What a glorious lecture. Historic. I've listened to this on flights a hundred million times and yet learn something new with each view. What a beauty economics is 😊
@34:38 shouldn't the cost be 398 since at a cost of 399 the 25 yr old male would just be indifferent between the choices, but at 398 his E(u) would be better if he's insured? Phenomenal work th0 MIT ❤️
I loved the lecture, but I do have 1 short question; all of the examples used have probability values, which is calculated risk, not Knightian uncertainty. How do we deal with that in economics?
Knightian uncertainty is not dealt with, it's ignored and economists work with theorems / idealized situations just to "illustrate principles" or "get insight into situations".
I do not get the idea of splitting one month's insurance if a risk of getting hit by car is not evaluated per month too. Otherwise, you should calculate 40 000 x times you get your salary under the period that the risk of 0,01 is for, which is probably the whole life, so that gives a tiny sum of some dollars per month, far below 399
I think that you neglect to consider the addictive property of gambling. Much like smoking. Everyone is not ignorant, entertainment is certainly coming close, but addiction seems to me the most plausible explanation, especially when I consider the types of regular lottery participants you mention.
If you go for this gamble, the expected utility you would get is 7.5, which is equivalent to $7.5^2 = $56.25 in terms of wealth (given the utility function: u^2 = c). As compared to the original utility you have which is 10 (i.e. $10^2 = $100 in term of wealth), the difference is $100 - $56.25 = $43.75, which means the fact that you are indifferent to attend the bet or pay $43.75 for not attending the bet. As a result, $43.74 is the price that you will definitely be willing to pay for not going for that gamble.
It was just a useful example because the model tampers off You can find other things if you take the time to model it but I'm not an expert at all so I hope someone else who is more acknowledged will try to answer you too
Hi guys. Hopefully someone can help clarify something here. At around 9:05 he says that the Expected Utility is less than the Initial utility, but surely that is based on his given utility function. Isn't that utility function just like a cost function, it could be anything, he's just plucked that function out of the air? Or is that specific equation something that needs to be remembered.
The specific square root functional form is just a choice of a utility function, but this choice was made because it shows a diminishing return of utility leading to the risk adverse conclusion. Basically, the "shape" of the utility function is more important in explaining the qualitative expectation of the behavior, the exact numbers e.g. bet rewards would depend on the exact form of the utility function. That said, utility functions in real life are extremely difficult to model and in real life, empirical estimates are usually made.
He is just calculating the expected utility of the bet, which is the sum of each outcome's utility multiplied by the probability of that outcome. So if he wins, he has 100+125=$225 and that gives a utility of root(225) and that has a 50% probability, so expected utility = root(225)/2 +root (100-100)/2.
Great reference. Note Expected Utility Theory (lecture) != Prospect Theory (Kahnemann). One is about risk aversion for rational agent vs another is loss aversion of people.
Sometimes it made me think that the teachers are so smart they made me wonder, becaus they're so on the topic, like they can be on a topic for a very long time.😂
Because that is the utility in case you win, which is given by the root of the total amount of money you would be having in that case. That is, the root of your initial wealth (100) + the amount you would win (12.50), hence root 112.5
