Crypto Users Are Comparatively Impatient
Written by Paul Veradittakit, Managing Partner, Pantera Capital
Compiled by: Yangz, Techub News
Token airdrops can be designed to take advantage of the fact that cryptocurrency users prefer instant gratification.
summary
A survey conducted by Pantera Research found that cryptocurrency users generally show a higher Present Bias and a lower Discount Factor, meaning they prefer instant gratification.
The Quasi-hyperbolic Discounting Model, characterized by parameters such as the immediate bias (ꞵ) and the discount factor (?), helps understand the tendency of individuals to prefer immediate returns over future returns, a behavior that is particularly evident in the volatile and speculative cryptocurrency market.
This research can be used to optimize token distribution, such as airdrops for early user rewards, decentralized governance, and marketing new products.
A classic story among Silicon Valley startups is PayPal’s decision to pay people $10 to use its product, and the logic behind this is that if you can pay people to use your product, then eventually the value of the network will be high enough that new people will join on their own and paid acquisition can stop. PayPal implemented this decision and maintained user growth after stopping payments, leading to network effects, so this decision seems to have worked.
In the cryptocurrency space, we’ve taken this approach and expanded upon it with airdrops, which pay not only new people but often users who continue to use the product over a period of time.
Hyperbolic Discounting Model
Frankly speaking, airdrops have become a multi-purpose tool, used to reward early adopters, decentralize protocol governance, and promote new products. The formulation of distribution criteria has become an art, especially when determining who to reward and the value of their efforts. In this context, the number of tokens allocated and the time of unlocking (usually through mechanisms such as vesting or gradual unlocking) both play an important role. These decisions should be based on system analysis rather than relying on speculation, emotions, or other precedents. Adopting a more quantitative framework ensures that airdrops are fair and strategically aligned with long-term goals.
The hyperbolic discounting model provides a mathematical framework for exploring how individuals make choices involving reward trade-offs over time. The model is particularly useful in areas where impulsivity and inconsistency can significantly influence decision making over time, such as financial decisions and health-related behaviors.
The model is driven by two population-specific parameters: the immediate propensity ꞵ and the discount factor ?.
Immediate trend
This parameter measures an individual's tendency to prioritize immediate rewards over long-term rewards. It varies between 0 and 1, with a value of 1 indicating no immediate preference, reflecting a balance between long-term rewards and an assessment of time consistency. When the value approaches 0, it indicates that the immediate preference is getting stronger, that is, a preference for immediate rewards is stronger.
For example, given a choice between $50 today and $100 in a year, a person with a high immediacy preference (close to 0) would prefer to receive $50 immediately rather than $100 in a year.
Discount factor?
This parameter measures the rate at which the value of future rewards decreases as the time to realization increases, reflecting the natural decline in perceived value as time passes. The discount factor is more accurately quantified over longer, multi-year time intervals. When evaluating two options over short periods (less than a year), this factor can show considerable variability because immediate circumstances can have a disproportionate impact on perceptions.
For the general population, research shows that the discount factor is usually around 0.9. However, in groups with a propensity to gamble, this number tends to be much lower. Research shows that the average discount factor for regular gamblers is usually just under 0.8, while compulsive gamblers tend to have a discount factor closer to 0.5.
Based on the above terminology, we can express the utility (U) of receiving a reward (x) at time (t) as: U(t) = tU(x)
The model captures how the value of rewards varies over time: immediate rewards are valued at their full utility, while the value of future rewards is adjusted downward to account for both immediate propensity and exponential decay.
experiment
Last year, Pantera Research Labs conducted a study to quantify the behavioral tendencies of cryptocurrency users. We surveyed participants with two simple questions to understand their preference for immediate rewards versus waiting for future value.
This approach helps us determine representative values for ꞵ and ?. Our results show that a representative sample of cryptocurrency users exhibits an immediate preference slightly above 0.4 and a significantly low discount factor.
The study showed that cryptocurrency users have ahigher-than-average tendency to act now and a lower discount factor, suggesting they may be impatient and prefer instant gratification over future gains.
This phenomenon can be attributed to several interrelated factors in the cryptocurrency market:
Cyclical market behavior: The cryptocurrency market is known for its volatility and cyclicality, with token prices often experiencing rapid swings. This cyclicality affects user behavior, as many traders are accustomed to trading frequently during these cycles rather than taking the long-term investment strategy more common in traditional finance. Frequent ups and downs may cause users to underestimate the future value of tokens, fearing that a potential recession could wipe out gains.
Bias towards Tokens: The survey specifically asked cryptocurrency users about their perception of the future value of tokens, revealing their deep biases related to token trading. This bias is related to the cyclical and speculative nature of token valuations, further deepening their cautious attitude towards long-term investment in the cryptocurrency space. In addition, if the survey used fiat currency or other forms of rewards to measure preferences, the discount factor of cryptocurrency users would likely be closer to the global average, suggesting that the nature of the reward may also have a significant impact on the observed discounting behavior.
Speculative nature of cryptocurrency applications: Today’s cryptocurrency ecosystem is deeply rooted in speculation and trading, and these characteristics are prevalent in the most successful applications. This trend shows that current users overwhelmingly prefer speculative platforms, and the survey results also reflect this, that users strongly prefer to obtain immediate financial gains.
Although the results of the study may differ from conventional human behavior norms, they truly reflect the characteristics and tendencies of the current cryptocurrency user group. This distinction is particularly important for projects that need to design airdrops and token distributions, because understanding these unique behaviors can help plan more strategies and design better reward system structures.
Take the approach of Drift, the sustainable product exchange for the Solana ecosystem, for example. The team recently launched its native token DRIFT with a delayed reward mechanism, which provides double rewards to users who wait 6 hours after the token is issued to claim the airdrop. The purpose of this mechanism is to alleviate robot congestion at the beginning of the airdrop and help stabilize the performance of the token by reducing the initial surge of sellers.
Results show that only 7,500 or 15% (at the time of writing) of the airdrop claimants did not choose to wait 6 hours to receive the doubled reward. Based on our research, if the reward value was doubled, Drift could easily extend the time to several months and statistically speaking, it should appease the majority of end users.
