* lottery B = $900
  * lottery A = 0.9 chance of $1000 and 0.1 chance of 0 + feeling stupid.

But I really believe, that it comes down to "how often" these chances come in ones life.

It can be seen in the gameshow "Who want's to be a millionaire". In Germany we have a fourth joker: people give up the savety-net at 16.000 for an aditional joker. till now everyone of the winners, who got the million, did not take this option.

16.000 is a lot of money, regarding, that you started with 0. on the other hand 500 (the second level net) is really not so much, when you are hanging at 125.000 and having to take a shot at the million. and falling down to 500 feels a lot more stupid. so people become risk adverse (risk of loosing a lot and feeling stupid) a lot faster and don't trust their answer when gambling for that million.

* lottery A = 0.9 chance of $1000 + feeling somewhat smart and 0.1 chance of 0 + feeling very stupid

more accurate?

You're creating a bit of a straw man when it comes to behavioral economists and their view of "rational actors" - the whole field is built around the understanding that there is more to economic decisions than expected value.

Question 5a is: $900 @ 100%, or $1000 @ 90%

Most people choose $900 @ 100%

Question 5b is: -$1000 + $100 @ 100%, or -$1000 + $1000 @ 10%

Most people choose -$1000 + $1000 @ 10%

Written this way, the false symmetry vanishes, and we see that in both cases, people are risk averse when the payoff is low, and risk-seeking when the payoff is high. Which is to say, people value life-changing sums super-linearly as compared to insignificant sums.

If I'm understanding you correctly, you're saying it's about the amounts; and I think I'd have to agree with you that for the examples to be equivalent, the amounts in 5b should be multiplied by 10. But then again, maybe that would introduce another comprehension hurdle or cognitive correction effect which would render the experiment invalid. Interesting question to ask the original researchers, although I presume that by now (after decades) they've addressed it somewhere already :)

A = $900 lost B = $9000 lost with a 90% chance

Those are odds I wouldn't want to try. I would take the instant loss knowing that my odds are just not good enough to win if I were to choose B.

A 90% chance of losing $9000 has an expected value of -$8100.

Do you want to be guaranteed you'll lose $900?

Or do you want a 10% chance you'll lose nothing at all, with a 90% chance you'll lose another $100?

So given a choice between being (nearly) totally wiped out, or having the chance of not being wiped out, people take the chance of keeping their cash.

Makes sense to me.

If I could pull $900-$1000 from my savings with no immediate consequences, I'd be more likely to spend the $900 at 100%. But if loosing $900-$1000 means I'll have to tell my landlord I'll be late with the rent and then finding someone to borrow it from, and paying it back with interest, the extra $100 aren't significantly more crippling - it's the transaction cost of going through all this bother that's problematic - I'll take a 10% chance.

Come to think of it, I actually did something like this: Prior to moving abroad a while ago, I consulted a lawyer to make sure I did everything right to avoid double taxation. That was a taking on a 100% chance of a rather big expense to avoid an unknown chance of an even larger expense.

I also agree that the reasoning changes a lot if this is a one-time event vs. a regular occurance.

But the point is that research has show that almost all people have this bias that makes them much more risk adverse when avoiding losses than when they don't already have the money.

We can speculate a lot on the reasons, but that speculation isn't tested yet.

http://joshua.schachter.org/2008/09/amateur-economist.html

People's behavior changes at the dollar price, too. $900 is different from $9 is different from $90000.

I tried to find a good resource that further explains this but wasn't able to quickly find one. If I am able, I will try and post one later today.

On a final note, all of the situations in this quiz are easily correctly answered (or in some cases, predicted) with a basic knowledge of statistics and economics.

- "Would you pay $900 for a 90% chance to win $1000?"

- "Would you pay $100 for a 10% chance to win $1000?"

So the distribution of results really is different. It's not just a phrasing trick. I would still say no to the first and yes to the second. I like positive outliers more than negative ones. This doesn't seem irrational to me.

I'd say that most people rarely have enough data to split probabilities into more than about five bins: ~0%, fairly unlikely, coin flip (~50%), fairly likely, and ~100%.

It's all about managing the risk: generally speaking, I can afford to risk $100, but not $900. Furthermore, I've already spent/borrowed the second $1000 (We're TAKING my existing money, which I presumably have already planned on having). So the first is a windfall, but the second is a real need. So I WON'T risk a large amount of money for a windfall, but I WILL risk a small amount in order to meet a real need. I'd say that's 100% rational.

Bob

In addition to that, there's very good reason for someone to act differently when it's a gain vs. loss at stake. For one thing, this difference is the whole reason that an insurance industry can exist! (And insurance, in turn, is the only reason many kinds of utility-enhancing ventures can exist at all!)

I used to wonder what the point of insurance was when you could just bear the risk yourself, but then I had an insight (that no one else arguing with me managed to bring up):

Diminishing marginal utility implies increasing marginal disutility!

Utility as a function of how much of a good G that you have, usually increases at a decreasing rate. The first n units provide more of a utility gain than the 2nd n units, and so on. For much the same reason, losing your first n units isn't as bad as losing the 2nd n units, and so on. (It may help to visualize U(n) as a logarithmic curve.)

This is why people can rationally regard it as better to have a guaranteed loss of (at least) N rather than a (1/x) chance of losing x times N, while not also buying a lottery ticket for N that offers a (1/x) chance of gaining x times N. And that, in turn, shows the fundamental asymmetry between insurance and gambling.

Kahnemann must be on the phone with VF right now demanding they correct this article in about ten places.

but I agree that people with same wealth level will weight risk factor differently ( in each gain or loss). in other words simple utility function is not enough!

1. choose a) 90% for $1000 or b) 100% for $900 a. you just got $1000. choose a) 10% chance to loose $1000 or b) 100% chance to loose $100.

according to rational actor theory, people should choose the same thing both times. however, people often don't. its the framing effect. decisions are different when framed as losses or games.

this builds the foundation for prospect theory. rational actor theory says that your preferences are consistent. prospect theory says that you have a reference point and your utility function is inconsistent and changes depending on your reference point.

- A 10% chance of losing (you don't get any money).

- A 10% chance of winning (you don't lose any money).

These two are options are in no way equivalent. In your version, you have:

- 10% chance of losing (you don't get $1,000).

- 10% chance of losing (you have to pay $1,000).

These situations are comparable and can be used to test the framing effect.

Maybe someone who read the book more recently could take a stab at describing it.

They don't: https://en.wikipedia.org/wiki/Expected_utility

In short, there are three types of people: risk-averse, risk-neutral, and risk-preferring. (In the general case, people can exhibit all three types of behavior at different income levels, but let's keep things simple).

Imagine a graph, with income on the x-axis and utility on the y-axis. A risk-averse person will have a concave utility function (like a square-root function), whereas a risk-netural and risk-preferring person would have a straight-line and a convex utility function, respectively.

You have two income levels: $0 and $1000. Now take the two points (0, U(0)) and (1000, U(1000)) and connect them with a straight line. Since we're dealing with a 90% chance = .9 probability, find the point on that line which is 90% of the way between the two points (closer to the second point). This point represents the utility received from the risky situation, or the expected utility. This is not the same thing as the utility of the expected value! Call this point A, with coordinates (Ax and Ay)

Compare U(Ax) this with the value U($900).

For a risk-neutral person, the two values will be exactly the same, as the utility function is a straight line. For a risk-averse person, the second value will be higher, as the utility function is concave with respect to the origin. For a risk-preferring person, the first value will be higher, as the utility function is convex with respect to the origin.

In practice, the utility function may not have a constant concavity, which explains why people buy insurance (which is only justified under risk-averse behavior) yet also buy lottery tickets or gamble at casinos (which is only justified under risk-preferring behavior).

> Who in their right mind would take the choice that could possibly leave them without a life-changing sum the next day?

As you can see, the answer to your question is, 'A person who is risk-preferring' (or operating under risk-preferring situations which are quite common in practice).

these are the properties of a "rational" utility function: http://en.wikipedia.org/wiki/Rational_choice_theory#Actions....

This not only explains why people play lottery and buy insurance (playing the lottery non-compulsively involves risking only small amounts of money; not having insurance involves risking large amounts of money), but it also explains why those close to retirement should have risk-averse portfolios, while the young should have risk-preferring portfolios (those close to retirement have few "samples" left to take as it were).

It was more like "90% chance of $1000, or 100% chance of $850" (i.e., something a little less than P(X)*X). That was the whole point), people are willing to pay a premium for certainty - and the contrary (are willing to pay a premium to turn a 0.01% chance into a 0% chance)

Now if the sums were $8.50 and $10.00 instead, I'd likely make the more rational choice (90% of $10), because such choices with smaller amounts of money come up far more often in my life: the sample size will be large enough that the mean approaches the expected value.

If only given question b and some time to think you would probably answer no (depending on your risk-preference). But because of question a, the amount and probability in question b seems insignificant which decreases your risk-averseness.

There are many many people who would argue with that.