At the top of a water park slide, you bristle with fear, despite clear knowledge that the slide having been operational for years without an accident. During plane rides, you may recall once feeling anxious during take off and landing which may hardly be remedied by someone telling you that the chance of accident is far lower than that of a car, which you may ride every day.
These illogical emotions which impact your behavior aren’t without scientific explanation. Enter psychological bias: loss aversion, the phenomenon that humans gravitate towards preventing the loss rather than the gain of something of equal value.
Early mammals developed the limbic system, including the emotion of fear and the high-leverage fight-or-flight response, to instinctively react to any (however small) possibility of threat and danger that could mean the loss of life. Even in today’s neocortex world, such mechanisms still are triggered by default, part of what Daniel Kahneman would refer to as System 1 thinking, even when completely unwarranted. Therefore, the mere possibility of losing something (loss aversion) often takes priority in formulating our actions. Sometimes, this causes us to miss out on risks objectively worth taking.
One immediate application is using this as an explanation of the Paradox of Choice. The current explanations revolve around fuzzy arguments like questioning yourself, self-blame, setting too high of an expectation. However, with loss aversion, the Paradox of Choice follows immediately as a corollary: every made decisions results in the loss of all existing alternatives (opportunity cost). When there’s no alternatives, we only see the act as a gain, explaining higher levels of satisfaction and confidence with our choice in this scenario.
The reason the underdog wins is because his/her life experiences makes him/her more inclined to not be affected by the loss aversion bias.
For a book with amazing anecdotes on this, check out Malcolm Gladwell’s David and Goliath (my favorite is how Gary Cohn became the President of Goldman Sachs). I included my own anecdote at the end of this post.
To apply this to life, there’re two advices you can take from this post:
- Directly: Think rationally around the loss aversion bias.
This can prove difficult, but I’ve devised a formal approach to do this that should prove fruitful even if you put a minor effort into following it.
First, excuse me as I borrow some jargon from the decision theory for AI agents.
Ideally, we want to consider the “expected utility” of some risk we want to take. We can define an outcome with utility as zero as maintain the status quo (no gain or loss), whereas positive utility results in a benefit and negative utility results in a loss. The “expected utility” is the integral of a utility-probability graph, where we essentially calculate a weighted “sum” of utility * probability(utility) where utility is the benefit we get from a possible outcome.
The accuracy of our calculation improves the more familiar we are with the space of outcomes, or the more utility probabilities we know.
To grossly simplify this process while maintain practicality, it suffices to consider the pessimistic, realistic, and optimistic outcomes, assign to each a rough probability of it occurring, and calculating the expected utility from those three data points (of course, more the merrier).
As an example… suppose my friend at school wanted to ask out a girl he’s been secretly admiring. Here’s how I would justify he should:
Optimistic: She says yes happily.
This likely leads to happiness from interaction, progress in relationship, along with bonuses of increases in self-confidence, social skills, popularity level amongst the union set of both you and her friend groups.
Realistic: She says sure but only a quick coffee grab to be polite but it’s clear she requites no romantic feelings.
Pessimistic: She says no or comes up with a lame excuse.
It’s clear she doesn’t like you. Depending on your values, you can argue this could result in positive utility, since now you’ve gathered the necessary info and can move on. However, for most people, this leads to a minor dip in confidence hence a minor negative utility.
Expected utility: (0.1)*(30) + (0.5)(10) + (0.4)(-10) = +4
Judgment: You should ask her out.
It’s worth remarking this is essentially how AI makes decisions: maximizing expected utility based on a fixed dataset. A major step to AGI will be adding the ability to seek out new information and incorporating into their model of the world (in interest of making better decisions).
- Indirectly: Adopt an underdog mentality.
This may or may not work, especially if you’re not an underdog. There’re also risks, so it’s unclear if this will yield direct benefits the way (1) does. However, this approach should still be considered because a) rational thinking is hard and b) becoming an underdog can allow you to tap into your “dark side” and attain extra emotional willpower.
The feeling of being misunderstood and the desire to prove others wrong, anecdotally speaking, is the single strongest source of motivation one can have. However, this also comes with risks of depression, loss of trust, etc. so it may not work for everyone.
When I arrived in my new school, I couldn’t fit in. Sent to counseling sessions to open up, I was dismissed to never succumb to much in the extrovert-dominant school. In middle school, I felt I had nothing to lose; I could start out with a blank canvas. I became a class clown. With all my insecurities at disposal, I realized, with self-deprecating humor, I could make others laugh while receiving the attention I always wanted.
Though I’ve always been cognizant of how my experiences have developed a kind of competitive edge for me, it’s only now I understand the scientific basis behind it. Because of my becoming comfortable with my insecurities, I gained enough leverage over them to develop the courage to take risks others don’t.
In my opinion, an AI (or modified human) of equal intelligence to us can easily become the most successful person in the world if it optimizes its decisions without the influence of loss aversion. It really can make that big of a difference.