One way to do it to get to the desired outcome is to replace U(x) with U(x,p) (with x being the money reward and p the probability to get it), and define U(x,p)=2x if p=1 and U(x,p)=x, otherwise. I doubt that this is a useful model of reality, but mathematically, it would do the trick. My stated opinion is that this special case should be looked at in the light of more general startegies/heuristics applied over a variety of situations, and this approach would still fall short of that.
I know Settlers of Catan, and own it. It's been awhile since I last played it, though.
Your point about games made me aware of a crucial difference between real life and games, or other abstract problems of chance: in the latter, chances are always known without error, because we set the game (or problem) up to have certain chances. In real life, we predict events either via causality (100% chance, no guesswork involved, unless things come into play we forgot to consider), or via experience / statistics, and that involves guesswork and margins of error. If there's a prediction with a 100% chance, there is usually a causal relationship at the bottom of it; with a chance less than 100%, there is no such causal chain; there must be some factor that can thwart the favorable outcome; and there is a chance that this factor has been assessed wrong, and that there may be other factors that were overlooked. Worst case, a 33/34 chance might actually only be 30/34 or less, and then I'd be worse off taking the chance. Comparing a .33 with a .34 chance makes me think that there's gotta be a lot of guesswork involved, and that, with error margins and confidence intervals and such, there's usually a sizeable chance that the underlying probabilities might be equal or reversed, so going for the higher reward makes sense.
[rewritten] Imagine you are a mathematical advisor to a king who asks you to advise him of a course of action and to predict the outcome. In situation, you can pretty much advise whatever, because you'll predict a failure; the outcome either confirms your prediction, or is a lucky windfall, so the king will be content with your advice in hindsight. In situation 2, you'll predict a gain; if you advised A, your prediction will be confirmed, but if you advised B, there's a chance it won't be, with the king angry at you because he didn't make the money you predicted he would. Your career is over. -- Now imagine a collection of autonomous agents, or a bundle of heuristics fighting for Darwinist survival, and you'll see what strategy survives. [If you like stereotypes, imagine the "king" as "mathematician's non-mathematical spouse". ;-)]
One way to do it to get to the desired outcome is to replace U(x) with U(x,p) (with x being the money reward and p the probability to get it), and define U(x,p)=2x if p=1 and U(x,p)=x, otherwise.
The problem with this is that dealing with p=1 is iffy. Ideally, our certainty response would be triggered, if not as strongly, when dealing with 99.99% certainty -- for one thing, because we can only ever be, say, 99.99% certain that we read p=1 correctly and it wasn't actually p=.1 or something! Ideally, we'd have a decaying factor of some sort that depends on...
Choose between the following two options:
Which seems more intuitively appealing? And which one would you choose in real life?
Now which of these two options would you intuitively prefer, and which would you choose in real life?
The Allais Paradox - as Allais called it, though it's not really a paradox - was one of the first conflicts between decision theory and human reasoning to be experimentally exposed, in 1953. I've modified it slightly for ease of math, but the essential problem is the same: Most people prefer 1A > 1B, and most people prefer 2B > 2A. Indeed, in within-subject comparisons, a majority of subjects express both preferences simultaneously.
This is a problem because the 2s are equal to a one-third chance of playing the 1s. That is, 2A is equivalent to playing gamble 1A with 34% probability, and 2B is equivalent to playing 1B with 34% probability.
Among the axioms used to prove that "consistent" decisionmakers can be viewed as maximizing expected utility, is the Axiom of Independence: If X is strictly preferred to Y, then a probability P of X and (1 - P) of Z should be strictly preferred to P chance of Y and (1 - P) chance of Z.
All the axioms are consequences, as well as antecedents, of a consistent utility function. So it must be possible to prove that the experimental subjects above can't have a consistent utility function over outcomes. And indeed, you can't simultaneously have:
These two equations are algebraically inconsistent, regardless of U, so the Allais Paradox has nothing to do with the diminishing marginal utility of money.
Maurice Allais initially defended the revealed preferences of the experimental subjects - he saw the experiment as exposing a flaw in the conventional ideas of utility, rather than exposing a flaw in human psychology. This was 1953, after all, and the heuristics-and-biases movement wouldn't really get started for another two decades. Allais thought his experiment just showed that the Axiom of Independence clearly wasn't a good idea in real life.
(How naive, how foolish, how simplistic is Bayesian decision theory...)
Surely, the certainty of having $24,000 should count for something. You can feel the difference, right? The solid reassurance?
(I'm starting to think of this as "naive philosophical realism" - supposing that our intuitions directly expose truths about which strategies are wiser, as though it was a directly perceived fact that "1A is superior to 1B". Intuitions directly expose truths about human cognitive functions, and only indirectly expose (after we reflect on the cognitive functions themselves) truths about rationality.)
"But come now," you say, "is it really such a terrible thing, to depart from Bayesian beauty?" Okay, so the subjects didn't follow the neat little "independence axiom" espoused by the likes of von Neumann and Morgenstern. Yet who says that things must be neat and tidy?
Why fret about elegance, if it makes us take risks we don't want? Expected utility tells us that we ought to assign some kind of number to an outcome, and then multiply that value by the outcome's probability, add them up, etc. Okay, but why do we have to do that? Why not make up more palatable rules instead?
There is always a price for leaving the Bayesian Way. That's what coherence and uniqueness theorems are all about.
In this case, if an agent prefers 1A > 1B, and 2B > 2A, it introduces a form of preference reversal - a dynamic inconsistency in the agent's planning. You become a money pump.
Suppose that at 12:00PM I roll a hundred-sided die. If the die shows a number greater than 34, the game terminates. Otherwise, at 12:05PM I consult a switch with two settings, A and B. If the setting is A, I pay you $24,000. If the setting is B, I roll a 34-sided die and pay you $27,000 unless the die shows "34", in which case I pay you nothing.
Let's say you prefer 1A over 1B, and 2B over 2A, and you would pay a single penny to indulge each preference. The switch starts in state A. Before 12:00PM, you pay me a penny to throw the switch to B. The die comes up 12. After 12:00PM and before 12:05PM, you pay me a penny to throw the switch to A.
I have taken your two cents on the subject.
If you indulge your intuitions, and dismiss mere elegance as a pointless obsession with neatness, then don't be surprised when your pennies get taken from you...
(I think the same failure to proportionally devalue the emotional impact of small probabilities is responsible for the lottery.)
Allais, M. (1953). Le comportement de l'homme rationnel devant le risque: Critique des postulats et axiomes de l'école américaine. Econometrica, 21, 503-46.
Kahneman, D. and Tversky, A. (1979.) Prospect Theory: An Analysis of Decision Under Risk. Econometrica, 47, 263-92.