00:01
We are estimating the probability of event by judging the ease with which relevant instances come to mind.
00:09
Is this an additive decision -making model, the representativeness heuristic, the availability heuristic, or a non -compensatory model? okay, so we're going to have to define these.
00:25
Let's start with a, an additive decision -making model.
00:29
What does this mean? it means that you think about all the separate features, and then you evaluate each option based on these features.
00:42
So you break your decision into all the features, and then your final decision is the addition of the features.
00:50
So that's not what we're looking at here.
00:52
This is something to do with choice.
00:54
For example, if you want to buy a new computer, you might think of all the different specifications, and the same based on the addition of how well it does in all of those different specifications.
01:06
So that's not what we're looking at.
01:08
Similarly, let's next look at d, the non -compensatory model.
01:13
The non -compensatory is different to be additive because in the non -compensatory, there are some must -have, but you cannot, that cannot be compensated for with something else.
01:28
So you want a computer and you say it has to have at least this much memory.
01:35
Anything less is just not considered.
01:38
That cannot be compensated for.
01:40
It has to have this amount of memory or more.
01:42
That would be a non -compensatory choice, whereas compensatory would be you prefer it, but it's not an absolute necessity.
01:51
So again, not the case, because we're not looking at the estimate of probability...