Are the Economic Troubles Good or Bad News for Applying Behavioral and Decision Sciences?

Exchanging some ideas with various people I’ve heard that due to the economic troubles in EU and USA it will be hard to find firms willing to apply insights from Behavioral and Decision Sciences. This thought made me wonder if the not so happy economic situation is a plus or a minus for applying behavioral and decision sciences.

On one hand, I would agree that many firms would be less open to “new stuff” and have a more prudent approach in the sense of going with what they know works. Similarly it is not unreasonable to focus on perfecting what “we know it works”.

On the other hand, companies and other organizations can take a more courageous approach and start experimenting with behavioral insights. The main argument in favor of trying out behavioral insights is that the “Tools” of behavioral and decision sciences are not very expensive and the results are (could be) quite strong.

When I say that using tools from behavioral and decision sciences is not very expensive, I mean that compared with other tools the behavioral ones are often easier to implement and the cost  of implementation is not skyrocketing. In no way are these tools “free” or even very cheap, but usually they cost a lot less than more traditional tools.

Yesterday I have attended a webinar from BrainJuicer and one of the examples given was placing scent dispensers in a shop. Now, let’s think for a second how much that would cost. Probably the costs were a couple of hundred euros for each shop. The results of using scent dispensers were surprisingly positive. The quality of the merchandise in the shop was rated considerably higher as compared with ratings from when the dispensers were not working. For the more financially oriented among you, the sales increased also.

Probably similar results could have been obtained through other means such as more advertising, changing some of the merchandise in the shop etc. But realistically speaking, do you believe that these “more traditional tools” would cost only a couple of hundred euros for each shop?

Earlier I said that companies and other organizations should “start experimenting with behavioral insights”. The key word is “experimenting” and here is why I believe that this should be done.

First, science has a lot of information and data on behavioral effects, but significantly less is known for practice. Don’t get me wrong, I’m not saying that what we know from science is useless, rather I support trying to apply the scientific insights in practice in a smart manner.

For example we know that the compromise effect occurs when there is ambiguity. The question is: how to optimally use the compromise effect in an on-line store? We can apply it to all products in the store and for all shoppers and then compare “before and after”. However, this is in no way optimal. Maybe “old” users of the web-shop don’t feel any ambiguity while “new” users feel it. Maybe in some product categories the compromise effect occurs, while in others it doesn’t; maybe it occurs in some product categories for new shoppers and not for old shoppers, while in other product categories it does not occur for either “new” or “old” users.

What is best? The answer is to experiment with these tools and simply see which works best for you and how does it work best.

Some more critical people would say that it is the fault of science that it has not answered these questions and until scientist come up with an answer practitioners shouldn’t bother. My answer to this is: scientists have the job of discovering such effects. It is your job to see if they work or not for your business.

Second, running some experiments is not very difficult. Of course there are some areas of business where it is more difficult than in others, but overall it is in no way Rocket Science. Businesses that have a strong component in the on-line environment have a big advantage because they can have more flexibility and ease in designing the stimuli and collecting the data. However, even less flexible businesses can experiment to some degree.

Research and statistics can be very scary for many people. In the case of experimental research things are significantly less scary than in other methodologies and if I (who am almost mathematically illiterate) could learn it, I am sure others can do it also. Of course, you can always hire a firm to do it for you.

Third, you can simply continue doing what you are doing already. I gave examples related to marketing, but this is not the only area where behavioral insights can help. For example if you want to cut some costs with office supplies, you can continue to send memos saying that people should not take home office supplies; you can continue to put sings around the office saying that people should be responsible and print both sides and so on.

If for example a government wants to improve on tax collection it can hire more people at the “financial authority” and start a wide campaign of punishing the wrong-doers. However this will not last for long and apart from increasing the level of stress in society it will not achieve long term changes.

At the same time firms and organizations can smart up and do some very simple and quite effective things such as setting the printer to print both sides by default.      

Starting to use behavioral insights is not a crazy adventure with a very small chance of success. At the same time it is not without any risks. In my view, the balance between costs + risks and potential rewards is in favor of the later.

Handle With Care! - Ethics in Applying Behavioral and Decision Sciences

In the past ten years behavioral and decision sciences have gained a lot of ground in both research and practice. Some very popular books that have “translated” the academic knowledge have helped a lot in this process. Now, 10 years is not that much for either science or practice. It is not exactly the very beginning, but for sure there isn’t a vast history behind using behavioral and decision sciences in practice.

In this post I will address several issues that have to do with the ethics of applying behavioral and decision sciences. Since academia has its own ethics codes and regulations I will not address the research related issues. However, there is no real ethics code of applying behavioral and decision sciences in practice. Let’s take a look at some potential issues.

Can applying the insights from these sciences be avoided? Take for example choice architecture, can it be avoided?  

The fast answer to this question is “Yes” and the reason for this is that in our minds it is always possible to not do something. In other words, if something can “be done” it also can “not be done”. In reality, however, things are not that straightforward.

Let’s think for example of the so often encountered “agree to terms and conditions” that we find on websites. The “check box” can be empty (not checked) as a default or it can be full (checked) as a default. We know that “the default option effect” is very powerful, thus being or not being checked makes a huge difference. But can we actually avoid the use / occurrence of this effect?

As you probably have figured, avoiding this effect is a lot harder than we initially thought.

Things are quite similar for other “tools” of choice architecture. For example the mere existence of a choice set influences the person’s choice. People don’t usually choose something that is not in the choice set. In other words, whenever a person is presented with a choice set there are elements of choice architecture present even without any intent.

Another example is the way in which elements of choice architecture are hard to avoid is the way in which we present options to be evaluated. We can present them in single evaluation mode (one at a time) or in joint evaluation mode (all at once). The options could be presented in a mix of both ways such as first present them one at a time and then allow for comparisons by showing them all at once. However, even this might influence the choice of a person.

I guess you got the main idea. Even if one would want to not use choice architecture (or other means of influencing people’s choices or decision) it is not really possible to fully avoid it.

Not being possible to fully avoid using elements of choice architecture does not mean that we have to use the entire set of tools form behavioral and decision sciences. In fact it would be wrong to use everything, but that’s another story. For example if we can’t avoid having a “checkbox” with a default of “checked” or “not checked” this does not mean that we have to use more tools such as peer or  authority recommendations.

The question now is “should we use more than just the things we can’t avoid?”. My answer is “yes”, but we have to “handle with care”. Tools from behavioral and decision sciences have significant effects on human judgment, decisions and behavior. Sometimes these effects are not necessarily large, but they exist and more or less they affect everyone. Using these tools responsibly is not an easy job.

Can we “overdo it”? The answer is “For sure!”. One of the areas in which tools from behavioral and decision sciences are used is marketing. Companies and their marketers want to sell more, increase profits and so on. Everything is legitimate. At the same time using too much the insights from these sciences can back-fire big time.

One way in which using too much of behavioral sciences insights can back-fire is customers returning products. For example, if a (on-line or brick and mortar) retailer increases its sales by using too much of behavioral insights it is not unlikely that some of the customers will return products that were bought under the influence of the behavioral tools. Sure, not each and every customer will return what she bought, but it is enough that a critical number of clients do so. An avalanche of people returning (slightly used) products will create considerable troubles with logistics and cash-flow. Again it is not necessary that all customers return products; a couple of hundred is enough to create problems.

In an earlier post, I have discussed the issue of Regret in decision making and subsequent behavior. Regret is a very powerful negative emotion that comes from the realization that “I could have done better”. Some people might regret buying stuff out of impulse (or mindless shopping) and this negative emotion will be linked with the brand of the seller. This does not apply only to physical stuff, but to services also.

In essence, a marketer who uses behavioral insights to increase sales should keep in mind that there is a need for a balance between present outcomes (sales increases) and future outcomes. Again, it is not necessary that a large number of people to react in a negative way. Considering the fact that many markets are more or less stagnating and that profit margins are getting smaller and smaller it is enough to just “overdo it just a bit” to get bad results.

On a more moral philosophy note, there is an issue of morality in using too much of behavioral sciences insights. I remember reading an article which advocated “reducing consumer surplus”. Consumer surplus is considered to be the difference between what a person would be willing to pay for something and what she actually pays. This consumer surplus can be seen in a different light such as opportunity to save more for one’s children’s education. Companies what to “reduce consumer surplus” and from their point of view it is legitimate to want higher profits. At the same time there is a bit more to life than just increasing profits. Anyhow, that’s my opinion.  

In a similar note, in a consumerist world we are surrounded by “Stuff”, most of which we don’t really need. If you think about the “personal storage space” business sector, the only reason it exists is that people buy a lot of stuff that they don’t need. Of course, not over-buying is due to the use of “tools” from behavioral and decision sciences, but using too much of these tools can lead to useless purchases.

Unfortunately there is no “golden rule” that says “from here on it is too much”. What is “too much” is up each and every one of us to decide. As far as I know there is no “code of ethics” in using behavioral insights in practice. There is some (formal) regulations (at least in the EU) on some aspects such as subscribing to newsletters (default option is “no”), but there is a long way from this to a “book of rules”.

Richard Thaler and Cass Sunstein have proposed the concept of “Libertarian paternalism”. This apparent oxymoron is considered to be a “self-regulatory mechanism for using behavioral insights”. In essence, Libertarian Paternalism combines two major philosophies that are in opposition. Libertarianism supports the idea of the rational independent agent. In other words, it claims that leaving people and markets free the optimal outcome for both the individual and society will emerge.  At the other end of the continuum is Paternalism which supports the idea that it is best if “someone” (authority) tells people what to do and makes decisions for them.

The combination of these two opposing philosophies goes like this. We use paternalism to “steer” (or nudge) people into the right direction and at the same time we do not restrict free choice.

For example one can use “the default option effect” to increase the number of people who make the “right” choice, but people are free to change the “default option” into another one at their choice.

Richard Thaler and Cass Sunstein claim that by allowing for free choice abuses can be prevented. By abuse they understand using behavioral insights for “bad” purposes. Indeed, by not restricting free choice people can make their own judgments and subsequently act accordingly. I for one, however, am not fully convinced that free choice is enough to prevent abuses. In the end “steering” people’s decisions and behaviors can be done in either the “bad” or the “good” direction.

I conclude this post with a warm recommendation: “Handle with care!"

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Keep Up With the Others - Social Competition

The third component of the social dimension form the 4D Model of Behavior is social competition. As stated in earlier posts human social relationships are on both dimensions (horizontal and vertical) of social hierarchy. We are influenced by the behavior (or mere presence) of people just like us (peers)  and we are influenced by authority.

Both peer and authority influences have evolutionary roots. The main idea is that as products of evolution, human have to achieve two evolutionary macro-goals: (1) Survival and (2) Successful reproduction. For more on this please read For What Are WeDesigned by Evolution to Be Good At … 

At the same time, evolution is not a “nice and fluffy” egalitarian process. Evolution, by its very nature, implies selection. In turn this implies that not all individuals get to send their genes into the next generation – successful reproduction. Some die before having offspring, others can’t find a mate with whom to have children with, others find a mate, but of low quality and subsequently their offspring are of low quality too and in a couple of generations their offspring will not have children and so on.

The main social consequence of selectivity in evolution is that people (as well as other species) compete with each-other. The area of competition is apparently broad, but in fact it can be reduced to a few dimensions.

First, we compete for attracting mates. Finding the best possible mate with whom to procreate is, from an evolutionary perspective, one of the top 3 most important goals in life. In order to ensure the perpetuation of your genes in the next generation, you need to find the best possible partner to have children with. Moreover, since humans need a lot of time and investment before becoming independent the partner should be good at parenting and be able to invest effort and resources in bringing up a child.

Second, we compete in order to defer same sex rivals. It is not enough to “impress” a potential mate, rather apart from impressing the potential mate we have to make sure that no other same sex individual impresses our potential mate.

The popular belief about competing for mates is that only males do it. This is true for species that have choosy females and males who would mate (including having offspring) with any female. Also these species are characterized by uneven parental investment. In other words the entire effort of raising the offspring is done by the female.

When it comes to humans, however, things are a bit different. In the case of humans the parental effort is divided (roughly equally) between the two parents. This implies that men are also choosy and would not have children with any human female. As a consequence, women compete also for attracting potential mates and defer same sex rivals.

Third, we compete for parental investment. Even as children we have to compete for the limited resources of the parents. These include material resources (including food), attention, time and so on. In today’s world it is not uncommon for families to have only one child, thus leading to a lack of need for competition. In the past, however, having only one child was extremely rare. Moreover, considering the infant and adult mortality rates it would also be unwise, from an evolutionary perspective, to not have more children.

We like to think that as parents we give equal attention and resources to our children, but the reality is slightly different. Even the smallest difference in parental investment given to children can make a huge difference in their later life. I don’t want to develop this argument further since it is not the topic of the post, but the key idea is that sibling competition exists.

Everybody who has children or had seen families with children has witnessed a rather disturbing situation in which a child does something dangerous (and somehow stupid) saying “Look at me what I can do…” This is an example of children competing for parental investment. By doing something dangerous the child communicates that he or she has good physical abilities and thus deserves parental resources.

Fourth, we compete for friends and allies. As a social species we have friends and allies; we also have enemies. From an evolutionary perspective having more and better friends and allies constitutes a very high benefit. Having more and better friends leads to the possibility of acquiring more resources. Think for example of collective hunting.  At the same time it offers better protection against threats such as predators and natural disasters.

Examples such as collective hunting and protection against predators are highly relevant for our distant ancestors’ lives and a lot less meaningful for modern life. At the same time, the main principles are more or less the same even in the XXI century. Having more and higher (social) quality friends can help you get a good job (please read access to resources). Similarly in case of a “bar fight” it is better to have more and stronger friends willing to help you.

The competition in social relationships comes from an inherent trait of humans, namely that we can have a limited number of meaningful social relationships. To make a long story short, a human can have meaningful relationships with at most 150 people.

Apart from the limited number of relationships that we can have, there is the issue of “is it worth being friends with you”. I don’t mean by this that friendships are based only on self-interest, but simply that having a social relationship with someone should be pleasant.

All these competitive relationships that humans have can be summed up into competition for one thing only, namely (social) STATUS.

Competition for status is deeply rooted into human nature. As you most likely have noticed, competition for status can have both positive and negative outcomes. For example athletic competition has overall a positive outcome, whereas escalated consumerism has negative outcomes. This, however, is a more philosophical discussion.

Let’s see how competition for status works. First, we should understand what “status” is. By its very nature status implies that others appreciate you for something. Who “others” are is not necessarily clear, in the sense that for sure “Others” is not the entire human species. Neither is it the entire society in which you live. For most people “others” represents their social group or a part of it. This includes friends, family, acquaintances, colleagues, potential mates etc.

Having broadly defined the “others” or in other words the audience, let’s take a look at what “something” is, namely for what is an individual appreciated by other individuals.

There are many things for what a person can be appreciated for. At the same time this broad array can be reduced to a few traits that have evolutionary meaning.

Let me explain a bit more on this evolutionary perspective. Taking the evolutionary perspective (which I believe is the best to explain most aspects of human nature), things must make sense for accomplishing the two main evolutionary macro-goals: survival and successful reproduction. In other words, if in the XXI century we appreciate someone for something, that something should be meaningful for our very distant ancestors too.

For example, people appreciate other people for being funny. From an evolutionary perspective, however, being friends or having children with someone that is funny has no benefit in order to survive or have high quality offspring. If, however, being funny is correlated with something more meaningful such as intelligence, then it makes sense to be friends or have children with someone funny. This is not because “funniness” gives an evolutionary benefit, but because more intelligent people are better at surviving (and protecting others) and intelligence is hereditary, thus your children will be more intelligent too.

Similarly, in the XXI century people appreciate athletes for their performances. At the same time being able to swim very fast or score many goals is not in itself meaningful from an evolutionary perspective. The underlying traits that allow athletes to be very good at sports, however, are evolutionary meaningful. Athletic performance can’t exist in the absence of physical fitness, good physical and mental health (lack of severe mental problems). All these traits are valuable when it comes to being friends and or having children with someone.

To conclude on what “something” is, what we appreciate is not necessarily relevant from an evolutionary perspective, but what we appreciate (the “something”) has to signal a trait relevant from an evolutionary perspective.

Evolutionary psychologist Geoffrey Miller suggests that what we are appreciated for (and subsequently gain status) are six personality traits which include Intelligence and the Big Five Personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism). To this I would add physical fitness (physical health and quality).

The main argument that Geoffrey Miller gives for this proposition is that these traits are relatively stable throughout life (though Conscientiousness increases with age) and are to a large degree hereditary. If you would like to learn more about Miller’s approach I strongly recommend reading his book Spent (later editions have the title “Must have”).

As a note, I will present each personality trait in future posts.

Having established who “Others” are and what “something” is, it is time to focus on the “how” of social competition. In other words, the next question that needs to be answered is “how do people (socially) compete?”

In order to answer this question, I suggest going a bit into the animal kingdom and taking a look at how animals compete for attracting mates, deferring same sex rivals, parental investment and in some cases for friends and allies. The competition for friends and allies is less pronounced in most non-primate species especially because many species are non-social.

Many animals have traits that offer no functional advantage and might be detrimental to survival, such as brightly collared feathers which can attract predators, or large tails that may impede an escape from a predator. The most common example is the peacock’s tail. It is large and brightly colored, but gives no real functional advantage. In order to grow and maintain such an ornament and for it to be beautiful, a peacock has to acquire a large quantity of metabolic resources (food) and to be healthy (not suffer from diseases or be infested with parasites). Moreover, in an environment where there are predators, a brightly collared and large tail is not exactly the best thing to have. The bright colors can attract the attention of predators, while the large size makes it difficult to flee from them.

If evolution would rely only on “survival of the fittest”, then such useless and even detrimental to survival features would have been “lost on the way”. Since they exist, however, they must have some beneficial role. Charles Darwin’s explanation was that these traits must have been kept throughout evolution because they were attractive to the opposite sex.

In other words, these useless and even detrimental features communicate something about the individual who possesses them that is liked by the opposite sex. Just like in the example of the athlete (footballer) who scores many goals, it is not the feature itself that is important, but rather the trait that it communicates. In the case of the peacock, the big, bright colorful tale says that this individual is highly physically fit (good genes) since it can gather all the food needed and can escape predators even with this huge handicap; in addition it has a good immune system since parasites didn’t damage the tail. If you would be a peahen hardwired by evolution to find the best possible mate to have offspring with, then a big, bright colorful tail is all you need to asses in order to say “yes” or “no” to mating with this peacock.

In scientific terms these useless and even detrimental features that are appreciated (liked) by others are called “costly signals”. A costly signal has four characteristics: (1) They have no apparent usefulness (or very low utilitarian value) or may even pose a threat to the owner; (2) Are costly in terms of resources needed to have such features; (3) Are visible or even conspicuous; (4) Are desirable themselves or signal a trait that is desirable by the opposite sex.

In the case of humans, there are two major particularities regarding costly signaling. First, the parental investment required to have and raise a human offspring that gets to have children of his / her own is very large (much larger than for most animals) and it is distributed between the two sexes. This implies that both males and females get to choose their mating partner and subsequently use costly signals in order to attract mates. This does not mean that men and women exhibit the same costly signaling behaviors, but rather that each sex tries to attract a suitable mate.

Second, human bodies do not have conspicuous features similar to the ones found in animals (e.g. large colored feathers etc.). This implies that most costly signals used by humans to attract mates, defer same sex rivals, compete for parental investment and attract friends and allies are behavioral and not anatomical. The two particularities described above lead to the conclusion that both sexes exhibit conspicuous behavior related to signaling their mating value.

When saying behavioral features, I include possessing things such as jewelry, expensive electronics, fashion products etc.

Overall, (most of) the human social competition is done through behaviors and possessions. Consciously or not we exhibit behaviors and buy stuff with the aim of communicating to others that we possess some desirable traits.         

We communicate our traits through other means than the costly and useless (from an utilitarian perspective) behaviors and possessions. In other words, communicating one’s social / mating value is done through behaviors and possessions that are not necessarily costly and useless. For example, being a hard worker is (usually) not useless and it signals desirable traits such as a high level of conscientiousness and self-control.

These behaviors and possessions that are used for communicating one’s traits and that are not costly signals are called in scientific terms “fitness indicators”.

Communicating one’s desirable traits (social / mating value) and subsequently entering a competition with other people is done through a mix of costly signals and fitness indicators.

As I mentioned earlier, competition among individuals aimed at gaining status is deeply rooted in human nature. Sure, some will say that it is good, some say that it is bad. They are “both” right. Sometimes it is useful to emphasize on competition. For example social competition is the driver of the luxury and mobile electronics business sectors and without emphasizing on “keeping pace” these markets would shrink considerably. Is this good? Depends for who, I guess.

Another example comes from Human Resources Management and concerns competition among co-workers. Competition exists whether you like it or not. The question is if you should encourage or discourage it. Think for example at “fashion” competition among co-workers. If one colleague starts wearing only high-end designer clothes and she is appreciated (and envied) by her colleagues, sooner or later another person will start wearing only designer clothes and this time even more expensive. Before you know it, the office will become a real “cat walk” and the consequence will be frustration and significantly lower disposable incomes (probably followed by a general request for pay raises).

In the end I would like to reach a rather sensitive topic, namely social competition based on consumerism. Everyone needs status and buying stuff is a simple way of doing so. I have nothing against capitalism and consumption (after all I lived in a communist and post-communist society for half of my life). At the same time, when social competition escalates in the area of consumerism, I believe that in the end we are all worse off.

Indeed firms sell more, but what happens is that we end up with a lot of stuff that we don’t use and with a lot of money spent on things that don’t make us happy. Social competition is natural and in fact very useful for society, but my belief is that consumerism is not the only “field of battle”.

Instead of buying the latest smartphone when the old (1 year) one works perfectly fine, wouldn’t it be better to use the 400-500 Euros to go on a nice weekend somewhere new? You can communicate your “Openness” either way. Instead of showing how smart we are by purchasing complicated to use devices that we show of in front of our social group, wouldn’t it be better to gather the group and play cards or scrabble that also show how smart we are?

To conclude, think of the following numbers:

A regular car costs about 15.000 euros. A SUV costs about 30.000. The difference is 15.000 which is the exact amount for doing a 1 year master at a good university in The Netherlands (EU tuition fee plus decent living expenses and money for books).

Both the SUV (upgrade from a regular car) and the 1 year Master studies are not essential to life, but if we want to show how great we are and get some benefits, which do you think should be prioritized? 

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Practical Applications of Prospect Theory

Prospect theory is one of the foundation stones of behavioral economics. Subsequently it has numerous implications for practice and in this post I will present four of them. These four principles were introduced by Richard Thaler. Mr. Thaler is an economist, but what made his work special is that he embraced behavioral insights and incorporated them in his work on economics.

I strongly recommend his book Nudge written with Cass R. Sunstein. It is a wonderful collection of how insights from behavioral sciences can be used.  

Before presenting the four principles proposed by Richard Thaler, I would like to invite you to take a look at the graph for the utility (value) function from prospect theory.

As you can see, this function is concave for gains and convex for losses. In simpler words, for gains: in the case of large amounts the benefit per unit decreases. Similarly for loses: in the case of large amounts the pain per unit decreases.

In addition, as you can see from the graph, the slope of the function is steeper for losses compared to the area of gains. This is loss aversion. In brief, a loss hurts more than the pleasure brought by an equal gain.

Keeping this in mind, here are the four principles of integration or segregation of outcomes proposed by Richard Thaler:

First, segregate gains. When faced with a large gain, the utility (benefit) per unit decreases (the function is concave). This implies that two smaller gains are better than a larger one with a value equal to the sum of the two smaller ones.

For example if you are to offer a future discount of 100 Euros in the form of a voucher, it will be better for the client to receive two vouchers of 50 Euros.

In mathematical expression this would go like this:

U(50) + U(50) > U(100)

Second, integrate losses. In the case of losses the function is concave, this implying that the pain of a large loss is smaller than the added pain of two smaller losses that summed up are equal in value with the larger loss.

For example if someone has to suffer a pay cut it is better to say that his overall annual salary will be smaller with 3000 Euros in total than to say that 2000 Euros will be cut from the actual salary and 1000 will be cut from the annual bonus.

In mathematical expression this would go like this:

U(-2000) + U(-1000) > U(-3000)

Don’t get me wrong, I am not saying that the person will not feel pain (disutility). It is just a comparison between two amounts of pain.

Third, cancel losses against larger gains. Since losses loom larger than gains, it is better to have a smaller gain than a larger gain and a loss.

For example, if you have two projects that might or might not pay off and one of them brings a profit of 1000 Euros, while the other failed to become profitable and lead to a loss of 200 Euros. Instead of seeing the outcome as 1000 Euros gain and 200 Euros loss it is much better to see it as 800 Euros gain.

In general losses bring a pain that is twice as large as the pleasure of a gain in equal amount would be. In this case the 200 Euros loss will be perceived as a “pain” that could be compensated by a gain of 400 Euros. This would leave you with the benefit (utility / pleasure) of only 600 Euros.

In mathematical expression this would go like this:

U(-200) + U(1000) < U(800)

Fourth, segregate “silver linings” (large loss, small gain). The utility function is concave in the case of losses, implying that the difference between in the pain generated by a loss of 1000 Euros and the pain generated by a loss of 1010 Euros will be very small. At the same time in the area of gains the function is convex, but only for larger values. This implies that when having to deal with a large loss and a small gain it is better to not integrate them.

For example, if your car broke down and you need to pay 1010 Euros for repairing it and in the same day you play the lottery and win 10 Euros, it is better to see these two outcomes separately. Truly, the pain of losing 1010 Euros is roughly the same as losing 1000. At the same time, the pleasure of wining 10 euros in a lottery is quite large in comparison with no wining anything.

In mathematical expression this would go like this:

U(-1010) + U(10) > U(-1000)

   

These principles proposed by R. Thaler seem common sense, but at the same time they are very important and sometimes we simply don’t take them into account.

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This post is documented from: Thaler, Richard H. (1985), "Mental Accounting and Consumer Choice," Marketing Science, 4 (3), 199-214.

Prospect Theory a “Descriptive Theory of Decision Making”

Prospect theory is one of the foundation stones of behavioral economics and behavior sciences overall. It was developed in the 1970s by Amos Tversky and Daniel Kahneman. For this work Daniel Kahneman received the Nobel Prize for Economics. Unfortunately Amos Tversky passed away a few years before the prize was awarded. 

The revolutionary nature of Prospect Theory came from the fact that it challenged a centuries old assumption, namely perfect rationality. It criticized the traditional (normative) model of decision making and replaced it with a positive one (how people actually make decisions).

In order to better understand what are the true innovations of Prospect Theory we should first take a look at the normative model, namely at the Expected Utility Model developed by Daniel Bernoulli in the XVIII-th century.

Before describing the Expected Utility Model, let’s make clear what Utility means. Utility can be perceived as “the amount of pleasure” or “the benefit” given by a certain amount of money. The main characteristic of Utility is that it is not linear. For small and moderate amounts of money it is in fact linear, but after a certain sum the utility per monetary unit starts to decrease.

For example, the utility of 20 Euros is twice the utility of 10 Euros. However, the utility of 2 million Euros is not twice the utility of 1 million Euros. The utility of 2 million Euros is smaller than twice the utility of 1 million Euros.

In mathematical expressions it would go like this:

U(20)=U(10) + U(10)

U(2.000.000) < U(1.000.000) + U(1.000.000)

The Expected Utility Model is straight-forward: The value of an uncertain outcome is the Utility multiplied by the probability of occurring.

To better understand, let’s assume that an alien comes from the sky and sits on your shoulder. The alien says it’s you lucky day and you get to play a series of games with him.  The alien takes out an intergalactic coin that is very similar to the earthly ones, namely it has two sides. If the side with the space ship comes up, you win 100 Euros. If the side with the “2 intergalactic credits” comes up, you win nothing. The coin is not tricked, namely there is a 50-50 chance of either side to come up.

The value of the alien’s proposition can be translated like this:

Expected Value = 0.5*U(100) + 0.5*U(0).

Another example would be if the alien would offer to throw a dice and if the side with “1” comes up you get 20 Euros, if the side with “2” comes up you get 2 Euros, if the side with “3” comes up you lose 10 Euros and if any other side comes up, nothing happens. The value of this proposition is:

E.V. = 1/6*U(20) + 1/6*U(2) + 1/6*U(-10) + ½*U(0).

I gave examples with “gambles” because they are the easiest, but the reality is that we make decisions about uncertain events all the time. For example, what are the chances of getting that nice job and what would the benefit (utility) be? Should I take an umbrella with me today or not? Will it be safer to go on the trip by car or by train?

I assume that you got the main idea of the Expected Value model. Before prospect theory (and even nowadays in some areas of science) this was assumed to be the way people make judgments about uncertain events.

Moreover, this is the RATIONAL way of making decisions. This is HOW WE SHOULD MAKE DECISIONS.

Since in the above paragraph you read “should” it implies that it is not the way we make decisions. Tversky and Kahneman developed prospect theory which describes how people actually make decisions under uncertainty.

  

Prospect theory keeps the “Utility function” or in other words it also uses the concept of utility. The benefit that money brings is not linear in both theory and practice. At the same time prospect theory brings two major new insights into the Expected value model.

First, prospect theory makes a very important distinction between gains and losses. The reality is that we humans do not think (perceive) the same way about what we gain and what we lose. Moreover, gains or losses are relative concepts. What is a gain or what is a loss depends on the reference against which we judge it.

To better understand consider that you are in the following situation. Your salary is 25.000 Euros per year and you have put in a lot of effort in the last year. Now you are up for a raise and in a couple of days the HR department will tell you what your salary will be next year. You can’t help and think about how much you would deserve and you come up with the number 32.000 Euros per year. When you have the meeting with the HR person, she tells you that you will get a raise of 5000 Euros, meaning that your salary next year will be 30.000 Euros.

Will you be happy about the raise? Most likely you will not be unhappy, but neither you will be happy. The explanation is that your reference point was the salary you believed that you deserve – 32.000 euros. In comparison with this amount, the 30.000 you were offered represents a loss of 2000 Euros.

At the same time, you actually get more money than last year and if you change the reference to your current salary, you will in fact experience a gain of 5000 euros.

The main implication of distinguishing between gains and losses is that people “suffer” from loss aversion. In more simple words, we hate to lose and we would put in more effort to avoid a loss than to achieve an equivalent gain.

Let’s illustrate this with the following example. The alien suggests the following bet:

50% chance of gaining 100 Euros and a 50% chance of losing 100 Euros.

What is your attitude about this gamble? Would you be willing to take it? It’s a 50-50 chance of either winning or losing 100 Euros. Will you take it?

Most likely your answer is a definite “NO” and most people in the world would be giving the same answer. From a strictly rational (normative economics) perspective, this answer is at least weird. The reason is that the objective value of this gamble is 100*0.5 + (-100)*0.5=0. If the objective expected value is zero, then you should be indifferent to the gamble and take it (or at least more people would take it). But you and most (normal) people in the world would not even consider taking this gamble.

The idea of being indifferent to a gamble basically means that you are equally inclined to take and to not taking it.

Now the alien can understand to a certain extent humans and he suggests the following bet:

50% chance of gaining 110 Euros and a 50% chance of losing 100 Euros.

What is your attitude about this gamble? Would you be willing to take it? It’s a 50-50 chance of either winning 110 Euros or losing 100 Euros. Will you take it?

Most likely your answer is still “No”. At the same time you have already learned how to compute the objective value of the gamble and by applying the very simple formula of 110*0.5 + (-100)*0.5=5. As you can see this gamble has a positive objective expected value. From a normative economics (rational) perspective everyone should be willing to take this gamble, but again most people are not willing to take it.

For more on loss aversion please check this post: Loss Aversion and its Implications

Roughly we perceive losses as twice as bad as an equivalent gain. In other words, the pain (dis-utility) of losing 100 Euros is (roughly) twice as large as the pleasure (utility) of gaining 100 Euros.

This would translate in a mathematical expression like this:

|U(-100)| = 2*U(100)

The loss aversion coefficient which in theory is called (lambda) λ is estimated to be approximately 2. This is a general estimate established after averaging values from large sample of people. What this means is that some people might have a loss aversion coefficient smaller or larger than “2”. To what extent this is relevant for your work remains to be established, the key idea is that there can be individual differences in the value of (lambda) λ.

The utility function has similar characteristics in both the gains and losses areas when it comes to linearity. In other words, as in the case of gains, when it comes to losses the (dis)utility of small and moderate amounts is linear. When it comes to large amounts, however, the (dis)utility becomes nonlinear.

To illustrate this, a loss of 20 Euros is equal to twice the loss of 10 Euros. However, a loss of 100.000 euros is smaller than twice the loss of 50.000 Euros.

In Mathematical expressions it goes like this:

U(-20)=U(-10) + U(-10)

U(-100.000) < U(-50.000) + U(-50.000)

The graphic illustration of the utility function is:

The image is a “screen shot” from the original paper introducing Prospect Theory by Tversky and Kahneman.  

To sum up the first part, Prospect theory keeps the concept of utility and its characteristic (non-linearity for large amounts). It makes a difference between gains and losses; in the area of losses the utility function has a steeper slope illustrating loss aversion (losses loom larger than equivalent gains). What is perceived as a gain or a loss is dependent on a reference point.

A major implication of distinguishing between gains and losses is the existence of a differentiated attitude towards risk. There can be two attitudes towards risk, namely Risk Seeking and Risk Averse (avoiding).

Prospect theory has concluded that in the area of gains, people are “Risk averse”, namely that we would reject a risky option if a certain one would be available.

Let’s illustrate this with an example. Imagine that you have to choose from the following options:

Option A. 50% chance of winning 100 Euros and 50% of wining nothing.

Option B. 50 Euros for sure.

Which would you prefer? Most likely you prefer option B and it is normal to do so. The expected value of option A is 50 Euros but it involves a risk, whereas option B has the same expected value, but it has no risk.

Now consider the following options:

Option C. 50% chance of winning 100 Euros and 50% of wining nothing.

Option D. 45 Euros for sure.

Which would you prefer? Most likely you prefer option D. In this case, however, the expected value of the preferred option is smaller than the expected value of the rejected option (C). In other words you would trade 5 euros for having certainty (a sure gain).  

In the area of losses things are different. When it comes to losses people are risk seeking, namely they would take a risk of losing a larger amount than accepting a sure loss.

Let’s illustrate this with an example. Imagine that you have to choose from the following options:

Option E. 50% chance of losing 100 Euros and 50% of losing nothing.

Option F. A sure loss of 50 Euros.

Which would you prefer? Most likely you prefer option E. In this case you would take a 50-50 risk of losing 100 Euros instead of accepting a sure loss of 50 Euros. From the point of view of expected value you should be indifferent between the two options.

Now consider the following options:

Option G. 50% chance of losing 100 Euros and 50% of losing nothing.

Option H. A sure loss of 45 Euros.

Which would you prefer? Most likely you prefer option G. In this case it is rational (from the point of view of expected value) to choose option H since its expected value is smaller than the expected value of option G. Still, the popular choice is option G, illustrating risk seeking in the area of losses.

To sum up attitudes towards risk, People are risk averse (avoiding) when it comes to gains and risk seeking when it comes to losses.

Second, prospect theory questions the way people judge probabilities. From a mathematical perspective probabilities are straight-forward. Probabilities are always between (including) 0 and 1 and they always add up to 1. People perceive probabilities in a different way than the mathematical view. Prospect theory introduces something called a weighting function for probabilities.

To make this a bit more clear, when judging on an uncertain event, even if we know the (real) probabilities we don’t actually take them into account to their “full value”. Instead we use the “probability weights” in making decisions.

Rephrasing this, there are objective probabilities (the real ones) and there are subjective probabilities (the weights we give to probabilities). The subjective probabilities are what we perceive the probability to be.

Having established that people do not perceive (use in judgment) probabilities as they are, the question is how do we perceive probabilities? The answer goes like this.

First, the “0” and “1” probabilities are perceived as they are. In other words certainty and impossibility are perceived as actual certainty and impossibility.

Second, small probabilities are either over-estimated, or ignored. The probability of a rare event that is salient in our mind will be overestimated. For example if you go for the first time by airplane, you will overestimate the probability of something going wrong. At the same time, probabilities of rare events that are not salient in our minds are simply ignored. For example if you take the subway every day to work, most likely you will not even consider the risk of dying in a subway accident.  

If you would like to learn more on this please check out “Certainty and Possibility Effects” 

Third, medium and large probabilities are underestimated. This means that we tend to perceive medium and large probabilities are smaller than they actually are. For example an objective (real) probability of 90% (or 0.9) is perceived (weighted) as a subjective probability of approximately 70% (or 0.7).

A graphic representation of the probability weighting function is presented in the next picture. This is in fact a screen-shot from the original paper introducing Prospect Theory byTversky and Kahneman. 

One issue is rather unclear in the graph above, namely up to what value are probabilities small and subsequently overestimated and from which value are probabilities medium and subsequently underestimated? Looking at the graph, one could say that the “critical value” is 10% (or 0.1).

In his book “Thinking Fast and Slow” Daniel Kahneman leaves this question open. However, in chapter 29 he presents a set of data that suggests that probabilities of 20% (or 0.2) are also overestimated.

In a paper published about 13 years later than the one introducing prospect theory, Tversky and Kahneman (1992) – click here for the paper  the authors refined their description of the probability weighting function and presented the following graph. 

From this graph we can deduce that the “critical value” is around 30% or 0.3.

The exact value of probability that marks the shift from over to under estimating probabilities can be important in some areas of activity such as insurance, while in many other areas is less relevant.

For your general knowledge, it is important to remember that small probabilities are overestimated while medium and large probabilities are underestimated. If, however, you are interested in more mathematical details on the probability weighting function, check out this article.  

To sum up on probabilities, probabilities of 0 and 1 are perceived as such; medium and large probabilities are underestimated; small probabilities are either overestimated or ignored. 

Before ending, I would like to give an advice... if you want to understand and work in behavioral economics, the following picture should be forever imprinted in your mind:

Source of the picture

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