id = ISSN|0938-2259 (Paper) ISSN|1432-0479 (Online) ( [http://www.sfb504.uni-mannheim.de/publications/dp04-28.pdf Publicly accessible, older version.] )

note_label|Samuelson|Samuelson, 1960|nonecite journal
last = Samuelson
first = Paul
year = 1960
month = Jan
title = The St. Petersburg Paradox as a Divergent Double Limit
journal = International Economic Review
volume = 1
issue = 1
pages = 31–37
doi = 10.2307/2525406

Bibliography

*cite journal
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journal = Journal of Economic Theory
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doi = 10.1016/0022-0531(77)90143-0

*cite journal
last = Durand
first = David
year = 1957
month = September
title = Growth Stocks and the Petersburg Paradox
journal = The Journal of Finance
volume = 12
issue = 3
pages = 348–363
url = http://links.jstor.org/sici?sici=0022-1082(195709)12%3A3%3C348%3AGSATPP%3E2.0.CO%3B2-R
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*cite web
url = http://cepa.newschool.edu/het/essays/uncert/bernoulhyp.htm
title = Bernoulli and the St. Petersburg Paradox
work = The History of Economic Thought
publisher = The New School for Social Research, New York
accessdate = 2006-05-30
curly = True

*cite book |title=Taking Chances |last=Haigh |first=John |year=1999 |publisher=Oxford University Press |location=Oxford,UK |isbn=0-19-850291-9 |pages=330 (Chapter 4)

External links

* [http://www.mathematik.com/Petersburg/Petersburg.html Online simulation of the St. Petersburg lottery]