Classroom B: Good morning. Sorry. Hey? How's everyone doing?
Ashley Wray: Great? How are you?
Classroom B: Alright
Classroom B: awesome. Okay. So
Classroom B: today we're we're switching gears and talking about applications of principles of memory for the last little while. And we're gonna shift gears a bit and talk about our ability and sometimes inability to
Classroom B: make good decision.
Classroom B: And of course, this is this is a, you know, working cognitive process for us.
Classroom B: We make decisions which in terms of more precise definition, most decisions may be researchers will sort of commitment to some sort of action or behavior like a choice to
Classroom B: do something either immediately or or in the in the future
Classroom B: and make those decisions based on what decision-making research is referred to as judgments, evaluations about you know, evaluations that support current state of our environment to help guide our decisions.
Classroom B: And
Classroom B: there's no, this isn't like an empirically validated estimate, but estimate. But we can sort of just generally speculate the way that we make like dozen of decisions, every.
Classroom B: some of them mundane, some of them great teens, some of them regular and sometimes much bigger decisions.
Classroom B: Sometimes they're good and sometimes they're bad.
Classroom B: Of course, like the the first thing that we need to do is determine. Okay, like what counts as a good decision based on your reading of the chapter, and just based on your own sort of speculation, because these are sort of
Classroom B: vague, sometimes ill-defined terms. What makes the decision. Good!
Classroom B: What? What are some of the criteria that would determine that? A decision that you made, and good decision rather than
Ashley Wray: oh, probability!
Ashley Wray: Sorry, Annie, you can go ahead.
Annie Lewis: No, the opposition thing
Ashley Wray: outcome outcomes probability and the utility of value to an individual.
Classroom B: Okay, yeah. So yeah, we. So we'll start with you most at outcomes as a as a start. There. Yeah. So I mean, that seems like one of the the clearest right here. Right did the decision resolved in some sort of you know. Positive benefit to
Classroom B: was the outcome positive versus the decision. Result in something they want as opposed to something don't want.
Classroom B: And and as I can say, that's sort of related
Classroom B: you know what's valued by the person well, when we talk about some of the typical economic theory that that have been developed to explain our our decision making. Well, we'll find one of major criticisms is that they, they tend to assume that we value one thing that has just not case.
Classroom B: but so just with that caveat, and after that, depending on what you value as a person did that decision result in a positive net increase to what you value.
Classroom B: I also heard something about probability in there actually, did you expand on that.
Ashley Wray: I guess in the textbook they use the example of gambling. If
Ashley Wray: you're more likely to win, then it's usually a better decision.
Classroom B: Yeah, yeah. So that your thing is sort of like, more like long term good decision making. And that that is important. Right? Cause sometimes the important. Sometimes I make a decision that does result in a positive outcome. It doesn't necessarily mean it was a good decision, for the example of a textbook. If you take a really risky example
Classroom B: that just happens to pay off. If if that was a decision that, like in in the long curve in the future.
Classroom B: hey? That position, and would generally be expected to do bad outcomes. The fact that it led to a good outcome this time around doesn't in and of itself make it a good decision like you gotta say, well, that was a that was, you know. Good luck.
Classroom B: I was a fluke
Classroom B: but you wouldn't want to make a decision. Yeah, this is what people typically describe when they talk about what makes something a good decision, something good. And then the other sort of common thing that people identify is how much it opens up opportunities for the future.
Classroom B: So people do kind of think in the short term and the long term when they're evaluating their decisions.
Classroom B: We also know that if we ask people to think about decisions they've made in their lives usually important decisions about the more relatively mundane
Classroom B: like, what should I have for lunch? But bigger decisions?
Classroom B: If you have people evaluate how how positive or negative the outcome was, and also happy. How important that decision was. We actually see a little bit of a generally positive prognosis in terms of our decision making.
Classroom B: The 2 things we find are that the good things we consider more good, and then we consider the bad things
Classroom B: I to
Classroom B: numerically to break down exactly what that means. You know your you are raining me positive or negative of an outcome of a decision on a scale pay to 5 positive 5.
Classroom B: And so anything above 0 is considered a good decision. Anything below 0 is considered a decision, and what you find is that on outrage
Classroom B: good decisions are rated as further away from 0. Correct.
Classroom B: That's what I mean by their their good decisions are more good than decisions are bad.
Classroom B: On top of that, people tend to rate the positive zoom as being more important.
Classroom B: This is sort of a general framework just to identify. You know, we're going to talk about the decision some
Classroom B: yeah of
Classroom B: what counts as as a good decision. And I think way of framing it is probably the most succinct way to to approach it? Does it increase what we count at?
Classroom B: Okay?
Classroom B: So if we trace the history of research on on decision making and and sort of in the same
Classroom B: framework that we've been adopted throughout the course we're starting with some
Classroom B: theoretical stuff like like sort of principles and groundwork, and that we start applying, acknowledging that at least in my
Classroom B: starting to go into more practical applications.
Classroom B: But if we face the history of the theoretical work, the first place where decision-making research. What happened was document psychology. But as I alluded to earlier in economics.
Classroom B: where where economists would be very much interested in what are the types of decisions that people make in the broader context of of like a nation or country
Classroom B: economic structure.
Classroom B: They're more concerned with the economic itself, but inherent in that economic structure is the fact that there are individual people that work as causal agents that influence that economic structure through their decision.
Classroom B: so
Classroom B: based on your reading of the chapter.
Classroom B: What is the sort of standard traditional economic perspective on humans? What are some of the key principles or
Ashley Wray: biases?
Classroom B: That's
Classroom B: good! That's a good guess. What you're actually probably identifying is more so. One of the weakest is that we'll come to economic theory, because most traditional economic theories would fairly, strongly deny that we act in ways that are biased.
Classroom B: Alright. It's gonna be a stupid thing where I can't actually draw on this. So I have to open up a whiteboard and then share it.
Ashley Wray: Availability, representativeness anchoring
Classroom B: you're still. You're still on those on those heuristics, so I think I can. I can try to guide us through here a little bit. I just have to. I'll probably have to clear some stuff off of this whiteboard. No, look, it's it's empty.
Okay, so let's let's start to break this down a little bit.
Classroom B: I will share this with zoom right now.
Classroom B: Okay, so the the traditional economic theory just to get a started discussion is is very much centered around a concept known as the maximization of expected utility.
Classroom B: So
Classroom B: bit of a mouthful, very technical sounding terminology, just in case that does sort of help push us in the right direction. Does anyone know what I mean by maximization of expected utility?
Classroom B: Well, then, let's let's break that down. So maximization of expected utility shortened to any new because it's bit of a mouthful it'd be in with a a basic sort of a basic series of steps that involves sort of a really straightforward, consistent, rational, essentially calculus of the
Classroom B: potential value of different choices and decisions we could make. So remember, this is an economic theory. So this is, basically exclusively going to be trained in terms of
Classroom B: making financial monetary decisions often under conditions of uncertainty and risk to some extent.
Classroom B: So yeah, we could imagine that you're trying to decide if we set up like investment portfolio, which, of course, are going to have. You know, different possible monetary gains that are associated with with different likelihoods or probability of of occurring. So the basic Meu framework starts might as well within within a potential investment, what we could call a a prospect
Classroom B: first identify. I'm missing a word here.
Classroom B: So the first step in a in a basic Meu decision-making process is just like within a particular investment portfolio or prospect, just like, first identify what are the possible outcomes? More than one possible outcome. We don't know which one in that table happen. So that's what I mean by an addition of uncertainty.
Classroom B: Next thing is to identify with any of those possible outcomes. What's the value like? What's the potential payout or possibly what's the potential loss? Like? Essentially, again, because this is an economic theory. What is the change in in monetary value associated with outcome? Which is what an economist would refer to as that outcome value.
Classroom B: If you identify the values. You also identify the probability of each outcome. So the likelihood that each outcome is going to occur
Classroom B: once you have the values and probabilities of each outcome, you multiply them for each outcome.
Classroom B: And then, in order to get the overall what's called the expected value or expected utility of that prospect investment whatever we're considering, you're just going to add up, or some
Classroom B: those products
for each outcome
Classroom B: questions about that. So Bar, we'll we're about to go through a concrete example just to make sure that we really really grasp it. But just clarification requirements at the point.
Classroom B: Okay, so let's say that we have. Oh, I have something in the chat.
Classroom B: Yeah. So yeah, Jared's sharing some some insights about this from from sociology. And just to sort of summarize that. Yeah, what you're identifying here is like the basic economic perspective is that we undermine assumptions that we're rational.
Classroom B: Another underlying assumption is that we seem to maximize, our monetary expected utility. And then we engage. And if we're looking at these steps, this is a very like thorough, systematic, rational calculus of of all of the the possible outcomes. Right? So you know, the the idea from the economic theory is that we're making some sort of monetary decision. Some sort of investment decision
Classroom B: we calculate expected utility so just to see how this might look, let's say that our prospect
Classroom B: but just we're going to say that the prospect of investment, the relatively simple one, is what you have 3 possible outcomes.
Classroom B: Okay, so Jaron's asking, how does rationality fit into this. Hold that to your back pocket bar a bit because we're we are going to come back to this. How it fits into the economic theory is the the economic perspective assumes as a starting point that humans act
Classroom B: rationally. Of course, we're going to see that that's going to be a very easy way to critique the traditional economic perspective. Because we don't. Yeah, it's an assumption exactly. It's an assumption. And worse than that, it's an assumption that we can very clearly demonstrate is not true.
perfect.
Classroom B: Let's say the first outcome is, it has a 50% chance for the probability of 0 point 5 of occurring and it's worth $1,000
Classroom B: it also has a a point to 5 probability of resulting in a $500 increase and a 0 point 2 5 probability of resulting in no monetary gains or $0 increase. So as a group, what would we? What are the steps we need to take to calculate the expected utility of investing in this process.
Classroom B: We've already kind of done diverse 2 steps right? I've I've given us the potential payouts and the probabilities.
Classroom B: I know it's not even 9. And they're making us do math.
Classroom B: Okay? So remember, the next step is, we take for each outcome. We take the the the payout value and multiply it by the probability. This is why we're dealing with probabilities rather than percent. Because probabilities are bound to a range of of 0 to one.
Classroom B: So our first outcome $1,000 payout with with a 50% chance or a point 5 probability to multiply those together. That means that the
Classroom B: value associated with that outcome is is $500.
Classroom B: So just 1,000 times 0 point 5
Classroom B: for the second outcome, $500 payout with a point 2 5 probability. If we multiply those together, we get 1, 25, and of course, for the final one, because the value is 0 multiplying. 0 gives us 0.
Classroom B: So based on that structure of payouts and and probabilities, the expected utility or expected value of that particular prospect is 625.
Classroom B: Now, what? That? Just to really emphasize what that means, what that means the reason that expected utility needed is in bank economic decision-making processes. A valuable rational process. Is that what that tells us is that in the long run. We repeated that prospect over and over and over again, large or infinite number of times. The average payout would be $625 for each reach out
Classroom B: that part. Make sense
Classroom B: like if you, if you did that like, if you just like, ran a simulation of those probabilities and those payouts a thousand times. You're going to get an average payout on very close to 225.
Classroom B: As the number of times you do, it increases the closer and closer you get that down.
Classroom B: So in terms of in terms of economic decision making.
Classroom B: Jared, so in this case you have, you have a point 7 5% chance of winning something.
Classroom B: Because it's only that point 2 5 probability, that third outcome, that results in nothing it. Just between the other 2 payouts you just get more or less depending on how things go, and but more importantly, for the the other way to think about it, instead of just winning or losing, is that the average payout for this prospect would be $625.
Classroom B: What that allows us to do as from an economic perspective is, determine 2 different things depending on the scenario, the simplest version. We're only looking at one prospect. If this is your say, this is an investment that you're gonna make, you're expecting on an on average, $625 payout. What that means is that you should be willing to. You shouldn't be willing to pay anything less than $625 for this investment.
Classroom B: usually with an investment you have to pay something in. If your expected return is 6, 25, then anything less than that will on average results, and it increase to your your value. So it'd be an irrational decision to, for example, pay $700 for this investment.
Classroom B: because that cost exceeds the effect utility.
Classroom B: So that's that's one type of economic decision that we might be make, the other is, we might be asked to choose between multiple prospects. And in that case it's well, if you're choosing between multiple prospects, you pick the one that has the highest expectability.
Classroom B: that making sense questions or thoughts about that
Classroom B: from like from an economic perspective
Classroom B: maximization of expected utility is like a rational, clear, and and effective way of making financial decisions. You know, of course you can. You can imagine that's like, well in in real life we don't have access to, or knowledge of, these valued probability structures with anything certainty.
Classroom B: But the general principle is that like, if you engage in expecting utility maximization in financial situation over the long term you are going to maximize your monetary payouts so it is a rational, accurate theory maximum, what we might call a normative theory.
Classroom B: What we mean by a normative theory is, it's a very good description of how we ought to make decisions.
Classroom B: What we're going to see very shortly is that it fails to be what's called a descriptive theory. which, as you might be able to put together is is a theory that actually describes what we do.
Classroom B: So normative is normative theories describe what we should do. Descriptive theories should explain what we actually
Classroom B: and you know, as as Darren was sort of identifying this assumption, that we act in rational ways is
Classroom B: not always true.
Classroom B: at least at least that be combined with the assumption that all we care about is our monetary outcomes. There are other things we care about.
Classroom B: Does anyone? Is anyone familiar with the set of sort of decision-making experiments in this economic framework known as the ultimate game?
Classroom B: It's a fairly straightforward experiment.
Classroom B: I talk about in other classes. So if it starts familiar, is like, let's imagine that Annie and athlete, this is the first 2 people I see on the screen are 2 participants in this experiment. What I might do is, I'm going to say, Andy, here's a hundred dollars
Classroom B: and you get to Andy now gets to decide how that $100 gets divided between herself and acting.
Classroom B: Now, Ashby still hasn't been in power in the scenario, because Ashley can then decide to accept or reject that offer. If she accepts it, then it gets the minor, not as Danny decided. If she rejects it, nobody gets anything
Classroom B: that's that make sense.
Classroom B: Okay? So according to expected utility theory, what is the smallest amount that Ashley should exempt
Classroom B: just according to maximization of expected utility
Classroom B: any thoughts.
Classroom B: And it's gonna be difficult to think. Cause you're probably going to have to given that this is a demonstration of why maximization of the expected utility is a bad theory. You're likely going to need to overcome your intuitive thoughts on like what is the smallest amount that actually would accept.
Classroom B: And remember that we're trying to ask, based on the principles of maximization of expected utility, what is the smallest amount that actually should accept if she would follow in the principles of expected utility
Classroom B: chat.
Classroom B: whatever has the highest probability. So remember, okay, let's let's break up the let's break down the value structure. So there's a bit of a variable keeper, because, of course, Annie gets to decide how much Ashley gets if she accepts the payment. So let's just call that variable X.
Classroom B: So if if Ashley accepts, she, her monetary payout is a 100% chance of X and X is just whatever Annie decides, that amount is going to be.
Classroom B: I
Classroom B: remember these scenarios that if actually rejects that, then nobody gets anything with me to say. Handing an active book at $0. So from Apple's economic perspective, the expected utility of rejecting the offer is $0.
Classroom B: So remember that. As you know, as Jaron was starting to identify, according to and EU theory, what Ashley should do to decide between these is pick, whichever one is higher. So Ashley should pick should accept according to expected utility you should pick
Classroom B: sorry should decide to accept the offer as long as X is greater than 0
Classroom B: right? We know that if if he rejects, then her expected utility is 0.
And so that means that, according to those rational calculus principles, if Annie offers anything above 0,
Classroom B: Ashley's correct economic decision to maximize expected utility is to
Classroom B: except, and in fact, there's actually no scenario in which the correct decision is to reject, because if Annie offers 0, then Ashley should, in terms of economics, be entirely ambivalent
Classroom B: about accepting a rejecting. because the expected value of both is 0.
Classroom B: So what that means is that if Annie offers $1
Classroom B: that is greater than 0, and according to utility theory, Ashley should accept.
Classroom B: If most of us were in this scenario, and Annie had $100 and designed as you allocate you $1. Would you accept?
Classroom B: Most people wouldn't.
Classroom B: And everyone's gonna differ on exactly how much they need to get from any in this perspective. But it's certainly going to be a lot more than wow.
Classroom B: Usually it's going to be somewhere that's approaching 50%
Classroom B: of the of the total value.
Classroom B: So a very clear violation of the principles of of me, you right me, you would say, like, Okay, big above 0 is a positive and the highest possible expected value in the scenario. But no does care about monetary stuff, right? We care about fairness.
Classroom B: you know, when it actually is faced with that $1. She's the typical participant of the experiment. She's thinking Annie's kind of a jerk on $1. Come on, that's not fair. I'm gonna say, no, so that Annie doesn't get any big jerk.
Classroom B: I don't care enough about that dollar. I care about things being fair.
Classroom B: So we we obviously don't just care about what it. So that's one of the major weeks
Classroom B: of expected utility theory.
Classroom B: but expected utility theory does have an answer to that question. It's a bit of a sloppy, argumentative move. But it just means okay, fine. We'll we'll relax that assumption that all we care about is monetary
Classroom B: value utility. You can just count as as actually was saying, the class just like some general combination of of things that out and in in non economic decision making. This is this is still the same principle. This is that idea of
Classroom B: they're making decisions such as like, Oh, like, which university could I go into? Could I buy this house? Should I buy this car? Could I take this person?
Classroom B: These are all decisions in which monetary value in some cases is one thing, but there are many dimensions
Classroom B: of things that we now do.
Classroom B: But under the hood it's still the exact same idea that we, we measure those different utilities of those different outcomes
Classroom B: just now. They're based on board, just like some more subjective psychological.
Classroom B: Utility metrics! But still that we then compare the ideas. Compare the prospects, compare the costs and benefits and maximize that utility.
Classroom B: So it changes a little bit, gets around that one problem, but it's still at its core that we are doing some rational, comprehensive calculus of all the different possible values
Classroom B: associated with a decision.
Classroom B: Okay?
Classroom B: But again, as we might imagine that still doesn't work terribly. Well.
Classroom B: okay.
And
Classroom B: okay. And then these, you guys come around and start to really start to throw more serious ranges. So Danny of Kahneman and enables diversity our 2 names that we're gonna come across a few times during during today's class. Because they they made what can sort of be considered 2 of the most substantial contributions to our psychological understanding
Classroom B: of of human decision making have a focus of those major contracts.
Classroom B: Jared, this, this isn't quite hitting dual process theory yet. That's the second contribution, especially from identify but what we're identifying here is, I've already been sort of alluding to this terminology when we're talking about the idea of a prospect. But their their theory of prospect theory
Classroom B: and the basic idea. I mean this, I mean this, this to like, emphasize how important this contribution was. This, this is the like, the development of what's called prospect theory in adaptation of demonstration of the weaknesses of expected utility. Theory
Classroom B: is actually ended up waiting.
Classroom B: Daniel Kahneman, a Nobel Prize in economics. Jursky was unfortunately deceased prior to the award, the Nobel Prize for thought award posthumously. But we can basically consider Terski is also having won a Nobel Prize in economics, because people
Classroom B: as the work that wits or not the not the person.
Classroom B: So in prospect theory, what caught him in diversity
Classroom B: do is demonstrate other systematic ways that we tend to
Classroom B: behave and decide inconsistently with the the basic calculations of unexpected. So
Classroom B: let's consider some of their their classic examples. We prospect a
Classroom B: so prospect A is just a 100% chance at $3,000.
Classroom B: So the of the the expected utility of this is relatively easy to calculate, because we just call the 5 3,000 by one. It's the only outcome. So that is just like the expected utility of prospect. A is $3,000
Classroom B: and trust what B is a bit more, although because there are 2 possible outcomes, we have an 80% chance of $4,000, but a 20% chance of winning nothing.
Classroom B: Now, if you do the expected utility calculation for this with you multiply 4,000 by Pointe. The expected. Utility of this gamble is $3,200, so it's slightly higher than prospect days. So, according to
Classroom B: expected utility theory, you should prefer prospect B.
Classroom B: Intuitively. What would your choice be between these 2 options?
Classroom B: The guaranteed option? A guaranteed option, a agreement or disagreement with Ashley?
Classroom B: Remember, it's just it's a subjective opinion on which one you value. So don't write or wrong, or according to the theory.
Classroom B: And and what actually is identifying is what the vast majority of people will prefer in this case. Usually so, the typical autumn and diversity experiment would be just bring people in and give them these types of decisions and given the choice between prospect A and prospect B.
Classroom B: Usually from experiment to experiment, like 70 to 80 people prefer prospecting.
Classroom B: so they prefer the the serving here
Classroom B: versus the potentially higher aid, but associated with the.
Classroom B: So this is, this is what's known as like sort of a certain preference for certain that we tend to over value at certain options.
Classroom B: And what if we just change the scenario? Now, we just add a negative sign to those those 2 values. So now we're talking about a much less preferable scenario where we're talking about prospect 8 is a 100% chance of losing $3,000.
Classroom B: Prospect B is an 80% chance of losing $4,000. And still that 20% chance of nothing. So now just that that expected utility changes to a negative.
Classroom B: So this is again expected. Utility theory is you should choose Prospect Day because it's a a smaller loss and based on the the certainty principle. You would also expect that we choose Prospect day. But that's not what people do.
Classroom B: Now, again, that sort of consisted like 70 to 80% of people prefer prospect beats. They prefer to take the risk of potentially losing more to try to get that 20% chance of not losing anything.
Classroom B: The people's preferences regarding certainty at risk change just based on whether we're talking about gains versus losses. So this is like one of the
Classroom B: major wrenches that gets thrown into this idea of expected utility theory that, you know we have. On one hand, we are risk averse
Classroom B: for the domain of games. We prefer certain. If we're talking about gaining potential outcomes, we are, we are risk adverse. We prefer to go for certain options. Even if the the risky scenario has a higher potential outcome
Classroom B: in the domain of losses. That preference reverses without risk seeking when we're talking about losses will risk the higher loss to avoid losing money. If it's an option.
Classroom B: there's actually there's there's an additional wrench in the pattern that the next one doesn't really dive into this pattern of being risk averse for gains and risk seeking for losses is true for moderate to high probabilities, but
Classroom B: it reverses with sufficiently low probabilities where we tend to. And we're gonna once we get like very, very low probabilities, especially with with high payouts or high losses. We now switch to being risk-seeking or gains and risk averse for losses.
Classroom B: And so that's what cognitive risk we refer to as the forefold
Classroom B: pattern, where we have 4 different scenarios depending on whether we're in games and losses, and whether we're at
Classroom B: moderate, high or low probabilities.
Classroom B: Now there, there are 2 very 2 very common, real world examples of those one or 2 third risks for losses when the probability resolution, low
Classroom B: and Wendy
Classroom B: monetary was, are relatively high. Anyone think of? There are those 2 examples
Classroom B: you're evacuating
Classroom B: like what's what's an example of a low probability being generated, one with heart risks.
Classroom B: No one ever bought a lottery ticket
Classroom B: or known anybody who has
Classroom B: break on the day of 3. Major. 3. Really, I, in most gambling scenarios in general. Are examples of risk seeking in the domain through looking at potential positive payouts. But those payouts have a very low probability, especially in the context of a lot of people
Classroom B: like the if you look at the, you know, if you look at the
Classroom B: look at like a sample like model 6 workout, or something
Classroom B: potential value of a win and multiply it by the probability that's that expected utility is almost 0
Classroom B: right? Just because I'm sure the the potential payout is massive, but the probability is so so low that, you know doing that calculation. Doing that multiplication ends up with an expected value. It's actually sort of
Classroom B: yeah expected value of a of nearly 0. Which means that when you incorporate the cost of watering tickets, your expected value is essentially negative. The cost of the
Classroom B: ticket.
Classroom B: you know. It's why it's such a major industry where it's like the payout is very much in favor of sell. We're a novel purchaser.
Classroom B: Okay.
Classroom B: what's a real life example in which we are risk adverse for low probability losses.
Classroom B: Everybody here, like Brandingston apartments, or or anything like that.
Classroom B: And we own a home
Classroom B: now in either of those scenarios. If, say there's a fire, and you lose one to your blog.
Classroom B: what happens?
Classroom B: Are you? Are you just out of luck. You lose all your possessions.
Classroom B: and then and then you have to start with the bill after taking your head. No, you you have insurance that that covers that right
Classroom B: now. Often, you know, we sort of drop in required to have home insurance or had insurance. But even in places where you're not required, people will purchase insurance.
Classroom B: That is the things a real-world example of people being risk averse for low probability losses, the probability of your house attaching higher and inclusive. Your possessions is very low.
Classroom B: but we are more than willing to make way more payments in order to avoid that low probability loss. So that's an example of risk. Adverse behavior in the domain of losses when the probabilities are quite low.
Classroom B: And again, remember that insurance companies make a lot of money, and they make money by setting premiums that you pay to have a a to outweigh the expected value
Classroom B: of anything that you might lose, which means they're the long drive, while they sometimes have to give. Mark pay outs in the long drive. Because of that expected utility calculation. They end up
Classroom B: any any questions about those patterns so far we'll we'll start to tie it all together into into the theoretical implications. So any questions or clarifications or thoughts about those patterns.
Classroom B: And so there. There are 2 things in prospect theory that that,
Classroom B: you know, can can adapt from expected utility theory to accommodate an entire set of results. The first one is that we tend to be what's called loss, averse.
Classroom B: So we experience what's called loss aversion.
Classroom B: But then, in general, we we what this means is that if we take, say, like a gain and a loss of equal value, so say, like gaining a hundred dollars or losing a hundred dollars. We tend to experience that loss of technically equal value, a a worth.
Classroom B: So we we experience losses as being more impactful than gates of equal value
Classroom B: that make sense.
Classroom B: which is partially in the in the context of moderate to high probabilities, explains why we we tend to now shift into risk seeking when we start talking about losses, because, due to the fact that we experience those losses as more impactful gain, we're willing to now take a risk
Classroom B: that more actual event that more tackle negative events
Classroom B: the the other change to accommodate those other 2 aspects of a normal pattern is that when we look at our assessments of probability or likelihood. The the change in the theory is that we tend to overweight low probabilities. So we consider low probabilities. We interpret low probabilities be higher than they are.
Classroom B: and then we corresponding. We underweight moderate and high probability less likely than they actually are.
Classroom B: Okay. Darren's asking, How is it a decision if it's under circumstances of risk? That's a good question. Because there is still a decision to be made there, there is uncertainty, and there is risk but you still get that choice between those 2 risky or non risky options. Right? So you're still making a decision just now, once you've made that decision, and there are elements that are out of your control.
Classroom B: But you still get to make the decision. In the first place.
Classroom B: you know, like once. So once you once you buy a lottery ticket, you don't have any control over the actual outcome. But you do have the decision as to whether or not to buy a lottery ticket
Classroom B: questions, clarifications, thoughts.
Classroom B: But even with those changes it's still somewhat under that that realm of utility right there, identifying systematic distortions to our perception of value and our perception of probability.
Classroom B: Okay, Jared, yeah, if you're still confused about that then feel free to send me an email. And we can. I can try to clear that for you. And but the short version is that the decision aspect. Is, that's yeah. And we're influenced by cultural factors. And but that's just identifying something that influences our our decision process.
Classroom B: Yeah, so we can. We can have a conversation about that over email.
Classroom B: Okay? So it's still like a utility calculator
Classroom B: sorting out that fundament. Diversity would, as we'll see
Classroom B: in in a bit. Also identify that we're probably not actually going through that very comprehensive calculation process, you know. Given those Zoom prospects certain $3,000, 8% after $4,000. You're probably making a barely snack to do right like you're not super hard about it.
Classroom B: And this is when we start to get more into this realm. People moving away from economic perspective and and moving into a field version as just a field of naturalistic decision.
Classroom B: So acknowledging the fact that we make a lot of decisions in our lives that don't really have anything to do with economic consideration.
Classroom B: we make decisions that have nothing to do with money finances changes in in value. Right? That.
Classroom B: like, you know, anybody who feels like contributing volunteering information like, you know, it's pretty early today. But what are some examples of decisions you need so far today.
Classroom B: what to have for breakfast? Yeah, what to have for breakfast.
Classroom B: Now I'm I'm doubtful that Ashley was engaging in some multi attribute utility measurements.
Classroom B: I chose the healthier option.
Classroom B: But did you did you take that healthy option and calculate its basic value to you. That also like weight that indicates the possible. You know your preference in terms of like tickets good, and things like that.
Classroom B: Probably not. So, I decided like, Oh, like I should go for the healthy octave.
Classroom B: I don't know how I'd assign those values. So no.
Classroom B: And what you're what you're sort of demonstrating is is like
Classroom B: a a single attribute decision like, well, I chose the healthy option. That's that's it. That's the only thing you consider. Really.
Classroom B: I want. I want to do the healthy options. Do you pick the healthy option
Classroom B: rather than identifying everything and waiting based on every possible attribute that you could potentially value.
Classroom B: Yeah, so these are the types of decisions we make are often not economic in nature. So
Classroom B: you know, research in naturalist decision making ends up with a fairly different story compared to what we get from a traditional economic perspective.
Classroom B: So
Classroom B: the the chapter outlines a case study in in Klein that I. This is a case of Nicholas Fire chief in response to
Classroom B: a a house fire and then Fire Chief has to make relatively quick snap decisions about what to do.
Classroom B: what are the what were the basic characteristics of that story like what maybe you had to describe the Bioges thought process. In that case, study. How would you describe it
Classroom B: if you need to refer back to the chapter and and get some of the details of that story. We need to be free.
Classroom B: Okay? So the the basic details of the story is so first, like, it's a basically a relatively short case study versus the fire tues there. So people obviously have already gathered evidence. And it's determined that at that moment in time. There is a fire that is, what? Sometimes a vertical fire what that means is that in this particular case the fire is contained to the building squadron
Classroom B: for the vertical higher in the sense that it's a relatively constrained vertical.
Classroom B: So the fire chief decision is, okay, we'll get the get the fire hose out. We're going to go to the top of the laundry shoe. We're going to pour water down the laundry shoe to extinguish the fire.
Classroom B: So they start that decision.
Classroom B: By the time that action is able to be implemented. Unfortunately, the fire has now expanded beyond the laundry. It's no longer fire spreading to other areas of the house. There's a large amount of smoke. Okay?
Classroom B: Decision is now okay. Abandon the fire hose down. The longitude idea is now a search and rescue. We put off here, go into the house, try to find anybody and then worry about getting rid of the file.
Classroom B: The other case outlined is the Us. Airways case. So what was the what was the year on that
Classroom B: the case studies 2,009 was around that time. Is the Us. Airways flight 1529. This is a case where a a bird flew into the engines. Cause engine failure plane was going very rapidly descending cause it didn't have enough power to to evolve to. This is the case where the pilot made the decision to land in Hudson River.
Classroom B: and happy ending. Everybody survived.
Classroom B: So the the question then becomes, what were the decision-making processes in these cases.
Classroom B: and one of the standard ways in naturalistic precision to that question
Classroom B: people how they made the decisions. And often this isn't the case of expert as the fire chief. Somebody who's worked their way up has really been recognized as as knowledgeable, and experts
Classroom B: hours on particular aircraft in order to be allowed to fly them.
Classroom B: so so these are, you know, expert decision making processes. And we just ask, well, how they made those decisions.
Classroom B: What do we find? Well, we find that. There is certainly not any evidence of any sort of like rational, comprehensive calculation of of different options.
Classroom B: And then so what we we tend to find in these type of scenarios is something that natural decision-making researchers refer to as the recognition prime decision making call
Classroom B: and we have. We have the the model out on here as we'll start to break down exactly how this works. But the the overarching idea. And this is one to relate very strongly to our our later discussion today. Of something that Ashley mentioned at the beginning class. This idea of heuristics during decision making.
Classroom B: And the key. One of the key components here that we're going to see in this model as well
Classroom B: is that our decision making in these models are very much the product of of
Classroom B: more fundamental, more sort of basic level. Relatively automatic cognitive processes. And this is where this recognition priming comes in.
Classroom B: So remember that these are. These are typically experts in these particular domains. So given that these people have a wide range of experience, there are different scenarios or situations that can be recognized with very little effort.
Classroom B: So this is what this the second box going going from top to bottom, means when it says, perceiving something as as typical, somebody'd be sort of a a prototypical scenario, or similar to, or analogous to, prototypical scenario.
Classroom B: so something like a vertical fire, you know, as a term, it has a definition in the situation or context of firefighter firefighting from from an expert perspective that is a prototypical, easily recognizable scenario.
Classroom B: So it doesn't require a careful analysis, something familiar, something that's been encountered many times before, something that could be automatically and effortlessly recognized.
Classroom B: Now, what that's going to produce is because of that experience you have. A bit of like a schema that people use to understand those prototypical scenarios, you know, when when the figure talked about expectancies, and there are certain things that we we expect the vertical fire to do.
Classroom B: there are certain things we're looking for in terms of change. So what are the things that are going to? What are some of the relevant queues that are gonna indicate that some people either stay as a vertical fire or or expand beyond it. What are some goals usually put out the fire? What are typical actions start at the top.
Classroom B: get rid of the fire.
Classroom B: Now from that process it moves on to the next stage of the model is because of the experience in memory with those types of stereos. It's relatively easy for somebody to sort of simulate
Classroom B: the possible outcomes based on different courses of action.
Classroom B: From there the decision process becomes very straightforward within that mental simulation relying on the basic fundamental processes of memory. If the action you've decided, if the action that you're evaluating is evaluated. That's likely to work.
Classroom B: Do it. If not, then you. You go back and
Classroom B: rearrange that prototype, rearrange that schema until you arrive at a course of action that eventually works.
Classroom B: So we can see this is very much a much
Classroom B: like we consider this. We consider this kind of make a less sophisticated, much more simplified, straightforward process compared to this complex waiting of different options and maximization of expected utility. It's a very simple process of, recognize the scenario.
Classroom B: recognize the the potential actions that can be taken to remedy that scenario and then simulate through to see whether it's going to work. If it works, do it, if it doesn't go back and try again.
Classroom B: This is what we mean by this recognition priming aspect of of decision making
Classroom B: that we recognize familiar situations and that very automatically, very effortlessly, guides our decision making process.
Classroom B: So if we, you know, start to break this down in terms of these 2 cases, it's like we have, we have 2 different things that happen based on these 2 case studies, which is why they're both barely
Classroom B: illustrative of this type of process. So in the in the firefighting example, the the major reason that the course of action had to change was because the situation itself changed.
Classroom B: So you know the the typical stage here is the fire chief recognizes and perceives the idea of a vertical fire as the current sort of description of of what's currently happening. There's a typical action based on that person's experience that will typically result in a vertical fire being solved
Classroom B: by that through that mental simulation. It's a cable start,
Classroom B: you know, administering water from the very top
Classroom B: point of the fire. Now we'll get rid of it.
Classroom B: so that that process is going through very quickly. Will it work? Yes, okay, you started implemented.
Classroom B: But in this scenario the problem is that in the process of implementing that course of action which, of course, has to unfold over time. The prototypical scenario changes which then just repeats that process where, because of the high degree of expertise and experience you're now perceiving a different prototypical scenario which affords a different set of outcomes, a different set of actions, a different set of expectations
Classroom B: which then goes through another quick mental simulation arriving at. And now a different solution to solve that new problem that's unfolded over time
Classroom B: make sense questions, clarifications.
Classroom B: Now the Us. And Airways case is slightly different, because now we can see cases in which that that process doesn't make it all the way down to the implement course of action stage until a couple of iterations through
Classroom B: so when when that pilot is describing that scenario the the sort of process that was going on is okay, like, here's a, here's a typical prototypical scenario engine failure. Okay, based on experience based on training based on on understanding of the scenario first course of action that would typically happen in in.
Classroom B: you know, if this type of engine failure scenario is proceed as as as as expected, to the runway that you're supposed to act on
Classroom B: in this case. Now you have that mental simulation and the mental simulation results. In Nope, there's not enough power. There's I want you drop altitude too quickly to make it to the intended runway, which in this case was already a airport.
Classroom B: So Nope, that doesn't work. So that goes to this bottom. Right box, will it work? No. go back? Reevaluate?
Classroom B: Okay, what's the other potential, plausible goal and typical action find a different runway.
Classroom B: Go through that simulation, Nope, there's nothing close enough.
Classroom B: Will it work? No
Classroom B: next option? Okay, land somewhere, sink land somewhere. That's not going to result in, you know, high damage to the plane, not going to result in a difficult crash. I can land in
Classroom B: water.
Classroom B: Hudson River is the closest good option simulate through it. Will it work? Yes.
Classroom B: and that mental simulation process is what people tend to describe.
Classroom B: at least in these expert scenarios. So it's not like some calculus and and sort of like rational meaning of optics. It's working through and using those processes of memory, those processes of of sort of like mental simulated perception, and basically just working through the scenario to see what the most likely is going to be
Classroom B: questions thoughts before we take a breather.
Classroom B: As as we're thinking. Through this, we can sort of like hopefully see some of the the fairly stark contrast between the sort of typical normative theories versus descriptive right? So we have normative economic theories that are, they are rational they. They involve, you know, comprehensive
Classroom B: thought through through all the possible scenarios they evolved. Deliberate systematic calculations. That will in the appropriate scenarios results in essentially optimize positioning, it will result in maximization of utility. However, utility is defined. In every single scenario.
Classroom B: What we're seeing is that there are many, many cases in those original economies per se demonstrations that we do not maximize utility, their systematic misfortune or errors that we make and then when we actually go and start to dig into the types of processes that expert decision makers engage in.
Classroom B: it doesn't look anything like maximization of expected utility.
Classroom B: The processes just seem different. They're based on relatively automatic, effortless, cognitive processes.
Classroom B: certainly less sophisticated and deliberate than the standard economic perspectives would identify.
Classroom B: So we're gonna take a break. We're gonna come back at around 100'clock.
Classroom B: And what we're gonna do is we're gonna want to dive more seriously into these more probably more reasonable, more accurate descriptive models of what we actually so we'll talk about. The idea of heuristics and biases. Will, you know, although it's not really described in this way in the chapter. But we just cover it as recognition.
Classroom B: Prying, decision-making model does like does connect very strongly to this idea of heuristic decision.
Classroom B: It is not necessarily free.
Classroom B: but it's relying on our heuristics. So we'll dive into some of that. We'll look at some of the ways that in certain settings it can use systematic errors. We'll also turn that around and say.
Classroom B: is it? But she risks
Classroom B: by large actually ends up being a fairly effective way of navigating our world.
Classroom B: We'll come through that, and then we'll get some chance to to work through some some application. Talk about some of the thoughts you had for your reading response, and things like that. But for now take 15, come back at 10, and then we'll keep an operation on.
Classroom B: So
Classroom B: we've identified so far that you know, we have this traditional economic perspective on on human decision making it, you know, it seems to be fairly valuable as a normative theory. In in the sense that it provides us with a a way of making decisions that's rational, systematic and tends to optimize
Classroom B: quality of decision making.
Classroom B: And what we've identified is that while we may send as a school normative theory it does
Classroom B: fairly poor Java to be describing what we actually do as humans. When we make our decisions, it makes a number of assumptions that are like fairly newly demonstrated, false.
Classroom B: and and it makes a bunch of incorrect predictions about the types of decisions. And
Classroom B: so we moved into more of a sort of naturalistic decision-making process, identifying that they're actually much more fluent, effortless, automatic auditor processes that we tend to use to guide our decisions.
Classroom B: And
Classroom B: there have been many of these types of automatic processes that have been identified. And this is the other contribution by contact. First.
Classroom B: the the identification of a number of what are called cognitive heuristics
Classroom B: that that tend to guide our our judgments and choice and decision making.
Classroom B: So what what is a heuristic
Ashley Wray: shortcut.
Classroom B: the shortcuts. So you want to expand on that. What what does it mean at a for a decision making for something to be a shortcut.
Ashley Wray: It's based on
Ashley Wray: previous experience.
Classroom B: Okay? And Jared saying, going by rule of thumb, yeah. So shortcut and rule of thumb are kind of 2
Classroom B: basic idea of conceptualizations that most people use.
Classroom B: So and then actually saying, you know, based on prior experiences, this is correct. But we're we're trying to identify even more fundamentally like what makes something at a cognitive decision making level a a shortcut.
Classroom B: you know, when and how does this end up working. So let's let's sort of identify some of the key components of of basic heuristic decision making processes.
Classroom B: Yeah. But as it adds just that general definition, it's like, some sort of cognitive shortcut or rule of thumb that we tend to follow for decision making.
Classroom B: And the the first characteristic identify that sort of define. The key characteristic of a heuristic is a process of attribute substitution
Classroom B: and what this means for Kahneman and diversity is that when we're faced with some sort of question that we need to answer, or a problem that we just need to solve in a decision-making process
Classroom B: or some decision-making situation. We're often not properly equipped to actually answer that question. So we think about the types of natural decision making situations we pride ourselves in
Classroom B: most of the time. We can identify reasons that something like a comprehensive utility calculation just doesn't make sense. And that's the one after we're talking about okay, like decision, deciding what to have for breakfast like, I don't even know how I would assign those weights. That's because that is the situation that we're typically faced with in our daily decision making processes.
Classroom B: We don't have, you know, clearly defined possible outcomes and and probabilities associated with those outcomes. It's all very well defined, Fuzzy like decisions that we have to make.
Classroom B: So because we're often not equipped to answer the actual question that we're based. We're based with. We, we tend to substitute it with easier similar question. That we are able to answer. Usually we're not aware of the fact that we're doing this. But kind of a diversity's first sort of theoretical claim is that we often engage in the process of attributes to Chip, and we'll see examples of these in different situations.
Classroom B: And the second component is the component of natural assessment. And what this means is that. And this connects to this idea of attribute substitution
Classroom B: attributes that we substitute in the decision that we make instead of the more complicated decision has to follow the constraints of being naturally accessible. In the sense that it relies on, or just natural naturally occurring cognitive processes. So what we tend to do is we tend to simplify a scenario.
Classroom B: to make it such that we can make a decision based on a shortcut, that is, that relies on our naturally occurring ever less automatic cognitive processes. So with those key components, we can see where this idea of natural assessment comes in to that recognition, private decision-making model.
Classroom B: the processes of retrieval from memory, the processes of development, prototypes, femas, processes of mental simulation. These are all things that our brains just do.
Classroom B: They don't require as though there are sort of like higher level, sophisticated bug processes. They're automatic. They're effortless.
Classroom B: We don't have to think about retrieving something from memory most of the time, especially if it's something highly familiar, or we just see it and we recognize it. We didn't have to try it to do that.
Classroom B: and in in their, in the sort of famous original heuristics paper by Product University, they identify, 3 different types of heuristics that, according to a large series of relatively straightforward experimental demonstrations that tend to be a good characterization of of our decision making processes. That, we had started to list, some of them earlier in the class. But what are the 3
Classroom B: basic heuristics that Kahneman diversity identified
Classroom B: you? You don't have to identify all 3. You can identify. What if you would like
Annie Lewis: availability.
Classroom B: availability? Yeah.
Classroom B: the availability heuristic.
Classroom B: hey? What else?
Annie Lewis: Representative, miss? Yep.
Classroom B: there's one more. Is it going to be Annie, or is somebody else want you? Oh, we have. Jaren coming to the chat, I think. Yeah. Anchoring and adjustment. We'll just we'll just shorten to anchoring.
Classroom B: Okay? So these are 3 examples of heuristic processes that diversity identified. We're gonna go through each one explain exactly how they work in the context of of these 2 components of attribute substitution and actual assessments, we'll look at some demonstrations in in experimental scenarios that seem to support the back, the finding or the claim that we use these heuristics to guide our decision processes.
Classroom B: So
Classroom B: availability or the availability heuristic? Does anyone take a shot at just providing a a relatively brief definition of the availability heuristic. What is it? How does it work
Annie Lewis: as the ease of which information or instance come to mind.
Classroom B: Yeah, okay, so what I'm saying is, it's the the heuristic. So and this is the natural assessment component of it. We tend to judge things very prudently based on either house. How easily things are are brought to memory! And or how many examples of something we can, we can bring them every other part of it. We use that to estimate the probability or likelihood that something is going to happen.
Classroom B: So that's basically, yeah, that's and that's that's what we mean by this attribute substitution. When we're faced with a problem of trying to determine how likely something is to happen.
Classroom B: We don't have access to those probability structures. So we substitute that with the easier or naturally occurring process of how easy is it for me to draw instances of that event from memory.
Classroom B: and and this leads to another. So some of the original demonstrations of this. I
Classroom B: sorry we lost connectivity briefly there. But I think it looks like we're back. I'm gonna share the screen again.
Classroom B: Okay? So again, the the finding that we're identifying right now is that we tend to judge a young man committing homicide. As more likely the committee
Classroom B: all right. Sorry again. Unfortunately, it seems like this might end up being a persistent problem. Hopefully, this is the last time it happens. But yeah, Zoom keeps just randomly shutting down for whatever reason.
Classroom B: So I'm I'm guessing this is going to be a very choppy section of the recording. Okay, other other illustration. This is the case in the textbook, where people are either asked
Classroom B: whether ask whether? Whether there are more words in English language. It's how it was. There was such a slightly different typically were asked to. If you imagine a typical 4 page like typical novel, and imagine 4 pages randomly selected from that novel you're then asked. Are there gonna be more words that start with the letter K, or have the letter K in the third position as the third letter.
Classroom B: and then what you, what you generally find is that people are are going to estimate that there are going to be many more words that begin with the letter K. But again, in reality you are on average, when there might be many, many more words where the letter
Classroom B: so in all these cases, what we end up finding like a deviation from a deviation between people's judgments of likelihood. And then the actual reality.
Classroom B: Again, availability is well. When we think about words, we tend to organize words based on their starting letter. So it's neat for us to think about words that start with the letter K.
Classroom B: You can do this yourself before we generate a budget example very quickly wherever those sleep.
Classroom B: it's a lot harder to generate words. Where K. Is the third thing, right?
Classroom B: So based on the availability heuristic the judgment becomes wow. Whereas with starting with the letter K must be more common because they're it's easier to remember. Recall example.
Classroom B: One of my, favorite examples of this. It's not I didn't find in the textbook is a
Classroom B: pictures have this.
Classroom B: Nobody tries to get to my my laptop. So I actually signed it. But that's fine.
Classroom B: This is still recording. All right, said recording was still going.
Classroom B: Yep.
Classroom B: one of my favorite examples of this from audio university is. They bring in 2 groups of people. And in both cases the first thing that people do is they have to recall times in their lives when they were very assertive, you know, when they stood up for themselves or set up for other people
Classroom B: to get what they want, or when it's fair, and then the only difference between the 2 groups is, one group is asked to recall. 6 times they were assertive, and the other group is asked to recall 12 times they were assertive.
Classroom B: Okay.
Classroom B: so and then the the second phase of the experiment is, you just want a scale of one to 10. Everybody's asked rate. How assertive they are, and what you find in this experiment is that you, on average, get much higher self ratings of assertive. This for the 6 Example group compared to the fall.
Classroom B: And I like this demonstration because it's like, now we have this clear distortion or distinguishing distinction between this this heuristic approach, and the more sort of like rational calculus approach by all consideration that we just like go through a rational calculus.
Classroom B: If you run all twice as many examples, twice as much evidence that you're an assertive person, you could rate yourself as more assertive. But you get the opposite.
Classroom B: Why. pretty easy to think of. 6 times it works or ocean.
Classroom B: But by the time you're getting to like the 9, 1011, 12, example, you're probably stretching a little bit. You're really digging deep in memory, and it's getting difficult.
Classroom B: So based on that attribute, substitution of availability. If it was easy to recall of it.
Classroom B: Sort of then you're gonna say, I must be a fairly assertive person.
Classroom B: If it was difficult to retrieve examples that you're gonna have the opposite attribution, opposite judgment, then I must not be a very assertive person. It was hard to recall why that was assertive.
Classroom B: even though the raw evidence is
Classroom B: questions about that.
Classroom B: Okay.
Classroom B: the representative heuristic. It applies to often similar types of settings where sometimes we're judging the likelihood of something happening. It's not the only application. But it's one of the most commonly.
Classroom B: Observed application.
Classroom B: what's the what's the basic idea? What's the what's the attribute substitution? What's the actual cognitive process that we're engaging in when we use the representative heuristic.
Classroom B: you guys provide a free cabinet offense.
Classroom B: Yeah. Okay? So, Darren, saying, judging by similarity to to stereotypes. And yeah, so that's that's definitely a part of it. And more so it's often related to sort of stereotypical or prototypical examples of things. But it's the at a cognitive level. The key thing here is a similarity.
Classroom B: Again, similar judgment is just like a core component of most of our part of the processes. So it definitely fits that actual assessment part and the sort of most common demonstration of this is that in the account
Classroom B: that's that's listed in the tax work.
Classroom B: So what you get is you get this old blurb about somebody named Linda. And you know, you get basic characteristics, and you know she was bright, intelligent, outspoken she was a major in gender setting university nuclear demonstration.
Classroom B: You get those basic. I'm paraphrasing. But those are the basic. And then you get you get a couple of different things. Aye.
Classroom B: the the textbook and most textbooks sort of miss. Part of this. It's not a direct one to one comparison, the same way that most textbooks identify. But then what you get is a bunch of possible descriptions of Linda as an add-off current
Classroom B: and you get, you know you get descriptions like how might we use it? That Linda is a teacher and get a bunch of the 2 things that are embedded within that long list
Classroom B: is you get. How likely is it that Linda is a bank tower. and how like we use it, that Linda is a feminist.
Classroom B: and you get 2 key findings demonstrating the importance of of representative
Classroom B: so the first one is that you could ask people how likely it is that Linda falls into these categories. They could also ask, how representative is this statement of Linda? And if you calculate the correlation between those 2, it's a near perfect growth
Classroom B: point 9 9, and the the highest correlation you can get is one.
Classroom B: So that? Doesn't we start to suggest that I think that we probably correspond like we would be representative sort of similarity judgments in our head.
Classroom B: And then those 2 key statements. The bank teller and feminist bank teller is where we start to see systematic error or a bias.
Classroom B: You know, we saw these systematic biases or errors of the bank based on availability. The error in the Linda case is that people get a much higher likelihood rating to feminist bank teller compared to bank teller.
Classroom B: why is that problematic based on your understanding of probability?
Classroom B: And we've we've identified. It is an error. So this is like making making that that those different judgments represents an error. What is the error based on probability?
Annie Lewis: Is it because, the probability of someone being a bank teller is a lot more common. And someone being that bank tailor who's a premise?
Classroom B: Yeah. So II, yeah, I yeah, I'd say that. That's a that's for a characterization. I just want to rephrase it a little bit the idea is that so what Annie saying is like, it's it's necessarily more likely for something and is correct. But what's impact? Why, Annie is correct.
Classroom B: there's there's a you know. Does anybody know how to calculate what are called conjunctive probabilities.
Classroom B: or, more simply, it's the the probability of 2 events. Both happen is what we mean by it. So it's an injunction, is it? Again.
Classroom B: so if we have 2 events X and Y, and and we want to calculate the probability of both X and Y happening is relatively straightforward. You just multiply the 2.
Classroom B: Which means that the yeah, the you know both scenarios, the conjunctive probability is going what we're then
Classroom B: individual probabilities. And in fact, the rule of constructive probabilities is that the the probability of 2 events, both occurring, cannot possibly be higher than either. One of those events occur.
Classroom B: Simpler way of putting that is. if Linda is a feminist baby teller, she's also a baby teller.
Classroom B: So, being a feminist bank teller is a conjunctive savings, you are a feminist. There's a certain probability of that. If you're a bank teller, there's a certain probability of that. So it's impossible, for
Classroom B: the statement. Window is a big tower. To be less likely than Linda is a feminine big teller at at best. If there's a 100% chance that wind is a feminist.
Classroom B: then that probabilities are equal.
Classroom B: because if Linda's a feminine bank teller, she's also a bank teller. So it violates the basic law of inductive probabilities
Classroom B: representing a systematic error, a systematic bias. The explanation from representativeness as a heuristic is fairly straightforward. Linda just seems more like a feminist than a big temp grace
Classroom B: based on that description, that description, you know, Linda, as a feminist, is more representative of our current understanding of Linda than windows a bank network.
Classroom B: Yeah, you're you're forgetting real odds in this case, largely because in this case it's like a very clear violation of of the laws of probability. But again, we're making that similarity based judgment. That would explain why we're making that fundamental error probability because based on attribute substitution. We're not making a judgment of probability, we're making a judgment of single error.
Classroom B: Sharon saying, if she's a feminist, would that make wind a better helper? Therefore a better bank tower? I mean. I guess it depends on your perspective, on those are helpful sometimes
Classroom B: the but the more fundamental aspect is just that like, if Linda is a feminist, make tower, she is a big teller, because we're not making a judgment about whether or not there's a good make tower.
Classroom B: But the basic probability structure. And and this is why this. Why, I'm highlighting that like this. These 2 things aren't set side by side, like most people. If they get the true statements side by side. Linda's a big teller. Our decision-making processes are good enough that most people will recognize. Oh, like well, she's a demonic teller.
Classroom B: but when they're embedded in the different sentences, or even those independent judgments. Then we get that substantial similarity event.
Classroom B: Some of the other applications of representative. Is anyone familiar with the concept of the gamblers.
Classroom B: It's the the classic demonstration is that like, if you give somebody a scenario where say, you're flipping a coin if if you start to get the same outcome in our sequence. Say you flip a coin, get like 11 heads in a row.
Classroom B: Most people will start to expect that the next foot of the coin is more likely to be prepared
Classroom B: because of that walk. Sequence they expect that's free to break.
Classroom B: of course, in reality every single coin, but is entirely independent, so the probability of tails is always 5. But the gamblers, fallacy reflects the binding that people will expect the likelihood of tails to increase as you get along.
Classroom B: The explanation based on quantum and diversity is well, we, we make a representative judgment. Where in, you know, in the long live we expect that the relative portion of heads and tails will be even
Classroom B: because in the long run that's correct. But the the similarity judgment is, we implicitly expect short-term odds to reflect long-term odds.
Classroom B: which is something that's not the case. When you have a short sequence of events, it's very easy for it to deviate from a long-term expectation. But we make a similarity judgment between the long-term outcome and short-term outcomes.
Classroom B: in some cases leading to errors, and in some cases leading to real life errors, because, of course, if somebody is more prone to this campus house, this is one of the things that allegedly leads to people with with gambling problems, you know, thinking that they're like you for a win is the so they just keep public money and then losing money.
Classroom B: because it doesn't matter how many times you've lost in a row, the probability structure of the next sample doesn't change. They're independent
Classroom B: if you want to. If you want to make people think that you're an absolute idiot, go into a Casino Google overlap table and place money on the last number that just happened. Everybody at the table is going to look at you like you're completely insane.
Classroom B: because, like, of course, it's not going to be the same number. We just got that one. But of course, you're the one who's corrected.
Classroom B: Okay? And finally, the heuristic of angry, what's the basic idea here?
Classroom B: Okay.
Classroom B: yeah, this is the one that people don't talk about as much and often gets missed. The the finding of anchoring or the longer version adjustment is the finding that we tend to be fairly strongly influenced by initial presentations of information.
Classroom B: even in cases where that information is either very implausible or but we'll also see cases not describing the textbook where we, that information is just entirely irrelevant to the problem at hand.
Classroom B: So the the
Classroom B: the example or demonstration example, where your task is participant is to estimate the age at which Gandhi died. But you start with like 2, one of depending on the group. You're in one of 2 questions, where the answer is, fairly long, because you're either asked what's gone deeper than 9 when he died.
Classroom B: that's or was he younger than 140 when he died. So yes.
Classroom B: so they're both really easy questions. We don't really need to think about that, because they're very any plausible values, but they influence people's decisions.
Classroom B: So when people are asked first asked whether Donnie was older than 9 when he died, and then they're given an estate asked to estimate the actual age of 55. The average age people estimated in this experiment was given
Classroom B: for the group that are asked whether he was older or younger than 140. Their average estimate was 60.
Classroom B: You get a 7 on average 17 year difference in estimate just caused by the initial, implausible, unrealistic numbers. And that's what we mean by there's like, there's a sort of like connection to that imaging within information which then the adjustment part of anchoring and adjustment is couldn't influence later, after the brilliant question.
Classroom B: that's what outlines that in more real life scenarios the same thing happens when people are asked to, for example, estimate the value of a house.
Classroom B: or and this will, of course, generalize, do other items as well, because one of the things that happened with with with a house sale is, there's an asking price.
Classroom B: So if you bring people in and give them an asking price that's going to influence their estimate of the time of the house.
Classroom B: So if you get a low asset price and overall assessment, and so lower the high asset price is overall assessment.
Classroom B: and to be higher.
Classroom B: This is true of people who are U.S.A. Agents as well.
Classroom B: They're less influenced by it than non-real eras, but they're still influenced by it.
Classroom B: There are other examples not described in the text that have, you know, these are at least like pieces of information that are at least connected to the next question some way but there are also that clear demonstrations where just completely irrelevant numeric information influences people's processes. So one of the examples, one of the original examples
Classroom B: is that people are asked to estimate the number of countries that are members of the web. And what they do first is they just before that experiment. They, they spin a wheel
Classroom B: that is rigged to land on specific numbers. And again, you have 2 groups. You can either land on, say, 5, or it can land on 60.
Classroom B: So completely irrelevant people still anchored to it.
Classroom B: After seeing 5, they get a lower estimate of the number of countries in the UN. Compared to 5, 16,
Classroom B: even though it's utterly drowned. Right?
Classroom B: One of those troubling demonstrations is they put people into auction, so they get they have some bank money to spend, and they get on items, and the first thing they do is they just
Classroom B: ran their social insurance number, but they have to write down the last 2 numbers of their social parents number people who happen to have higher values for those L 2 digits ended up bidding more money
Classroom B: because they had recently seen a water shelter.
Classroom B: This is where you get these negotiation tactics. Right? So you start with a high price that you're selling.
Classroom B: which is, when you anchor people to that amount. And hopefully.
Classroom B: and, as you can see very likely end up
Classroom B: talking to the person to say, All right
Classroom B: questions. Thoughts.
Okay?
Classroom B: So this, so far, I mean, this is a pretty pessimistic view on our our, the effectiveness of our decision making processes based on these heuristics and the fact that it's usually framed as heuristics and biases. And we've gone through these different demonstrations where these heuristic decision making processes lead to systematic biases. And
Classroom B: and so when I said, That's a pessimistic view of decision making is suggest that our we have these natural cognitive limitation that drive us to use the heuristics would, too, balancing.
Classroom B: Now, the fortunate thing is that the actual.
Classroom B: the actual evidence and and perspective on this suggest that is definitely much more optimistic. It suggests that these curiosity making processes while you can generate laboratory experiments which they do errors entirely
Classroom B: by and large.
Classroom B: they lead to in a realistic, natural decision-making setting. They tend to be through decisions.
Classroom B: And then th, this is an idea. That's often attributed to a researcher Germany named Eric, who was often seen as sort of an opponent of Kahneman and Diversity. My view has always been that neither of them reach other carefully enough, because they actually kind of say the same thing caught him in diversity
Classroom B: say that same thing, that while we can identify these biases in certain scenarios, they're lightly adaptive in the sense that they are. They representing fine and tuning between our processes and our.
Classroom B: But the general idea of understanding that potentially artificial debate
Classroom B: is that in real settings these heuristics tend to leave them accurate decisions.
Classroom B: There's some good demonstrations on this.
Classroom B: So first of all, we think about availability as a he was. That's that letter K. Example.
Classroom B: There are only 5 consonants in in the English language, for which it's true that they're more common in the third position than the first position.
Classroom B: So for every other consonant in the alphabet. First letter decision process
Classroom B: leads to accurate decisions. It's only in the special cases where it leads to errors
Classroom B: so overall in our natural language environments. That's a good heuristic.
Classroom B: you know, availability more broadly outside of artificial scenarios like letters. Generally.
Classroom B: it's easier to recall things that we encounter. Frequently
Classroom B: we encounter them frequently because they happen more often.
Classroom B: It can be distorted by by disproportionate reports in various forms of media, but generally things that are more familiar that are easier to recall for memory.
Classroom B: They they are easy to retrieve from memory, because they happen more often, which means that in many cases availability is a good year thing.
Classroom B: In fact, here's one queer demonstration of this, and this is what gigawrins were called the recognition.
Classroom B: I generally consider it just a an extreme form of availability heuristic. The recognition heuristic is just, not not even how easily can I retrieve this from memory? It's an even simpler question of, can I retrieve this from memory?
Classroom B: But do I recognize this as an event that that happens?
Classroom B: Here's a demonstration of this and a demonstration of the utility of this type of availability, heuristic or recognition heuristic
Classroom B: in in a now fairly well known experiment, and there are 2 versions of it. They bring people into a laboratory, and they're making either decisions about about stock changes.
Classroom B: So our our, you know, our companies and various stocks went to go up or down. And then another scenario. They're asked to
Classroom B: determine or or predict the outcomes of various football methods.
Classroom B: Your view of the ball is good, so like what America's calls offer. and in each case they they divide people into novices and experts, notices or people that just don't have much experience with that in the stock case. You get actual expert stockbrokers, and in the football case you get people who would identify themselves, as
Classroom B: you know, pre-existing football hands. So you've asked very few nomisons.
Classroom B: What you find in these scenarios and many other scenarios is that the offices do just as well as for sometimes better than the so-called experts in making these decisions.
Classroom B: If you look, if you go through that naturalistic decision making process and ask people to describe how they made those decisions, the experts engage in this complicated calculus thinking about a bunch of things stockbrokers, you know. We'll think about all the different trends that happen in the past little while football fans will think about like what's happening on different teams. What have the previous practice look like but to the office
Classroom B: they just pick what they recognize
Classroom B: is this talk going to outperform this one? Well, I've heard of Google, I haven't heard of this one. So I'm going to say, Google is probably going to do better
Classroom B: if you're a if you're not a football fan, I don't. I've never heard of this team, but I've heard of Germany.
Classroom B: so I'm going to pick that
Classroom B: now because of the structure of our environment. This works. You tend to hear about companies and stocks that do. Well. you kind of hear about sports teams that win.
Classroom B: So if you're relying on that very, very simple heuristic based on the actual structure of the real world environments, they tend to lead to good decisions.
Classroom B: I would even argue that this this game created in the piece of real estate. asking prices pretty strongly correlated with the value of the house in the real world. Right?
Classroom B: Sure you can have an experimental artificial scenario where you sort of like fudge the asking price. So that's quite different from the actual value. That's a little bit of passing prices to be maybe a little bit higher than the actual assessed value of the house. But if it's a a seller's market, but in general there's gonna be a strong correlation between the value of a house, right?
Classroom B: So you know, adjusting your estimate of the actual value of the house based on that price in the real world. Isn't that irrational? Because it's predictive? It's it's useful information.
Classroom B: And and a lot of Peter Rether's work is identifying a lot of these very simple heuristics that, rather than identifying the ways they produce biases and errors and identifying the ways in which they lead to good decisions.
Classroom B: The recognition heuristic is a good example of that.
Classroom B: There's the gaze heuristic in in baseball is when you're outfielder catching, catching a fly ball. One of the easiest ways to make sure that you're in the correct location is just to move with the ball to make it such that if you're looking at it, the angle between you and the ball doesn't change.
Classroom B: If you do that, it's a very simple thing to do, a very simple decision rule. You will always be in the right
Classroom B: as long as you can get there quickly.
Classroom B: You don't have to do any complicated calculation of the trajectory of the ball.
Classroom B: Simple, easy, fine duck.
Classroom B: And what we're identifying here is that these, the terminology that people use here is. These heuristics are what are called ecologically valid heuristics. They're ecologically valid in the sense that they are. Of course, this is different from
Classroom B: black holidays concept we covered at the beginning of the course. They're ecologically valid in the sense that they are appropriate based on our real world environment.
Classroom B: Yeah, the computer scientist, for Simon describes this as imagining cognition of being one weighing it in a pair of scissors and environment, being the other played in that panor, and the only way in which a pair of scissors is going to be useful is those waves fit together.
Classroom B: So we don't necessarily need sophisticated, complicated, cognitive decision making processes need decision-making processes that are well tuned to our environment.
Classroom B: I've been talking a while and want to lose some time for for discussion, but before we move into that any thoughts questions.
Classroom B: The the general view is that, like these, the benefit of these years and processes is that there are often framed, and what are called fast and frugal decision making processes. They're fast in the sense they don't take very long. They're frugal in the sense that they don't require a lot of contact
Classroom B: because it's a rapper.
Classroom B: Those are very well tuned for the types of decisions we need to make in many scenarios. We often don't have the time to sit down and deliberate about every possible outcome, especially when we don't have all the information available. You often make
Classroom B: quick decisions.
Classroom B: Fortunately our decision-making processes through these heuristics have developed in a way that they can lead to error, depending on the scenario, but more often than not they can lead to good decisions.
Classroom B: So what I would like to do for the last half hour or so is, as I mentioned at the beginning of last. This chapter is kind of largely theoretical, and not a lot of direct application for some, but not much. Which means that the reading response questions we kind of require a more extrapolation
Classroom B: on your part.
Classroom B: So what I'd like to just spend some time discussing the the response to that that broader question of what are some of the things that we can do to, you know? Facilitate decision making at a societal
Classroom B: it could be in any any sort of gonna give you some time to discuss it as a group, and then I'll I'll pop in and see where your thoughts is going.
Classroom B: Does that make sense?
Classroom B: I'm gonna stop the recording, which I'll have to do from here.