The Sunk Cost Effect

Credit: The Center for Advanced Hindsight

This cartoon’s pretty brilliant.

According to WhatIs.com, the sunk cost effect is the tendency for humans to continue investing in something that clearly isn’t working. This tendency is due to time/effort/money already invested in the endeavor at hand. In the cartoon to the right, the sunk cost effect is shown by the dog’s irrational behavior to follow through with further digging even after not finding his bone after digging a bit.

This behavioral quirk is quite popular in the investing field. Instead of cutting losses, investors have an inclination to hold onto shares of a company after the shares fall in value, in hopes that the value would increase. These investors are reluctant to admit that they’ve made a bad investment and would prefer to hold onto those shares instead of reinvesting in another company.

To provide another example: I’ve recently been debating about switching my paid music streaming subscription service from Spotify Premium to Amazon Music Unlimited. Being that I’m an Amazon Prime member, my Amazon Music Unlimited subscription would cost $7.99 per month. In my current Spotify Premium plan, I’m dishing out $9.99 per month. If following rational economic theory, I would switch my subscription over to Amazon because… well, it just makes sense financially! However, in being the “irrational” consumer that I am, I haven’t switched over due to the fact that I keep thinking about all the time I’ve invested in creating my Spotify playlists - a clear case of the sunk cost effect.

Poker Face

The game of poker provides a great framework for understanding not only the problems in decision-making, but also how we can solve for them. In poker, you don't know your opponents cards and you don't know what they're thinking. In settings in which only partial information is provided, decision-making becomes difficult, tricky, and often contaminated by bias. Essentially, the decisions we encounter in our every day lives are similar to making bets in poker games in that we don’t necessarily have all the information when making decisions. Every decision is a bet.

Renowned professional poker player turned professional speaker and decision strategist, Annie Duke merges her experience in poker with her cognitive psychology graduate work at UPenn to examine decision-making. In her most recent book “Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts”, Annie explains how to best manage having less than perfect information when making decisions - situations we likely find ourselves encountering on a daily basis.

I came across the above Google Talk recently. Some key take-aways from the discussion include:

• What prevents people from making good decisions? “I think it's a collision of a couple of problems. I think that the first piece is uncertainty, and uncertainty comes from two places. It comes from hidden information. So this is where we can see how poker might be a really good way to look at decision making (cards are face down).... But, even if we did have perfect information - say for example, about a coin, and we know that heads or tails it's going to flip 50% of the time, we also have the intervention of luck, which is the other form of uncertainty... The second problem is that nature, the way that our brains are built has given us a lot of rope to sort of hang ourselves with the uncertainty...”

• It would be impractical to wait to have all the necessary data to make a decision in many cases.

• There are type 1 errors and type 2 errors. Type 1 errors are false positives and type 2 errors are false negatives. As humans, we're built for false positives. We connect occurrences together and believe that there’s causality. Being that we think that things are falsely connected together, a problem arises.

• We're inherently built for tribes, which causes problems for decision-making. These “echo chambers” we live in stress the fact that people outside of our tribe are not trusted sources of information, leading to a lack of outside perspectives.

• "Motivated reasoning" is a large problem. While confirmation bias is based on a prior belief we might have, motivated reasoning is when we’re motivated to confirm the belief we have and that motivation drives our information processing. When confronted with information that disconfirms us, we’ll work hard to discredit it, and on the opposite side the coin, when confront we’re confronted with information that confirms us, we don't put any effort into discrediting it.

• After receiving the results from an unsuccessful bet, your ability to go back and analyze the decision process is distorted - aka “resulting”.

• How can we separate outcomes from decision quality? When possible, deconstruct decision before outcomes have occurred.

Ultimately, what I extracted from the Google Talk, is that approaching decisions in a probabilistic way, making sure to include the opinions of people who offer different viewpoints, and learning the right lessons from outcomes are all habits that’ll improve decision quality. If you’re a little bit better at making good decisions, that’s going to compound over time since better overall decision quality will lead to better quality outcomes.

How Does Spotify Tap Into Consumer Psychology?

Like some media platforms nowadays (think YouTube or Pandora for example), Spotify has two principle revenue drivers within their music-as-a-service, business model: an ad-based, free streaming service and a paid premium subscription tier.

... and, in similar fashion to most platform-based tech companies, the goal of the business is to have consumers open up the app and keep them there for as long as possible. The more time we spend in the Spotify app, the more music we consume, the more the artists get paid, and Spotify takes a cut of that profit. It's a beneficial situation for us as consumers (we get to jam out to the music we like), and lucrative for the artists and Spotify.

For the last few weeks, I've been reflecting on the ways in which Spotify utilizes nudge theory on consumers to use the music platform and keep them there. One thing I came to notice between Spotify and other apps I currently have installed on my phone is the lack of Spotify mobile phone push notifications.

Push notifications serve as a great way for brands to stay top of mind and I've been wondering why Spotify doesn't send more notifications. The default notifications setting allows Spotify to send mobile phone notifications so unless you change it, you should be receiving push notifications. But, since having the the app installed on my phone for several years now, I can't recall ever receiving a push notification. Wouldn't it be useful for Spotify, if say, when one of the artists I'm following releases a new album, I'd receive a heads up from the mobile phone app so I can open the app and give the album a listen? I receive "New Release" emails from Spotify, but being how tied we are to our phones nowadays, I would think that push notifications would be a way to reach and nudge consumers into using the app.

Since push notifications aren't a major part of their strategy, how does Spotify draw you and keep you listening in the app? While I've noticed the usage of the default effect and social proof (both behavioral economics nudges) within the app, it's the familiarity heuristic that struck me as most prevalent as I conducted my user testing. More on the familiarity heuristic coming up...

Part of the value proposition of Spotify is their hyper personalization. The usage of big data provides a variety of personalized flavors of recommendations - whether it be their Discover Weekly feature or the Daily Mix playlists - and machine learning algorithms brining it all together.

That being said, we should also note the emotional connection we have with music. As humans, we have the tendency to form associations in our memory with different songs. As a result of these associations, songs becomes stimuli that have the ability set off internal triggers within ourselves, whether it be a sense of joy from the memory of a night out with friends after hearing a song played that night or a sense of depression after listening to a song that reminded you of a deceased relative.

At times we want to relive those moments. We dig up these events from our memory through associated songs, providing a sense of nostalgia and experiencing those emotions again. This is where the familiarity heuristic comes into play.

Understood to be a cognitive "rule of thumb", the familiarity heuristic is our tendency to form decisions favoring what we know (the "familiar") instead of what we don't. We understand what songs impact our moods and in what ways. There are songs that cheers us up, chill us out, and so on. Being that we have a massive selection of music to choose from on music streaming platforms, we can feel overwhelmed with the vast catalogue available. Unless you're specifically searching for new music to listen to, as music consumers we rely on the familiarity heuristic to help us decide what we end up listening to. 

When it comes to our consumption, we rely on those associated emotions to help us make that decision. Once we make a song selection, Spotify's recommended-songs feature kicks in and plays a suggested track based on the original one. In using context- and content-based data to make their suggestion, Spotify is pretty accurate in pinning down your mood and, as a result, will keep the songs aligned with your mood.

Spotify clearly has a deep understanding of not only the psychology of their consumers and the link between music and emotion (which could be one of the many reasons why Mood Playlists were made available on the Spotify platform), but also an awareness of our mobile phone usage. With users spending increasingly more time within the Spotify app, Spotify's doing something right in terms of keeping consumers engaged within the app. Instead of potentially irritating people with push notifications from another mobile phone app, Spotify's usage of machine learning is undoubtedly a strategic way to implement nudge theory on a media platform like their own.

How Moneyball Led To Understanding Behavioral Economics

Remember Moneyball? You know, the movie with Brad Pitt and Jonah Hill that came out a few years ago? If you haven't seen it or don't remember what movie I'm referring to, the film was centered on the 2002 Oakland A's baseball team and their General Manager (now Executive Vice President) Billy Beane, who used an evidence-based, statistical approach to recruit undervalued baseball players and take the financially strapped team on a stellar winning streak.

What does Moneyball have to do with behavioral economics?

Well, the film's based on a book written by American Author Michael Lewis, which was published in 2003. Unbeknownst to Lewis, behavioral economics was at the core of Billy Beane analytical method, showing that statistics can do a lot better than intuitions. According to The New Yorker, Lewis read The New Yorker's book review, began to take an interest in the whole topic of human rationality, and, improbably, decided to write a book about Kahneman and Tversky. Lewis kindly even gave them credit for setting him down that path.

At this point, it'd be safe to assume that readers/behavioral economics aficionados are familiar with the names Daniel Kahneman and Amos Tversky.

Unexpectedly, writing Moneyball eventually led Lewis to write his latest publication, The Undoing Project: A Friendship That Changed Our Minds, the story behind the two Israeli psychologists who eventually shifted the world view from the economic theory of homo economicus to "homer economicus", aka what Dan Ariely describes as the human tendency to be predictably irrational.

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I recently came across an interview featuring Lewis in which he dives into the process of writing the story of the relationship between Kahneman and Tversky. Some of the key take-aways from the interview include:

• Moneyball was about misjudging assets (in this case, baseball players). Players that looked like they were "players" or good-looking players had a tendency to be overvalued.
• "What people do together can get very interesting if the right people collaborate".
• Kahneman was "immune to social embarrassment".
• No one could see how the relationship between Kahneman and Tversky worked - "Amos had no time for difficult people. He thought life was pretty simple and he thought people were basically pretty simple and the complexity they added was mostly bullshit. But every now and then, a complicated person entered his life, because this person was complicated in an interesting way."
• Kahneman brought an artistic imagination to the work of the two scientists.
• What Amos did was take the ideas of Kahneman and try to turn it into math and logical to frame them into a way to publish them in a academic papers.
• The backdrop of Israel, being part of the Israeli army and what they saw in the war played into their insight into Prospect Theory.
• As a young Jewish boy, Kahneman and his family hid from the Nazi's in France, having decided not to go back to Israel due to his father misjudgment of the intention of the Germans.
• The unpublished study on "rules of human imagination" - People have a tendency to create alternative realities, involving "if only" statements.
• People don't decide between things. People decide between description of things. If you change the description, you change the decision.
• The ultimate point of Kahneman and Tversky's work was that "fallibility is human".
• While you can't see your mistakes, sometimes other people can. Does that mean we're condemned to a cycle where we need someone else to check our biases? Yes.

I quite enjoyed the interview and would suggest it to anyone looking for a little context behind what led to the findings of behavioral economics. Here's another book to add to my summer reading list.

"Satisficers"

It's quite fascinating to reflect on how irrational people can be.

And it's perfectly normal to be irrational. We can't be completely rational all the time right? 

This irrationality is part of our bounded rationality. According to Herbert Simon, recipient of the Nobel Prize in Economics in 1975, people have cognitive limitations and because of that, we go about making sub-optimal decisions in our everyday lives. Through this lens, people unconsciously act as "satisficers" in their decision-making, seeking to make a satisfactory decision instead than an optimal one. In order to facilitate the decision-making process, we act on instincts, habits, heuristics, cognitive illusions, and biases - leading us to "satisficing" decisions.

This is innate within all people. During this unconscious part of the decision-making process, we formulate stories - stories that support our beliefs.

Thomas Gilovich, Author and Professor of Psychology at Cornell University who has contributed to the behavioral economics field, describes these stories as a result of "Patternicity", the tendency to create meaning from events. While this evolved trait (think hunter-gatherers hearing rustling in the grass and believing it might be a predator) greatly served our ancestors, "patternicity" leads to irrationality, poor decision-making & bias.

While the topic of ethics relating to behavioral economics makes for another blog post, marketers have been using the underlying biases in "satisficers" to create effective marketing campaigns advantages for decades. From loss aversion to anchoring, there are plenty of biases for marketers to utilize. With that being said, potential consumers can offset this targeting by being "intuitive scientists" - a nice little secret from Thomas in the video above.

Image Credit: Behavioral Scientist