Pantera Research: Crypto Users Lack Patience, Instant Gratification Over Future Gains

Original Title: “Crypto Users Are Comparatively Impatient”

Author: PAUL VERADITTAKIT

Translation: Shenchao TechFlow

Do Cryptocurrency Users Need Intervention?

• A study by Pantera Research Lab found that cryptocurrency users exhibit a higher current preference and a lower discount factor, indicating a strong inclination towards instant gratification.

• The quasi-hyperbolic discounting model, characterized by parameters such as present bias (ꞵ) and discount factor (𝛿), helps to understand individuals' preference for immediate rewards over future gains, a behavior particularly evident in the volatile and speculative cryptocurrency market.

• This research can be used to optimize token distribution, such as rewarding early users, decentralized governance, and marketing new product airdrops.

Introduction

In the classic startup story from Silicon Valley, PayPal decided to pay users $10 to use its product. The logic behind this was that if you can pay people to join, eventually the network value will be high enough that new users will join for free, and you can stop paying. This seems to have worked, as PayPal was able to stop paying and continue to grow, thus driving network effects.

In the cryptocurrency space, we have adopted and expanded this approach by airdropping not only to pay people to join but also typically requiring them to use our products for a period of time.

Quasi-Hyperbolic Discounting Model

Airdrops have become a multifaceted tool for rewarding early users, decentralized protocol governance, and promoting new products. Particularly when determining who should receive rewards and the value of their efforts, establishing distribution criteria has become an art. In this context, the quantity and timing of token distribution (often through mechanisms like vesting periods or gradual releases) play a crucial role. These decisions should be based on systematic analysis rather than relying on guesswork, emotions, or precedents. Using a more quantitative framework can ensure fairness and align with long-term strategic goals.

The quasi-hyperbolic discounting model provides a mathematical framework for exploring individuals' choices when weighing rewards across different time points. Its application is particularly relevant in areas where impulsivity and temporal inconsistency significantly affect decision-making, such as financial decisions and health-related behaviors.

The model is driven by two specific parameters: present bias (ꞵ) and discount factor (𝛿).

Present Bias (ꞵ):

This parameter measures an individual's tendency to prioritize immediate rewards over delayed ones. Its value ranges between 0 and 1, where 1 indicates no present bias, reflecting a balanced, time-consistent evaluation of future rewards. The closer the value is to 0, the stronger the present bias, indicating a strong preference for immediate rewards.

For example, when choosing between receiving $50 today or $100 a year later, a person with a high present bias (close to 0) would prefer to take the $50 immediately rather than wait for the larger amount.

Discount Factor (𝛿):

This parameter describes the rate at which the value of future rewards declines as the time to realization increases, considering its naturally diminishing perceived value with delay. The discount factor is more accurately quantified over longer, multi-year intervals. When evaluating two options in the short term (less than a year), this factor exhibits considerable variability, as immediate circumstances may disproportionately influence perceptions.

For the general population, studies indicate that the discount rate is typically around 0.9. However, among groups with a strong propensity for gambling, this value is often significantly lower. Research shows that the average discount factor for habitual gamblers is slightly below 0.8, while for problem gamblers, it approaches 0.5.

Using the above terminology, we can express the utility U of receiving reward x at time t with the following formula:

U(t) = tU(x)

This model captures how the value of rewards changes over time: immediate rewards are assessed at full utility, while future rewards are adjusted based on present bias and exponential decay.

Experiment

Last year, Pantera Research Lab conducted a study quantifying the behavioral tendencies of cryptocurrency users. We surveyed participants through two simple questions, aimed at measuring their preferences for immediate payments versus future value.

This approach helped us determine representative values for ꞵ and 𝛿. Our research found that a representative sample of cryptocurrency users exhibited a current preference slightly above 0.4 and a significantly lower discount factor.

The study revealed that cryptocurrency users' current preference is above average and their discount factor is low, indicating a tendency towards impatience and a preference for instant gratification over future gains.

This can be attributed to several interrelated factors in the cryptocurrency environment:

• Cyclical Market Behavior: The cryptocurrency market is known for its volatility and cyclical nature, with tokens frequently experiencing rapid value fluctuations. This cyclicality influences user behavior, as many are accustomed to navigating these cycles rather than adopting the longer-term investment strategies more common in traditional finance. Frequent ups and downs may lead users to steeply discount future values, fearing that potential downturns could erase profits.

• Stigma of Tokens: The survey specifically inquired about tokens and their perceived future value, which may highlight the entrenched stigma associated with token trading. The stigma surrounding token valuations linked to cyclicality and speculation reinforces a cautious attitude towards long-term investments. Moreover, if the survey measured fiat currency or other forms of rewards, the results might align more closely with global averages, suggesting that the nature of the reward could significantly influence observed discounting behavior.

• Speculative Nature of Cryptocurrency Applications: Today's cryptocurrency ecosystem is deeply rooted in speculation and trading, characteristics that are particularly prevalent in its most successful applications. This tendency indicates that current users overwhelmingly prefer speculative platforms, a preference reflected in the survey results, showing a strong inclination towards immediate financial gains.

While the findings may differ from typical human behavioral norms, they reflect the characteristics and tendencies of the current cryptocurrency user base. This distinction is particularly important for designing airdrop and token distribution projects, as understanding these unique behaviors can lead to more strategic planning and reward system structures.

For example, Drift, a perpetual contract DEX on Solana, recently launched its native token DRIFT. The Drift team incorporated a time delay mechanism in its token distribution strategy, offering double rewards to users who wait 6 hours to claim airdrops after the token launch. The time delay aims to alleviate congestion typically caused by bots in the early stages of airdrops and help stabilize token performance by reducing the initial surge of sellers.

In fact, only 7.5k or 15% (at the time of writing) of potential claimants did not wait the 6 hours to receive double rewards. Based on our research findings, Drift could delay for several months and statistically meet the needs of most end users.