Tax Refunds: Traditional vs Behavioral Economics Explained
(And Why That Matters for Game Learning Systems) by Julia Keren-Detar
Ever wonder how instructional designers create training games that really work? It’s part game design knowledge and a lot of behavioral psychology. Cognitive bias is something we think about often. It’s very important to understand how cognitive bias work. Take the 2018 tax changes for example. A lack of understanding these biases can lead to a disconnect between what economists or financial advisors think people should react and how they actually react to a system as complex as money. Likewise, designing a training game with this lack of understanding can have similar disconnects. Using my knowledge from creating commercial games and tutorial systems, I’ll be sharing a series of blog posts aimed at explaining the important relationship cognitive biases have on designing systems of learning.
This year there was an outpouring of dismay, frustration and disappointment from people who received their tax refunds. It seems that on average tax refunds are down about 17% from last year. Because of the large and complex tax cut legislation passed last year, the IRS updated their tax withholding formula, trying to figure in the reduced taxes more accurately. As a result, people who were expecting larger refunds were disappointed and vented their frustration on social media.
Many traditional economists including financial planners were surprised by the reaction. The majority of people ended up paying much less in taxes overall, so why were they upset at the tax bill? Following this lamentation on twitter, many wrote of the importance of having a small to no refund check. After all, a refund is money the taxpayer overpaid, basically giving an interest free loan to the government. That is not prudent financial planning. Yet in many of the comments in these articles, people talked about how good a large refund check made them feel, especially when it seems so hard to save money. So why the disconnect?
Traditional vs. Behavioral economics
Traditional or market economics has been around since the founding of capitalism (around the late 18th century). It is the belief that if left free to choose, people will make rational decisions, picking the best outcome for themselves financially. Whether that is buying a good quality product at a low price, negotiating pay or investing, people are driven by self interest which can help regulate and balance an economy. If you sell a product, you want the most profit you can get, but if you look to buy the same product, you want the lowest price you can get. The two opposites will set a fair market price.
Behavioral economics is much newer, around 50 or so years. It was born out of new findings that psychologists (Most notably Kahneman and Tversky) were discovering. As much as we think we are logical thinkers making choices in our best interest, we are actually predictably irrational and are bound to cognitive biases that control how we think about decision-making, especially when it comes to something as complex as money. This is the core of this disconnect. On the one hand, having small to no refund checks is the logical, smartest choice when it comes to financial planning. However, that is competing against a few core biases we all have as humans that make large refund checks so hard to give up.
This is a well known cognitive bias that we evolved to have. It was designed to make sure that we are risk averse: to ensure we think about the resources on hand, so we have enough subsistence to survive the environment we live in. Loss aversion is the sense that losing something is twice as painful as gaining something of similar value. It is what makes giving up personal items during spring cleaning so hard. What makes smaller refund checks so painful, especially if a larger refund was expected. If you gave people the same amount of money as a bonus rather than a refund, it wouldn’t impact them as much as taking that money away. The scale also makes a difference. Due to the change of withholding, the IRS actually increased peoples’ refund pay. But the 10 or 20 dollar difference per paycheck barely registers compared to a one time large lump sum amount. The difference of a few hundred bucks is much more noticable and affects our judgement more. In this case, the refund is a one time, noticeable and expected payout. Many people claim it is their forced savings account, one that is out of mind until tax time comes around, and if that amount is significantly less than they were expecting, that experience is quite painful. Similar to winning a lottery, having an expected-yet-unknown payday amount feels great. That feeling is addictive and memorable, and outweighs the logical decision to plan properly and avoid a large refund.
What does this mean for game learning systems?
Understanding how cognitive biases influence how we process the world around us can help us design better products, and be better equipped to predict people’s behavior when using those products. Otherwise, we’ll often be surprised with how people respond compared to the original intent. Many such surprises happen in game learning systems, particularly in how people learn. We usually associate learning with memorization and repetition, but that doesn’t actually produce an effective teaching. Instead learning happens through play, experimentation, struggle, observed failure, exploration and questioning. Providing the right answer is only half the battle: the other is recognizing your mistakes and correcting them on your own. Game systems are perfect for utilizing this behavior effectively. Games are systems that challenge players to figure out the patterns, systems or problems presented to them in creative ways. While using game systems for learning can be effective and powerful, we must first understand how to harness our knowledge of cognitive biases in order to design them effectively.
Julia Keren-Detar is a game and UX designer who has been making games for over a decade. She is currently a game designer for The Game Agency as well as for Untame, a studio shared by her and her husband. She has worked on many commercial game titles including Arkadium’s hit Facebook game “Mahjongg Dimensions Blast”, HitPoint Studio’s “Hell Mary’s” iOS card battle game and Untame’s “Mushroom 11” which won an Apple Design award and a Google Play award. Julia is also a hobby game historian and an economic and personal finance junkie.