A crash course in behavioral economics terms for cryptocurrencies

I put this list together because I haven’t seen enough discussion around behavioral economics in the cryptocurrency space. There are a lot of arguments, and even entire systems, built on the assumption that people act with cold hard economic rationality. That assumption is simply not true.

People make decisions in strange ways. We are constrained by effort, evolutionary oddities, our inability to focus, and time. As a result, we are as Dan Ariely puts it, predictably irrational. I’ve sought to outline some key terms from the field of behavioral economics as well as give examples of how they are applicable to cryptocurrencies. My examples are not exhaustive, some of these terms have entire books written about them, and there are a plethora of different ways these concepts can be applied. I encourage you to think of your own examples as you read! Some of these concepts are more relevant to investing, some of them are more relevant to mechanism design. If you have anything to add then please feel free to comment or get in touch.

Anchoring

A specific form of priming whereby initial exposure to a number serves as a reference point and influences subsequent judgements about value.

Crypto Example

The price of a cryptocurrency at ICO launch can serve as an anchor. Future prices are often talked about in relation to the price at ICO, as if that was not just some arbitrary number itself.

Alternatively, the price point someone enters a market at can serve as a reference for how they think about future movements. You would think about Bitcoin in very different ways if you had entered at $100, $1000, $10000, or $19000.

Further reading

Tutor2u’s video on Anchoring

Scott, P. J., & Lizieri, C. (2012). Consumer house price judgments: New evidence of anchoring and arbitrary coherence. Journal of Property Research, 29, 49–68.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science(New Series), 185, 1124–1131.

Wansink, B., Kent, R. J., & Hoch, S. J. (1998). An anchoring and adjustment model of purchase quantity decisions. Journal of Marketing Research, 35(1), 71–81.

Base rate bias

If presented with related base rate information (general) and specific information the mind tends to ignore the former and focus on the latter.

Crypto Example

Thinking that since the cryptomarket in general is up, your specific investment must also go up.

Alternatively, imagine for a second a cryptocurrency that has a lot of sound “fundamentals” such as a strong developer community, team, and token use case. You would imagine that this cryptocurrency will outperform the market. Base rate bias would be to overweight specific information, such as a delay in the release of a feature, and regard the cryptocurrency as a bad investment when the specific information could simply be a bump in the otherwise upwards road.

Further reading

Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237–251.

Bounded rationality

In decision making, individual’s rationality is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make a decision.

Crypto Example

A user may erroneously validate a bad submission to a token curated registry because they were didn’t know how to evaluate it, it took a lot of effort, or they didn’t have the time to properly make a decision.

Further reading

Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of economics, 69(1), 99–118.

Commitment mechanism

Tools used to counteract people’s lack of willpower and to change their behaviors. The greater the cost of breaking a commitment, the more effective it is. Commitment mechanisms are integrally tied to time inconsistency.

Frequently used in dieting, exercise, or saving. Examples include buying long-term gym memberships, cutting up credit card to avoid using them, or not keeping alcohol in the house to avoid drinking it.

Crypto Example

Smart contracts that lock your Ether up for a year so that you HODL it and don’t panic sell.

Further reading

Strecher, V. J., Seijts, G. H., Kok, G. J., Latham, G. P., Glasgow, R., DeVellis, B., … & Bulger, D. W. (1995). Goal setting as a strategy for health behavior change. Health education quarterly, 22(2), 190–200.

Confirmatory bias

Seeking out, or evaluating, information in a way that fits their existing beliefs.

Crypto Example

Filling your email and Twitter feed with Bitcoin Maximalists and getting the rest of your news from /r/bitcoin.

Crowding out of intrinsic incentives

The idea that monetary incentives, or punishments, can undermine intrinsic motivations.

Crypto Example

People may work for free on an open source project for ideological reasons or to contribute to collective good, but may not have performed the same work if offered payment.

Further readings

Why We Collaborate_In this hour, TED speakers unravel ideas behind the mystery of mass collaborations that build a better world._www.npr.org

Decoy effect

The decoy effect occurs when people’s preference for one option over another changes as a result of adding a third option.

As an example, the Economist once offered all their web content for $59, a subscription to the print edition for $125, or a combined print and web subscription also for $125. Behavioral economist Dan Ariely surveyed his students about this pricing structure; 84% opted for the combination deal and 16% for the web subscription. However, when he repeated the poll without the unpopular print only option 32% chose the print and web option and 68% chose the web only option.

Crypto Example

Utility tokens could use the decoy effect in a similar way to the Economist to nudge people towards more expensive options.

Denomination effect

People are less likely to spend larger currency denominations than their equivalent value in smaller denominations.

Crypto Example

People would are more likely to spend small denominations, such as 100 installments of 10 Ripple, than they are larger, such as 1 payment of 1000 Ripple.

Further reading

Raghubir, P., & Srivastava, J. (2009). The denomination effect. Journal of Consumer Research, 36(4), 701–713.

Dictator game

A two player game where one player, “the dictator”, determines how to split an endowment of money between themselves and the second player. The second player has no influence over the outcome of the game. In theory the dictator should take the entire endowment out of self interest, however, a robust body of literature shows that the dictator often allocates money to the second player.

Crypto Example

There are no direct implications for the dictator game, but what it does tell us is that there exist motives for people’s actions other than self interest. Potential explanations are that people are concerned for others (altruism) or averse to inequitable outcomes (inequality aversion).

Diminishing marginal sensitivity

People are more sensitive to changes near their status quo than to changes far from their status quo. This concept is at the core of prospect theory.

In the image below small gains to the status quo provide huge utility, but after a certain threshold they drastically drop off. The gains between point 1 and 2 are significant, but there is very little extra utility gained.

Crypto Example

The difference in utility gained from making $100,000 in returns versus $200,000 in returns pales in comparison to the utility difference between $1 returns and $100,000 returns.

Similarly, the pain felt from losing an initial $100,000 is greater than the additional pain from losing another $100,000.

Discounted utility model

Discounted utility models are the most commonly used frameworks for analysing choices overtime. They capture the simple idea that (most) economic agents prefer current rewards to delayed rewards of similar magnitudes. The most widely used discounting model assumes that total utility can be broken down into a weighted sum of utility flows over time. Expressed mathematically this is equal to…

Crypto Example

Users prefer to receive rewards now rather than later. The longer you make a user wait for a reward the more you will have to reward them. Moreover, decisions being made should be seen in the lens of a stream of future utilities as opposed to a on-off decision.

Endowment effect

We overvalue things that we own, regardless of their objective market value. This is illustrated simply in two example: people become reluctant to part with goods they own for their cash equivalent and when people are willing ot pay less for a good than they are willing to accept when selling it.

The endowment effect is an illustration of status quo bias and can be explained with loss aversion and prospect theory.

Crypto Example

Simply by owning a cryptocurrency you value it more than you would otherwise. In traditional finance traders have been known to stick with assets they own even if they become unprofitable simply because of their emotional attachment to them.

Further readings

Kahneman, D., Knetsch, J., & Thaler, R. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.

Framing

A cognitive bias whereby people react to a particular choice in different ways depending on how it is presented; e.g. as a loss or a gain. People avoid risk when a positive frame is presented but seek risks when a negative frame is presented.

Framing can also be explained with loss aversion and prospect theory.

Crypto Example

The ways in which you describe a decision, such whether or not to stake or not, can significantly affect the outcome.

Likewise, how information is displayed to you will affect how you perceive that information. Take a step back, reframe the information, and examine how it feels in a different light.

Further readings

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.

Levin, I. P., Schneider, S. L., & Gaeth, G. J. (1998). All frames are not created equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes, 76, 149–188.

Gambler’s fallacy

The belief that a certain random event is more or less likely to occur given a previous event or a series of events.

For example, if you were flipping a coin and it landed on heads 10 times in a row, the probability that it lands on tails next is still 50%. Believing anything else is an example of the gambler’s fallacy.

Crypto Example

Believing that since you have lost money on the past 15 trades you are bound for a profitable trade.

Hard-Easy effect

Overestimating the probability of one’s success at a task perceived as hard, and underestimating one’s likelihood of success at a task perceived as easy.

Crypto Example

Overestimating your likelihood of beating the market but underestimating your likelihood of being able to figure out how to buy alt-coins.

Hedonic editing

Hedonic editing is combining events to affect their perceptions. Prospect theory teaches us that people have a high level of loss aversion as well as diminishing marginal sensitivity to gains and losses. By grouping losses together we can minimize the total disutility felt and by separating gains we can maximize utility felt.

Take the following gains and losses:

Gains

10

10

Losses

10

10

And the corresponding utilities, note the diminishing marginal sensitivity and loss aversion.U(20) = 35

U(10)= 20

U(-10) = -60

U(-20) = -95

The possible ways you can present these gains and losses are…

Gains of 10 and 10 → U(10) + U(10) = 40

A gain of 20 → U(20) = 35

Losses of 10 and 10 → U(-10) + U(-10) = -120

A loss of 20 → U(-20) = -95

Out of all of these combinations, the way to maximize utility would be to present a united loss of 20 and separate gains of 10 and 10 for a total of -55. On the other end, the worst way to display things would be a total gain of 20 and separate losses of 10 and 10 for a total of -85.

Crypto Example

The above concept is easily applicable to losses and returns made from investing.

Hindsight bias

The tendency to believe that an event was more predictable than it actually was. In turn, this can lead to oversimplification of causes.

Crypto Example

After the long run up in Bitcoin looking back and attributing it to simple causes or labeling it as predictable after the fact.

Hyperbolic discounting

The tendency for people to increasingly choose a smaller reward sooner over a larger reward later as the delay occurs sooner rather than later. When people are offered a larger reward in exchange for waiting a set amount of time, they are more willing to wait as the rewards happen further in the future. In other words, people avoid waiting more as the wait nears the present. Also known as time inconsistency.

Crypto Example

Think about whether you would rather have 10 BTC now or 12 BTC in six months. Now, consider whether you would rather than 12 BTC in three months or 12 BTC in nine months. These are the same pair of options at different distances, and if you didn’t choose the same option in both situations, you show time inconsistency and hyperbolic discounting.

Ikea effect

Consumers place a disproportionately high value on products they assemble or make themselves.

Crypto Example

Things that people have helped make, whether software or digital assets, they place a level of ownership over.

Further reading

Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love. Journal of Consumer Psychology, 22, 453–460.

Inequality aversion

The tendency for people to resist inequalities.

Crypto Example

People may be willing to act out of their self interest in mechanisms where you would normally assume they act only out of self interest.

Further reading

Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114, 817–868.

Law of small numbers

The tendency to take a small sample to be indicative of the entire population.

Crypto Example

If a fund manager has had three above-average years in a row, many people will conclude that the fund manager is better than average, even though this conclusion does not follow from such a small amount of data.

Libertarian paternalism

The idea that it is both legitimate and possible for private and public institutions to affect behavior while respecting freedom of choice. Often times this is relevant to specific UI design choices, such what the default settings are or prompting a user to make a specific and important decision.

The way that you frame interfaces matters because they will affect people’s choices. Moreover, forcing people to make choices can also push them to socially optimal outcomes.

Crypto Example

In the future when we have a truly open financial system powered by cryptocurrency there will be UIs to interact with our “bank accounts” (wallets). Built into these systems will be some sort of savings or investment portfolio similar to what we have today. An example of libertarian paternalism would be prompting people to make a decision about whether they want to set aside a portion of their income for their savings or not. The user still retains fully autonomy over their choice, but the prompting leads more people to choose to save.

A more paternal alternative would be to design the system such that users are by default saving a portion of their income but have the option of opting out.

Further reading

Thaler, R. H., & Sunstein, C. R. (2003). Libertarian paternalism. American economic review, 93(2), 175–179.

Loss aversion

The idea that the pain of losing is more powerful than the pleasure of gaining. Estimates for how much more powerful vary, but are generally thought to be over 2x.

Crypto Example

Losing $100,000 is more quite a bit more painful than gaining $100,000.

Further reading

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.

Mental accounting

The tendency for people to separate their money into different mental accounts based on a variety of subjective criteria, such as the source of the money and intent for each account.

For example, people are more likely to frivolously spend money they have won at the casino than money they have earned working.

Crypto Example

Money made from an ICO is put in a separate mental account than money from other sources. Since it doesn’t feel as real as money from an hourly wage, you might be more apt to spend it without second thought.

Money illusion

The tendency to think of currency in nominal terms rather than real terms.

Crypto Example

Not recognizing the depreciation in the purchasing power of the dollar in the past decades.

Naif vs Sophisticate

Naive decision makers (“Naifs”) are not aware of their expectations or how that affects their utility. Sophisticates are aware of their expectations and behavioral biases and adjust their actions accordingly.

For example, a naif allows themselves to get extremely excited for Christmas presents every year, regardless of what they expect to get. A sophisticate tempers their excitement and expects to get nothing, knowing that their utility from receiving anything will be much greater then.

Narrative fallacy

Our limited ability to look at facts without creating a narrative out of them.

Crypto Example

Picking out a “market trend” from a random series of movements.

Narrow Framing

The tendency to make investment decisions without considering the context of your overall portfolio.

Crypto Example

Investing in a new decentralized exchange without taking into account your portfolio is already over exposed to decentralized exchanges.

Omission/Commission bias

Omission bias is the tendency to judge harmful actions as worse than equally harmful omissions. For example, if you were to recommend a friend with an allergy eat a food with that specific allergen unbeknownst to them, that would be seen as worse than passively watching as they chose and ate that same food.

Crypto Example

Consider a situation where someone owes you Ether. Omission/commission bias would be being more upset at someone for having sent Ether to the wrong wallet then if they had simply not sent Ether at all.

Paradox of choice

Having an excessive amount of options in a particular decision can lead to worse outcomes. Choice overload can lead you to question the decisions you make before you even make them, set you up for unrealistically high expectations, and make you blame yourself for any and all failures.

Crypto Example

Giving users too many choices for goods and services to exchange for a utility token with may lead them to a bad choice.

Priming

Engaging people with a task or exposing them to a stimuli to activate associated memories with the goal of influencing their performance on a subsequent task.

Crypto Example

Being exposed with certain information can affect your valuation about a cryptocurrency.

Further reading

Chartrand, T. L., Huber, J., Shiv, B., & Tanner, R. (2008). Nonconscious goals and consumer choice. Journal of Consumer Research, 35, 189–201.

Principal-agent problem

When one person or entity (the “agent”) is able to make decisions and/or take actions on behalf of, or that impact, another person or entity: the “principal”. Often times this becomes problematic when agents and principals have diverging interests.

Crypto Example

In any mechanisms you design there are multiple parties, each of which has their own incentives, varying expectations, and different actions they can take. To design robust and sustainable mechanisms you should lay all of these things out and try to model your system.

Projection bias

People’s assumption that their preferences will remain the same over time.

Crypto Example

Assuming that your portfolio risk preferences will remain the same over time. Alternatively, assuming the agents acting within your mechanisms will have the same preferences over time.

Prospect theory

A theory that describes the way people choose between probabilistic alternative involving risk, where the probabilities of outcomes are unknown. Prospect theory dictates that people make decisions based on relative gains and losses instead of in absolute terms. Moreover, people use heuristics, several of them are included in this article, to evaluate these gains and losses. Lastly, it prescribes that people have loss aversion, or that they feel losses more deeply than equivalent gains.

Crypto Example

The loss of 1 BTC is felt much more strongly than a gain of 1 BTC. Moreover, gains and losses show diminishing marginal sensitivity, or they are felt much less strongly after an initial threshold.

Prospect theory also informs how people make decisions regarding risk. We are risk averse to gains but risk seeking when it comes to losses. Consider the following situation where you are given two choices. Either a sure gain of $900, or a 90% chance of $1000 and a 10% chance of 0.

The expected outcome of both choices is the same. Most people will avoid the risk and take the $900. However, consider the same situation with losses instead.

Diagrams and examples from https://www.nngroup.com/articles/prospect-theory/

A majority of people would prefer the riskier second option in the hopes of avoiding the loss. We are risk-seeking with losses and risk-averse with gains. Be mindful of these biases and invest accordingly.

Representativeness heuristic

People judge the probability that an object or event A belongs to a certain class B by looking at the degree to which A resembles B. In doing so, we neglect information about the general probability of B occurring (its base rate) (Kahneman & Tversky, 1972).

Consider the following:

Bob is an opera fan who enjoys touring art museums when on holiday. Growing up, he enjoyed playing chess with family members and friends. Which situation is more likely?

A. Bob plays trumpet for a major symphony orchestraB. Bob is a farmer

A large proportion of people will choose A in the above problem, because Bob’s description matches the stereotype we may hold about a classical musicians rather than farmers. In reality, the likelihood of B being true is far greater, because farmers make up a much larger proportion of the population.

Crypto Example

Judging the probability that a new cryptocurrency will produce returns based off whether it resembles previous cryptocurrencies that have been lucrative.

To be more specific, you’d be using a representativeness heuristic by investing in a new coin because it is a smart contract platform given that smart contract platforms were recently successful investments.

Further reading

Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430–454.

Risk-dominant strategies (minimax)

Instead of maximizing utility an agent aims to _mini_mize the _maxi_mum loss scenario. A slight variant is to _max_imize the _min_imum gain (maximin). Originally applicable as a strategy in two player games but broadly applicable, especially in situations with high uncertainty.

Crypto Example

Instead of trying to maximize your portfolio’s gains, you could seek to minimize the maximum possible loss you make.

Alternatively, users could deploy this same strategy in the games/mechanisms you design.

Satisficing

Instead of optimizing, people tend to make decisions by satisficing, or a combination of sufficing and satisfying. Satisficing individuals can be thought of as having some basic acceptable criteria to measure options against and choosing an option that satisfies those, instead of the “best” option.

Crypto Example

When constructing your portfolio, you may not seek to find the optimal trade, but instead one that satisfices a series of criteria such as a minimal threshold of expected return given its volatility.

Self-attribution bias

The tendency to ascribe success to innate aspects, such as talent or foresight, while blaming failures on outside influences.

Crypto Example

Attributing your portfolio’s success to your genius while blaming its failures on outside forces. In recent times this would have been timing Ether’s bounce back from ~$300 but missing the top and attributing that to “FUD” or insiders.

Status quo bias

The preference for the current state of things. Strongly related to anchoring and prospect theory. Status quo bias is less prominent when there are small transaction costs to change and small sunk costs.

Crypto Example

The irrational tendency to not want to adjust your portfolio.

The planning fallacy

A bias whereby people generally underestimate the amount of time needed to complete a future task.

Crypto Example

Underestimating the time that it will take you to do due diligence on a new investment.

Underestimating the time that it will take your users to complete an action.

Time inconsistency

People’s preferences change over time in such a way that they may become inconsistent with previous or future preferences. For example, you may use your credit card and rack up debt today without giving it a second thought, basking in the instant gratification of new things. Later on you might feel regret when you must begin to pay off your debt. As a result of these changing preferences, you probably won’t make the decision that will maximize your utility.

Crypto Example

Deciding to HODL and committing this by sending your Ether to a smart contract to lock it up today but regretting the opportunity cost of that decision tomorrow.

Ultimatum game

A game with two players whereby one is endowed with a sum of money and tasked to split it with another player. The player endowed with the sum of money must propose a split of the sum to the other player. The other player may accept the proposed split or reject it. In the case that they reject the proposed split both players receive nothing.

When carried between members of a shared social group people offer fair (i.e 50:50) splits and often times will reject offers of less than 30%.

Crypto Example

Similarly to the dictator game, there are no direct implications for the dictator game, but what it does tell us is that there exist motives for people’s actions other than self interest. Potential explanations are that people are concerned for others (altruism) or averse to inequitable outcomes (inequality aversion).

Connect with me

A special shout out goes to Ashley Hodgson for introducing me to a lot of these topics. Thanks also to Travis Kling for his thoughts on this article.

If you enjoyed my article then you can follow me here or on Twitter. Feel free to reach out, I appreciate feedback!