Blockchain platforms attempt to expand their user base by awarding tokens to users, a practice known as issuing airdrops.
Empirical data and related work implies that previous airdrops fall short of their stated aim of attracting long-term users, partially due to adversarial farmers who game airdrop mechanisms and receive an outsize share of rewards.

In this work, we argue that given the futility of fighting farmers, the airdrop business model should be reconsidered: farmers should be harnessed to generate activity that attracts real users, i.e., strengthens network effects.
To understand the impact of farmers on airdrops, we analyze their performance in a market inhabited by two competing platforms and two tiers of users: real users and farmers.
We show that counterintuitively, farmers sometimes represent a necessary evil — it can be revenue-optimal for airdrop issuers to give some tokens to farmers, even in the hypothetical case where platforms could costlessly detect and banish all farmers.

Although we focus on airdrops, our results generally apply to activity-based incentive schemes.

§ INTRODUCTION

Blockchain platforms may choose to airdrop (i.e., distribute) tokens to their users in an attempt to attract new users.
Generally, the amount of rewards a user can receive is limited, and eligibility for tokens is conditioned on performing various tasks <cit.>, possibly involving some form of PoH or KYC procedures <cit.>.

Unfortunately, airdrops attract the attention of farmers who employ sophisticated tactics to increase their rewards, such as creating sybil accounts to pose as multiple users <cit.>, and even paying others to circumvent PoH measures <cit.>.
To curtail farming, some issue retroactive airdrops which are not announced in advance.
Instead, eligibility for rewards is based on users' past interactions with the issuing platforms, also allowing issuers to screen users that exhibit farmer-esque behavior <cit.>.
However, some “pre-farm” retroactive airdrops <cit.>, with empirical analyzes showing that farmers receive a large share of their rewards <cit.>.
Given the persistence and ubiquity of farmers, a question is raised:

Can farmers be used to the benefit of the ecosystem?

§.§ This work

We initiate the study of the aforementioned question.
Our main contribution is an economic analysis of airdrops in an adversarial setting, where farmers attempt to receive rewards without contributing to the economic activity of the platform.
In particular, our analysis accounts for the potential impact of farmers on network effects, a phenomenon in which an individual's utility from a service depends on the size of its userbase, and which previous work highlights as crucial for an airdrop's success <cit.>.

We introduce the general setting of our work by analyzing a motivating example in <ref>, which considers a newcomer blockchain seeking to create or expand its userbase.
While honest users may prefer an incumbent blockchain with a large userbase, farmers are naturally attracted to the blockchain that executes an airdrop.
If airdrop eligibility criteria target activity that strengthens network effects, farmers lured by these rewards effectively increase the value proposition of the newcomer with respect to honest users.
However, the profitability of the blockchain crucially hinges on setting the airdrop's rewards correctly, so that they ensure profits from users offset losses from farmers.

We continue in <ref> by establishing an empirical basis for our model.
Thus, we collect data on recent airdrops and review previous empirical works, using both to study real-world user behavior before, during, and after an airdrop.
We present evidence supporting previous work: while airdrops may produce a positive short-term impact on various metrics of interest, the primary one to sustain a positive impact over longer periods of time is the TVL.
We then go over previous empirical work showing that airdrops fall in the hands of the “wrong” type of users: many airdrop recipients sell most of their tokens, usually within a very short period of time, and cease using the issuing platform, even in the case of retroactive airdrops.

Building on insights gleaned from the aforementioned findings, we present a formal model for the setting in <ref>.
One of our primary goals is to define a framework that captures the effect of airdrops on user share in a competitive market.
Thus, the model considers a market composed of multiple blockchains and users, where the latter may belong to one of two user tiers: honest users, or airdrop farmers.
Honest users will not engage in farming;
for example, they may abhor farming and view it as unethical, or simply lack the technological skills to farm and do not find it worthwhile to obtain them.
On the other hand, farmers do not hold such reservations and will farm airdrops, if it is profitable for them to do so as dependent on the measures blockchains take against farming and on farmers' own techniques to avoid detection and lower eligibility costs.

Our analysis extends the established line of Hotelling-esque <cit.> works that examine competition between two firms over consumers that are distributed along a continuous line, where if a point on the line represents a marginal consumer that prefers firm $1$ over $2$, then all the consumers to its left do so as well, and vice versa (if it prefers $2$ over $1$, then all to the right do so as well).
In particular, we find that such works on the impact of digital piracy on competition are related to the current setting (see <ref>): pirates may obtain services or products for free (e.g., a video game), but their contribution to network effects also attracts non-pirate consumers (e.g., consider that having more players in a massively online video game may cause gamers to enjoy it more, even if some players did not pay for the game).
However, prior work considered pirates with limited consumption and whose harm to revenue is due to not paying for their consumption.

On the other hand, our setting is more complex:
if profitable for them, farmers may create a potentially unlimited number of fake accounts, a practice also known as a sybil attack <cit.>.
Moreover, their contribution to revenue is possibly negative, if airdrop rewards are costly to produce (e.g., consider token giveaways).
Both comprise but a part of the impact of farmers, who may attempt to minimize costs entailed in becoming eligible for an airdrop through additional means.
For example, in case a minimal number of transactions is listed as part of an airdrop's eligibility criteria, farmers can create many transactions in a cheap manner either by transacting when fees are low.

To demonstrate the impact of these changes, we show in <ref> that when considering a fixed reward for each eligible user, a setting similar to previous work, an opposite effect to Jain's <cit.> famous result emerges: farmers may significantly harm the revenue of an airdrop issuer even when considering network effects.
Indeed, some rely on such fixed airdrops, where each eligible user receives a fixed amount of tokens that is determined in advance, à la Worldcoin's $25$ tokens per incoming user <cit.>.

Although this may paint a bleak picture, we analyze an alternative design: the proportional drop, where a predefined number of tokens is split equally among eligible users.
Intuitively, we show in <ref> that this scheme naturally leads farmers to limit the amount of sybil accounts that they create.
Thus, while an airdrop issuer would give rewards to farmers, the latter's contribution to network effects can outweigh the bounded revenue losses.
We show in <ref> that this implies that even if the issuer could somehow costlessly detect all sybil accounts, it is profitable to not do so.

§.§ Organization

We begin by reviewing related work in <ref>, and by analyzing a motivating example in <ref>.
We proceed by going over empirical findings in <ref>, and use them as the basis of our model, which we define in <ref>.
We present our analysis in <ref>.
We conclude and advance interesting directions for future work in <ref>.

§ RELATED WORK

In this section, we discuss related papers on airdrops.
As far as the authors are aware, our work is the first to present a theoretical economic analysis of airdrops that considers farmers.
To paint a complete picture, we cover a wide range of additional work in <ref>.

§.§ Airdrop Design Space

Allen <cit.> conducts case studies of eight platforms that performed notable airdrops (Arbitrum, Blur, dYdX, ENS, Evmos, Looksrare, Optimism and Osmosis), and uses them to outline the design space of airdrop mechanisms;
e.g., airdrops can be retroactive or proactive, may condition eligibility on various criteria such as supplying funds to liquidity pools, etc.
The author finds that retroactive airdrops may be less useful in expanding a platform's userbase beyond its existing users.
Notably, the author quotes the founder of the blockchain platform Blur, who said in an interview (see 22:36 in <cit.>):

“...it seems like a somewhat of a waste to do a completely retroactive airdrop because when it's retroactive, if it's purely retroactive and everyone was already using the protocol, then you’re really just burning a lot of dry powder that a protocol could be using to actually grow itself...”

Allen  <cit.> extend the analysis to twelve platforms (Auroracoin, Decred, Livepeer, Stellar, Uniswap, Bankless DAO, Osmosis, dYdX, ENS, Evmos, Bored Ape Yacht Club, Optimism), and furthermore present potential reasons for platforms to perform airdrops, with the two main ones being marketing (i.e., creating and/or enlarging a platform's userbase) and decentralization of ownership.

Lommers  <cit.> provide a high-level overview of factors that may affect the performance of blockchain airdrops, and conclude that successful airdrops should not only focus on an issuing platform's existing userbase, but also reach out to those who are not already using the platform.
Thus, the authors argue that to succeed in attracting new users, retroactive airdrops should be preceded by signaling on behalf of issuing platforms, e.g., they should hint that a retroactive airdrop is planned.

§.§ Empirical Studies of Airdrops

Fan  <cit.> perform a case study of ParaSwap's airdrop using data from November 2021 and April 2022, and find that $41\%$ of the airdrop's recipients sold their rewards immediately, with an additional $22\%$ being so-called airdrop hunters who employ sybil accounts.
This is in spite of the measures taken by ParaSwap: the airdrop was retroactive, and sybil accounts were disqualified from being eligible for rewards <cit.>.

Messias, Yaish and Livshits <cit.> empirically evaluate airdrops performed by several platforms (ENS, dYdX, 1inch, Gemstone, and Arbitrum), and show that generally, a large percentage of airdrop recipients tend to sell the tokens they receive relatively quickly after the airdrop.
Notably, the airdrops performed by ENS and dYdX were retroactive <cit.>.

Guo  <cit.> find that airdrops positively affect user engagement, including the engagement of ineligible users.
Furthermore, they find that, despite airdrop recipients commonly selling their tokens, this does not extend to ineligible users, implying that panic selling herd behavior does not form as a result of farmers exiting the market.

Makridis  <cit.> examine a sample of 51 DEX, finding that airdrops led to a statistically significant increase of $13.1\%$ in the market capitalization growth rate of issuing platforms, and that the effect persists over time.

§.§ Airdrop Implementation

Fröwis and Böhme <cit.> analyze the operational costs of airdrops, and find that these generally scale linearly with the number of eligible users.
They analyze both a “push” distribution strategy, where the platform actively sends out rewards to users, and a “pull”-based approach, where eligible users have to send a transaction to receive their reward.

Wahby  <cit.> present a cost-efficient airdrop mechanism that preserves the privacy of recipients by allowing them to claim rewards without revealing their identities, even if they lost some of their private keys.
Their mechanism allows users to generate proofs showing that they did not receive any funds, to account for the possibility that an adversarial issuer claims rewards have been handed out without actually sending them.

§.§ Network Effects in Adversarial Settings

Some considered network effects in the blockchain setting, notably <cit.>.
However, we differ from these, as we focus on an adversarial setting where part of the market uses a blockchain solely to receive subsidies (e.g., an airdrop) rather than extract value from the provided services.
We find that the setting is close to that of the literature on network effects models for digital product markets in the presence of piracy.

Jain <cit.> shows that piracy allows price-conscious consumers to receive a de facto discount on products, while also inadvertently contributing to sales from customers who abstain from piracy, as these value products more due to the network effects generated by the pirates.
This elegant work served as an inspiration to ours, yet the difference in our models is significant.
In Jain's work, pirating digital products does not incur any cost for both pirates and firms, while in our case farming may result in capital expenses for farmers and in revenue for issuing blockchains.
Thus, the positive impact of farmers delicately depends on various parameters, such as the cost of issuing the airdrop and the strength of network effects.

Kúnin and Žigić <cit.> extend Jain's model to incorporate vertical product differentiation, e.g., each of the competing firms can offer multiple products of varying quality.
Their analysis demonstrates that given the choice between a high-quality product and a lower-quality one, if piracy prevention is enforced, then price-conscious consumers may prefer to purchase the lesser product, rather than obtain a pirated copy of the higher-quality one.

§ MOTIVATING EXAMPLE

We now go over an example to illustrate how farmers can help grow a platform's userbase.

Consider an incumbent blockchain called “Arbithouse” which allows users to send transactions at an average fee of $3$ tokens per transaction, and a newcomer blockchain called “Pessimism” that has average fees equal to $2$ tokens.
Our market is populated by one user who is flexible in choosing a blockchain, and four who are accustomed to Arbithouse and would stick with it, even if others are cheaper.
The flexible user wishes to issue one transaction, and would extracts a value of $3$ from doing so.
Moreover, due to network effects, the user produces an additional utility of $1$ per user on its chosen blockchain.
In total, our user would receive a utility of $4$ from using Arbithouse, and $1$ from Pessimism.

Thus, while Pessimism beats Arbithouse on price, it finds that attracting users is hard, leading it to issue an airdrop, where eligibility is conditioned on supplying a predetermined amount of funds into some DeFi liquidity pool, after which users receive some tokens as a reward (i.e., this is a push airdrop, per <ref>).
Given the market, Pessimism can afford to issue an airdrop of at most $4$ tokens per user:
even under the simplifying assumption that Pessimism earns $2$ tokens per transaction (e.g., has no operating costs), it can break even only if eligible users transact at least twice.
Recall that users must supply liquidity to become eligible, implying that eligibility requires at least one transaction.
So, for Pessimism to break even, eligible users need to send at least another transaction.
Unfortunately, this implies that even if the flexible user opts to become eligible for the airdrop, its utility from using Pessimism is at most $3$, lower than from using Arbithouse.

Recall that this example considers a push airdrop.
In the case of a pull issuance strategy, the user's utility from participating in the airdrop would have been lower, due to the fees required for the additional transaction.

Now consider the aforementioned market, after augmenting it with an airdrop farmer who does not genuinely wish to use a blockchain, and would not interact with it besides for the sake of receiving an airdrop:
The farmer will choose Pessimism, as Arbithouse does not have an airdrop.
Although the expenses associated with providing liquidity to the contract are the same for both farmers and regular users, farmers employ sophisticated tools to reduce the costs of transacting, for example, by sending transactions when fees are low, allowing our farmer to send transactions at the low price of $1$ token.
Furthermore, farmers can create multiple sybil identities to earn additional rewards; assume that our farmer can create at most $4$ sybil accounts without being caught by the blockchain's anti-farmer detection schemes.

Interestingly, the current setting allows Pessimism to set a lower airdrop of $1.1$ tokens and make a profit:
while it would incur a loss of $0.4$ tokens due to issuing the airdrop to the farmer's sybil accounts, these very accounts contribute to the utility the flexible user assigns to using Pessimism, which now equals $5$ – enough to “poach” the user from Arbithouse.
We note that the user would not choose to become eligible for the airdrop, as its transaction costs are higher than the reward.
In total, given the revenue that is earned generated by flexible user, the airdrop would result in a net profit of $1.6$ tokens for Pessimism.

§ EMPIRICAL FOUNDATIONS

In this section, we provide empirical foundations for our model.

Airdrops can Contribute to Network Effects
The literature on network effects considers cases where the utility users extract from a platform increases as a function of the platform's user base.
One expects honest users (i.e., non-farmers) to naturally interact with blockchain platforms in a manner that strengthens network effects, for example, by trading on DEX and thus paying fees to liquidity providers <cit.>, or by providing funds to lending pools <cit.>.
However, farmers who wish to maximize their profits may avoid such behavior if it is not beneficial to them.
However, airdrops can condition eligibility on performing actions that increase the issuing platforms' TVL (i.e., the total value of the digital assets that are locked on the platform), for example, by requiring users to provide funds to specific liquidity pools.
Indeed, several airdrops have adopted this approach <cit.>.

With respect to TVL, the related work of Messias, Yaish and Livshits <cit.> finds that it is positively affected by airdrops, and that this impact persists over relatively long periods of time compared to the short-lived impact on other metrics such as the number of active accounts on the platform.
To reach this conclusion, the performance of Arbitrum <cit.> and Optimism <cit.> is compared around the time of the former's airdrop (issued on March '23 <cit.>), while not considering any of the latter's four airdrops (issued on on May '22, June '23, September '23, and February '24 <cit.>).
Notably, the two platforms are the leading Ethereum L2 by several metrics <cit.>.

We complement the findings of <cit.> by analyzing a longer timeframe which includes both Arbitrum's airdrop and all four of Optimism's airdrops, covering January '22 to January '24.

In spite of three of Optimism's airdrops taking place after Arbitrum's single one, Arbitrum generally leads Optimism with respect to the fees spent by users (see <ref>) and TVL (see <ref>).

To highlight the impact of airdrops on the tug of war between the two platforms, we follow previous work and depict the ratio between the number of unique addresses active in each in <ref>, the ratio between the number of transactions processed by each in <ref>, the ratio of the total fees expended by users in <ref>, and the ratio between the TVL on each in <ref>.
The empirical data depicted show that airdrops may positively impact the various metrics in the short term, with the TVL metric enjoying a positive impact that is sustained between the airdrops considered.

Our model considers eligibility criteria that strengthen network effects, implying that both honest users and farmers contribute to network effects.

Total transaction fees paid per day across various Ethereum L2, in USD.

The ratio between the total daily transaction fees paid in Arbitrum and Optimism, in percent.

Although the daily amount of transaction fees paid by Arbitrum users spiked around their airdrop, it did not enjoy a sustained increase when compared to competitors.

The ratio between the unique addresses active per day in Arbitrum and Optimism.

The ratio between the total number of transactions processed by Arbitrum and Optimism.

When examined through the lens of the daily unique active addresses and transaction number, airdrops produce a positive effect, albeit for the short-term.

The amount of TVL per day across various Ethereum L2, in USD.

The ratio between the TVL of Arbitrum and Optimism.

The TVL metric enjoys a sustained positive change as a result of airdrops.

Farmers Differ in Capabilities
Empirical evidence shows that farmers vary in their ability to avoid being uncovered by airdrop issuers, who commonly attempt to detect farmers and discredit suspicious accounts from being eligible for rewards.
These efforts are primarily based on identifying sybil accounts, i.e., several accounts that belong to one user.
For example, the Arbitrum Foundation enlisted the aid of an analytics firm to detect farmers, with the firm publicly disclosing their sybil-detection measures <cit.>.
Despite these efforts, some found that not all farmers were detected <cit.>.
Notably, a recent empirical study pegs the fraction of “same-person” addresses among Arbitrum airdrop recipients at nearly $50\%$, with almost $22\%$ of addresses belonging to a sybil cluster of $20$ or more related addresses <cit.>.
Some farmers publicly claim that to avoid detection, they operate their farming operations manually and, furthermore, resort to using a low number of sybil accounts <cit.>.

We incorporate these findings into our model by allowing issuers to set a sybil-resistance level, where a fraction of farmers can be detected.
Moreover, our analysis considers that farmers may reduce the number of accounts they create to avoid detection.

Airdrop Dump does not Extend to Other Users
Several studies measure the fraction of recipients who dump (i.e., sell most of) rewards shortly after receiving them, finding that these range between $35\%$ and $95\%$ <cit.>.
Recent work shows that post-airdrop dumps do not cause similar behavior among users who do not receive rewards <cit.>.

Given that the dumping of tokens by farmers does not affect non-farmers, our model does not account for such considerations in the utility function of honest users.

Farmers do not Organically Use Platforms
Recent work suggests that farmers use platforms that issue airdrops just to become eligible, and stop doing so after receiving rewards.
For example, $50\%$ of the recipients of Uniswap's airdrop stopped using the platform altogether within a month <cit.>, and after a year, this rose to roughly $84\%$ of recipients.
Fan  <cit.> find that $61\%$ of the recipients of ParaSwap's airdrop stopped using the platform.
Even when farmers must use issuing platforms to become eligible for airdrops, they may employ tools to reduce the associated costs <cit.>.
For example, if eligibility hinges on sending transactions, farmers can account for the seasonality of transaction fees to lower their expenses <cit.>.
Moreover, in case eligibility is conditioned on completing seemingly free-of-charge tasks (e.g., watching videos or sharing posts on social networks), economies of scale and technological skills confer an advantage to well-oiled farming operations that can automate them, or employ cheap labor when PoH measures are used <cit.>.

Our model accounts for these findings:
While honest users have some intrinsic value for the services offered by an issuing platform and therefore actually use it, farmers do not value these services, and, moreover, incur lower airdrop eligibility costs.

§ MODEL

We now formally define our model, with all notations summarized in <ref>.

We consider two blockchains, denoted by $\chain_1$ and $\chain_2$.
Let $\userbase_i$ be the number of users active on blockchain $\chain_i$, i.e., the blockchain's userbase.

Blockchain $\chain_i \in \left\{\chain_1, \chain_2\right\}$ can run an airdrop campaign, where airdrops are defined according to the following parameters.

* Eligibility criteria.
If $\chain_i$ holds an airdrop, it specifies an eligibility criteria that users must satisfy to receive rewards.
We denote the number of eligible users by $\eligible_i$.

* Rewards.
Each eligible user receives a reward equal to $\reward_i$.

* Airdrop type.
We examine two types of airdrop mechanism.
In a fixed drop, eligible users receive a reward of $\fixed_i \in \mathbb{R}$ tokens, where in a proportional drop, a distributor allocates a budget of $\budget_i \in \mathbb{R}_+$ tokens to distribute to users, with each eligible user receiving $\frac{\budget_i}{\eligible_i}$ tokens.
Platforms can choose not to issue an airdrop at all (that is, the case where $\fixed_i = 0 = \budget_i$), issue an airdrop that belongs to exactly one type (the case corresponding to either $\fixed_i = 0$ or $\budget_i = 0$), or combine the two types (i.e., $\fixed_i,\budget_i > 0$).
To capture all cases, we denote the amount of tokens distributed to the $i$-th user by:

\begin{equation}
\label{eq:GeneralReward}
\reward_i \define \fixed_i + \frac{\budget_i}{\eligible_i}
\end{equation}

* Issuance costs.
For each fixed reward issued, blockchain $\chain_i$ incurs a cost of $\dropCost_i \in \mathbb{R}_+$.
Although it is natural to assume that $\dropCost_i = \fixed_i$, we generalize to settings in which issuance costs are distinct from rewards.
This applies, for example, to cases where airdrop rewards are NFT <cit.>.

* Eligibility costs.
To satisfy the airdrop criteria of blockchain $\chain_i$, users incur costs equal to $\cost_i \in \mathbb{R}$.
wlog, costs are collected by the blockchain, as costs that cannot be collected (see <ref>) can be accounted for by offsetting user utility by the corresponding negative value.

* Sybil-resistance level.
We say $\chain_i$ has a resistance level of $\resist_i$ if it can detect a fraction $\resist_i \in \left[0, 1\right]$ of actors who create sybil accounts.

Generally, eligibility costs can be taken to mean a variety of things.
For example, airdrops may require users to send some number of transactions to become eligible, or that eligibile users claim their rewards by sending at least one transaction (e.g., pull airdrops, as described in <ref>), and in that case, the cost amounts to the corresponding transaction fees.
In other cases, eligibility depends on performing a variety of tasks, such as providing identification documents for a KYC procedure, or sharing posts on social media related to the platform that issues the airdrop.
Thus, costs can also be taken to mean the effort required for eligibility, such as the time expenditure, or the privacy risks associated with KYC procedures <cit.>.

One may design airdrops where the amount of rewards a user receives scales with costs, i.e., the amount of tokens allocated is proportional to a user's total expenditure on fees.
Another alternative is to issue nontransferable rewards, such as fee discounts on future transactions.
Such designs are naturally farmer-proof, in the sense that farming them cannot confer any profit.
However, our focus is on commonly-used mechanisms that are not farmer-proof.
The two airdrop types we analyze are interesting when considering farmers, especially when economies of scale and technological skills confer an advantage to well-oiled farming operations.

Actor Tiers
Actors belong to two different market segments: honest users, and farmers.
Each user of both tiers chooses a single blockchain in a manner that maximizes its utility.

Honest Users
There are $\numHonest$ honest users.
An honest user's utility (see <ref>) is informed by the value they gain from using the chosen blockchain, the presence of network effects, the user's bias towards a specific blockchain, whether the user is eligible for the chosen blockchain's airdrop, and the degree of complementarity between the airdrop and the value assigned to using the blockchain.

* Usage utility.
Users wish to use blockchain services and assign a value of $\val \in \mathbb{R}_+\cup\{0\}$ to doing so.
Given a choice of blockchain $\chain_i$, users pay $\fee_i$ in transaction fees, which are collected by the blockchain.

* Network effects.
Following the literature <cit.>, user utility increases linearly with the user base $\userbase_i$ of its chosen blockchain.
Given the per-capita strength of the network effect is $\netfx \in \mathbb{R}$, then a user's utility increases by $\netfx \cdot \userbase_i$.

* Bias.
Users are innately biased towards some blockchain, and will use it even if both are equal.
Per the classic Hotelling model <cit.>, users with bias $\bias \in \left[0,1\right]$ who choose blockchain $\chain_i$ incur a cost of $-\vert i-1 -\bias \vert$.
Intuitively, the proximity of $\bias$ to $0$ or $1$ determines a preference of either $\chain_1$ or $\chain_2$, respectively, rising from the disutility of choosing against one's bias, also known as Hotelling's “transportation cost”.
In-line with previous work <cit.>, biases are sampled in an iid manner from a uniform distribution.

* Airdrop eligibility.
Users are rational and would participate in an airdrop only if it increases their utility.
If blockchain $\chain_i$ issues an airdrop, users can pay the eligibility cost $\cost_i$ and receive the corresponding reward $\reward_i$.

* Complementarity.
The service provided by a blockchain and the added utility of owning the blockchain's tokens may possess a degree of complementarity.
In our case, this may be, for example, because tokens received as part of the airdrop allow participation in the blockchain's governance process <cit.>.
Indeed, Makridis  <cit.> found that airdrops of governance tokens positively impact market capitalization.
Following prior art <cit.>, if a user receives an airdrop, its usage utility minus the bias increases by a factor $1+\valScale \in \mathbb{R}$, i.e., it is $(1+\valScale) (\val - \vert i - 1 - \bias \vert)$.

An honest user with bias $\bias$ who chooses blockchain $\chain_i \in \left\{\emptyset, \chain_1, \chain_2\right\}$ receives a utility equal to:

\begin{equation*}
\utility_\honest \left( \bias, i \right)
\define
\begin{cases}
(1+\valScale) (\val - \vert i - 1 - \bias \vert)
- \fee_i + \netfx \userbase_i
+ \reward_i - \cost_i
,  & \text{\small chose $\chain_i \in \{\chain_1, \chain_2\}$ and eligible}
\\
\val - \vert i - 1 - \bias \vert - \fee_i + \netfx \userbase_i
,  & \text{\small chose $\chain_i \in \{\chain_1, \chain_2\}$ and ineligible}
\\
0, & \text{\small otherwise}
\end{cases}
\end{equation*}

Airdrop Farmers
There are $\numFarmer$ farmers.
Farmer utility (see <ref>) differs from honest users' in several important aspects.

* Single-minded.
Farmers do not organically interact with a blockchain, and do so solely to receive its airdrop.
Thus, they do not receive a utility of $\val$ from using a blockchain of their choice and do not incur the matching amount of $\fee_i$ fees.
Furthermore, they are unbiased and do not consider network effects when choosing a blockchain.

* Sybil capacity.
Farmers may create multiple sybil accounts to increase their profits.
We denote the number of sybils a farmer can create by $\sybilCap \in \mathbb{N}\cup\{0,\infty\}$.

* Lower eligibility cost.
To capture the ability of farmers to incur lower eligibility costs per account, we scale their costs by a factor $\costScale \in \left[0, 1\right]$.

For each sybil account deployed to blockchain $\chain_i \in \left\{\emptyset, \chain_1, \chain_2\right\}$, an airdrop farmer receives a utility equal to:

\begin{equation*}
\utility_\farmer \left( \bias, i \right)
\define
\begin{cases}
\reward_i - \costScale\cost_i, & \text{\small deployed to $\chain_i \in \{\chain_1, \chain_2\}$ and eligible}
\\
0,                             & \text{\small otherwise}
\end{cases}
\end{equation*}

The Game
Our game proceeds as follows.

* First, both blockchains simultaneously announce their transaction costs and, furthermore, whether or not they are issuing airdrops.
In case a blockchain announces an airdrop, the corresponding eligibility criteria is also published.

* Then, each actor (i.e., user or farmer) performs the following:

* Selects a blockchain, and if an airdrop was announced, whether to become eligible.
If the actor is a farmer, it furthermore chooses the number of sybils to create.
All choices are made in a manner that maximizes the actor's equilibrium utility.

* If the actor is an honest user, it interacts with the selected blockchain and pays the corresponding fees, which are given as revenue to the chosen blockchain.

* If the actor chose to do so, it satisfies the chosen blockchain's eligibility criteria and receives the associated rewards.

Similarly to other works on network effects <cit.>, we analyze rational expectations equilibria <cit.>, where if all actors are rational, then their ex-ante estimation of a value accurately predict its realization in equilibrium.
Thus, if users expect blockchain $\chain_i$ to have a userbase of $\overset{\sim}{\userbase_i}$, the realization in equilibrium is $\overset{\sim}{\userbase_i} = \userbase_i$.

§ EQUILIBRIUM MARKET SHARE

We begin by analyzing the equilibrium market share of each blockchain.
wlog, consider blockchain $\chain_i$.
Let $\bias_{i,\honest_{\xmark}}^*$ be the bias of the marginal honest user who is indifferent between choosing blockchain $\chain_i$ and not using any blockchain.
Given that the blockchain decides to issue an airdrop, let $\bias_{i,\honest_{\cmark}}^*$ be the bias of the marginal honest user who chooses $\chain_i$ yet is indifferent between becoming eligible for its airdrop and opting-out of it, and denote the number of farmer-created sybil accounts that are eligible for the airdrop of $\chain_i$ by $\eligible_{i,\farmer}$.

To avoid degenerate cases where all users would never use a blockchain without receiving an airdrop, we only consider equilibria where all game parameters are set such that:

\begin{equation}
\label{eq:Degenerate}
\le
\bias_{1,\honest_{\cmark}}^*
\le
\bias_{1,\honest_{\xmark}}^*
\le
\bias_{2,\honest_{\xmark}}^*
\le
\bias_{2,\honest_{\cmark}}^*
\le
\end{equation}

Thus, these biases define the market share of the blockchain in each user segment.
The share of those who do not choose to qualify for an airdrop is equal to $\vert \bias_{i,\honest_{\cmark}}^* - \bias_{i,\honest_{\xmark}}^*\vert$.
Furthermore, for blockchain $\chain_1$, the share of eligible honest users is $\bias_{1,\honest_{\cmark}}^*$, and for $\chain_2$, it is $1 - \bias_{2,\honest_{\cmark}}^*$.
Moreover, we can succinctly decompose the userbase of the blockchain into the different constituents that contribute to network effects, i.e., the honest users, and sybil accounts who are eligible for rewards:

\begin{align}
\label{eq:UserBase}
\userbase_i
\numHonest \vert i - 1 - \bias_{i,\honest_{\xmark}}^* \vert + \eligible_{i,\farmer}
\end{align}

We now derive the bias of the two marginal types of honest users, starting with $\bias_{i,\honest_{\xmark}}^*$.

The equilibrium bias of an honest user who is indifferent between using blockchain $\chain_1$ and not using any of $\chain_1, \chain_2$ is:

\begin{equation*}
\bias_{1,\honest_{\xmark}}^*
\frac{
\val - \fee_1 + \netfx \eligible_{1,\farmer}
}{1 - \netfx \numHonest}
\end{equation*}

Equivalently, for blockchain $\chain_2$ we have:

\begin{equation*}
\bias_{2,\honest_{\xmark}}^*
\frac{
\val - \fee_2 + \netfx \left( \numHonest + \eligible_{2,\farmer} \right)
}{1 + \netfx \numHonest}
\end{equation*}

The share of honest users who choose $\chain_i$ equals the bias $\bias_{i,\honest_{\xmark}}^*$ of a user who is indifferent between using it and not using any blockchain at all, and can be found by equating the utility of such users to $0$:

\begin{equation*}
\val - \bias_{i,\honest_{\xmark}}^* - \fee_i + \netfx \userbase_i
\end{equation*}

When solved, we get:

\begin{equation}
\label{eq:BiasIneligibleUserbase}
\bias_{i,\honest_{\xmark}}^*
\val - \fee_i + \netfx \userbase_i
\end{equation}

By substituting <ref> into <ref>, one finds:

\begin{equation*}
\bias_{i,\honest_{\xmark}}^*
\val - \fee_i + \netfx \userbase_i
\val - \fee_i + \netfx \left(
\numHonest \vert i - 1 - \bias_{i,\honest_{\xmark}}^* \vert + \eligible_{i,\farmer}
\right)
\end{equation*}

Now, solving for $i = 1$:

\begin{equation*}
\bias_{1,\honest_{\xmark}}^*
\frac{
\val - \fee_1 + \netfx \eligible_{1,\farmer}
}{1 - \netfx \numHonest}
\end{equation*}

Equivalently, for $i = 2$:

\begin{equation*}
\bias_{2,\honest_{\xmark}}^*
\frac{
\val - \fee_2 + \netfx \left( \numHonest + \eligible_{2,\farmer} \right)
}{1 + \netfx \numHonest}
\end{equation*}

By <ref>, the honest market share is monotonically increasing in the number of sybil accounts.
However, although a naïve interpretation of the result may present farmers in a positive light, note that user share alone does not capture the whole picture.
In particular, while honest users contribute to the revenue of the blockchain, farmers potentially harm it.
We examine these effects in more depth in later parts of our analysis.

We continue with honest eligible users.

The equilibrium bias of honest users who opt-in to the airdrop of $\chain_1$ is:

\begin{equation*}
\bias_{1,\honest_{\cmark}}^*
\val + \frac{\reward_1 - \cost_1}{\valScale}
\end{equation*}

And, the bias of those who qualify for the airdrop of $\chain_2$ is:

\begin{equation*}
\bias_{2,\honest_{\cmark}}^*
1 - \left( \val + \frac{\reward_2 - \cost_2}{\valScale} \right)
\end{equation*}

To derive $\bias_{i,\honest_{\cmark}}^*$, consider an honest user who chooses blockchain $\chain_i$, yet is indifferent between opting in for the airdrop, and not being eligible for it.
If the bias of such users is $\bias_{i,\honest_{\cmark}}^*$, then their utility from both options is equal:

\begin{align}
\label{eq:IndifferentUser}
(1+\valScale) (\val - \vert i - 1 - \bias_{i,\honest_{\cmark}}^* \vert) - \fee_i + \netfx \userbase_i + \reward_i - \cost_i
\val - \vert i - 1 - \bias_{i,\honest_{\cmark}}^* \vert - \fee_i + \netfx \userbase_i
\end{align}

Note that the identical terms on both sides cancel-out:

\begin{equation*}
\valScale \cdot (\val - \vert i - 1 - \bias_{i,\honest_{\cmark}}^* \vert) + \reward_i - \cost_i
\end{equation*}

By solving the equation, we find that the bias of the indifferent user of chain $\chain_1$ is:

\begin{equation*}
\bias_{1,\honest_{\cmark}}^*
\val + \frac{\reward_1 - \cost_1}{\valScale}
\end{equation*}

And, the equivalent result for $\chain_2$ is:

\begin{equation*}
\bias_{2,\honest_{\cmark}}^*
1 - \left( \val + \frac{\reward_2 - \cost_2}{\valScale} \right)
\end{equation*}

By definition, both eligible and non-eligible honest users enjoy network effects to a similar degree.
Therefore, intuitively, the choices of which blockchain to use and whether to become eligible for its airdrop can be separated, where the former choice may be informed by network effects and thus by the blockchain's userbase, while the latter choice does not depend on such considerations.

§ IMPACT OF SYBILS

In <ref>, we show that if farmers can create as many sybil accounts as they want, issuing a fixed airdrop does not result in losses in two cases: when issuance costs for the platform are lower than the eligibility costs of farmers, or when all sybil accounts are detected.

Consider the case where $\chain_i$ issues an airdrop that involves a positive fixed reward $\fixed_i > 0$, and that farmers can set $\sybilCap$ arbitrarily.
If $\dropCost_i \le \costScale\cost_i$, then it is revenue-optimal to set a sybil-resistance level of $\resist_i = 0$.
Otherwise, setting $\resist_i = 1$ is revenue-optimal.

Recall that according to the definition of farmer utility (see <ref>), a farmer can profit from deploying at least one sybil account to $\chain_i$ if $\cost_i < \frac{\reward_i}{\costScale}$.
wlog, consider a purely fixed airdrop, i.e., the total rewards allocated proportionally is $\budget_i = 0$.
Note that this setting is the most extreme possible from the perspective of farmers: if they can profit here, then they would also be able to profit if $\budget_i > 0$.
By <ref>, and from the assumption of the current case that $\budget_i = 0$, we get:

\begin{equation}
\label{eq:InftyFixedFarmerCondition}
\cost_i
\frac{\reward_i}{\costScale}
\frac{\fixed_i + \frac{\budget_i}{\eligible_i}}{\costScale}
\frac{\fixed_i}{\costScale}
\end{equation}

In the case where $\dropCost_i \le \costScale\cost_i$, one can deduce from <ref> that $\dropCost_i \le \costScale\cost_i < \costScale\frac{\fixed_i}{\costScale} = \fixed_i$.
In words, the net revenue from each sybil account is non-negative, as the income from each is at least equal to the airdrop issuer's costs.
Furthermore, by <ref>, we have $\costScale \in \left[0, 1\right]$, which means that the income from each honest user is at least equal to that from a sybil account.
In total, this implies that the blockchain cannot lose money from issuing the airdrop to any given single account, whether it belongs to an honest user or a farmer.
Finally, consider that the contribution of farmers to the blockchain's revenue is both direct, via the income they generate, and also indirect: as dependent on the magnitude of network effects, an honest user's utility increases if more sybils are active on the blockchain, implying that utility-maximizing users are more likely to choose the blockchain.
It follows that sybil resistance values greater than $0$ harm profits, meaning that setting $\resist_i = 0$ is optimal.

On the other hand, when $\dropCost_i > \costScale\cost_i$, the per-farmer revenue is negative.
In particular, as the sybil capacity of farmers is unbounded, the airdrop issuer faces unbounded losses for any sybil resistance value lower than 1, implying that the revenue-optimal level is $\resist_i = 1$.

In practice, platforms may not be able to detect all sybil accounts, in which case <ref> implies that a fixed airdrop should be issued only if eligibility costs for farmers are higher than issuance costs.
However, platforms do not necessarily know the methods used by all farmers to reduce their costs, which implies that in certain cases, farmer eligibility costs may be difficult to estimate.
This can be avoided by defining eligibility criteria that incur some strictly positive cost, such as sending a single transaction.
This allows one to obtain a lower bound on eligibility costs that can be used instead of concrete ones.
Later, in <ref>, we consider another natural method to circumvent this rather restrictive result altogether.

When airdrop issuance costs are high and setting $\resist_i = 1$ is unfeasible (a realistic case, when considering the findings of recent work <cit.>), we show in <ref> that adopting a purely proportional airdrop can limit the number of sybils that farmers create.
Intuitively, this is because farmer profits are bounded, and even more so, their marginal returns decrease as a function of the number of sybil accounts they create.

Consider the case where $\chain_i$ issues a purely proportional airdrop and farmers can set $\sybilCap$ arbitrarily.
In equilibrium, the total number of sybil accounts active on $\chain_i$ is:

\begin{equation*}
\eligible_{i,\farmer}
\frac{\budget_i}{\costScale\cost_i} - \numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert
\end{equation*}

In a purely proportional airdrop we have $\fixed_i = 0$, thus the analog of the farmer profitability condition given in <ref> for the current case is:

\begin{equation}
\label{eq:InftyPropFarmer}
\cost_i
\le
\frac{\reward_i}{\costScale}
\frac{\fixed_i + \frac{\budget_i}{\eligible_i}}{\costScale}
\frac{\frac{\budget_i}{\eligible_i}}{\costScale}
\frac{\budget_i}{\costScale\eligible_i}
\end{equation}

Our remark that additional sybil accounts may “eat” into farmer profits is on display in <ref>: a higher value of eligible accounts $\eligible_i$ lowers farmers' profitability threshold.

We continue by deriving the equilibrium value of $\eligible_i$, and decompose it into the number of honest eligible users $\eligible_{i,\honest}$, and the amount of eligible farmer accounts $\eligible_{i,\farmer}$.
Thus, one can write:

\begin{equation}
\label{eq:DecomposeEligible}
\eligible_i
\eligible_{i,\honest} + \eligible_{i,\farmer}
\end{equation}

By combining <ref>, we obtain:

\begin{align}
\label{eq:InftyPropFarmerProf}
\cost_i
\le
\frac{\budget_i}{\costScale\eligible_i}
\frac{\budget_i}{\costScale\left(\eligible_{i,\honest} + \eligible_{i,\farmer}\right)}
\frac{\budget_i}{\costScale\left(\numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert + \eligible_{i,\farmer}\right)}
\end{align}

In equilibrium, farmers create accounts until reaching their break-even threshold, i.e., when the inequality of <ref> becomes an equality:

\begin{equation*}
\eligible_{i,\farmer}
\frac{\budget_i}{\costScale\cost_i} - \numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert
\end{equation*}

To understand how the number of sybil accounts may impact the profitability of the blockchain, we first derive the bias of the marginal honest eligible user.

Consider the case where $\chain_i$ issues a purely proportional airdrop.
In equilibrium, the bias of the marginal honest eligible user in $\chain_i$ is:

\bias_{i,\honest_{\cmark}}^*
\vert i - 1 - \left(
\val + \frac{\cost_i}{\valScale}\left(\costScale - 1\right)
\right)\vert

Due to our case's assumption that $\fixed_i = 0$, we can combine <ref>:

\begin{align}
\bias_{i,\honest_{\cmark}}^*
\vert i - 1 - \left( \val + \frac{\reward_i - \cost_i}{\valScale} \right) \vert
\vert i - 1 - \left( \val + \frac{\frac{\budget_i}{\eligible_{i,\honest} + \eligible_{i,\farmer}} - \cost_i}{\valScale} \right)\vert
\nonumber \\&
\vert i - 1 - \left( \val + \frac{\frac{\budget_i}{\numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert + \eligible_{i,\farmer}} - \cost_i}{\valScale} \right)\vert
\end{align}

Given <ref>, we substitute $\eligible_{i,\farmer}$ and reach a clean solution for $\bias_{i,\honest_{\cmark}}^*$:

\begin{align}
\label{eq:InftyPropIndifferentUser}
\bias_{i,\honest_{\cmark}}^*
\vert i - 1 - \left(
\val + \frac{
\frac{\budget_i}{\numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert + \eligible_{i,\farmer}}
- \cost_i
\right)\vert
\nonumber \\&
\vert i - 1 - \left(
\val + \frac{
\frac{\budget_i}{\numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert + \frac{\budget_i}{\costScale\cost_i} - \numHonest\vert i-1- \bias_{i,\honest_{\cmark}}^*\vert}
- \cost_i
\right)\vert
\nonumber \\&
\vert i - 1 - \left(
\val + \frac{
\costScale\cost_i
- \cost_i
\right)\vert
\vert i - 1 - \left(
\val + \frac{\cost_i}{\valScale}\left(\costScale - 1\right)
\right)\vert
\end{align}

We continue with <ref>.

Consider the case where $\chain_i$ issues a purely proportional airdrop.
In equilibrium, it is revenue-optimal to set $\resist=0$.

Consider that each honest user produces a revenue of $\fee_i$ from fees, while eligible users also contribute $\cost_i$ per user due to eligibility costs.
With respect to farmers, each one contributes less: $\costScale\cost_i$.
Note that the total expenses for a proportional drop are fixed, thus the net revenue made by the blockchain equals the revenue, minus some constant.
When not considering expenses, the total revenue is:

\begin{equation}
\label{eq:RevInftyProp}
\rev_i
\fee_i \numHonest \vert i - 1 - \bias_{i,\honest_{\xmark}}^* \vert
\cost_i \numHonest \vert i - 1 - \bias_{i,\honest_{\cmark}}^* \vert
\costScale\cost_i\eligible_{i,\farmer}
\end{equation}

If $\resist$ is set to $1$, then a farmer that found it profitable to create sybil accounts would still farm, but with a single account.
Thus, we have that $\costScale\cost_i\eligible_{i,\farmer}$ cannot exceed $\costScale\cost_i\numFarmer$, i.e., the revenue earned from farmers is harmed.
Moreover, by <ref>, this harms the market share among honest users, implying that it is revenue optimal to set $\resist = 0$.

As <ref> shows, the bias of the marginal honest eligible user is monotonically decreasing as $\costScale$ approaches $0$.
This implies that the presence of farmers with lower eligibility costs decreases the percentage of honest users among the population of airdrop recipients, which may have negative repercussions.
For example, if the airdrop is of a governance token, farmers can raise governance proposals to increase their rewards, potentially leading to a takeover of the platform <cit.>.

§ DISCUSSION

In this work, we define and analyze an economic model of cryptocurrency airdrops that operate in an adversarial setting, where farmers may try to receive an outsize amount of rewards.
To ground our work in real-world user behavior and farming practices, we collect and analyze data from high-profile airdrops issued by blockchain platforms, and from related empirical work (<ref>).
Given our findings, we advance a model (<ref>) that builds on the existing literature on network effects, and extends it to incorporate crucial aspects of the blockchain setting, such as the ability of farmers to create sybil accounts.

In our analysis (<ref>), we find that in the case where farmers can create a large number of sybils, fixed airdrops can result in significant losses for platforms that cannot detect all farmers.
As a mitigation, we analyze a mechanism where rewards are split proportionally, and show that it caps possible losses.

While our analysis in this paper focuses on blockchains, our results are generally applicable to settings where users may engage in farmer-esque tactics to opportunistically obtain rewards.

§.§ Future Work

In this section, we discuss how our results provide a framework that opens avenues for novel research that could serve to better inform future airdrop mechanisms.

Additional impact of farmers
Our analysis shows that platforms may find it profitable to set their sybil-resistance levels lower than $1$, i.e., allow some farmers to be eligible for an airdrop.
We note that setting such low levels can be appealing for other reasons, for example, to avoid mislabeling honest users as farmers, which can cause frustration in the community <cit.>.
Farmers may even try to intentionally cause others to be mislabeled by tainting them (i.e., by sending small amounts of funds so that they seem to be related to clusters of sybil accounts), or by preventing others from interacting with the blockchain <cit.>, thereby decreasing honest competition while increasing their own profits.
The aforementioned considerations illustrate that the impact of farmers goes beyond the models analyzed by the traditional economic literature.
In particular, tainting represents a novel risk that arises due to the blockchain setting and points to interesting directions for future research that are highly relevant for airdrop mechanism designers, such as mapping out the range of adversarial actions that farmers can take both against airdrop issuers (e.g., creating sybils in a manner that avoids detection) and against others vying for airdrop rewards (e.g., increasing their share of rewards by tainting others).

Long-term dynamics
Like previous work on network effects <cit.>, we analyze a one-shot setting.
However, several notable platforms such as Optimism and Blur have issued multiple consecutive airdrops, perhaps in a bid to maintain the positive short-term impact of airdrops over a longer period of time <cit.>.
Such practices suggest that a long-term analysis of platform competition when considering network effects may be valuable in our context, à la Halaburda and Yehezkel <cit.>, Bakos and Halaburda <cit.> and Halaburda  <cit.>.

In equilibria where issuing several airdrops is profitable, one may also measure the impact this competition has on the prices offered by the platforms, or on the effort that users invest in becoming eligible <cit.>.

Retroactive airdrops
Intuitively, the uncertainty introduced by retroactive airdrops should make sybil attacks riskier and thus less profitable <cit.>.
However, some researchers and practitioners argue that retroactive airdrops may be less useful in expanding a platform's userbase beyond its existing users (see <ref>), and furthermore, empirical works that find that the recipients of such airdrops commonly exhibit farmer-esque behavior (see <ref>).
However, retroactive airdrops are still used in practice, making them good candidates for future theoretical work to better understand their underlying economics.
Notably, Chitra <cit.> advances an agent-based simulation in which retroactive airdrops are modeled as American perpetual options with a random start, under assumptions that do not generally apply, primarily that all actors know (1) that an airdrop will be issued, (2) the total amount of tokens that are issued, (3) the eligibility cost (which is furthermore assumed to be fixed for all).
This presents an interesting framework that is worthwhile to explore.

§ REPRODUCIBILITY

We provide complete reproduction details in <ref>.

Missing 'biblatex' package
The bibliography requires the 'biblatex' package.

abstractThe long-awaited Arbitrum has finally released its airdrop news. Along with the airdrop news, they also released their rules for checking Sybil addresses. We found that the above Sybil detection rules will cause significant loopholes. After many confrontations between Sybils and the project party, Sybils often use exchanges on a large scale for depositing their funds. This will result that these Sybils are not excluded from the Aribtrum's airdrop. Through our internal same-person/Sybil address recognition model, we successfully identified more than 279,328 same-person addresses and 148,595 Sybil addresses that received the airdrop.
titleAdvanced Analysis For Arbitrum Airdrop

abstractStop manually farming crypto airdrops. Airbot transforms the way you farm with advanced automation tools. Our bots handle everything from A-Z, maximizing your potential gains. Whether you're new or experienced, we will help elevate your farming game to the next level.
titleRedefining Airdrop Farming

Elsevier BV

abstractWeb3 projects have distributed billions of dollars of tokens at zero cost to users through airdrops. Designing airdrop criteria, however, has largely progressed through trial-and-error experiments of protocols, and remains a key strategic design challenge. This paper examines airdrop design as coevolutionary process and makes three contributions. First, applying a coevolutionary lens to airdrops, where participants (namely protocols and recipients) learn and adapt their strategies (e.g., new criteria, on-chain farming) both within and between web3 ecosystems. Second, nine airdrop cases studies (e.g., Optimism, Arbitrum, Blur) that demonstrate the expanding set of airdrop strategies (e.g., task-based claiming). Third, implications for airdrop design including more feedback loops to facilitate learning (e.g., multi-round airdrops), ongoing innovation in airdrop criteria (e.g., randomness), and a reversion back to simple designs for some projects (because of the costs of airdrop design). Ultimately this paper informs how protocols and airdrop recipients coordinate to build and maintain communities in Web3.
journaltitleSSRN Electronic Journal
titleCrypto Airdrops: An Evolutionary Approach
Cryptocurrency airdrop,Airdrop,Token airdrop,Web3,Web3 strategy,airdrop design

abstractA cryptocurrency token airdrop is a novel means of distributing rights over a blockchain project to a community of users and owners for free. The market value of these airdrop giveaways is often upwards of hundreds of millions of dollars. This paper considers why projects might choose this unusual and costly means of token distribution. It considers a selection of high-profile airdrops as case studies between 2014 and 2022. This is the first comprehensive analysis of the rationales and mechanisms of Web3 token airdrops. We find that two primary rationales for airdrops are marketing (to attract new users and to maintain a community) and decentralisation of ownership and control of a project (building community, providing regulatory protection, and enhancing security). The paper contributes to an understanding of business practice and strategy in the emerging cryptocurrency and blockchain industry.
journaltitleJournal of Business Research
titleWhy airdrop cryptocurrency tokens?

Rochester, NY

abstractPlatform-specific digital tokens are becoming increasingly common with the proliferation of initial coin offerings (ICOs). In addition to a novel financing mechanism, such tokens can help address the coordination problem that is common in network adoption. We develop a model to investigate the use of tradable digital tokens to solve this coordination problem. Our analysis shows that platform-specific tokens, due to their tradability and consequent higher value if the platform succeeds, can provide another tool to overcome the coordination problem in a platform adoption setting and to support equilibria favorable to the platform.We find that if the platform is not facing capital constraints, the most profitable strategy is the traditional strategy to subsidize adoption. If the platform is capital constrained, however, then token issuance provides an alternative that is increasingly attractive as the platform's cost of capital increases. With tokens, the platform trades off future revenue for present revenue, which helps finance solving the coordination problem. In that sense, even pure utility tokens have certain characteristics of equity: (1) early adopters share the future gains if the platform succeeds, and (2) the tokens provide an alternative when traditional financing is too costly or not available to the platform.
titleThe Role of Cryptographic Tokens and ICOs in Fostering Platform Adoption
economics of IS,ICOs,blockchain,platforms,network effects

Rochester, NY

abstractBlockchain technologies are designed to promote decentralization and self-governancein economic and social settings. In the context of platforms, an early claim of someproponents of these technologies was that blockchains would promote disintermediation,replacing intermediaries with decentralized governance, for instance leading toplatforms governed by decentralized autonomous organizations (DAOs).We have seen some elements of that (e.g., in DeFi–Decentralized Finance), butupon closer examination, most of the “decentralized" platforms are actually controlledby a small number of agents, or even a single agent, that effectively act as intermediaries.In this work we study the underlying dynamics and explore the potential fordecentralized governance of blockchain-based platforms by modeling a simpli ed settingwith a one-sided platform and comparing control by a centralized intermediary tocontrol by a DAO that makes decisions via majority voting of its governance tokens.Our analysis suggests that a “democratic" DAO with governance tokens equallydistributed among potential users would set a low access price for the platform andmaximize network size and total surplus. If tokens can be traded, however, there is astrong tendency for concentration of control, which makes it challenging to maintaindecentralized governance. This tendency becomes stronger if at least a certain fractionof small holders is myopic, and would sell their tokens without considering the futureimpact of their actions. Concentration in the ownership of governance tokens can belimited either by design or by regulation, but implementation and enforcement wouldbe challenging. Intermediaries may be changed by blockchain technologies, but theyare not likely to disappear.
shorttitleWill Blockchains Disintermediate Platforms?
titleWill Blockchains Disintermediate Platforms? The Problem of Credible Decentralization in Daos
blockchain,platforms,decentralization,Digital Autonomous Organizations

abstractIn today's episode, David sits down with Pacman, the Founder of the NFT trading marketplace, Blur. Blur recently launched their $BLUR token and Pacman joins us to share the entire airdrop story, the different phases that went into it, and what's next for the fascinating OpenSea competitor.}
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\field{year}{2023}
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\field{abstract}{Nansen aided in the development of the Arbitrum airdrop distribution, which is supported by on-chain data analyses facilitated by Nansen Query. TL;DR - The Arbitrum Foundation, in partnership with Offchain Labs, has just announced the airdrop of the first governance token for Arbitrum, one of the leading layer 2 blockchains - Offchain Labs, The Arbitrum Foundation, and Nansen have worked together on designing a token distribution model that bases eligibility for Arbitrum community members on their organic participation in the Arbitrum ecosystem - This distribution model relies on Nansen on-chain data and labels, allocating eligibility points to wallets according to their past on-chain activity, with wallet addresses gathering at least three points to gain eligibility to the airdrop - The list of eligible wallets represents 625,143 addresses or ~28\% of those having bridged to Arbitrum One since inception and as of the late snapshot date of February 6, 2023 - Protocols that have a community-governed treasury that have been pushing the Arbitrum ecosystem forward were also invited to participate in the distribution of a portion of the tokens for their own communities - Check out our Arbitrum Airdrop Analysis dashboard containing all the metrics you'll want to keep an eye on: ARB Airdrop Analysis}
\field{month}{3}
\field{title}{"All for One and One for All" - An on-chain distribution model for the Arbitrum community}
\field{year}{2023}
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\endentry
\entry{beres2021blockchain}{inproceedings}{}
\name{author}{4}{}{%
given={I.\bibnamedelimi A.},
giveni={I\bibinitperiod\bibinitdelim A\bibinitperiod}}}%
given={A.\bibnamedelimi A.},
giveni={A\bibinitperiod\bibinitdelim A\bibinitperiod}}}%
familyi={Q\bibinithyphendelim C\bibinitperiod},
\list{location}{1}{%
{Los Alamitos, CA, USA}%
\list{publisher}{1}{%
{IEEE Computer Society}%
\strng{namehash}{8e116e30618afd5fa5e519ff7dbe724c}
\strng{fullhash}{bec150c5415ba15f69e382042b128b1f}
\strng{bibnamehash}{bec150c5415ba15f69e382042b128b1f}
\strng{authorbibnamehash}{bec150c5415ba15f69e382042b128b1f}
\strng{authornamehash}{8e116e30618afd5fa5e519ff7dbe724c}
\strng{authorfullhash}{bec150c5415ba15f69e382042b128b1f}
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\field{labeltitlesource}{title}
\field{abstract}{Ethereum is the largest public blockchain by usage. It applies an account-based model, which is inferior to Bitcoin's unspent transaction output model from a privacy perspective. Due to its privacy shortcomings, recently, several privacy-enhancing overlays have been deployed on Ethereum, such as non-custodial, trustless coin mixers and confidential transactions. In our privacy analysis of Ethereum's account-based model, we describe several patterns that characterize only a limited set of users and successfully apply these “quasi-identifiers” in address deanonymization tasks. Using Ethereum Name Service identifiers as ground truth information, we quantitatively compare algorithms in a recent branch of machine learning, the so-called graph representation learning, as well as time-of-day activity and transaction fee based user profiling techniques. As an application, we rigorously assess the privacy guarantees of the Tornado Cash coin mixer by discovering patterns of careless usage to link the mixing parties. To the best of our knowledge, we are the first to propose and implement Ethereum user profiling techniques based on quasi-identifiers.}
\field{booktitle}{2021 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS)}
\field{month}{8}
\field{title}{Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users}
\field{year}{2021}
\field{pages}{69\bibrangedash 78}
\range{pages}{10}
\verb{doi}
\verb 10.1109/DAPPS52256.2021.00013
\endverb
\verb{urlraw}
\verb https://doi.ieeecomputersociety.org/10.1109/DAPPS52256.2021.00013
\endverb
\verb{url}
\verb https://doi.ieeecomputersociety.org/10.1109/DAPPS52256.2021.00013
\endverb
\keyw{privacy;analytical models;machine learning algorithms;conferences;bitcoin;tornadoes;task analysis}
\endentry
\entry{biaoxu2018pricing}{article}{}
\name{author}{2}{}{%
family={Biao\bibnamedelima Xu},
familyi={B\bibinitperiod\bibinitdelim X\bibinitperiod},
given={Zhong\bibnamedelima Yao},
giveni={Z\bibinitperiod\bibinitdelim Y\bibinitperiod}}}%
\list{publisher}{1}{%
{Taylor & Francis}%
\strng{namehash}{41446dfec46f54adbab8e94d86fcef9b}
\strng{fullhash}{41446dfec46f54adbab8e94d86fcef9b}
\strng{bibnamehash}{41446dfec46f54adbab8e94d86fcef9b}
\strng{authorbibnamehash}{41446dfec46f54adbab8e94d86fcef9b}
\strng{authornamehash}{41446dfec46f54adbab8e94d86fcef9b}
\strng{authorfullhash}{41446dfec46f54adbab8e94d86fcef9b}
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\field{abstract}{In this paper, we model a fully covered duopoly market in which two firms offer a differentiated information product that exhibits positive network effects and a complementary premium service to consumers. For each firm, there are two marketing strategies: the freemium strategy and the bundling strategy. We find that, under the market equilibrium, a firms’ decision whether to employ the freemium strategy or not depends largely on the quality of the information product compared to its rival. When the information product quality is similar and the products’ intrinsic values are sufficiently large, both firms will be better off by adopting the freemium strategy, while the bundling strategy will prevail if the products’ intrinsic values are sufficiently small. Additionally, when the magnitude of complementary effects or network effects exceeds a given threshold, both firms’ profit can be enhanced by an increase in the degree of product complementarity or in the intensity of network effects. We also demonstrate that a firm can benefit from an increasing market size only if the intrinsic value of its information product is sufficiently large. Finally, we extend our model to the uncovered market and derive the equilibrium prices and profits.}
\field{journaltitle}{International Journal of Production Research}
\field{number}{12}
\field{title}{Pricing strategies for information products with network effects and complementary services in a duopolistic market}
\field{volume}{56}
\field{year}{2018}
\field{pages}{4243\bibrangedash 4263}
\range{pages}{21}
\verb{doi}
\verb 10.1080/00207543.2018.1425558
\endverb
\verb{urlraw}
\verb https://doi.org/10.1080/00207543.2018.1425558
\endverb
\verb{url}
\verb https://doi.org/10.1080/00207543.2018.1425558
\endverb
\endentry
\entry{chitra2022retroactive}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{977ec1fe2d75bcf746b15bd156dbd3d6}
\strng{fullhash}{977ec1fe2d75bcf746b15bd156dbd3d6}
\strng{bibnamehash}{977ec1fe2d75bcf746b15bd156dbd3d6}
\strng{authorbibnamehash}{977ec1fe2d75bcf746b15bd156dbd3d6}
\strng{authornamehash}{977ec1fe2d75bcf746b15bd156dbd3d6}
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\field{sortinithash}{4d103a86280481745c9c897c925753c0}
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\field{labeltitlesource}{title}
\field{title}{Retroactive Airdrops are Exotic Options}
\field{year}{2022}
\verb{urlraw}
\verb https://gauntlet.network/reports/retroairdrop
\endverb
\verb{url}
\verb https://gauntlet.network/reports/retroairdrop
\endverb
\endentry
\entry{cong2020tokenomics}{article}{}
\name{author}{3}{}{%
given={Lin\bibnamedelima William},
giveni={L\bibinitperiod\bibinitdelim W\bibinitperiod}}}%
\name{editor}{1}{}{%
\list{publisher}{1}{%
{Oxford University Press (OUP)}%
\strng{namehash}{74fa9bc66916c782dc5038f3becf35e4}
\strng{fullhash}{74fa9bc66916c782dc5038f3becf35e4}
\strng{bibnamehash}{74fa9bc66916c782dc5038f3becf35e4}
\strng{authorbibnamehash}{74fa9bc66916c782dc5038f3becf35e4}
\strng{authornamehash}{74fa9bc66916c782dc5038f3becf35e4}
\strng{authorfullhash}{74fa9bc66916c782dc5038f3becf35e4}
\strng{editorbibnamehash}{3e78dec9928a53006f52c6f1275da16e}
\strng{editornamehash}{3e78dec9928a53006f52c6f1275da16e}
\strng{editorfullhash}{3e78dec9928a53006f52c6f1275da16e}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{We develop a dynamic asset pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium price of tokens is determined by aggregating heterogeneous users’ transactional demand, rather than discounting cash flows as is done in standard valuations models. Endogenous platform adoption builds on user network externality and exhibits an -curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users’ transaction costs on the platform by allowing users to capitalize on platform growth. The resultant intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.}
\field{issn}{1465-7368}
\field{journaltitle}{The Review of Financial Studies}
\field{month}{8}
\field{number}{3}
\field{title}{Tokenomics: Dynamic Adoption and Valuation}
\field{volume}{34}
\field{year}{2020}
\field{pages}{1105\bibrangedash 1155}
\range{pages}{51}
\verb{doi}
\verb 10.1093/rfs/hhaa089
\endverb
\verb{urlraw}
\verb https://doi.org/10.1093/rfs/hhaa089
\endverb
\verb{url}
\verb https://doi.org/10.1093/rfs/hhaa089
\endverb
\endentry
\entry{copeland2023we}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{6de07e3668ba5e8ae6d704311cfd121f}
\strng{fullhash}{6de07e3668ba5e8ae6d704311cfd121f}
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\strng{authorbibnamehash}{6de07e3668ba5e8ae6d704311cfd121f}
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\strng{authorfullhash}{6de07e3668ba5e8ae6d704311cfd121f}
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\field{sortinithash}{4d103a86280481745c9c897c925753c0}
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\field{labeltitlesource}{title}
\field{abstract}{Airdrop hunters aim to make money by speculating on potential airdrops and farming them in advance. While the tactic can be highly profitable, it comes with financial risk and ethical questions.}
\field{month}{4}
\field{title}{'We made close to \$1 million': Inside the murky world of airdrop farming}
\field{year}{2023}
\verb{urlraw}
\verb https://web.archive.org/web/20230916094618/https://www.theblock.co/post/225215/we-made-close-to-1-million-inside-the-murky-world-of-airdrop-farming
\endverb
\verb{url}
\verb https://web.archive.org/web/20230916094618/https://www.theblock.co/post/225215/we-made-close-to-1-million-inside-the-murky-world-of-airdrop-farming
\endverb
\endentry
\entry{cosmocam19732024why}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{b5e067ec35aff412bbb45d007446ad02}
\strng{fullhash}{b5e067ec35aff412bbb45d007446ad02}
\strng{bibnamehash}{b5e067ec35aff412bbb45d007446ad02}
\strng{authorbibnamehash}{b5e067ec35aff412bbb45d007446ad02}
\strng{authornamehash}{b5e067ec35aff412bbb45d007446ad02}
\strng{authorfullhash}{b5e067ec35aff412bbb45d007446ad02}
\field{sortinit}{c}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{month}{2}
\field{title}{Why didn’t I get my airdrop? What changed between the confirmation of coins that would be received (248) and the airdrop?}
\field{year}{2024}
\verb{urlraw}
\verb https://twitter.com/cosmocam1973/status/1755097457678323880
\endverb
\verb{url}
\verb https://twitter.com/cosmocam1973/status/1755097457678323880
\endverb
\endentry
\entry{cryptonerd20172023linea}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{f53b9418aefd443ff81e026dc946a9bb}
\strng{fullhash}{f53b9418aefd443ff81e026dc946a9bb}
\strng{bibnamehash}{f53b9418aefd443ff81e026dc946a9bb}
\strng{authorbibnamehash}{f53b9418aefd443ff81e026dc946a9bb}
\strng{authornamehash}{f53b9418aefd443ff81e026dc946a9bb}
\strng{authorfullhash}{f53b9418aefd443ff81e026dc946a9bb}
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\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{month}{6}
\field{title}{Linea quests suck! Worst experience ever}
\field{year}{2023}
\verb{urlraw}
\verb https://twitter.com/CryptoNerd_2017/status/1674024296623263748
\endverb
\verb{url}
\verb https://twitter.com/CryptoNerd_2017/status/1674024296623263748
\endverb
\endentry
\entry{arbitrum2023arb}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{cf7167c568199dc8ed44e67050c025fe}
\strng{fullhash}{cf7167c568199dc8ed44e67050c025fe}
\strng{bibnamehash}{cf7167c568199dc8ed44e67050c025fe}
\strng{authorbibnamehash}{cf7167c568199dc8ed44e67050c025fe}
\strng{authornamehash}{cf7167c568199dc8ed44e67050c025fe}
\strng{authorfullhash}{cf7167c568199dc8ed44e67050c025fe}
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\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
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\field{labeltitlesource}{title}
\field{title}{\$ARB airdrop eligibility and distribution specifications}
\field{year}{2023}
\verb{urlraw}
\verb https://web.archive.org/web/20230316130818/https://docs.arbitrum.foundation/airdrop-eligibility-distribution
\endverb
\verb{url}
\verb https://web.archive.org/web/20230316130818/https://docs.arbitrum.foundation/airdrop-eligibility-distribution
\endverb
\endentry
\entry{deadspyexx2023airdrop}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{0167dff1291896090b1335e2ce7c2e6b}
\strng{fullhash}{0167dff1291896090b1335e2ce7c2e6b}
\strng{bibnamehash}{0167dff1291896090b1335e2ce7c2e6b}
\strng{authorbibnamehash}{0167dff1291896090b1335e2ce7c2e6b}
\strng{authornamehash}{0167dff1291896090b1335e2ce7c2e6b}
\strng{authorfullhash}{0167dff1291896090b1335e2ce7c2e6b}
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\field{labeltitlesource}{title}
\field{abstract}{The bot can transfer in ZkSync. Software designed to automate the process of participating in airdrops, making it easier for users to receive airdropped tokens across various platforms. It might help users find and participate in airdrops, manage multiple wallet addresses, and keep track of their airdrop rewards efficiently.}
\field{title}{Airdrop Hunt Bot}
\field{year}{2023}
\verb{urlraw}
\verb https://web.archive.org/web/20230916101556/https://github.com/deadspyexx/airdrop-hunt-bot
\endverb
\verb{url}
\verb https://web.archive.org/web/20230916101556/https://github.com/deadspyexx/airdrop-hunt-bot
\endverb
\endentry
\entry{deng2023monopoly}{inproceedings}{}
\name{author}{5}{}{%
\list{publisher}{1}{%
{AAAI Press}%
\strng{namehash}{14282b41062c382dc44600054ce4a323}
\strng{fullhash}{7c3914a3ddf8d10837239e34ad927e2d}
\strng{bibnamehash}{7c3914a3ddf8d10837239e34ad927e2d}
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\strng{authorfullhash}{7c3914a3ddf8d10837239e34ad927e2d}
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\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
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\field{abstract}{We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design and symmetric contestants. The goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) form an equilibrium in the model with competition among contests. Under a very natural assumption these contests are in fact dominant, and the equilibria that they form are unique. Moreover, equilibria with the optimal contests are Pareto-optimal even in cases where other equilibria emerge. In many natural cases, they also maximize the social welfare.}
\field{booktitle}{Proceedings of the {Thirty}-{Seventh} {AAAI} {Conference} on {Artificial} {Intelligence} and {Thirty}-{Fifth} {Conference} on {Innovative} {Applications} of {Artificial} {Intelligence} and {Thirteenth} {Symposium} on {Educational} {Advances} in {Artificial} {Intelligence}}
\field{isbn}{9781577358800}
\field{month}{2}
\field{series}{{AAAI}'23/{IAAI}'23/{EAAI}'23}
\field{shorttitle}{From monopoly to competition}
\field{title}{From monopoly to competition: optimal contests prevail}
\field{urlday}{11}
\field{urlmonth}{6}
\field{urlyear}{2024}
\field{volume}{37}
\field{year}{2023}
\field{urldateera}{ce}
\field{pages}{5608\bibrangedash 5615}
\range{pages}{8}
\verb{doi}
\verb 10.1609/aaai.v37i5.25696
\endverb
\verb{urlraw}
\verb https://doi.org/10.1609/aaai.v37i5.25696
\endverb
\verb{url}
\verb https://doi.org/10.1609/aaai.v37i5.25696
\endverb
\endentry
\entry{dotan2023vulnerable}{inproceedings}{}
\name{author}{5}{}{%
giveni={H\bibinithyphendelim C\bibinitperiod}}}%
\list{location}{1}{%
{Copenhagen, Denmark}%
\list{publisher}{1}{%
{Association for Computing Machinery}%
\strng{namehash}{cfeb5133408420acac69310f84a61c71}
\strng{fullhash}{d3722167dd1c172180deabed3eb7a296}
\strng{bibnamehash}{d3722167dd1c172180deabed3eb7a296}
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\field{labeltitlesource}{title}
\field{abstract}{Decentralized Finance (DeFi) platforms are often governed by Decentralized Autonomous Organizations (DAOs) which are implemented via governance protocols. Governance tokens are distributed to users of the platform, granting them voting rights in the platform's governance protocol.Many DeFi platforms have already been subject to attacks resulting in the loss of millions of dollars in user funds. In this paper we show that governance tokens are often not used as intended and may be harmful to the security of DeFi platforms. We show that (1) users often do not use governance tokens to vote, (2) that voting rates are negatively correlated to gas prices, (3) voting is very centralized. We explore vulnerabilities in the design of DeFi platform's governance protocols and analyze different governance attacks, focusing on the transferable nature of voting rights via governance tokens. Following the movement and holdings of governance tokens, we show they are often used to perform a single action and then sold off. We present evidence of DeFi platforms using other platforms' governance protocols to promote their own agenda at the expense of the host platform.}
\field{booktitle}{Proceedings of the 2023 Workshop on Decentralized Finance and Security}
\field{isbn}{9798400702617}
\field{series}{DeFi '23}
\field{title}{The Vulnerable Nature of Decentralized Governance in DeFi}
\field{year}{2023}
\field{pages}{25\bibrangedash 31}
\range{pages}{7}
\verb{doi}
\verb 10.1145/3605768.3623539
\endverb
\verb{urlraw}
\verb https://doi.org/10.1145/3605768.3623539
\endverb
\verb{url}
\verb https://doi.org/10.1145/3605768.3623539
\endverb
\keyw{decentralized finance,blockchains,governance}
\endentry
\entry{douceur2002sybil}{inproceedings}{}
\name{author}{1}{}{%
given={John\bibnamedelima R.},
giveni={J\bibinitperiod\bibinitdelim R\bibinitperiod}}}%
\name{editor}{3}{}{%
\list{location}{1}{%
{Berlin, Heidelberg}%
\list{publisher}{1}{%
{Springer Berlin Heidelberg}%
\strng{namehash}{9f4ba4fbc08914d0e8142c74dd162fff}
\strng{fullhash}{9f4ba4fbc08914d0e8142c74dd162fff}
\strng{bibnamehash}{9f4ba4fbc08914d0e8142c74dd162fff}
\strng{authorbibnamehash}{9f4ba4fbc08914d0e8142c74dd162fff}
\strng{authornamehash}{9f4ba4fbc08914d0e8142c74dd162fff}
\strng{authorfullhash}{9f4ba4fbc08914d0e8142c74dd162fff}
\strng{editorbibnamehash}{0903cb3b0145c36180c7cdae3cebc50e}
\strng{editornamehash}{0903cb3b0145c36180c7cdae3cebc50e}
\strng{editorfullhash}{0903cb3b0145c36180c7cdae3cebc50e}
\field{sortinit}{D}
\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Large-scale peer-to-peer systems face security threats from faulty or hostile remote computing elements. To resist these threats, many such systems employ redundancy. However, if a single faulty entity can present multiple identities, it can control a substantial fraction of the system, thereby undermining this redundancy. One approach to preventing these ``Sybil attacks'' is to have a trusted agency certify identities. This paper shows that, without a logically centralized authority, Sybil attacks are always possible except under extreme and unrealistic assumptions of resource parity and coordination among entities.}
\field{booktitle}{Peer-to-Peer Systems}
\field{isbn}{978-3-540-45748-0}
\field{title}{The Sybil Attack}
\field{year}{2002}
\field{pages}{251\bibrangedash 260}
\range{pages}{10}
\endentry
\entry{droppables2023kyc}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{7186bbddb2c11ddca37166b954c9d237}
\strng{fullhash}{7186bbddb2c11ddca37166b954c9d237}
\strng{bibnamehash}{7186bbddb2c11ddca37166b954c9d237}
\strng{authorbibnamehash}{7186bbddb2c11ddca37166b954c9d237}
\strng{authornamehash}{7186bbddb2c11ddca37166b954c9d237}
\strng{authorfullhash}{7186bbddb2c11ddca37166b954c9d237}
\field{sortinit}{D}
\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
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\field{labeltitlesource}{title}
\field{abstract}{As discussed in our article Outsmarting the System: Sybil Attacks in Airdrop Farming projects are starting to take additional measures to ensure their airdrops are fair and prevent bad actors gaming the system. One of these methods starting to appear in the requirements for receiving an airdrop is KYC. Projects such as SupraOracles and Linea make it a prerequisite for taking part meaning that farming on multiple wallets is hard (unless you’re an international secret agent with multiple identities). In this article we’ll look at what KYC is, it’s importance and impact and an option for a digital residency for KYC purposes.}
\field{month}{6}
\field{title}{KYC: The New Normal for Crypto Airdrops?}
\field{year}{2023}
\verb{urlraw}
\verb https://web.archive.org/web/20230916100712/https://droppables.medium.com/kyc-the-new-normal-for-crypto-airdrops-71e1ec953c3f
\endverb
\verb{url}
\verb https://web.archive.org/web/20230916100712/https://droppables.medium.com/kyc-the-new-normal-for-crypto-airdrops-71e1ec953c3f
\endverb
\endentry
\entry{drucker2012simpler}{inproceedings}{}
\name{author}{2}{}{%
given={Fabio\bibnamedelima A.},
giveni={F\bibinitperiod\bibinitdelim A\bibinitperiod}}}%
given={Lisa\bibnamedelima K.},
giveni={L\bibinitperiod\bibinitdelim K\bibinitperiod}}}%
\list{location}{1}{%
{Valencia, Spain}%
\list{publisher}{1}{%
{Association for Computing Machinery}%
\strng{namehash}{dc8abdffc12aa2e578bce41b6045417b}
\strng{fullhash}{dc8abdffc12aa2e578bce41b6045417b}
\strng{bibnamehash}{dc8abdffc12aa2e578bce41b6045417b}
\strng{authorbibnamehash}{dc8abdffc12aa2e578bce41b6045417b}
\strng{authornamehash}{dc8abdffc12aa2e578bce41b6045417b}
\strng{authorfullhash}{dc8abdffc12aa2e578bce41b6045417b}
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\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Multi-level marketing refers to a marketing approach in which buyers are encouraged to take an active role in promoting the product. This is done by offering them a reward for each successful referral of the product to other prospective buyers. To encourage potential customers to buy early and to give referrals to influential people, these mechanisms also reward indirect referrals --- a direct referral linked to the buyer through other direct referrals. Doing so can make the referral/reward system vulnerable to sybil attacks --- where profit maximizers create several replicas in order to maximize their rewards.In this paper we propose a family of mechanisms for which sybil attacks are not profitable. We do this by modifying any mechanism that satisfies certain natural properties of sensible reward mechanisms to obtain one that is invulnerable to sybil attacks by profit maximizers while preserving its natural properties. Our modified mechanisms are also collusion proof. Finally, we give a concrete example of a natural mechanism that is sybil proof and simple to implement.}
\field{booktitle}{Proceedings of the 13th ACM Conference on Electronic Commerce}
\field{isbn}{9781450314152}
\field{series}{EC '12}
\field{title}{Simpler sybil-proof mechanisms for multi-level marketing}
\field{year}{2012}
\field{pages}{441\bibrangedash 458}
\range{pages}{18}
\verb{doi}
\verb 10.1145/2229012.2229046
\endverb
\verb{urlraw}
\verb https://doi.org/10.1145/2229012.2229046
\endverb
\verb{url}
\verb https://doi.org/10.1145/2229012.2229046
\endverb
\keyw{mechanism design,recommender systems,social networks}
\endentry
\entry{edwards2022retroactive}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{6bebc0665eff062a335f6c2ada44f710}
\strng{fullhash}{6bebc0665eff062a335f6c2ada44f710}
\strng{bibnamehash}{6bebc0665eff062a335f6c2ada44f710}
\strng{authorbibnamehash}{6bebc0665eff062a335f6c2ada44f710}
\strng{authornamehash}{6bebc0665eff062a335f6c2ada44f710}
\strng{authorfullhash}{6bebc0665eff062a335f6c2ada44f710}
\field{sortinit}{E}
\field{sortinithash}{8da8a182d344d5b9047633dfc0cc9131}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Here's how to game the system and potentially earn yourself some tokens. Being early to a new DeFi ecosystem can be very profitable. Early adopters of Uniswap, Yearn Finance and Curve received airdrops of free tokens, simply for using those platforms. The Uniswap airdrop tokens were worth a cool \$17,600 at their peak, while a single Yearn token was worth as much as \$90,000 this year. More recently, DyDx airdropped tokens to its users, with some earning six-figure sums. These protocols are staples of DeFi, so it's kind of like getting rewarded to use Netflix, Amazon or Twitter. Not a bad outcome for using something that was already handy.}
\field{month}{1}
\field{title}{Retroactive airdrop farming: How to prepare yourself for 2022}
\field{year}{2022}
\verb{urlraw}
\verb https://web.archive.org/web/20220120123746/https://www.finder.com.au/retroactive-airdrop-farming-2022
\endverb
\verb{url}
\verb https://web.archive.org/web/20220120123746/https://www.finder.com.au/retroactive-airdrop-farming-2022
\endverb
\endentry
\entry{emek2011mechanisms}{inproceedings}{}
\name{author}{4}{}{%
\list{publisher}{1}{%
\strng{namehash}{82b2ec3a3d0c940c41ddcf8558d3b171}
\strng{fullhash}{df9013be0c026dd6345bdb3e07465761}
\strng{bibnamehash}{df9013be0c026dd6345bdb3e07465761}
\strng{authorbibnamehash}{df9013be0c026dd6345bdb3e07465761}
\strng{authornamehash}{82b2ec3a3d0c940c41ddcf8558d3b171}
\strng{authorfullhash}{df9013be0c026dd6345bdb3e07465761}
\field{sortinit}{E}
\field{sortinithash}{8da8a182d344d5b9047633dfc0cc9131}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Multi-level marketing is a marketing approach that motivates its participants to promote a certain product among their friends. The popularity of this approach increases due to the accessibility of modern social networks, however, it existed in one form or the other long before the Internet age began (the infamous Pyramid scheme that dates back at least a century is in fact a special case of multi-level marketing). This paper lays foundations for the study of reward mechanisms in multi-level marketing within social networks. We provide a set of desired properties for such mechanisms and show that they are uniquely satisfied by geometric reward mechanisms. The resilience of mechanisms to false-name manipulations is also considered; while geometric reward mechanisms fail against such manipulations, we exhibit other mechanisms which are false-name-proof.}
\field{booktitle}{Proceedings of the 12th ACM conference on Electronic commerce}
\field{month}{6}
\field{series}{EC ’11}
\field{title}{Mechanisms for multi-level marketing}
\field{year}{2011}
\verb{doi}
\verb 10.1145/1993574.1993606
\endverb
\endentry
\entry{etzion2014complementary}{article}{}
\name{author}{2}{}{%
giveni={M\bibinithyphendelim S\bibinitperiod}}}%
\list{publisher}{1}{%
\strng{namehash}{8da24b1fe0d8dc983f62b25b5117ab03}
\strng{fullhash}{8da24b1fe0d8dc983f62b25b5117ab03}
\strng{bibnamehash}{8da24b1fe0d8dc983f62b25b5117ab03}
\strng{authorbibnamehash}{8da24b1fe0d8dc983f62b25b5117ab03}
\strng{authornamehash}{8da24b1fe0d8dc983f62b25b5117ab03}
\strng{authorfullhash}{8da24b1fe0d8dc983f62b25b5117ab03}
\field{sortinit}{E}
\field{sortinithash}{8da8a182d344d5b9047633dfc0cc9131}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{A growing number of firms are strategically utilizing information technology and the Internet to provide online services to consumers who buy their products. Online services differ from traditional services because they often promote interactivity among users and exhibit positive network effects. While the service increases the value obtained by consumers, network effects are known to intensify price competition and thus may reduce firms' profits. In this paper, we model the competition between two firms that sell a differentiated product when each firm can offer a complementary online service to its customers. We derive the market equilibrium and determine how firms should adjust their strategies to account for network effects. We find that when the service exhibits network effects, a firm's decision whether or not to offer the service depends on both the competitor's decision and the competitor's service quality. When the service does not exhibit network effects, this is not the case. In addition, we show that a firm can benefit from the technological ability to offer the service, and from an increase in the strength of network effects or in the market size of the service, only when the value customers derive from the direct functionalities (those that do not rely on the network) of the service are sufficiently high. As a result, a firm's investment in the direct functionalities of its service increases with the strength of network effects of the service as long as the marginal development cost is not too high. Finally, we show that inefficiencies in terms of the number of firms offering the service as well as the total number of service users may prevail.}
\field{journaltitle}{Mis Quarterly}
\field{number}{1}
\field{title}{Complementary online services in competitive markets: Maintaining profitability in the presence of network effects}
\field{volume}{38}
\field{year}{2014}
\field{pages}{231\bibrangedash 248}
\range{pages}{18}
\verb{urlraw}
\verb https://www.jstor.org/stable/26554876
\endverb
\verb{url}
\verb https://www.jstor.org/stable/26554876
\endverb
\endentry
\entry{fan2023altruistic}{inproceedings}{}
\name{author}{4}{}{%
\list{location}{1}{%
{Hamburg, Germany}%
\list{publisher}{1}{%
{Association for Computing Machinery}%
\strng{namehash}{27dcaa8063545a61118082365c96553e}
\strng{fullhash}{48530e7e8099bac78099ad22371ac87e}
\strng{bibnamehash}{48530e7e8099bac78099ad22371ac87e}
\strng{authorbibnamehash}{48530e7e8099bac78099ad22371ac87e}
\strng{authornamehash}{27dcaa8063545a61118082365c96553e}
\strng{authorfullhash}{48530e7e8099bac78099ad22371ac87e}
\field{extraname}{1}
\field{sortinit}{F}
\field{sortinithash}{2638baaa20439f1b5a8f80c6c08a13b4}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Regardless of which community, incentivizing users is a necessity for well-sustainable operations. In the blockchain-backed Web3 communities, known for their transparency and security, airdrop serves as a widespread incentive mechanism for allocating capital and power. However, it remains a controversy on how to justify airdrop to incentive and empower the decentralized governance. In this paper, we use ParaSwap as an example to propose a role taxonomy methodology through a data-driven study to understand the characteristic of community members and the effectiveness of airdrop. We find that users receive more rewards tend to take positive actions towards the community. We summarize several arbitrage patterns and confirm the current detection is not sufficient in screening out airdrop hunters. In conjunction with the results, we discuss from the aspects of interaction, financialization, and system design to conclude the challenges and possible research directions for decentralized communities.}
\field{booktitle}{Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems}
\field{isbn}{9781450394215}
\field{series}{CHI '23}
\field{title}{Altruistic and Profit-Oriented: Making Sense of Roles in Web3 Community from Airdrop Perspective}
\field{year}{2023}
\verb{doi}
\verb 10.1145/3544548.3581173
\endverb
\verb{urlraw}
\verb https://doi.org/10.1145/3544548.3581173
\endverb
\verb{url}
\verb https://doi.org/10.1145/3544548.3581173
\endverb
\keyw{Network analysis,Unsupervised learning,Airdrop,Decentralized community}
\endentry
\entry{fan2023strategic}{inproceedings}{}
\name{author}{6}{}{%
familyi={M\bibinithyphendelim C\bibinitperiod},
given={David\bibnamedelima C.},
giveni={D\bibinitperiod\bibinitdelim C\bibinitperiod}}}%
\name{editor}{2}{}{%
given={S.\bibnamedelimi Matthew},
giveni={S\bibinitperiod\bibinitdelim M\bibinitperiod}}}%
\list{location}{1}{%
{Dagstuhl, Germany}%
\list{publisher}{1}{%
{Schloss Dagstuhl -- Leibniz-Zentrum für Informatik}%
\strng{namehash}{d2cd418fe2241bd4bd03209d1ac6977c}
\strng{fullhash}{4ce5a386fb0df58a85aaf5f1b48dadbd}
\strng{bibnamehash}{4ce5a386fb0df58a85aaf5f1b48dadbd}
\strng{authorbibnamehash}{4ce5a386fb0df58a85aaf5f1b48dadbd}
\strng{authornamehash}{d2cd418fe2241bd4bd03209d1ac6977c}
\strng{authorfullhash}{4ce5a386fb0df58a85aaf5f1b48dadbd}
\strng{editorbibnamehash}{880ca7b63a53b5d88a041b49dc62fc70}
\strng{editornamehash}{880ca7b63a53b5d88a041b49dc62fc70}
\strng{editorfullhash}{880ca7b63a53b5d88a041b49dc62fc70}
\field{extraname}{2}
\field{sortinit}{F}
\field{sortinithash}{2638baaa20439f1b5a8f80c6c08a13b4}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Uniswap v3 is the largest decentralized exchange for digital currencies. A novelty of its design is that it allows a liquidity provider (LP) to allocate liquidity to one or more closed intervals of the price of an asset instead of the full range of possible prices. An LP earns fee rewards proportional to the amount of its liquidity allocation when prices move in this interval. This induces the problem of strategic liquidity provision: smaller intervals result in higher concentration of liquidity and correspondingly larger fees when the price remains in the interval, but with higher risk as prices may exit the interval leaving the LP with no fee rewards. Although reallocating liquidity to new intervals can mitigate this loss, it comes at a cost, as LPs must expend gas fees to do so. We formalize the dynamic liquidity provision problem and focus on a general class of strategies for which we provide a neural network-based optimization framework for maximizing LP earnings. We model a single LP that faces an exogenous sequence of price changes that arise from arbitrage and non-arbitrage trades in the decentralized exchange. We present experimental results informed by historical price data that demonstrate large improvements in LP earnings over existing allocation strategy baselines. Moreover we provide insight into qualitative differences in optimal LP behaviour in different economic environments.}
\field{annotation}{Keywords: blockchain, decentralized finance, Uniswap v3, liquidity provision, stochastic gradient descent}
\field{booktitle}{5th Conference on Advances in Financial Technologies (AFT 2023)}
\field{isbn}{978-3-95977-303-4}
\field{issn}{1868-8969}
\field{series}{Leibniz International Proceedings in Informatics (LIPIcs)}
\field{title}{{Strategic Liquidity Provision in Uniswap V3}}
\field{volume}{282}
\field{year}{2023}
\field{pages}{25:1\bibrangedash 25:22}
\range{pages}{-1}
\verb{doi}
\verb 10.4230/LIPIcs.AFT.2023.25
\endverb
\verb{urlraw}
\verb https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.25
\endverb
\verb{url}
\verb https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.25
\endverb
\endentry
\entry{foundation2024optimism}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{9c178a3d0bd2bb035d04cc800604c344}
\strng{fullhash}{9c178a3d0bd2bb035d04cc800604c344}
\strng{bibnamehash}{9c178a3d0bd2bb035d04cc800604c344}
\strng{authorbibnamehash}{9c178a3d0bd2bb035d04cc800604c344}
\strng{authornamehash}{9c178a3d0bd2bb035d04cc800604c344}
\strng{authorfullhash}{9c178a3d0bd2bb035d04cc800604c344}
\field{sortinit}{F}
\field{sortinithash}{2638baaa20439f1b5a8f80c6c08a13b4}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{title}{Optimism}
\field{year}{2024}
\verb{urlraw}
\verb https://www.optimism.io/
\endverb
\verb{url}
\verb https://www.optimism.io/
\endverb
\endentry
\entry{friends2023farming}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{bc111146fb69f20d6e8e0b015f27f033}
\strng{fullhash}{bc111146fb69f20d6e8e0b015f27f033}
\strng{bibnamehash}{bc111146fb69f20d6e8e0b015f27f033}
\strng{authorbibnamehash}{bc111146fb69f20d6e8e0b015f27f033}
\strng{authornamehash}{bc111146fb69f20d6e8e0b015f27f033}
\strng{authorfullhash}{bc111146fb69f20d6e8e0b015f27f033}
\field{sortinit}{F}
\field{sortinithash}{2638baaa20439f1b5a8f80c6c08a13b4}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{title}{Farming Tasks/Routes}
\field{year}{2023}
\verb{urlraw}
\verb https://web.archive.org/web/20230916101504/https://farmer-friends.gitbook.io/untitled/overview/farming-tasks-routes
\endverb
\verb{url}
\verb https://web.archive.org/web/20230916101504/https://farmer-friends.gitbook.io/untitled/overview/farming-tasks-routes
\endverb
\endentry
\entry{froewis2019operational}{inbook}{}
\name{author}{2}{}{%
\list{publisher}{1}{%
{Springer International Publishing}%
\strng{namehash}{f69b4501c0b149eb80a2aef10b52db19}
\strng{fullhash}{f69b4501c0b149eb80a2aef10b52db19}
\strng{bibnamehash}{f69b4501c0b149eb80a2aef10b52db19}
\strng{authorbibnamehash}{f69b4501c0b149eb80a2aef10b52db19}
\strng{authornamehash}{f69b4501c0b149eb80a2aef10b52db19}
\strng{authorfullhash}{f69b4501c0b149eb80a2aef10b52db19}
\field{sortinit}{F}
\field{sortinithash}{2638baaa20439f1b5a8f80c6c08a13b4}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Efficient transfers to many recipients present a host of issues on Ethereum. First, accounts are identified by long and incompressible constants. Second, these constants have to be stored and communicated for each payment. Third, the standard interface for token transfers does not support lists of recipients, adding repeated communication to the overhead. Since Ethereum charges resource usage, even small optimizations translate to cost savings. Airdrops, a popular marketing tool used to boost coin uptake, present a relevant example for the value of optimizing bulk transfers. Therefore, we review technical solutions for airdrops of Ethereum-based tokens, discuss features and prerequisites, and compare the operational costs by simulating 35 scenarios. We find that cost savings of factor two are possible, but require specific provisions in the smart contract implementing the token system. Pull-based approaches, which use on-chain interaction with the recipients, promise moderate savings for the distributor while imposing a disproportional cost on each recipient. Total costs are broadly linear in the number of recipients independent of the technical approach. We publish the code of the simulation framework for reproducibility, to support future airdrop decisions, and to benchmark innovative bulk payment solutions.}
\field{booktitle}{Data Privacy Management, Cryptocurrencies and Blockchain Technology}
\field{isbn}{9783030315009}
\field{issn}{1611-3349}
\field{title}{The Operational Cost of Ethereum Airdrops}
\field{year}{2019}
\field{pages}{255\bibrangedash 270}
\range{pages}{16}
\verb{doi}
\verb 10.1007/978-3-030-31500-9_17
\endverb
\endentry
\entry{gafni2021worst}{article}{}
\name{author}{3}{}{%
\list{language}{1}{%
\strng{namehash}{2beeb42bf99da7d37567fbaf6ae3cce7}
\strng{fullhash}{2beeb42bf99da7d37567fbaf6ae3cce7}
\strng{bibnamehash}{2beeb42bf99da7d37567fbaf6ae3cce7}
\strng{authorbibnamehash}{2beeb42bf99da7d37567fbaf6ae3cce7}
\strng{authornamehash}{2beeb42bf99da7d37567fbaf6ae3cce7}
\strng{authorfullhash}{2beeb42bf99da7d37567fbaf6ae3cce7}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Weighted voting games apply to a wide variety of multi-agent settings. They enable the formalization of power indices which quantify the coalitional power of players. We take a novel approach to the study of the power of big vs. small players in these games. We model small (big) players as having single (multiple) votes. The aggregate relative power of big players is measured w.r.t. their votes proportion. For this ratio, we show small constant worst-case bounds for the Shapley-Shubik and the Deegan-Packel indices. In sharp contrast, this ratio is unbounded for the Banzhaf index. As an application, we define a false-name strategic normal form game where each big player may split its votes between false identities, and study its various properties. Together, our results provide foundations for the implications of players’ size, modeled as their ability to split, on their relative power.}
\field{issn}{1076-9757}
\field{journaltitle}{Journal of Artificial Intelligence Research}
\field{month}{9}
\field{title}{Worst-case {Bounds} on {Power} vs. {Proportion} in {Weighted} {Voting} {Games} with an {Application} to {False}-name {Manipulation}}
\field{urlday}{11}
\field{urlmonth}{6}
\field{urlyear}{2024}
\field{volume}{72}
\field{year}{2021}
\field{urldateera}{ce}
\field{pages}{99\bibrangedash 135}
\range{pages}{37}
\verb{doi}
\verb 10.1613/jair.1.13136
\endverb
\verb{urlraw}
\verb https://www.jair.org/index.php/jair/article/view/13136
\endverb
\verb{url}
\verb https://www.jair.org/index.php/jair/article/view/13136
\endverb
\keyw{autonomous agents,game theory,decision theory,mathematical foundations}
\endentry
\entry{gafni2023optimal}{article}{}
\name{author}{2}{}{%
\list{publisher}{1}{%
{Open Publishing Association}%
\strng{namehash}{313874b71e3f6c5360d244bd5a412e1f}
\strng{fullhash}{313874b71e3f6c5360d244bd5a412e1f}
\strng{bibnamehash}{313874b71e3f6c5360d244bd5a412e1f}
\strng{authorbibnamehash}{313874b71e3f6c5360d244bd5a412e1f}
\strng{authornamehash}{313874b71e3f6c5360d244bd5a412e1f}
\strng{authorfullhash}{313874b71e3f6c5360d244bd5a412e1f}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{In robust decision making under uncertainty, a natural choice is to go with safety (aka security) level strategies. However, in many important cases, most notably auctions, there is a large multitude of safety level strategies, thus making the choice unclear. We consider two refined notions: (i) a term we call DSL (distinguishable safety level), and is based on the notion of ``discrimin'', which uses a pairwise comparison of actions while removing trivial equivalencies. This captures the fact that when comparing two actions an agent should not care about payoffs in situations where they lead to identical payoffs. (ii) The well-known Leximin notion from social choice theory, which we apply for robust decision-making. In particular, the leximin is always DSL but not vice-versa. We study the relations of these notions to other robust notions, and illustrate the results of their use in auctions and other settings. Economic design aims to maximize social welfare when facing self-motivated participants. In online environments, such as the Web, participants' incentives take a novel form originating from the lack of clear agent identity -- the ability to create Sybil attacks, i.e., the ability of each participant to act using multiple identities. It is well-known that Sybil attacks are a major obstacle for welfare-maximization. Our main result proves that when DSL attackers face uncertainty over the auction's bids, the celebrated VCG mechanism is welfare-maximizing even under Sybil attacks. Altogether, our work shows a successful fundamental synergy between robustness under uncertainty, economic design, and agents' strategic manipulations in online multi-agent systems.}
\field{issn}{2075-2180}
\field{journaltitle}{Electronic Proceedings in Theoretical Computer Science}
\field{month}{7}
\field{title}{Optimal Mechanism Design for Agents with DSL Strategies: The Case of Sybil Attacks in Combinatorial Auctions}
\field{volume}{379}
\field{year}{2023}
\field{dateera}{ce}
\field{pages}{245\bibrangedash 259}
\range{pages}{15}
\verb{doi}
\verb 10.4204/eptcs.379.20
\endverb
\endentry
\entry{gafni2022greedy}{misc}{}
\name{author}{2}{}{%
\list{publisher}{1}{%
\strng{namehash}{133766b22351dc7c5a16e359a23017a2}
\strng{fullhash}{133766b22351dc7c5a16e359a23017a2}
\strng{bibnamehash}{133766b22351dc7c5a16e359a23017a2}
\strng{authorbibnamehash}{133766b22351dc7c5a16e359a23017a2}
\strng{authornamehash}{133766b22351dc7c5a16e359a23017a2}
\strng{authorfullhash}{133766b22351dc7c5a16e359a23017a2}
\field{extraname}{1}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Decentralized cryptocurrencies are payment systems that rely on aligning the incentives of users and miners to operate correctly and offer a high quality of service to users. Recent literature studies the mechanism design problem of the auction serving as a cryptocurrency's transaction fee mechanism (TFM). We present a general framework that captures both myopic and non-myopic settings, as well as different possible strategic models for users. Within this general framework, when restricted to the myopic case, we show that while the mechanism that requires a user to "pay-as-bid", and greedily chooses among available transactions based on their fees, is not dominant strategy incentive-compatible for users, it has a Bayesian-Nash equilibrium where bids are slightly shaded. Relaxing this incentive compatibility requirement circumvents the impossibility results proven by previous works, and allows for an approximately revenue and welfare optimal, myopic miner incentive-compatible (MMIC), and off-chain-agreement (OCA)-proof mechanism. We prove these guarantees using different benchmarks, and show that the pay-as-bid greedy auction is the revenue optimal Bayesian incentive-compatible, MMIC and 1-OCA-proof mechanism among a large class of mechanisms. We move beyond the myopic setting explored in the literature, to one where users offer transaction fees for their transaction to be accepted, as well as report their urgency level by specifying the time to live of the transaction, after which it expires. We analyze pay-as-bid mechanisms in this setting, and show the competitive ratio guarantees provided by the greedy allocation rule. We then present a better-performing non-myopic rule, and analyze its competitive ratio. The above analysis is stated in terms of a cryptocurrency TFM, but applies to other settings, such as cloud computing and decentralized "gig" economy, as well.}
\field{title}{Greedy Transaction Fee Mechanisms for (Non-)myopic Miners}
\field{year}{2022}
\verb{doi}
\verb 10.48550/arXiv.2210.07793
\endverb
\verb{urlraw}
\verb https://arxiv.org/abs/2210.07793
\endverb
\verb{url}
\verb https://arxiv.org/abs/2210.07793
\endverb
\keyw{Computer Science and Game Theory (cs.GT),Theoretical Economics (econ.TH),FOS: Computer and information sciences,FOS: Economics and business}
\endentry
\entry{gafni2024competitive}{misc}{}
\name{author}{2}{}{%
\strng{namehash}{133766b22351dc7c5a16e359a23017a2}
\strng{fullhash}{133766b22351dc7c5a16e359a23017a2}
\strng{bibnamehash}{133766b22351dc7c5a16e359a23017a2}
\strng{authorbibnamehash}{133766b22351dc7c5a16e359a23017a2}
\strng{authornamehash}{133766b22351dc7c5a16e359a23017a2}
\strng{authorfullhash}{133766b22351dc7c5a16e359a23017a2}
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\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Decentralized cryptocurrencies are payment systems that rely on aligning the incentives of users and miners to operate correctly and offer a high quality of service to users. Recent literature studies the mechanism design problem of the auction serving as a cryptocurrency's transaction fee mechanism (TFM). We find that a non-myopic modelling of miners falls close to another well-known problem: that of online buffer management for packet switching. The main difference is that unlike packets which are of a fixed size throughout their lifetime, in a financial environment, user preferences (and therefore revenue extraction) may be time-dependent. We study the competitive ratio guarantees given a certain discount rate, and show how existing methods from packet scheduling, which we call "the undiscounted case", perform suboptimally in the more general discounted setting. Most notably, we find a novel, simple, memoryless, and optimal deterministic algorithm for the semi-myopic case, when the discount factor is up to ≈ 0.770018. We also present a randomized algorithm that achieves better performance than the best possible deterministic algorithm, for any discount rate.}
\field{month}{2}
\field{title}{Competitive Revenue Extraction from Time-Discounted Transactions in the Semi-Myopic Regime}
\field{year}{2024}
\field{dateera}{ce}
\verb{doi}
\verb 10.48550/arXiv.2402.08549
\endverb
\endentry
\entry{galxe2023introducing}{misc}{}
\name{author}{1}{}{%
\strng{namehash}{f9c34167970cb3cf9ae552c74e1dd41d}
\strng{fullhash}{f9c34167970cb3cf9ae552c74e1dd41d}
\strng{bibnamehash}{f9c34167970cb3cf9ae552c74e1dd41d}
\strng{authorbibnamehash}{f9c34167970cb3cf9ae552c74e1dd41d}
\strng{authornamehash}{f9c34167970cb3cf9ae552c74e1dd41d}
\strng{authorfullhash}{f9c34167970cb3cf9ae552c74e1dd41d}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{A Public-Testnet Loyalty Program to the Core of zkEVM by ConsenSys}
\field{month}{5}
\field{title}{Introducing Linea Voyage, presented by Galxe and Linea}
\field{year}{2023}
\verb{urlraw}
\verb https://web.archive.org/web/20230502144418/https://blog.galxe.com/linea-voyage-public-testnet-quest-with-galxe-fd58e6958114?gi=f4cc46b4ab02
\endverb
\verb{url}
\verb https://web.archive.org/web/20230502144418/https://blog.galxe.com/linea-voyage-public-testnet-quest-with-galxe-fd58e6958114?gi=f4cc46b4ab02
\endverb
\endentry
\entry{gan2023decentralized}{article}{}
\name{author}{3}{}{%
given={Jingxing\bibnamedelima (Rowena)},
giveni={J\bibinitperiod\bibinitdelim (\bibinitperiod}}}%
\list{publisher}{2}{%
{Institute for Operations Research}%
{the Management Sciences (INFORMS)}%
\strng{namehash}{e296fea24f093c4bdeadae55578838fe}
\strng{fullhash}{e296fea24f093c4bdeadae55578838fe}
\strng{bibnamehash}{e296fea24f093c4bdeadae55578838fe}
\strng{authorbibnamehash}{e296fea24f093c4bdeadae55578838fe}
\strng{authornamehash}{e296fea24f093c4bdeadae55578838fe}
\strng{authorfullhash}{e296fea24f093c4bdeadae55578838fe}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Traditional two-sided platforms (e.g., Amazon, Uber) rely primarily on commission contracts to generate revenues and fuel growth, whereas their decentralized counterparts (e.g., Uniswap, Filecoin) often forego these in favor of token retention. What economics underpin this choice? We show that with properly designed initial coin offerings (ICOs), both mechanisms can independently alleviate market failures at the initial fundraising stage and incentivize long-term platform building. However, they achieve this in different ways. Although commission contracts often lead to higher profits for founders, token retention leads to higher service levels, benefiting the users and service providers. In essence, token retention surrenders a fraction of earnings to better align with the tenets of decentralized governance. Combining both mechanisms can add value, but only in relatively limited cases. These findings offer guidance and a possible rationale for why platforms may want to favor one mechanism over the other or use both.}
\field{issn}{1526-5501}
\field{journaltitle}{Management Science}
\field{month}{11}
\field{number}{11}
\field{title}{Decentralized Platforms: Governance, Tokenomics, and ICO Design}
\field{volume}{69}
\field{year}{2023}
\field{pages}{6667\bibrangedash 6683}
\range{pages}{17}
\verb{doi}
\verb 10.1287/mnsc.2021.02076
\endverb
\endentry
\entry{gandal2016can}{article}{}
\name{author}{2}{}{%
\list{publisher}{1}{%
{MDPI AG}%
\strng{namehash}{457ee5f3833e8730f58835eb050a36dc}
\strng{fullhash}{457ee5f3833e8730f58835eb050a36dc}
\strng{bibnamehash}{457ee5f3833e8730f58835eb050a36dc}
\strng{authorbibnamehash}{457ee5f3833e8730f58835eb050a36dc}
\strng{authornamehash}{457ee5f3833e8730f58835eb050a36dc}
\strng{authorfullhash}{457ee5f3833e8730f58835eb050a36dc}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{issn}{2073-4336}
\field{journaltitle}{Games}
\field{month}{7}
\field{number}{3}
\field{title}{Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market}
\field{volume}{7}
\field{year}{2016}
\field{pages}{16}
\range{pages}{1}
\verb{doi}
\verb 10.3390/g7030016
\endverb
\endentry
\entry{gent2023cryptocurrency}{article}{}
\name{author}{1}{}{%
\strng{namehash}{12fe94d93f1ba3415dc9ff326febbf56}
\strng{fullhash}{12fe94d93f1ba3415dc9ff326febbf56}
\strng{bibnamehash}{12fe94d93f1ba3415dc9ff326febbf56}
\strng{authorbibnamehash}{12fe94d93f1ba3415dc9ff326febbf56}
\strng{authornamehash}{12fe94d93f1ba3415dc9ff326febbf56}
\strng{authorfullhash}{12fe94d93f1ba3415dc9ff326febbf56}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{In a college classroom in the Indian city of Bangalore last August, Moiz Ahmed held up a volleyball-size chrome globe with a glass-covered opening at its center. Ahmed explained to the students that if they had their irises scanned with the device, known as the Orb, they would be rewarded with 25 Worldcoins, a soon-to-be released cryptocurrency. The scan, he said, was to make sure they hadn't signed up before. That's because Worldcoin, the company behind the project, wants to create the most widely and evenly distributed cryptocurrency ever by giving every person on the planet the same small allocation of coins.}
\field{issn}{1939-9340}
\field{journaltitle}{IEEE Spectrum}
\field{month}{1}
\field{number}{1}
\field{title}{A Cryptocurrency for the Masses or a Universal ID?: Worldcoin Aims to Scan all the World's Eyeballs}
\field{volume}{60}
\field{year}{2023}
\field{pages}{42\bibrangedash 57}
\range{pages}{16}
\verb{doi}
\verb 10.1109/MSPEC.2023.10006664
\endverb
\endentry
\entry{guo2023spillover}{inproceedings}{}
\name{author}{3}{}{%
prefix={li\bibnamedelima yongjun},
prefixi={l\bibinitperiod\bibinitdelim y\bibinitperiod}}}%
\strng{namehash}{249dced6b7f59bf58d1e1f867b0a6498}
\strng{fullhash}{249dced6b7f59bf58d1e1f867b0a6498}
\strng{bibnamehash}{249dced6b7f59bf58d1e1f867b0a6498}
\strng{authorbibnamehash}{249dced6b7f59bf58d1e1f867b0a6498}
\strng{authornamehash}{249dced6b7f59bf58d1e1f867b0a6498}
\strng{authorfullhash}{249dced6b7f59bf58d1e1f867b0a6498}
\field{sortinit}{G}
\field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{The emergence of tokenization platforms based on blockchain technology has led to the use of free airdrop to replace traditional expensive financial incentives to enhance user engagement. However, critics argue that such incentives may devalue tokens and prompt nonrecipients to panic sell. To investigate the impact of airdrops, we conducted a quasi-experiment on Axie Infinity. Our findings indicate that airdrops significantly enhance engagement among both recipients and nonrecipients. Mechanism analysis shows that cross group spillover effects stems from expectation of another airdrop program and increased market liquidity. While recipients tend to immediately sell tokens and often sell more tokens than received, we did not find evidence of nonrecipients panic selling tokens. Furthermore, we investigated the heterogeneous effects of airdrops. Our work contributes to the ongoing debate of the effectiveness of airdrops and provide insights into the study of tokenization platforms.}
\field{booktitle}{ICIS 2023 Proceedings}
\field{title}{Spillover Effects of Airdrops: Evidence from Tokenization Platforms}
\field{volume}{7}
\field{year}{2023}
\verb{urlraw}
\verb https://aisel.aisnet.org/icis2023/user_behav/user_behav/7/
\endverb
\verb{url}
\verb https://aisel.aisnet.org/icis2023/user_behav/user_behav/7/
\endverb
\endentry
\entry{halaburda2020dynamic}{article}{}
\name{author}{3}{}{%
\list{publisher}{1}{%
\strng{namehash}{3a814adc5856e252850177a9b5c6dddb}
\strng{fullhash}{3a814adc5856e252850177a9b5c6dddb}
\strng{bibnamehash}{3a814adc5856e252850177a9b5c6dddb}
\strng{authorbibnamehash}{3a814adc5856e252850177a9b5c6dddb}
\strng{authornamehash}{3a814adc5856e252850177a9b5c6dddb}
\strng{authorfullhash}{3a814adc5856e252850177a9b5c6dddb}
\field{sortinit}{H}
\field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{We consider dynamic competition among platforms in a market with network externalities. A platform that dominated the market in the previous period becomes “focal” in the current period, in that agents play the equilibrium in which they join the focal platform whenever such equilibrium exists. Yet when faced with higher-quality competition, can a low-quality platform remain focal? In the finite-horizon case, the unique equilibrium is efficient for “patient” platforms; with an infinite time horizon, however, there are multiple equilibria where either the low- or high-quality platform dominates. If qualities are stochastic, the platform with a better average quality wins with a higher probability, even when its realized quality is lower, and this probability increases as platforms become more patient. Hence, social welfare may decline as platforms become more forward looking.}
\field{issn}{1756-2171}
\field{journaltitle}{The RAND Journal of Economics}
\field{month}{3}
\field{number}{1}
\field{title}{Dynamic competition with network externalities: how history matters}
\field{volume}{51}
\field{year}{2020}
\field{pages}{3\bibrangedash 31}
\range{pages}{29}
\verb{doi}
\verb 10.1111/1756-2171.12304
\endverb
\endentry
\entry{halaburda2016role}{article}{}
\name{author}{2}{}{%
\strng{namehash}{7aab4a3993afcb60d6ee152265476057}
\strng{fullhash}{7aab4a3993afcb60d6ee152265476057}
\strng{bibnamehash}{7aab4a3993afcb60d6ee152265476057}
\strng{authorbibnamehash}{7aab4a3993afcb60d6ee152265476057}
\strng{authornamehash}{7aab4a3993afcb60d6ee152265476057}
\strng{authorfullhash}{7aab4a3993afcb60d6ee152265476057}
\field{sortinit}{H}
\field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{This paper considers platform competition in a two-sided market that includes buyers and sellers. One of the platforms benefits from a favorable coordination bias in the market, in that for this platform it is less costly than for the other platform to convince customers that the two sides will coordinate on joining it. We find that the degree of the coordination bias affects the platform's decision regarding the business model (i.e., whether to subsidize buyers or sellers), the access fees, and the size of the platform. A slight increase in the coordination bias may induce the advantaged platform to switch from subsidizing sellers to subsidizing buyers, or induce the disadvantaged platform to switch from subsidizing buyers to subsidizing sellers. Moreover, in such a case the advantaged platform switches from oversupplying to undersupplying sellers, and the disadvantaged platform switches from undersupplying to oversupplying sellers.}
\field{journaltitle}{Journal of Economics \& Management Strategy}
\field{number}{2}
\field{title}{The Role of Coordination Bias in Platform Competition}
\field{volume}{25}
\field{year}{2016}
\field{pages}{274\bibrangedash 312}
\range{pages}{39}
\verb{doi}
\verb 10.1111/jems.12163
\endverb
\verb{urlraw}
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/jems.12163
\endverb
\verb{url}
\verb https://onlinelibrary.wiley.com/doi/abs/10.1111/jems.12163
\endverb
\endentry
\entry{hall2023what}{report}{}
\name{author}{2}{}{%
given={Andrew\bibnamedelima B},
giveni={A\bibinitperiod\bibinitdelim B\bibinitperiod}}}%
given={Eliza\bibnamedelima R},
giveni={E\bibinitperiod\bibinitdelim R\bibinitperiod}}}%
\list{institution}{1}{%
{Stanford University, Graduate School of Business}%
\strng{namehash}{7802d539aa6a5705fc60d0986a44d8e0}
\strng{fullhash}{7802d539aa6a5705fc60d0986a44d8e0}
\strng{bibnamehash}{7802d539aa6a5705fc60d0986a44d8e0}
\strng{authorbibnamehash}{7802d539aa6a5705fc60d0986a44d8e0}
\strng{authornamehash}{7802d539aa6a5705fc60d0986a44d8e0}
\strng{authorfullhash}{7802d539aa6a5705fc60d0986a44d8e0}
\field{sortinit}{H}
\field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{How can we democratically govern the AI, social media, and online platforms of the future? Today, low participation is a major barrier to community governance online. We leverage a digital quasi-experiment that allows us to study the links between incentives and political participation at a scale and granularity not previously possible. We focus on a web3 startup called Optimism that distributed digital tokens worth roughly \$28 million USD to more than 300,000 active participants in its governance system as part of a sequence of rewards meant to encourage long-run engagement. Studying 1.2 million unique wallet addresses, we find that the reward scheme induces new users to participate in Optimism’s governance, has larger effects for smaller token holders, and leads voters to spread their votes across delegates more widely. Together, the results suggest that reward schemes that give people a durable stake in the community and promise a sequence of future rewards are able to broaden participation in online democracy noticeably, at least in the short run under the proper conditions.}
\field{title}{What Kinds of Incentives Encourage Participation in Democracy? Evidence from a Massive Online Governance Experiment}
\field{type}{techreport}
\field{year}{2023}
\verb{urlraw}
\verb https://www.gsb.stanford.edu/faculty-research/working-papers/what-kinds-incentives-encourage-participation-democracy-evidence
\endverb
\verb{url}
\verb https://www.gsb.stanford.edu/faculty-research/working-papers/what-kinds-incentives-encourage-participation-democracy-evidence
\endverb
\endentry
\entry{hatfield2023simple}{misc}{}
\name{author}{2}{}{%
given={John\bibnamedelima William},
giveni={J\bibinitperiod\bibinitdelim W\bibinitperiod}}}%
given={Scott\bibnamedelima Duke},
giveni={S\bibinitperiod\bibinitdelim D\bibinitperiod}}}%
\strng{namehash}{a05e8e28fedf7d548f2950ca92d0534f}
\strng{fullhash}{a05e8e28fedf7d548f2950ca92d0534f}
\strng{bibnamehash}{a05e8e28fedf7d548f2950ca92d0534f}
\strng{authorbibnamehash}{a05e8e28fedf7d548f2950ca92d0534f}
\strng{authornamehash}{a05e8e28fedf7d548f2950ca92d0534f}
\strng{authorfullhash}{a05e8e28fedf7d548f2950ca92d0534f}
\field{sortinit}{H}
\field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Firms find it valuable to lock in repeat customers, and they often do so via loyalty programs such as frequent-flyer miles or discounts for returning customers. We introduce a model of loyalty programs in the presence of competition and show that, surprisingly, such programs result in higher prices for all consumers in equilibrium. However, competitors may also find it valuable to identify repeat customers of other firms and convince them to switch; such efforts are called "vampire attacks" – and are particularly easy to execute in the context of Web3 platforms that record their transactions on a public blockchain ledger. We show that vampire attacks can facilitate price competition; this implies that Web3 platforms may face greater competitive pressure than more traditional firms with loyalty programs.}
\field{journaltitle}{Available at SSRN 4377561}
\field{title}{A Simple Theory of Vampire Attacks}
\field{year}{2023}
\verb{doi}
\verb 10.2139/ssrn.4377561
\endverb
\endentry
\entry{heimbach2023risks}{inproceedings}{}
\name{author}{3}{}{%
\list{location}{1}{%
{Cambridge, MA, USA}%
\list{publisher}{1}{%
{Association for Computing Machinery}%
\strng{namehash}{4e11c3aed1811ddd9760034bad38d8cd}
\strng{fullhash}{4e11c3aed1811ddd9760034bad38d8cd}
\strng{bibnamehash}{4e11c3aed1811ddd9760034bad38d8cd}
\strng{authorbibnamehash}{4e11c3aed1811ddd9760034bad38d8cd}
\strng{authornamehash}{4e11c3aed1811ddd9760034bad38d8cd}
\strng{authorfullhash}{4e11c3aed1811ddd9760034bad38d8cd}
\field{sortinit}{H}