Behavioral Bias Bingo: Decoy Effect

/Behavioral Bias Bingo: Decoy Effect

Behavioral Bias Bingo: Decoy Effect

By | 2017-08-18T17:09:36+00:00 June 26th, 2014|Behavioral Finance|2 Comments

“When my information changes, I alter my conclusions. What do you do, sir?”

– John Maynard Keynes

How does the Decoy Effect work? Here is a simple example from Huber, Payne and Puto (1982):

  1. A store owner has two camel hair jackets priced at $100 and $150 and finds that the more expensive jacket is not selling.
  2. A new camel hair jacket is added and displayed for $250;
  3. The new jacket is added and displayed for $250; the new jacket does not sell, but sales of the $150 jacket increase.

Here, the $250 jacket “decoy” increased sales of the $150 jacket. The decoy, by changing the choice context, affects how we perceive the group of jackets.

Amos Tversky has noted that humans have a natural tendency to categorize, and organize similar things into groups. That is, when things are similar, they can be for us largely indistinguishable from one another, with no individual item that predominates.

Inexpensive pens, for example, are known to have a high elasticity of demand. There are many available substitutes, and mostly we don’t care which pen we use. Or perhaps we have a preference for, say, blue pens, or metals pens, but these are subjective choices and shouldn’t change, at least based on how our pen options are arranged.

When we are presented, however, with an alternative within a group, that is perhaps differentiated along some dimension, humans systematically alter how we evaluate and assess other members of the group, and in ways that can be manipulated.

The decoy effect is also known as the “asymmetric dominance effect,”  due to the way an item that dominates a group along some dimension affects our perception of the other items. Asymmetric dominance occurs when one item in the group is dominated across every dimension versus another item, but is dominated only partly by another item. Due to this cognitive bias, when we receive new information, we sometimes update our beliefs in a flawed way, and act in ways that conflict with our internal subjective preferences.

Huber, Payne and Puto (1982) wrote a research paper on the decoy effect, entitled, “Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis.” (Click here for a copy) In their study,they examined six product categories: cars, restaurants, beer, lotteries, film, and television sets, and tested the effect using choices made by 153 students. They found strong evidence of the decoy effect. (You can find details of the methods and results in their paper)

Below we offer a simplified version of the decoy effect (these are not actual research results, but demonstrate the point).

Game: Which TV will you choose?

 

Situation 1: Suppose you want to buy a TV and must choose between two models. The TVs have similar features, but differ in size and price. Which one would you choose?

  • A: 42 Inch TV at price of $400.
  • B: 48 Inch TV at price of $500.

This is a trick question, as there’s no right answer. There are only two attributes to consider: size and price, and it’s up to you which you prefer. You have your preferences and they are what they are. The point of the research, however, is that these internal preferences should represent your subjective probabilities, and you should abide by them consistently. Yet the research shows these can be affected (or manipulated) by a decoy.

Situation 2: Now suppose a new TV set is added to the mix, TV C, which has similar features to the previous two TVs. Now which of the three do you choose?

  • A: 42 Inch TV at price of $400.
  • B: 48 Inch TV at price of $500.
  • C: 48 Inch TV at price of $550.

Obviously, TV C is the decoy.  The research shows that consumers who original chose TV A, based on its low price, switched to TV B in the presence of the decoy. Wait, what?! They are rational human beings! Why should that be?

Consider that TV B was weaker than TV A on the price dimension, but we have extended the price dimension, from $400-$500 up to $400-$550. The researchers hypothesized that the extension of the range of the prices caused consumers to reduce the perceived importance of price differences along that extended price dimension. In the presence of the decoy, the $100 price advantage of TV A over TV B seems less extreme, since now there’s a TV that’s even MORE expensive than TV B. TV B looks just a little bit better now than it did before, since it’s not as “weak” as it was in Situation 1 on pricing, although nothing has changed since you answered the first question.

Situation 3: Now instead of TV C, another model, TV D, is added to your choice set, and it also has similar features to the A and B models. Now which do you choose?

  • A: 42 Inch TV at price of $400.
  • B: 48 Inch TV at price of $500.
  • D: 42 Inch TV at price of $450.

Here we have another decoy, TV D.  The research shows the decoy works again, this time by causing consumers to switch from TV B and choose TV A instead — the opposite of the Situation 2! Wait, what?! Why should that be?

Consider that TV A is stronger than TV B on the price dimension. This time, however, we have not extended the range of prices (it’s still $400-$500), but instead we have increased the frequency of observations along the price dimension (adding $450 in between $400 and $500). It was hypothesized that increasing the number of observations along a price dimension has a tendency to increase our perceived importance of price differences. Perhaps our internal thought process is, “Hmmm, I have more independent pieces of information about price? Aha! Price must be more important!” Not necessarily. Also, the addition of TV D’s $450 price has the effect of enlarging or spreading the differences between individual observations. Now, with intermediate price points that are $50 apart ($400, $450, $500), suddenly the the $100 difference between TV A and TV B appears much greater than before.

Applications in Finance:

The decoy effect would appear to hold in financial markets as well. Paris (2011) did research on how a decoy can affect investor decisions using similar methods as explored above, but changed the research target to stocks. A copy of paper can be found here.

Situation 1: Which stock do you choose?

  • Stock A: long-term growth of 20%, and dividend yield of 2%
  • Stock B: long-term growth of 10% and dividend yield of 7%

This situation establishes internal investor preferences for yield and growth.

Situation 2: Now which do you choose?

  • Stock A: long-term growth of 20%, and dividend yield of 2%
  • Stock B: long-term growth of 10% and dividend yield of 7%
  • Stock C: long-term growth of 15% and dividend yield of 1%

The decoy, Stock C, served its purpose, causing more investors to prefer Stock A under these choice conditions. Stock C is asymmetrically dominated by Stock A in both growth and yield. Also notice that we have increased the frequency of observations along the growth dimension, causing investors to increase the perceived importance of growth over income. The example also extended the dividend dimension, causing investors to reduce the perceived importance of that dimension.

Situation 3: Now which do you choose?

  • Stock A: long-term growth of 20%, and dividend yield of 2%
  • Stock B: long-term growth of 10% and dividend yield of 7%
  • Stock D: long-term growth of 8% and dividend yield of 4.5%

The decoy in this situation caused investors to prefer Stock B. Stock D, the decoy, is asymmetrically dominated by Stock B in both growth and yield. Here, we have extended the growth dimension, causing investors to reduce its importance. We have also increased the frequency of observations along the dividend dimension, causing investors to weight dividends more highly than before.

Now maybe you’re smart enough not to invest in the decoy. But the next step is to make sure that the decoy does not influence your personal subjective preferences for dividend or growth, whatever they may be.

Reference:

Joel Huber, John W. Payne and Christopher Puto. (June 1982). “Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis”. Journal of Consumer Research. 9(1), 90-98.


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About the Author:

David Foulke
Mr. Foulke is currently an owner/manager at Tradingfront, Inc., a white-label robo advisor platform. Previously he was a Managing Member of Alpha Architect, a quantitative asset manager. Prior to joining Alpha Architect, he was a Senior Vice President at Pardee Resources Company, a manager of natural resource assets, including investments in mineral rights, timber and renewables. He has also worked in investment banking and capital markets roles within the financial services industry, including at Houlihan Lokey, GE Capital, and Burnham Financial. He also founded two technology companies: E-lingo.com, an internet-based provider of automated translation services, and Stonelocator.com, an online wholesaler of stone and tile. Mr. Foulke received an M.B.A. from The Wharton School of the University of Pennsylvania, and an A.B. from Dartmouth College.

2 Comments

  1. Michael Milburn July 7, 2014 at 8:44 pm

    Interesting articles on this site. Many of the articles here remind me of Dan Arielly’s books and his class on irrationality on coursera.

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