The Framing Effect

In this video I introduce Tversky and Kahneman’s work on the framing effect and how consideration of benefits or losses can influence the choices that people make and their willingness to take risks. I consider a few everyday examples of this, then consider how the framing of default options may also influences the choices we make, as demonstrated in Eric Johnson and Daniel Goldstein’s work looking at opt-in and opt-out organ donation programs in different countries.

Daniel Kahneman – Thinking, Fast and Slow (Amazon) http://amzn.to/2nAWnop

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Video Transcript:

Hi, I’m Michael Corayer and this is Psych Exam Review. In this video we’re going to look at another effect that was studied by Amos Tversky and Daniel Kahneman. So for this I’d like you to imagine a hypothetical situation that we have a rare virus outbreak and it’s going to affect this 600 person village. So what you have to do is decide one of two programs in order to confront this situation. If you choose program A then 200 people will be saved. If you choose program B then there’s a one-third chance that everyone will be saved and a two-thirds chance that no one will be saved. So which of these programs would you adopt?

Now half of the participants in Tversky and Kahneman’s study received the same question about this rare virus outbreak in a village of 600 people but the program options that they received were slightly different. What they received said okay if you choose program A 400 people will die. If you choose program B then there’s a one-third chance that no one will die and a two-thirds chance that everyone will die. Which program would you like to choose?

Now you can think about your own answer when I first framed this question. Of course the participants didn’t know that there were different versions of these forms. You can think about which program sounds better in either case. What Tversky and Kahneman found is that in the first situation Program A was popular. People liked saving the 200 lives.

In the second version, same question and mathematically identical answers, right, you can probably notice that, you know, program A is the same thing here, program B is the same thing but in the second case program B was more popular. So what’s going on?

Well, what’s the difference between these framings here? How are they, how are the programs framed in different ways? Well, the first framing is all about people being saved and the second is about people dying. So based on this Tversky and Kahneman suggested that when it comes to thinking about benefits people like to be certain. They like certainty. They’d like to know that this is what I’m getting, this is the benefit of this program: 200 people are going to be saved. That sounds good.

But when it comes to thinking about losses, people want to avoid losses and they’re willing to take risks to avoid losses. So we start thinking about the people that are going to die, you’re thinking about the losses and as a result you’re willing to take riskier options. That’s why program B is suddenly more popular because people like, you know, I don’t like to think about 400 people dying, I will take a chance to try to save everyone. I will risk it, I’ll roll the dice and see what happens. Maybe we can save everyone, maybe we can avoid these losses altogether.

Ok so this shows how the way that we frame choices, the way we describe them, the words that we choose, and whether we’re thinking about the benefits or the losses influences our decision making. We see this all the time in daily life. You see medications, for instance, focus on their benefits. They tell you that they have a you that they have a 30% failure rate. Or when you go to a gas station would you rather see a sign that there’s a cash discount if you pay with cash or that there’s a credit card fee? Do we think of this as a reward for using cash, a benefit, or do we think of it as a loss, as a penalty for using a credit card? Maybe that will influence which people prefer.

We also see this in politics. We see that the way that certain programs are described will influence whether people like them or not. Do we decide that a program should be described as helping the needy or is it providing welfare, which is a term that has negative connotations? People want to avoid a welfare state and so they may find that they like a program that provides assistance to the needy and they don’t like a program that’s providing welfare, even though the program may be exactly the same thing.

So how we frame things matters. It influences the choices that people make and how we frame the default options also matters. So now we’ll look at a study by Eric Johnson and Daniel Goldstein and what Johnson and Goldstein did was they looked at organ donation programs and they looked at whether the program was an opt-in program or an opt-out program.

So what that means is in an opt-in program you have to choose to be a part of the organ donation program. In other words, you’re not in the program until you check that little box that says I want to join the organ donation program. In an opt-out program it says you’re in the organ donation program. If you don’t want to be in the organ donation program then you have to check this box that says I do not want to be in the organ donation program.

So the question is, how does this influence what people choose? The answer seems to be that if it’s an opt-in program, enrollment rates are lower. Countries that have an opt-in program tend to have lower rates of enrollment in their organ donation programs. Countries that have an opt-out situation where you’re part of the program unless you choose not to be tend to have much higher rates of enrollment in their organ donation programs.

So what this demonstrates is that how things are framed, how we see things as a default option of being in this program or not, has an influence on the decision we make. This is a decision that, you know, is tough to make. Perhaps I mean there’s, you know, philosophical and religious implications of thinking about what’s going to happen to your body after you die. I mean that might be a hard question to try to even begin to think about and so rather than think about it the easier choice is just let this form decide. The people who designed it, let’s just go with whatever happens and then I don’t have to think about it. Then I don’t have to consider all the implications of this.

Now this also means that we’re sort of sacrificing our free will a bit to the people who design the forms, right? We tend to just go with however things are framed and we tend not to think about you know whether or not we really want to be a part of this program or not. Now of course you can say for yourself “well that’s how other people behave and not me. I would think about it and I would have my choice of whether I’m in the organ donation program or not” but if we look at the average you’re probably like most people and you probably won’t actually do that.

You’ll tend to go with the crowd just like many other people do. Ok, so this shows how framing really does matter and if you think about how we design questions, how we design possible programs, whether we focus on the benefits or the losses and that’s going to influence the decisions that people make and how risky people might want to be in making those decisions. I hope you found this helpful, if so, please like the video and subscribe to the channel for more.

Thanks for watching!

The Representativeness Heuristic

In this video I describe another heuristic identified by the work of Amos Tversky and Daniel Kahneman. The representativeness heuristic is a shortcut that we use when attempting to estimate the odds of something being true, such as whether an interview profile came from a lawyer or an engineer. Rather than using relevant base rate information, participants showed a tendency to rely on prototypes when making this decision.

 

Daniel Kahneman – Thinking, Fast and Slow (Amazon) http://amzn.to/2nAWnop

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Video Transcript:

Hi I’m Michael Corayer and this is Psych Exam Review. In this video we’re going to look at another example of a heuristic identified by the work of Amos Tversky and Daniel Kahneman.

So for this I’d like you to imagine a hypothetical situation. Here I want you to imagine that I have conducted 100 interviews with 70 engineers and 30 lawyers and what I’m going to do is I’m going to mix up all of these assessments and I’m going to randomly pull out a profile and read this person’s profile to you. Your job is to guess what are the odds you think this person is a lawyer or an engineer.

Ok, so we’ll start with, let’s imagine I pull out the profile of a man named Adam and I tell you that Adam seemed to be outgoing, he was interested in politics, and he displayed particular skill in argument. So what do you think about Adam? What are the odds Adam is a lawyer or an engineer?

Ok, let’s look at another profile. Let’s imagine I pull out a profile of Dick here. It says Dick is a thirty year old man. He’s married with no children, he’s shown high ability and motivation, and has been quite successful in his field, and he’s well-liked by his colleagues. So what do you think about Dick? What are the odds Dick is a lawyer or an engineer?

So if you were thinking about these people here and you thought Adam was probably a lawyer and you thought Dick was a little harder, you might have said, you know, “I couldn’t really tell, 50/50 chance. I mean he could be a lawyer, he could be an engineer, I really don’t know”. Well if you thought this way, which is how most of the participants in Tversky and Kahneman’s study thought, then you’ve demonstrated this representativeness heuristic.

So why is that? Well what you’re doing if you thought this way is you’re comparing to your prototypes of what you think an engineer or a lawyer is, right? You’re trying to decide which category to put this person in and so you compare them to sort of a mental prototype of what is a lawyer or what is an engineer. If you were thinking this way when trying to figure out whether Adam or Dick was a lawyer or an engineer, you were doing it wrong.

there’s a much better way to determine this because what you were ignoring is the base rate information. All right, I actually told you at the start the odds that either one of them is a lawyer or an engineer, right? I told you there were down right here. You probably didn’t include that in your analysis of each profile right? If you include that, you would just say engineer every time, right? for Dick, “well 50/50 chance” and that’s actually what many participants said. It’s an equal chance of lawyer or engineer.

Well, no 70/30, I mean he’s probably an engineer and even with the case for Adam, you know, I told you he’s interested in politics and skilled in argument. That was to sort of prime your prototype of what you might associate with lawyers. But of course there are engineers who are interested in politics and there are engineers who are good at argument and so, you know, the odds are still very much in favor of engineer even though he sounds like a lawyer.

Ok, so what’s happening here? In the availability heuristic, remember, we said that our mind substituted. It substitutes a different question. We’re not good at estimating frequency and so what we do is we just see how easily does it come to mind, right? And we answer that question instead. So what’s the question here that triggers this representativeness heuristic and what question do we do we replace it with?

Well the idea is we’re not good at calculating odds. We don’t like doing probability; our minds are really not equipped to do this. So whenever we ask this question, as I was doing here, which is “what are the odds of something, what are the odds of X?”, you know, in this case it was “what are the odds this person is a lawyer?”. Well as soon as we start thinking about this our mind says, “you know what I’m not doing odds. There’s probability, these calculations and statistics, it’s very very complicated, let’s not try to actually do all of that. That’s going to take a long time and, you know, I don’t have the time to do that. Instead let’s take a shortcut. Let’s answer a different question.”

So the shortcut is to say “how well does this match my prototype?”. Now here’s a question that we can answer. How well does this match my prototype of that. So instead of “what are the odds of this, this person is a lawyer” say well “how well does this person match my prototype of a lawyer?”. There’s a question that we can answer.

We can say “Oh Adam, he sounds like a lawyer, you know, he fits my profile, my prototype fits, my sort of stereotypes about what a lawyer is or what an engineer is”. So the idea is well if it matches my prototype then I’ll say that the odds are high and if it doesn’t match my prototype I’ll say that the odds are low.

Of course we can think about other situations beyond this hypothetical here where this is a real problem. We don’t want to just be making decisions based on how well things match our prototype. I mean imagine that you were interviewing for a job and maybe what’s really happening is it’s just a matter of seeing how well you match the prototype of the interviewer for this particular position. Well what’s my prototype of this position and does this person match? Ok, they seem to match, give them the job, right?

We don’t want that sort of thing happening and it’s even worse if you think about other situations. I mean, what are the odds this person is a criminal? What are the odds this person is a terrorist? Here we can see why things like racial profiling occur because what happens is that’s a really hard question to answer.

What are the odds that this person is a terrorist? How do we go about doing that? How well does this person match my prototype of a terrorist, in other words, how well do they match the stereotypes that I have about what, you know, who terrorists are? Of course that’s not a very fair way of trying to answer this question, right? We’re going to ignore other potentially relevant information when we’re comparing to a prototype so there’s a danger to this representativeness heuristic and how it can lead us to make errors that can have important consequences.

Ok, so that’s this representativeness heuristic; the idea that we make comparisons to prototypes rather than actually looking at the base rates of things and actually trying to figure out the odds of something occurring. I hope you found this helpful, if so, please like the video and subscribe to the channel for more. Thanks for watching!

The Availability Heuristic

In this video I provide an introduction to behavioral economics and the work of Amos Tversky and Daniel Kahneman by describing a heuristic we use when attempting to assess the frequency of events. The availability heuristic is a shortcut that estimates frequency based on how available an event is to us, or how readily we can bring examples to mind. This can cause us to make errors in estimating frequency because ease of recalling events does not necessarily mean that they are more frequent; they may simply be more memorable (such as terrorist attacks, planes crashes, and child abductions). This can cause us to overestimate the likelihood of certain events occurring, while we underestimate the risks posed by events which are actually more frequent.

 

Daniel Kahneman – Thinking, Fast and Slow (Amazon)
http://amzn.to/2nAWnop

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Video Transcript:

Hi, I’m Michael Corayer and this is Psych Exam Review. In this video and the next few videos we’re going to look at some examples of heuristics.

So heuristics are these mental shortcuts that we use when we’re solving problems and making decisions. The heuristics that we’ll look at are mostly going to come from the work of Amos Tversky and Daniel Kahneman. Tversky and Kahneman essentially provided the foundation for a new field of study known as behavioral economics.

Behavioral economics is a combination of economics and psychology and it questions this traditional economic assumption that people act rationally, right? That people make rational choices in order to optimize their output. What behavioral economics does is show actually people don’t act rationally very often. They rely on heuristics when they’re making decisions and this means that they’re going to make predictable types of errors in their decision-making.

Daniel Kahneman won the Nobel Prize for this research in shared the prize although unfortunately he had passed away in 1996 and wasn’t eligible. If you’d like to find out more about Tversky and Kahneman’s research and behavioral economics in general I highly recommend Daniel Kahneman’s book Thinking Fast and Slow.

Ok, so let’s look at an example of one of these heuristics. This first one is the availability heuristic. So the availability heuristic is something that might occur whenever we’re trying to answer the question of “how frequent is X?”. This is a hard question to answer. So here’s an example that Tversky and Kahneman used. They asked participants to estimate “do you think English has more words that start with the letter K or more words that have K as the third letter?”.

You can think about this yourself. Which do you think is more frequent? If you’re like most of the participants you probably think that there’s more words that start with the letter K than have K as the third letter. If you think this way, you’re wrong because there’s actually nearly three times as many words that have K as the third letter than words that start with the letter K. So why was this so hard to get correct?

Well it turns out this question of how frequent something is is really difficult. How frequent is X? It’s like I don’t know where to begin answering that question. How do I estimate the frequency of something?

So our minds really aren’t equipped to answer this question but rather than admitting defeat what they do is they substitute a different question. This is a question that’s a little bit easier for our mind to answer. So what’s the question that we substitute? The question that we ask instead and we don’t realize that we’ve switched them is “how easily does X come to mind?”.

There’s a question that can be answered. Just try to bring it to mind and see how easy or hard it is and the idea is we make the assumption that if something is easy to bring to mind, you know, if I can come up with a lot of examples of something, it’s probably common. So it’s probably a frequent event but if it’s really hard to bring something to mind I can’t come up with any examples then it’s probably not very frequent.

This is probably what happened with this question about the letter K. The thing is it’s easy to bring words to mind based on their first letter. It’s hard to bring them to mind based on their third letter but this actually doesn’t have anything to do with their frequency, right? It comes with how we think about words. It’s easy to bring them to mind by the first letter even if they’re not particularly frequent. So you can think about words that start with the letter K and you can come up with a list pretty easily; kitchen, kite, kick, kangaroo, but when you try to think about words with K as their third letter, it’s hard to do.

We’re not used to thinking about words this way and so it’s not a question that you normally ask and even if you see words with K as the third letter you might not acknowledge it. So it’s harder to bring words of that ilk to mind. Ok, so of course this heuristic is used more than just when we’re thinking about words and letters, right?

It’s really used whenever we have this question about frequency. How frequent is X? That’s hard to do so our mind uses a shortcut, says “well how easily can I think of examples of it? I’ll estimate frequency based on that”.

This is why people are more afraid to ride on a plane than in a car, even though they’re much more likely to be injured or killed in a car crash than in a plane crash because when people ask “How frequently do planes crash?” they can think of examples of plane crashes really easily because they’re memorable right? We see big news stories about them and so people think about plane crashes and they bring several to mind but when it comes to car crashes, there’s thousands and thousands of car crashes every year that we simply don’t hear about. So we can’t bring those examples to mind and as a result we assume that car crashes are less frequent than they actually are and that plane crashes are more frequent than they actually are.

Now we also see this when parents think about their children’s behavior. We have parents that won’t let their children walk to school because of fear of child abductions. Child abductions are incredibly rare; they almost never happen, right? But when they do happen, we hear about them. The big news stories, this means we can bring them to mind easily and that means we think that they’re more frequent than they actually are. And other behaviors that are actually real risks to children that are common, we don’t think of as being much of a problem. So I mean parents will drive their kids to school even though the risk of being injured in a car crash is much greater than the risk of being abducted by a predator.

Or, you know, we let children eat hot dogs. The risk of choking to death on a hotdog is a greater risk to a child than an abduction or a risk of drowning, but, you know, we let kids go to barbecues and pool parties without a second thought. Yet those are things that post real risks to children, whereas the risk of child abduction is nearly zero but because of how we bring things to mind and how we estimate frequency, we are likely to overestimate the risk of certain things and underestimate the risks of others.

Ok, so that’s essentially the availability heuristic; this idea that we aren’t good at estimating frequency and so we base it on how available things are to our mind. How easily do they come to mind, and we estimate frequency based on that. Ok, I hope you found this helpful, if so, please like the video and subscribe to the channel for more.

Thanks for watching!

Problem Solving: Algorithms vs. Heuristics

In this video I explain the difference between an algorithm and a heuristic and provide an example demonstrating why we tend to use heuristics when solving problems. While algorithms provide step-by-step procedures that can guarantee solutions, heuristics are faster and provide shortcuts for getting to solutions, though this has the potential to cause errors. In the next few videos we’ll see examples of heuristics that we tend to use and the potential decision-making errors that they can cause.

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Video Transcript:

Hi, I’m Michael Corayer and this is Psych Exam Review. In this video I want to explain the difference between an algorithm and a heuristic and then provide an example that will hopefully help you to see why it is that we tend to use heuristic.

First, what is an algorithm? Well an algorithm is a step by step procedure for solving a problem. The downside of an algorithm is that it tends to be slow because we have to follow each step. We have to move through the process but on the positive side an algorithm guarantees that we’ll get to the solution. If we can follow all the steps, then we will find the solution to the problem. So an algorithm is guaranteed to work but it’s slow.

What’s a heuristic? Well, a heuristic is sort of a mental shortcut. It’s not a step-by-step procedure and so the downside of a heuristic is, because it’s not a step-by-step procedure it doesn’t guarantee that we’ll actually get to the solution. It might not work so a heuristic is not guaranteed.

We don’t know if it will get us to the correct solution but it’s faster, right? Because it’s a shortcut it skips a lot of the steps and says well these probably aren’t going to get to the solution so I’m just going to skip them. Now it could be the case that those steps would have gotten to the solution or would have gotten to a better solution but the heuristic says I mostly am concerned with getting a quick answer rather than the always correct answer.

Ok, so let’s look at an example of a sort of everyday problem that you might experience and how you would solve it using an algorithm versus using a heuristic. So let’s imagine that I’m in my apartment and I want to go somewhere and so of course I need to take my keys with me and I can’t find them. They’re not on my desk where I usually put them and so I have this problem of how do I find my keys.

Well I know that they’re inside the apartment because I’m inside the apartment and I must have unlocked the door to get in. So one thing that I could do is I could follow an algorithm for solving this problem. I could say okay if the keys are definitely in the apartment somewhere then a step-by-step procedure for finding them will be to start in one corner of the apartment and to slowly look in every single location expanding out from that corner of the apartment until I’ve searched every square inch of the apartment and if I do that it is guaranteed that I will find my keys because they must be in the apartment somewhere.

That approach, that step-by-step procedure will guarantee that I find them but as you can already see it’s going to be slow, right? Because I’m not allowed to skip ahead I have to start in one corner and progress through the entire apartment. Now that’s probably not how you look for your keys if you can’t find them. What do you do instead?

You use a heuristic. You use a shortcut. You say “there’s probably a lot of places that I can just not look in because my keys probably aren’t there.” Now I don’t know that for sure because I don’t know where my keys are but a heuristic that I might use in this approach to solving this problem would be to say “why don’t I look the last place that I remember having them” or maybe I should start by looking in the door, maybe I’ve left them in the door when I unlocked it, right?

Now this doesn’t guarantee that my keys are going to be there but there’s a higher likelihood and it’s going to be faster if I look in the two or three places that my keys are usually found then. I’m probably going to find them. It’s not guaranteed; I might not find my keys in those two or three locations. I might say “okay maybe they’re in the pocket of the pants that I was wearing yesterday or maybe they’re in the door or maybe I set them down on the kitchen table instead of in my bedroom” right?

So what a heuristic does, it says check those places first, take a shortcut. Don’t start looking under the couch, I mean the odds of them being under the couch are probably pretty low. They’re not necessarily zero but they’re low. So start with the places that they might be and then move out from there. Now again, this doesn’t guarantee that I’ll find the keys but it’s going to be a lot faster and maybe I try that and it still doesn’t work. Then maybe eventually I have to resort to my algorithm approach but most of the time we use heuristics.

We have shortcuts that we use that allow us to skip a lot of potentially unnecessary steps. So in the next few videos we’ll look at some other types of heuristics that we use in our decision-making. We’ll also see the situations where they can lead us astray; they can lead us to think that we’ve found the answer when in fact we haven’t or we found an incorrect answer. Ok, I hope you found this helpful, if so, please like the video and subscribe to the channel for more.

Thanks for watching!