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An Analysis of p+ε Attacks on Various Models of Schelling Game Based Systems

Published onOct 22, 2021
An Analysis of p+ε Attacks on Various Models of Schelling Game Based Systems


Smart contracts allow flexible new forms of trust-free cooperation. This gives mechanism designers a new platform in which they can apply game-theoretical principles to engineer desirable outcomes. However, as we have seen with the emergence of “dark DAOs,” hostile attackers can just as easily deploy smart contracts that coordinate collusion and otherwise attempt to undermine the goals of a given game-theoretical structure. Indeed, attacks on incentive structures that would previously have seemed elaborate and difficult to organize can now execute automatically via smart contracts, requiring no trust in the attacker. In this work, we will analyze the “p+ϵp+\epsilon attack” on Schelling point-based cryptoeconomic systems. Particularly, we will consider design choices that one can apply, slightly modifying these systems, to increase resistance to these types of attacks.

1. Introduction and past work

In a coordination game, participants are allowed to choose from several options, and they have an incentive to choose the option that is also chosen by other participants. A standard example of this phenomenon is the process of deciding whether to drive on the left or right side of a road. To the degree that the payoff matrix is symmetric between the various options, which option participants coordinate on can be thought of as somewhat arbitrary.

Figure 1: An example payoff matrix for a voter USR\mathcal{USR} in a symmetric coordination game for p>0p > 0 constant.

However, as was noted by Thomas Schelling [1], when one of the options is somehow distinguished, it can serve as a “focal point” or “Schelling point” on which participants can coordinate. This effect is particularly relevant when participants lack other means of communication that they could use to coordinate. Then, a “Schelling game” is a coordination game where one option is such a focal point, that is distinguished for reasons external to the payoff structure of the game. This is in contrast to approaches based on participants coordinating based on asymmetries in the payoff structure of the game, such as when equilibria are Pareto-ranked; see e.g., [2], [3], [4] for work in this direction.

Schelling games are the basis for many blockchain “oracles,” i.e., systems used to import information onto a blockchain platform that is not checked as part of the consensus protocol of that blockchain. Here, the principle is that if a group of voters are asked to choose between options while reporting on some question about the external world, they will tend to gravitate towards the true response, if only because they view it as a distinguished answer that other voters will expect them to choose. Such oracles are important for reporting on-chain results of elections for prediction market contracts, rainfall amounts for drought insurance contracts, etc. Moreover, this structure also arises implicitly in fundamental ways in blockchain systems; in [5] it is pointed out that in consensus algorithms based on proof-of-work and the longest chain rule such as Bitcoin [6], the choice of which fork to follow can be thought of as a “vote” having the structure of a Schelling game. See Section 4 for further details on these examples.

An intriguing new attack, the p+ϵp+\epsilon attack, designed to warp the incentive structures of these games was proposed in a blog post by Vitalik Buterin [5], crediting the idea for the attack to Andrew Miller. In general, a p+ϵp+\epsilon attack that attempts to make the malicious choice YY win should pay to participants who vote YY an additional ϵ\epsilon more than what they would receive for voting XX, contingent on XX winning. In the case of the game of Figure 1, this yields a payoff matrix for the participant USR\mathcal{USR} of:

Then voting YY is a weakly dominant strategy in this game, so the attacker might hope that the vast majority of participants will vote YY causing the attacker’s choice to win even as she is not required to pay out any bribes. Hence, when participants are rational and self-interested this attack can warp the incentives of the system with no costs to the attacker, i.e., “for free.” Indeed, one observes that there are (at least) the following two equilibria in this game:

  • all voters vote YY

  • N2\lfloor \frac{N}{2}\rfloor votes for YY and N2+1\lfloor \frac{N}{2}\rfloor+1 votes for XX.

In [5], several defenses to attempt to resist such attacks are considered, including, notably, a defense that builds on the second type of equilibrium above, where one is at a “tipping point” between XX and Y.Y. Here, if a rational actor with vv votes thinks, based on her knowledge of how other participants were likely to vote, that she has the decisive vote, then she maximizes her return by splitting up their vv votes so that XX wins by a single vote. Thus, as many votes as possible receive the additional ϵ\epsilon while remaining in the situation where the attacker loses and actually has to pay out the bribes. Then this idea, when considered from the perspective of a group of voters coordinating rather than a single participant, is the basis for the response of “counter-coordination” considered in [5]. As pointed out in [5], a problem with counter-coordination in games of this form is that each counter-coordinator, if they believe that the counter-coordination will succeed and the attack will fail independently of whether they individually participate or not, is strictly better off taking the full bribe. Hence, counter-coordination can only function to the degree that participants are either altruistic or believe their participation is likely to make a difference in the success of the endeavor. Thus, this situation exhibits characteristics of a tragedy of the commons [7].

More generally, this idea of a bribe that is only paid out in the case that the bribe is unsuccessful in changing the outcome was already considered in a thought experiment proposed by Warren Buffett. As part of an editorial written for the New York Times in 2000, Buffet highlighted absurd aspects of the United States campaign finance system and arguing for campaign finance reform [8]. It has been remarked that situations similar to the p+ϵp+\epsilon have existed in other pre-blockchain situations, particularly surrounding share buying offers in acquisitions of companies; indeed, there is even an example of such a situation where the intended victim of the attack launched a second p+ϵp+\epsilon attack in support of its side [9]. Also, the p+ϵp+\epsilon attack has been considered in classifications of types of attacks on cryptoeconomic systems [10] particularly towards the perspective of whether these attacks are viable on proof-of-stake consensus systems as well as proof-of-work systems [11], [12]. Nonetheless, considering the widespread use of Schelling-based structures (explicitly or implicitly) in blockchain-based systems, this attack has so far received relatively little attention from the research community.

2. Possible structures of Schelling games

In this section, we will recall various elements that can be considered in the design of a Schelling game. In all of these situations we will take p,ϵR>0p,\epsilon\in\mathbb{R}_{>0}.

  • Users can be required to place a deposit d0d\geq 0 which they lose if their vote disagrees with the ultimate result. Thus, this allows penalties to voters as well as rewards.

  • We can also have appeals. Appeals result in delaying the result of a vote by an additional tt blocks per appeal, up to some maximum number of appeal rounds lN{}l_{***}\in\mathbb{N}\cup \left\{\infty\right\}. Each appeal will typically consume some additional resources, with the idea that more resources can be put towards this ruling. So, ideally, this ruling is more likely to be correct. Concretely, if we suppose that there are MiM_i voters in the iith appeal round, a Schelling game of the form of Figure 1 might require O(Mip)O(M_ip) resources to provide for payouts to voters. Of particular note for our purposes, we will generally consider the winner of a game, as used for determining which payouts are made, to be the option that receives the most votes in the last appeal round.

    Appeals can be triggered in a variety of ways, such as by allowing users to pay a fee, or by random chance.1 Some of our results will assume the model where users can trigger an appeal in the iith appeal round by paying a fee of (at most) RiR_i. Nevertheless, a user that decides to trigger such an appeal may ultimately pay less than the RiR_i; for example, if there are multiple users who decide to appeal, they might be able to split the fee between them, one of them might be randomly chosen to pay the full fee, or they might all have to pay the full fee with the excess amounts burned, depending on the appeal system used. While our results are general enough to handle these different possibilities, we nevertheless assume the following continuity property: if USR1,,USRN\mathcal{USR}_1,\ldots,\mathcal{USR}_N are users that appeal in round ii with probabilities yi,1,,yi,N[0,1]y_{i,1}, \ldots, y_{i,N}\in[0,1] respectively, then

    fk(yi,1,,yi,k1,yi,k+1,yi,N)f_k(y_{i,1}, \ldots,y_{i,k-1},y_{i,k+1},\ldots y_{i,N})(1)
    =E[amount of fees USRk pays to appeal]=E[\text{amount of fees }\mathcal{USR}_k\text{ pays to appeal}]

    is continuous in yi,1,,yi,Ny_{i,1},\ldots, y_{i,N}. Furthermore, we assume 0f(yi,1,,yi,k1,yk+1,yi,N)Ri0\leq f(y_{i,1}, \ldots,y_{i,k-1},y_{k+1},y_{i,N})\leq R_i.

In our work, we will only consider binary Schelling games: namely, games where a set of voters select between two different outcomes. Nonetheless, one can construct different payoff matrices for games that preserve the basic idea of a Schelling game. The payoff matrix that we saw in the Introduction, when modified to include deposits is:

Definition 2.1: We define a simple Schelling game to be one where a participant USR\mathcal{USR} can vote one of two options with the payoffs given by:

A p+ϵp+\epsilon attack transforms the payoff matrix to:

Concretely, this is achieved as the attacker pays to USR\mathcal{USR} an amount of p+d+ϵp+d+\epsilon if USR\mathcal{USR} finds herself in the case of the bottom-left entry of the table.

Another alternative that is employed in some applications (see Section 4) is the following structure:

Definition 2.2: We define a symbiotic Schelling game to be one where a participant USR\mathcal{USR} contributes a deposit dd and can vote for one of two options with the payoffs given by:

where xx is the number of votes (other than that of USR\mathcal{USR}) for XX.

A p+ϵp+\epsilon attack on this game gives the following payoff table:

Finally, as another alternative, we could also divide all amounts—both the positive sum resources that the participants can receive and the lost deposits—between voters (in a given appeal round) on the winning side. This has the effect that rewards are larger in the event of narrow decisions.

Definition 2.3: We define a redistributive Schelling game to be one where a participant USR\mathcal{USR} contributes a deposit dd and can vote for one of two options with the payoffs given by:

where xx is the number of votes (other than that of USR\mathcal{USR}) for XX.

A p+ϵp+\epsilon attack on this game gives the following payoff table:

Note that, in a redistributive Schelling game, if a given voter is the only person to vote for XX in her round and then XX ultimately wins, she receives all of the MpMp and all of the lost deposits for her round. Then, in order for an attacker to make voting YY a dominant strategy and to incentivize voters to not take a chance on trying to get this “lone voice of reason jackpot,” an attacker needs to be willing to offer bribes that are O(M)O(M) to each voter, for a total of O(M2)O(M^2), in the most extreme case.

Note that these three structures are normalized so that they have the same maximum total payout to voters - MpMp. However, the full MpMp is only guaranteed to be paid out in the redistributive Schelling game case.

We will see in Section 6 and Section 7 that redistributive Schelling games with appeal systems have a certain level of robustness against p+ϵp+\epsilon attacks, essentially due to the nature of “lone voice of reason jackpot.” Hence, we also propose adapted attacks, which, while they can be applied to any of the payoff structures discussed above, are designed to preserve the spirit of a p+ϵp+\epsilon attack while removing or limiting the “lone voice of reason jackpot” in redistributive games:

  • “Last round only attack”: an attacker can only pay bribes to participants in the last, decisive appeal round—without specifying what this round is in advance

  • “Capped attack”: an attacker can offer a bribe in all rounds, but she caps how much a given voter can receive as a bribe by H>0H>0.

Deposits and (a form of) appeal systems,2 were all already considered as possible defenses against p+ϵp+\epsilon attacks in [5] in the context of Schelling games of the form of Definition 2.1. The possibility that participants can lose deposits has already been evoked as a basis for increased security against p+ϵp+\epsilon attacks in the context of proof-of-stake consensus algorithms [11] (compared to proof-of-work where miners do not typically stake a deposit3). However, the interplay between these elements, and particularly how they interact with the choice of games of the form of Definition 2.1, Definition 2.2, or Definition 2.3, seems to have not been fully explored.

3. Our contribution

In this work we extend the analysis presented in [5] on simple Schelling games of the form of Definition 2.1 to symbiotic Schelling games of the form of Definition 2.2 and redistributive Schelling games of the form of Definition 2.3. Moreover, we will see how various defenses against p+ϵp+\epsilon attacks, such as deposits and appeal systems, interact with each other and with the choice of payoff structure.

In Section 6 and Section 7, we see that, under a variety of measures of difficulty of an attack, redistributive games, particularly when there is an appeal structure, have increased resistance to p+ϵp+\epsilon attacks. Specifically, in Section 6, we see that, under certain conditions, a p+ϵp+\epsilon attack on a redistributive Schelling game with an appeal system has budget requirements which grow quadratically in the number of participants and hence are likely to be prohibitive in many cases. In Section 7, we see that there are “counterattacks” against p+ϵp+\epsilon attacks on redistributive Schelling games with appeal that are stable in a mixed strategy equilibrium and result in the attack failing with positive probability greater than a lower bound that we are able to calculate explicitly.4

However, in Section 9, we will analyze the “last round only” and “capped” p+ϵp+\epsilon attacks, where the attacker adapts the spirit of a p+ϵp+\epsilon attack to avoid some of these defenses. While we will see that these adapted versions are less prohibitive, even in the context of a redistributive Schelling game with an appeal system, accepting the bribe is no longer a dominant strategy, and hence these attacks are no longer “free.” Thus, these attacks seem to be somewhat less dangerous. In Section 8, we consider the situation where rational participants assign a moral (or cognitive effort) cost to accepting the bribe, and we observe mixed strategy equilibria where the attack fails some percentage of the time.

Finally, in Section 10, we will consider the results of some preliminary experiments around p+ϵp+\epsilon attacks as conducted on the Kleros platform.

4. Examples

A number of blockchain-based systems involve voting that, implicitly or explicitly, can be seen in the context of Schelling point based games.

  • A number of token-curated lists involve voting on candidates for admission to the list where voters are rewarded via a Schelling point game, see [13]. Notable examples include lists of true or fake news for decentralized news aggregation services or lists of products that meet certain criteria for sale in decentralized marketplaces. Here the voters for the list, typically a pool of token holders, vote on whether a given entry deserves admission to the list, with rewards (and in some cases penalties) applied to voters based on whether or not they agree with the majority vote. This process serves as an oracle for blockchain applications that use these lists to gain information about whether submissions satisfy the listing criteria. See [14], [15] for examples.

  • Truthcoin [16] proposes a more general purpose oracle system based on a Schelling point mechanism; however, its payoff structures are somewhat more complicated than those considered in Section 2 as Truthcoin creates an interdependence of votes on different questions, which are then resolved using a singular value decomposition on the matrix of votes.

  • Kleros [17] is a blockchain-based system for dispute resolution that functions on a Schelling point-based mechanism. It is, then, a type of oracle that is specialized in bringing on-chain information necessary to resolve disputes. In any given case, for example between two parties to a small-scale freelancing dispute, a number of “jurors” are drawn randomly from among a pool of token holders. These “jurors” then vote on the result of the dispute, and they are rewarded or penalized based on whether or not they vote for the majority result. Note that Kleros uses a redistributive Schelling game structure and has an explicit appeal mechanism satisfying the properties described in Section 1.

  • One of the original examples of [5] is that of the proof-of-work consensus mechanism. A given block producer can expect to receive pp in block rewards, but only if her blocks are ultimately in the longest chain. Then suppose that we have two rival chains on which one can mine, chains XX and Y,Y, where on each chain a transaction has been accepted that conflicts with the other chain. Over short time frames (compared to the difficulty adjustment period), then, mining a block on each chain would require approximately equal resources and produce a constant reward dependent only on the corresponding chain ultimately winning out. Hence, for each block someone accepting the p+ϵp+\epsilon bribe mines on the YY chain assuming the XX chain ultimately becomes the longest chain, the attacker must pay the block rewards plus ϵ\epsilon. This structure then resembles that of Definition 2.1.

    However, imagine heuristically that we have very short block times (relative to the period of time in which the attacker needs to wait to have a sufficient number of block confirmations) and that difficulty adjusts continuously. Then suppose a single miner Bob that represents a proportion zz of all mining resources is mining on the XX chain for some period of time while all other miners, representing 1z1-z resources, are mining on the YY chain. This can be thought of as zz percent of the “votes” being cast for XX and 1z1-z percent being cast for YY over this period. By considering longer amounts of time (heuristically discretizing them into distinct periods) we can think of the votes as being broken into appeal periods in the sense of Section 1. From the perspective of the XX chain, Bob should get all of the block rewards over this period of time. If the option XX ultimately gives the longest chain (beyond the number of blocks required for “coinbase maturity”), then all of these rewards that Bob receives will be usable. In order to incentivize rational miners who are willing to accept bribes, but do not think that the attacker has the resources to maintain the attack over the long term, to not be tempted by this “lone voice of reason jackpot,” it is not sufficient for the attacker to pay the miners who support YY on a per-block basis. Rather, she must pay them the amount that they would have received by mining on the other chain during this period, up to the total block rewards plus epsilon. This structure resembles that of Definition 2.3.

  • The Serenity proof-of-stake version of Ethereum [18] (currently in Phase 0 of its release) uses the symbiotic Schelling game model of Definition 2.2. This choice was motivated to make the protocol less vulnerable to “discouragement attacks” [18], [19]. Note that as this protocol, in contrast to proof-of-work, is designed to have finalization guarantees [20], one cannot consider waiting extra blocks as an “appeal” in the same way.

5. Model of actors

In this work, our attacker will generally have the ability to offer some type of p+ϵp+\epsilon bribe. In situations where there is an appeal structure, one typically thinks of this structure as being a defense; however, is a double-edged sword as we then may also allow our attacker the ability to make malicious appeals. However, we do not consider attackers with the ability to tamper directly with votes. In particular, we do not consider attackers capable of performing 51%51\% attacks or denial of service attacks on the consensus protocol of an underlying blockchain platform where the vote is taking place, nor do we consider attackers that have votes themselves or that are capable of directly controlling users’ votes, e.g., via malware.

Moreover, in Section 7 where we discuss “counterattacks” against p+ϵp+\epsilon attacks, we will need to assume a more rigid pattern for attacker behavior. Indeed, there we consider a p+ϵp+\epsilon attack in a multi-round situation, and we model the attacker as offering the bribe in each round until

  • Her attack succeeds and YY wins the game; or

  • The attacker stops, either because she has reached the maximum appeal round or because the required bribes exceed predetermined some budget,5

and not taking other actions.

For some results we will require specific behavioral assumptions of voters. A particular set of hypotheses that we will often make on voters is the following:

Definition 5.1: Voters are said to satisfy * if:

  • Each vote (across all rounds) is controlled by a unique entity.6

  • Voters are economically rational, namely, they take actions that maximize their expected return in a risk-neutral fashion (possibly assigning extra, specified internal costs to immoral or cognitively difficult actions, see Section 8).

  • The structures of the games, including the presence of attacks, is common knowledge among the voters.7

  • If the payoff matrix is symmetric between the “honest choice” XX and the “dishonest choice” YY, for example for participants in an appeal round where the p+ϵp+\epsilon bribe is no longer provided, voters vote for the “honest choice” XX with probability one.

6. Budget requirements of p+ϵp+\epsilon attacks on various structures

In this section we will begin to consider measures of the difficulty for an attacker to launch p+ϵp+\epsilon attacks in the models of Definition 2.1, Definition 2.2, and Definition 2.3, with and without appeal. Particularly, we will calculate the required capital that must be locked up in each of these scenarios.

Proposition 6.1:

  • A p+ϵp+\epsilon attack on a simple Schelling game of the form of Definition 2.1 with MM voters and no appeal requires a budget of M2(p+d+ϵ)\lfloor\frac{M}{2}\rfloor(p+d+\epsilon). More generally, a p+ϵp+\epsilon attack on a simple Schelling game of the form of Definition 2.1 with successive appeal rounds of M1,M2,,MkM_1,M_2,\ldots,M_k voters respectively requires a budget of (Mk2+i=1k1Mi)(p+d+ϵ).\left(\lfloor\frac{M_k}{2}\rfloor+\sum_{i=1}^{k-1}M_i\right)(p+d+\epsilon).

  • A p+ϵp+\epsilon attack on a symbiotic Schelling game of the form of Definition 2.2 with MM voters and no appeal requires a budget of M2(p2+d+ϵ))\lfloor\frac{M}{2}\rfloor\left(\frac{p}{2}+d+\epsilon)\right). More generally, a p+ϵp+\epsilon attack on a symbiotic Schelling game of the form of Definition 2.2 with successive appeal rounds of M1,M2,,MkM_1,M_2,\ldots,M_k voters respectively requires a budget of Mk2(p2+d+ϵ)+i=1k1Mi(p+d+ϵ).\lfloor\frac{M_k}{2}\rfloor\left(\frac{p}{2}+d+\epsilon\right)+\sum_{i=1}^{k-1}M_i (p+d+\epsilon).

  • A p+ϵp+\epsilon attack on a redistributive Schelling game of the form of Definition 2.3 with MM voters and no appeal requires a budget of

    (2d+2p+ϵ)M2 \sim (2d+2p+\epsilon)\frac{M}{2}

    with the asymptotic as MM increases.

    More generally, a p+ϵp+\epsilon attack on a redistributive Schelling game of the form of Definition 2.3 with successive appeal rounds of M1,M2,,MkM_1,M_2,\ldots,M_k voters respectively requires a budget of


PROOF: We consider the redistributive Schelling game case. Take xix_i to be the number of participants who vote for XX in the iith round. The attacker must pay out (Mixi1)d+Mipxi+1+d+ϵ\frac{(M_i-x_i-1)d+M_ip}{x_i+1}+d+\epsilon to each of the MixiM_i-x_i participants who vote YY in the iith round. However, the attacker only has to make these payments if xkMk2x_k\geq \lceil \frac{M_k}{2} \rceil, causing the attack to fail. However, one can take any xi0x_i\geq 0 for i<ki<k. Note that this upper bound is in fact realized for xi=0x_i=0 for i<ki<k and xk=Mk2.x_k=\lceil \frac{M_k}{2} \rceil. The other cases are similar.


Hence, even when d=0d=0 and with no appeal mechanism, moving from the simple Schelling game to the redistributive model roughly doubles the budget required by the attacker from M2(p+ϵ)\lfloor\frac{M}{2}\rfloor (p+\epsilon) to M(p+ϵ2)M(p+\frac{\epsilon}{2}). The difference in budget required between a redistributive Schelling game versus the other models is much more dramatic in the case of appeals. The simple Schelling game has a required budget that grows linearly in MiM_i for all ii, whereas the redistributive Schelling game has a required budget that grows quadratically in MiM_i for i<ki<k.

Example 6.2: We consider the implications of Proposition 6.1 on some of the examples we considered in Section 4, particularly those related to consensus mechanisms. Namely, for each proof-of-work as it exists for both Bitcoin and Ethereum, the heuristic version of proof-of-work presented in Section 4 where difficulty adjusts continuously, and the Serenity proof-of-stake system, we will consider the budget required to do each of two different p+ϵp+\epsilon attacks: a censoring attack and a double spend attack. See Figure 2 for a summary of the results of these calculations.

  • In a censorship attack on a proof-of-work system, the attacker incentivizes all miners to mine a fork where a given transaction TX\mathcal{TX} is not included; denote this branch by YY. A priori, starting from any block in YY a miner could insert TX\mathcal{TX} in a child block resulting in several different branches competing with YY to be accepted by the network. However, we consider the worst case for the attacker, where all mining resources that accept TX\mathcal{TX} are concentrated in a single branch XX. Then, if the attacker wants to censor TX\mathcal{TX} in the branch accepted by the network at a fixed time TT, this is equivalent to having YY win a simple Schelling game with no appeal and MM equal to the number of blocks until TT, allowing one to apply Proposition 6.1 directly. As of April 2021, for Bitcoin these values are an average block time of roughly 99 minutes and a block reward of 6.256.25 BTC plus average fees of approximately .6.6 BTC, and for Ethereum one has an average block time of 13.313.3 seconds with an average block reward of roughly 3.13.1 ETH, including fees. [21]

  • A double spend p+ϵp+\epsilon attack on a proof-of-work network has a similar structure. Here an attacker wants two conflicting transactions TX1\mathcal{TX}_1 and TX2\mathcal{TX}_2 to receive enough confirmations KK to be accepted by two different vendors so that she receives products corresponding to each of these transactions, while only paying for one of them. We analyze the budget required for an attacker strategy where the attacker waits for one of the transactions, for example TX1\mathcal{TX}_1, to receive KK confirmations on branch XX and then launches a p+ϵp+\epsilon attack to incentivize miners to revert to the last block where TX1\mathcal{TX}_1 was not included and mine an alternative branch YY until it surpasses XX to become the longest chain and also has (at least) KK confirmations. Suppose the attacker offers the p+ϵp+\epsilon bribe to miners who mine blocks K+1,,2K+1K+1,\ldots, 2K+1 (the attacker could offer the bribe over a wider range, allowing for her to still win even if some minority of miners mine on XX at the expense of requiring a larger budget). Then, this corresponds to a simple Schelling game between XX and YY with M=2K+1M=2K+1 votes where the first KK votes are already set to XX. Due to this restriction on the voting pattern, one cannot apply Proposition 6.1 directly; nonetheless one sees by a similar argument that the payouts required by this attack in the most extreme case where exactly KK of the remaining K+1K+1 participants mine on YY gives the same required total payouts as those in Proposition 6.1. For the values for Bitcoin that we present in Figure 2, we take the standard choice of K=6,K=6, [6] and for Ethereum we take K=20K=20, corresponding to the number of confirmations required by large exchanges to process deposits [22].

  • We now consider similar attacks under the heuristic model of proof-of-work with rapidly adjusting difficulty discussed in Section 4. We develop on this heuristic, noting additional approximations as necessary. Particularly, we suppose that the collective mining power of the community consists of MM identical ASICs each of which can attempt tt nonces per minute. This collective mining power remains fixed over the period of the attack. Furthermore, as the difficulty of different blocks varies over the course of a confirmation period, we assume that nodes verifying the confirmation status of a block check for a total amount of difficulty KK in subsequent blocks rather than simply a given number of subsequent blocks. Suppose TT is the time at which the attacker wants the transaction TX\mathcal{TX} to be censored. Denote by T0T_0 the time at which TX\mathcal{TX} becomes known, and denote by time T1T_1 a point at which the difficulty on the fork XX where TX\mathcal{TX} is included has adjusted to the point so that a given nonce has a 1τ\frac{1}{\tau} chance of producing a block. (In particular, this supposes that enough miners mine on XX during the transition between T0T_0 and T1T_1 to trigger the difficulty adjustment.) Then suppose that the attacker manages to incentivize all miners to mine on the fork YY where TX\mathcal{TX} is not included until time T2T_2 when all miners switch to mining the fork XX. For the attacker to incentivize miners to not defect, she needs to provide them with a reward of at least

    E[Mine on Xat a SingleTime Increment]E\left[\begin{array}{c} \text{Mine on }X \\ \text{at a Single}\\ \text{Time Increment} \end{array}\right]

    at each of the tt increments per minute at which they mine between T1T_1 and T2T_2. Hence, to cover bribes in this scenario, the attacker needs

    (T2T1)MtE[Mine on Xat a SingleTime Increment](T_2-T_1)\cdot M\cdot t\cdot E\left[\begin{array}{c} \text{Mine on }X \\ \text{at a Single}\\ \text{Time Increment} \end{array}\right]
    (12TDifficulty Transition Periods)\approx \left(\frac{1}{2}T-\begin{array}{c} \text{Difficulty Transition } \\ \text{Periods} \end{array}\right)
    MtAvg. Block Rewardτ,\cdot M\cdot t\cdot \frac{\text{Avg. Block Reward}}{\tau},

    where we note that, as all miners are mining on YY between T1T_1 and T2T_2 and are all mining on XX after T2T_2, discounting the assumed to be short periods of difficulty adjustment, these periods that produce roughly equal total difficulty are of roughly equal length.

  • We take the assumptions related to difficulty adjustment from the heuristic above and have the same structure of double spending attacks as for standard proof-of-work. Similarly to above, we denote by T0T_0 the point at which TX1\mathcal{TX}_1 is mined in a block and by T1T_1 the point at which the chain XX has accumulated enough difficulty to be viewed as confirmed and the attacker launches the attack. Denote by T2T_2 the time at which the difficulty on chain YY has adjusted to reflect the entire community mining on it and at which the difficulty on chain XX has adjusted so that a given nonce has a 1τ\frac{1}{\tau} chance of producing a block. We consider a scenario where all NN miners mine on YY until some time T3T_3 when exactly K1K-1 total difficulty is mined on chain YY, at which point all miners switch back to mining on XX. For the attacker to incentivize miners to not defect, she now needs to be able to cover total bribes of

    (T3T2)MtE[Mine on Xat a SingleTime Increment].(T_3-T_2)\cdot M\cdot t\cdot E\left[\begin{array}{c} \text{Mine on }X \\ \text{at a Single}\\ \text{Time Increment} \end{array}\right].
    (Avg. Typical Confirmation TimeDifficulty Transition Periods)\approx \left(\begin{array}{c} \text{Avg. Typical } \\ \text{Confirmation Time} \end{array}-\begin{array}{c} \text{Difficulty Transition } \\ \text{Periods} \end{array}\right)
    MtAvg. Block Rewardτ,\cdot M\cdot t\cdot \frac{\text{Avg. Block Reward}}{\tau},

    where we note that, as all miners are mining on YY between T2T_2 and T3T_3 and discounting the assumed to be short periods of difficulty adjustment, T3T2T_3-T_2 is roughly the time that would normally be required for a transaction to be confirmed.

  • We now consider a similar censorship attack on the proof-of-stake system of Serenity [18], adapted to account for its somewhat different structure. Here blocks are grouped into “epochs” and the participants of the validator pool are tasked with providing an attestation to one block per epoch. Then the protocol attempts to determine a sequence of “checkpoint” blocks, consisting of a single checkpoint per epoch. Attestations contain references: to the previous block, a “target” checkpoint for the current epoch, and a “source” checkpoint from a a previous epoch [23]. Then a checkpoint bb is considered “justified” if at least two thirds of validators (weighted by deposit) provide an attestation with target bb and source aa, where aa is a justified checkpoint for a previous epoch. An epoch is considered “finalized” if its checkpoint aa is justified and at least two thirds of validators attest to a checkpoint in the immediately following epoch with aa provided as the source [24]. In the event of a fork, (self-consistent) attestations to the different branches would disagree on the previous block, the previous block and the target checkpoint, or all three elements, depending on the depth of the divergence point of the forks; validators are rewarded or penalized based on how many of these elements they correctly specify, conforming to the state of the chain as it is eventually finalized in some future epoch.8

    In the censorship attack that we consider, we suppose that the attacker’s goal is to prevent a given transaction TX\mathcal{TX} from appearing in a block in a finalized epoch by a fixed time TT. Suppose that the transaction TX\mathcal{TX} to be censored9 appears in epoch i0i_0. Then, considering that in order to be finalized, a checkpoint on some fork must be included as a target and then a source in two-thirds of attestations of two consecutive epochs, to prevent finalization of an epoch including TX\mathcal{TX} it is sufficient for an attacker to offer p+ϵp+\epsilon bribes to validators who attest a fork YY that does not include TX\mathcal{TX} in every other epoch: i0+1,i0+3,.i_0+1,i_0+3,\ldots. Hence we assume that the attacker only offers bribes for these epochs.10 Note that if a fork XX that includes TX\mathcal{TX} in epoch i0i_0 ultimately finalizes, then any attestation for YY will be incorrect on the previous block, the target, and the source (assuming that i0i_0 is justified) in any of the epochs considered. Serenity has a base reward BB that is calculated in terms of the number of active validators [18]. It uses11 a symbiotic Schelling game under “normal operations” with the choice between XX and YY corresponding to p=3Bp=3B and d=3Bd=3B. However, if more than EE epochs pass without being finalized, then the incentive structure changes to a simple Schelling game with p=0p=0 and d=6B+ld=6B+l, where ll is the “inactivity leak,” an amount proportional to the validator’s remaining balance that increases with the number of epochs that are not finalized. Then, in the most extreme case where all MM validators accept the bribe in each epoch jj for j=i0+1,i0+3,,jTEpoch Time (min)j=i_0+1,i_0+3,\ldots, j\leq \lfloor \frac{T}{ \text{Epoch Time (min)}}\rfloor, but XX is finalized by having checkpoints in epochs TEpoch Time (min)1\lfloor \frac{T}{\text{Epoch Time (min)}}\rfloor-1 and TEpoch Time (min)\lfloor \frac{T}{\text{Epoch Time (min)}}\rfloor justified, then the attacker must pay out:

    M(6B+ϵ)E2+M\cdot (6B+\epsilon)\lfloor \frac{E}{2} \rfloor+
    M(6B+ϵ)(T2Epoch Time (min)E21)M\cdot (6B+\epsilon)\left( \lfloor \frac{T}{2\cdot \text{Epoch Time (min)}} \rfloor-\lfloor \frac{E}{2} \rfloor-1 \right)
    +j=i0+1,i0+3,,j<TEpoch Time (min)lj+δ,+\sum_{j=i_0+1,i_0+3,\ldots, j< \lfloor \frac{T}{\text{Epoch Time (min)}}\rfloor}l_j+\delta,

    where taking the continuous approximation to the inactivity leak of [23]

    j=i0+1,i0+3,,j<T6.4minlj\sum_{j=i_0+1,i_0+3,\ldots, j< \lfloor \frac{T}{6.4 \text{min}}\rfloor}l_j\approx
    12MInitial Validator Balance(1eT6.4Epoch Time (min)2/2α),\frac{1}{2}\cdot M\cdot\text{Initial Validator Balance}\cdot \left(1-e^{-\lfloor \frac{T}{6.4 \text{Epoch Time (min)}}\rfloor^2/2\alpha}\right),

    with α\alpha a fixed parameter and δ\delta is the total bribe that must be paid out in the last round. As the last round finalizes and hence converts back to a symbiotic Schelling game, we have12 δM(6B+ϵ)\delta\leq M\cdot(6B+\epsilon). In Figure 2, we take M=120000M=120000, B=8100B=8100 gwei (based on current values as of April 2021 [25] calculating BB from MM using the formulas of [23]), E=4E=4, Initial Validator Balance=3232 ETH, Epoch Time=6.4 min, and α=224\alpha=2^{24}, the (anticipated, long-term) value of α\alpha [23].

  • To the degree that a double spend on a system using Serenity would require two conflicting transactions to be finalized, this is only possible insofar as it leads to a permanent fork of the network. One could consider attacks that attempt to trigger such a split by incentivizing validators to attest in such a way such that the inactivity leak eventually results in each fork perceiving the validators on the other forks as being penalized to the point where the remaining validators suffice to finalize [18], [24]. However, as such an attack requires some, but not all, validators voting for each fork, it does not fit into structures that we have considered in this section.

Figure 2: Budget requirements of various p+ϵp + \epsilon attacks on block production protocols.

So to the degree that the specific censorship attacks we consider on the PoW and the PoS versions of Ethereum are comparable, we see in Figure 2 that censorship attacks on the PoS version require smaller budgets at short intervals of time, but substantially larger budgets on longer time scales due to the inactivity leak. On the other hand, the structure of Serenity provides greater resistance to double spending attacks, even removing the types of attacks we consider on PoW as possibilities. Note that it remains possible for an attacker to launch adapted versions of these attacks that might have different budget requirements; see Section 9 and particularly Example 9.1, where we consider some modified versions of the censorship attack that provide a different set of tradeoffs for the attacker.

In the context of performing a p+ϵp+\epsilon attack on a proof-of-work system, one can compare these deposit sizes to those of other smart contract-enforced attacks described in [26]. Note that in some such applications on proof-of-work systems, particularly those in which a payout is made to “uncle blocks” as in Ethereum, an attack could reduce their payouts by these amounts, hence reducing the costs of a failed attack in a manner similar to the uncle-mining based attacks presented in [27]. Nonetheless, as uncle block rewards on Ethereum are only made to the extent that the uncle blocks are subsequently referenced in blocks in the main chain [28], and are hence not guaranteed, we have not taken them into account in the budget requirements shown in Figure 2. Note that the quadratic costs manifest themselves in the heuristic version of PoW that we consider with quickly adjusting difficulty in that, even when one accounts for the balancing average block reward against τ\tau to produce some average reward per unit time, there is an additional term of MM, the number of active miners, in the required budgets.

Remark 6.3: Note that in systems such as [29] and [17] where deposits are made in a system token, p+ϵp+\epsilon bribes must also be paid in this token.13 Then, by having quadratic budget requirements in the redistributive case, one can have situations where the budget requirement of a sufficiently high appeal round exceeds the number of total tokens in circulation and is hence impossible. For example, in the Kleros “general court” where (as of April 2021) d=620d=620 PNK tokens of which the total circulating supply is 524 million [30], an appealable redistributive Schelling game vote with M=905M=905 participants would require a bribe budget of at least 9052620905^2\cdot 620, exceeding the supply.

Remark 6.4: The large budget requirements of p+ϵp+\epsilon attacks on redistributive Schelling games with an appeal system represent an implicit cost to the attacker from the opportunity costs of having their capital locked up during the vote.

7. Counterattacks against p+ϵp+\epsilon attacks

We will further see that the large budgets required to bribe participants in a redistributive Schelling game with appeals can be used as a weapon against the attacker. Specifically, we describe a counterattack that is possible in Schelling games where the appeal system is such that a voter in round ii can trigger an appeal from the iith round to thest round by paying RiR_i. Here the actions available to voters in round ii are:

  • Vote XX or vote YY

  • Independent of her choice of vote, to trigger an appeal or not

See Section 5 for our model of possible actions taken by the attacker. Then, voters can take the following strategy:

Namely, voters are said to be counterattackers if they vote to accept the bribe and then appeal in their vote round. Counterattack 1 depends on the idea that if there is an appeal to a round where the attacker can no longer commit to enough money to pay required bribes, voters will revert back to the honest choice, XX. Then the attacker’s own bribes are turned against her as voters are willing to pay appeal fees in exchange for some percent chance of ultimately receiving the bribe. Consequently, as we will see below, this counterattack is particularly effective in redistributive Schelling games. See Figure 3 for an illustration of this idea.

Figure 3: Example of multi-round counterattack in a redistributive Schelling game where voters can choose options favoring Alice or Bob, and Bob is executing a p + ϵ attack. Voters receive payouts from the coherence game and the attack contract, which are symbolized by the scale and the image of the attacker Bob respectively. Participants who execute Counterattack 1 have an amount deducted corresponding to paying to trigger an appeal. Participants are grouped horizontally by appeal round. In the first two rounds, counterattackers are shown on the left and non-counterattackers on the right; in the third round, we suppose Bob can no longer lock up enough funds to cover all of the bribes required of him, so voters default back to the “honest” choice of voting for Alice. Note that if either of the counterattackers in the first two rounds had voted for Bob without counterattacking, Bob would have won and all voters would have only received the coherence payout of p.

Concretely, if an attacker has a given budget bb, this imposes limits on the number of appeal rounds for which she can commit to paying out p+ϵp+\epsilon bribes against a given game. For our purposes, voters will not know the attacker’s budget bb exactly. However for some of our results, we will model voters as viewing this budget as a random variable BB. Namely, a given attacker’s budget is a sample from some fixed, known probability distribution fBf_B, Prob(b1Bb2)=b1b2fB(x)dx\text{Prob}(b_1\leq B \leq b_2)=\int_{b_1}^{b_2}f_B(x)dx, that captures the variability of budgets seen across the universe of all possible attackers.

Then, we define another random variable as a function of BB by

L=min{lN:B<costs to continue attack until lth round}.L_*=\min\left\{l\in\mathbb{N}:B<\text{costs to continue attack until }l\text{th round}\right\}.

Namely, LL_* reflects a voter’s probabilistic assessment of the first round in which the attacker will not be capable of locking up the amounts required to continue the attack. These costs to continue the attack include the bribes considered in Proposition 6.1 as well as potentially other costs (such as appeal fees depending on the appeal fee model).

Then, participants update their beliefs on this distribution as the attacker continues the attack in successive rounds. Specifically, if the attacker continues the attack through the iith round, voters will estimate her probability of continuing it further to the jjth round for jij\geq i as Prob(L>jL>i)\text{Prob}(L_*>j|L_*>i).

Remark 7.1: If an attacker committed to a large attack budget at the beginning of an attack—for example, by placing it in a smart contract that could only be used to fund p+ϵp+\epsilon bribes—then we could consider this information as simply being included in the voters’ prior beliefs on BB (namely that voters imagine the attacker as being drawn from some corresponding subpopulation). For this work, we do not consider more sophisticated behavior in how the attacker provides information about her budget, such as by committing an additional amount, beyond what is required of her, in a smart contract during an intermediate round after the attack has begun, see Section 5.

Remark 7.2: One could have potential complications from situations where the attacker has the budget required to pay partial, but not full bribes in a given round. In this section, we suppose that in any given appeal round the attacker credibly commits to paying all of the bribes for that round, or does not commit to paying any bribes, see Section 5. Indeed, heuristically, one could imagine that each participant in Counterattack 1 places sufficient funds in a smart contract to automatically appeal the following two rounds, avoiding effects from rounds with partial bribes at the expense of increasing RiR_i. Attackers who spread out their budget further to pay partial bribes over a larger number of rounds are considered in Section 9.

While voters observe LL_* with uncertainty, there are sometimes natural upper bounds that they will have on it. Indeed, if the Schelling game permits a maximum total number of rounds of l<l_{***}<\infty, then P(L>l+1)=0P(L_*>l_{***}+1)=0. Moreover, we define

l=min{lN{}:Prob(L>l)=0}.l_{**}=\min\left\{l\in \mathbb{N}\cup\left\{\infty \right\}:\text{Prob}(L_*>l)=0 \right\}.

Then ll+1l_{**}\leq l_{***}+1. One sometimes has better natural upper bounds on ll_{**}. Particularly, in Schelling games where bribes must be paid in terms of an asset with a limited supply, the quadratic growth of the required bribes seen in Proposition 6.1 can cause them to exceed the total supply, as discussed in Remark 6.3. In this case, there is a round where it is impossible to continue the attack, even as appeal is still possible, namely l1<ll_{**}-1<l_{***}.

As we will require the same set of assumptions on our parameters in several of our results, we will condense notation by saying:

Definition 7.3: (Mi,Ri,c1,c2,c3,d,p)(M_i,R_i,c_1,c_2,c_3,d,p) are said to be well-designed if

  • any user can trigger an appeal from the iith round to the i+1i+1st round by paying RiR_i

  • Mi+1c1MiM_{i+1}\leq c_1 M_i for all ii, for some c1R>0c_1\in \mathbb{R}_{>0}.

  • Mi2M_i\geq 2 for all ii.

  • Ric2Mi+1pR_i\leq c_2M_{i+1}p for some c2R>0c_2\in\mathbb{R}_{>0}.

  • d>c3pd>c_3p, where c3=max{2c1c21,0}c_3=\max\left\{2c_1c_2-1,0\right\}.

Note that the constraints that make up the property of being “well-designed” are generally not very burdensome. Indeed, the designer of a Schelling game based system can ensure that the MiM_i grow reasonably and require sufficiently large deposits relative to payoffs so that these types of constraints hold before and independent of any eventual attacks.

Then we see:

Proposition 7.4: Suppose that an attacker with a budget drawn from a distribution fBf_B launches a p+ϵp+\epsilon attack on a multi-round redistributive Schelling game such as in Definition 2.3 where round ii voters can trigger an appeal from the iith round to the i+1i+1st round by paying RiR_i. Suppose:

  • Voters satisfy *. Moreover, fBf_B is common knowledge to voters.

  • (Mi,Ri,c1,c2,c3,d,p)(M_i,R_i,c_1,c_2,c_3,d,p) are well-designed.

  • l1<ll_{**}-1<l_{***}, and in particular ll_{**} is finite.

Then there exists a pure strategy equilibrium where some (distinguished) voter executes Counterattack 1 (i.e., takes the bribe and then appeals) in each round until the attacker is no longer capable of locking up sufficient resources to continue the bribe. Hence, in this equilibrium, the honest choice XX eventually wins with probability 11.

PROOF: We consider a situation where all voters vote YY in any round where the p+ϵp+\epsilon bribe is available, and one (distinguished) voter per round performs Counterattack 1. Hence, this voter pays

Ric2Mi+1pc1c2MipR_i\leq c_2M_{i+1}p\leq c_1c_2M_{i}p

to cover any required appeal fee in the iith round. We will show that this situation is an equilibrium.

Eventually, one arrives at a round (at latest ll_{**}) where the attacker can no longer fund her attack. At this point, XX wins. In any round where the p+ϵp+\epsilon attack bribes are available, voting XX, whether subsequently appealing or not, is a dominated strategy.

Hence, we need only consider the strategies of voting YY and performing Counterattack 1, or voting YY without performing Counterattack 1. Then, following the payoffs shown in Definition 2.3, participants receive (Mi1)d+Mip+ϵ(M_i-1)d+M_ip+\epsilon if YY wins from the coherence game and the bribe.

For the distinguished voter, if she deviates from the equilibrium by not paying the appeal fees, YY wins and her total payoff is pp. On the other hand, if she pays the appeal fees and all of the other voters follow the candidate equilibrium, XX wins and the distinguished voter receives a total payoff of (Mi1)d+Mip+ϵRi(M_i-1)d+M_ip+\epsilon-R_i. Hence, as d>(2c1c21)pd>(2c_1c_2-1)p and Mi2M_i\geq 2,

(Mi1)(2c1c21)p+Mipc1c2Mipp\geq(M_i-1)(2c_1c_2-1)p+M_ip-c_1c_2M_ip\geq p

so the distinguished voter does not have an incentive to deviate.

Similarly, we take the perspective of one of the non-distinguished voters. Then, as long as the other participants do not deviate from the candidate equilibrium, XX wins regardless. Hence, if the non-distinguished voter does not counterattack, she receives (Mi1)d+Mip+ϵ(M_i-1)d+M_ip+\epsilon, and if she counterattacks she receives (Mi1)d+Mip+ϵRi(M_i-1)d+M_ip+\epsilon-R_i. Hence, the non-distinguished voters also do not have an incentive to deviate.


Nonetheless, the equilibrium of Proposition 7.4 seems to be one that voters would be unlikely to fall into. Specifically, coordinating on which voters should be distinguished to pay the appeal fees seems difficult, as while each voter would prefer that some voter perform Counterattack 1, they would prefer that it not be themselves. Here equilibrium formation is particularly difficult as one does not necessarily have repeated games where the same groups of voters are offered a p+ϵp+\epsilon bribe again. As users have the additional infrastructure of smart contracts at their disposal, one might propose that the voters in each round place a deposit in a smart contract that chooses one of them and obliges her to pay the appeal fees; however, this merely displaces the issue as then voters prefer that some voter(s) join this smart contract coordination while preferring it to not be themselves.

In fact, in any given appeal round, our situation is similar to a multiplayer version of the anti-coordination game of “Chicken” [31]; Proposition 7.4 essentially corresponds to the pure strategy equilibria observed in that game. In the following result we see that there can also exist a mixed strategy equilibrium where voters (of a given appeal round) act symmetrically, each paying the required appeal fees with some positive probability, mirroring the mixed equilibria observed in “Chicken.” Due to its symmetry, this equilibrium seems somewhat more natural and more likely to arise in practice than that of Proposition 7.4. However, in order to show that voters will estimate the probability of the counterattack continuing a sufficient number of rounds beyond their own as being high enough that the counterattack is likely to eventually succeed and that it is worthwhile for them to counterattack themselves, we will require additional assumptions on the budget of the attacker.

Theorem 7.5: Suppose that an attacker with a budget drawn from a distribution fBf_B launches a p+ϵp+\epsilon attack on a multi-round redistributive Schelling game such as in Definition 2.3 where round ii voters can trigger an appeal from the iith round to the i+1i+1st round by paying RiR_i. Suppose:

  • Voters satisfy *. Moreover, fBf_B is common knowledge to voters.

  • (Mi,Ri,c1,c2,c3,d,p)(M_i,R_i,c_1,c_2,c_3,d,p) are well-designed.

  • l1<ll_{**}-1<l_{***}, and in particular ll_{**} is finite.

  • The distribution fBf_B is such that for all i,jNi,j\in\mathbb{N} such that iji\leq j and il2i\leq l_{**}-2,

    Prob(L>j+1L>i+1)Prob(L>jL>i).\text{Prob}(L_*>j+1|L_*>i+1)\leq \text{Prob}(L_*>j|L_*>i).(2)

Then there exists a (potentially mixed strategy) equilibrium where each voter USRk\mathcal{USR}_k in round ii funds appeals following Counterattack 1 with some probability yi,k[0,1]y_{i,k}\in [0,1], and

Prob(X wins)supl0N3{Tl0},\text{Prob}(X\text{ wins})\geq \sup_{l_{0}\in\mathbb{N}_{\geq 3}}\left\{T_{l_0} \right\}, (3)



Here if Prob(L>l0L>2)=1\text{Prob}(L_*>l_0|L_*>2)=1, for the sake of computing the supremum, we take the convention that Tl0=T_{l_0}=-\infty. However, as l<l_{**}<\infty, there is some value of l0l_0 for which this supremum is finite. Indeed, for dd sufficiently large relative to pp, c1c_1, c2c_2, and the distribution fBf_B, Inequality 3 is positive. We will explore this bound in the case of specific distributions that satisfy Inequality 2 in Corollary 7.7.

Remark 7.6: While this counterattack requires no altruism on the part of its participants, there are several practical limitations to this approach. The amounts that the counterattackers must pay in appeal fees become quite large (though much smaller than the budget required by the attacker as in Proposition 6.1), and may exceed the liquidity or risk thresholds of the counterattackers. Also, an important element of this scheme is that counterattackers are incentivized to appeal because their potential reward is very large relative to the chance that their individual participation changes the result. This depends on the “lone voice of reason jackpot” that comes from all voters in these rounds voting YY. In practice, a substantial number of voters will vote XX (and not appeal) either because they are altruistic or they are unaware of the attack. To the degree that the percentage of such voters is significant but less than half, this actually works against the counterattack.

Before we continue with the proof of this result, we see that the condition of Theorem 7.5 given by Inequality 2 is satisfied by reasonable models of the attacker’s budget. Indeed, one expects that the size of the budget for an attack roughly corresponds to the attacker’s overall wealth. Then, standard, classical models for distributions of wealth are given by Pareto distributions [32]. More recent work [33] suggests that wealth is distributed exponentially for less wealthy segments of the population and then distributed by a Pareto distribution in the tail of wealthier individuals. As the p+ϵp+\epsilon attack requires large budgets (see Proposition 6.1), we would expect that attackers would already be a wealthier subpopulation of the general population of users; hence, in the following corollary and example, we will model attacker wealth by a Pareto distribution for simplicity. Nonetheless, as we have assumed a maximum appeal round, l1l_{**}-1, at which the attacker can possibly commit to paying the bribe, a model for the attacker budget with an infinite tail is inappropriate. However, as such upper bounds often arise in practical applications, truncated versions of the Pareto distribution have also been widely applied [34]. Then, we consider truncated Pareto distributions:

Prob(Bx)={αLαxα11(LH)α,for LxH0,for x>H},\text{Prob}(B\geq x)=\left\{\begin{array}{lr} \frac{\alpha L^{\alpha}x^{-\alpha-1}}{1-\left(\frac{L}{H}\right)^\alpha}, & \text{for } L\leq x\leq H\\ 0, & \text{for } x>H\\\end{array}\right\},

for 0<L<H0<L<H. We see that such distributions satisfy Inequality 2. Note that the lower bound we obtain on Prob(X wins)\text{Prob}(X \text{ wins}) does not depend on ll_{**}, so one can approximate a standard Pareto distribution arbitrarily well.

Corollary 7.7: Let fBf_B be a truncated Pareto distribution with support between LL and HH and α>0.\alpha>0. Then suppose that an attacker with a budget drawn from the distribution fBf_B launches a p+ϵp+\epsilon attack on a multi-round redistributive Schelling game such as in Definition 2.3 where round ii voters can trigger an appeal from the iith round to the i+1i+1st round by paying RiR_i. Suppose:

  • Voters satisfy *. Moreover, fBf_B is common knowledge to voters.

  • (Mi,Ri,c1,c2,c3,d,p)(M_i,R_i,c_1,c_2,c_3,d,p) are well-designed.

  • c1N>1c_1\in\mathbb{N}_{>1}, and Mi+1=c1MiM_{i+1}=c_1 M_i for all ii (i.e., the number of voters grows exactly exponentially).

  • l1<ll_{**}-1<l_{***}, and in particular ll_{**} is finite.

  • There exist constants d1,d2R0d_1,d_2\in\mathbb{R}_{\geq 0} such that the additional budget to continue the attack, from the i1i-1st round to the iith round, is exactly d1Mi2+d2Mid_1M_i^2+d_2M_i for all ii.

Then there exists a (potentially mixed strategy) equilibrium where each voter USRk\mathcal{USR}_k in round ii funds appeals following Counterattack 1 with some probability yi,k[0,1]y_{i,k}\in [0,1], and

Prob(X wins)supl0N3{1ql0α+12c1c2p(l02)(d+p)(1ql0α+1)},\text{Prob}(X\text{ wins})\geq\sup_{l_{0}\in\mathbb{N}_{\geq 3}}\left\{ 1-q_{l_0}^{\alpha+1}-\frac{2c_1c_2p(l_{0}-2)}{(d+p)\left(1-q_{l_0}^{\alpha+1} \right)} \right\},

where we compute the explicit quantity

d1M1c121c14+d21c11c12d1M1c12(1+1c121)d21c1(1+1c11)d1M1c121c12l0+d21c11c1l0d1M1c12(1+1c121)d21c1(1+1c11).\frac{d_1\frac{M_1}{c_1^2-1}c_1^4+d_2\frac{1}{c_1-1}c_1^2-d_1\frac{M_1}{c_1^2}\left(1+\frac{1}{c_1^2-1}\right)-d_2\frac{1}{c_1}\left(1+\frac{1}{c_1-1}\right) }{d_1\frac{M_1}{c_1^2-1}c_1^{2l_0}+d_2\frac{1}{c_1-1}c_1^{l_0}-d_1\frac{M_1}{c_1^2}\left(1+\frac{1}{c_1^2-1}\right)-d_2\frac{1}{c_1}\left(1+\frac{1}{c_1-1}\right)}.

Moreover, if d1=d+pd_1=d+p and d2=ϵd_2=\epsilon (namely the additional budget of continuing the attack in the iith round corresponds exactly to the amounts required for the bribes seen in Proposition 6.1), by minimizing the bound over all choices of ϵ\epsilon, we see

Prob(X wins)supl0N3{1q,l0α+12c1c2p(l02)(d+p)(1q,l0α+1)},\text{Prob}(X\text{ wins})\geq\sup_{l_{0}\in\mathbb{N}_{\geq 3}}\left\{ 1-q_{*,l_0}^{\alpha+1}-\frac{2c_1c_2p(l_{0}-2)}{(d+p)\left(1-q_{*,l_0}^{\alpha+1} \right)} \right\},

where we compute the explicit quantity

q,l0=max{c14c1211c12(1+1c121)c12l0c1211c12(1+1c121),1c11c121c1(1+1c11)1c11c1l01c1(1+1c11)}.q_{*,l_0}=\max\left\{\frac{\frac{c_1^4}{c_1^2-1}-\frac{1}{c_1^2}\left(1+\frac{1}{c_1^2-1}\right)}{\frac{c_1^{2l_0}}{c_1^2-1}-\frac{1}{c_1^2}\left(1+\frac{1}{c_1^2-1}\right)},\frac{\frac{1}{c_1-1}c_1^2-\frac{1}{c_1}\left(1+\frac{1}{c_1-1}\right)} {\frac{1}{c_1-1}c_1^{l_0}-\frac{1}{c_1}\left(1+\frac{1}{c_1-1}\right)} \right\}.

The proof of this corollary depends on showing that the tail of the distribution decreases quickly enough that Inequality 2 holds in this case. Then one computes the explicit lower bound given by Inequality 3. This is a rather straightforward calculus argument, and is included in Appendix A for completeness.

Example 7.8: Take the attacker budget to be distributed by a truncated Pareto distribution with α=2.1\alpha=2.1 - the value observed for UK wealth distribution in [33], the cost of continuing the attack from the i1i-1st to the iith round to be exactly the Mi2(d+p)+MiϵM_i^2(d+p)+M_i\epsilon that must be locked up in bribes according to Proposition 6.1, c1=c2=2c_1=c_2=2, and d15pd\geq 15p. Then, one can use Corollary 7.7 with l0=3l_0=3 to see that, in the mixed strategy equilibrium, XX wins and the attack fails at least 39.1%39.1\% of the time. This implies a corresponding non-zero lower bound on the expected cost of the attack. Note that this lower bound holds for any ϵ\epsilon that the attacker might choose. Generally, there is a trade-off that can be made between decreasing the size of the deposit dd relative to pp and increasing the lower bound on the rate at which p+ϵp+\epsilon attacks fail, see Figure 4.

Figure 4: We show lower bounds on the rate of failure of p+ϵp + \epsilon attacks against redistributive Schelling games in the mixed strategy equilibrium of Corollary 7.7 when α=2.1,c2=2,\alpha = 2.1, c_2 = 2, and for varying choices of c1c_1. In general there is a trade-off between increasing the required deposits (relative to payoffs) and attack resistance. We see that for d/p=10d/p = 10 we already have meaningful lower bounds on the rate of attack failure.

We will now develop the tools necessary to prove Theorem 7.5, fixing the following notation that we will use towards this proof over the remainder of this section. We will show the existence of yi,k[0,1]y_{i,k}\in [0,1] such that each voter in round ii, USRk\mathcal{USR}_k, choosing to trigger an appeal with probability yi,ky_{i,k} gives rise to the equilibrium discussed in the statement of Theorem 7.5. For given choices of yi,ky_{i,k}, we denote by

KikBernoulli(yi,k)K_i\sim \sum_{k}\text{Bernoulli}(y_{i,k})

the number of participants who choose to appeal in the iith round. Similarly, fixing a distinguished participant USR=USRk0\mathcal{USR}=\mathcal{USR}_{k_0}, we denote by

Kikk0Bernoulli(yi,k)K'_i\sim \sum_{k\neq k_0}\text{Bernoulli}(y_{i,k})

the number of participants other than USR\mathcal{USR} who choose to appeal in the iith appeal round.

Moreover, again from the perspective of USR\mathcal{USR} who considers counterattacking, namely triggering an appeal, in round ii, we denote

Pi(XC)=Prob(Xin ith round andwinsUSR counterattacks)P_i(X|C)=\text{Prob}\left(\begin{array}{ccc} X &|& \text{in }i\text{th round and} \\ \text{wins}&|&\mathcal{USR}\text{ counterattacks} \end{array} \right)
Pi(XN)=Prob(Xin ith round and USRwinsdoesn’t counterattack),P_i(X|N)=\text{Prob}\left(\begin{array}{ccc} X &|& \text{in }i\text{th round and }\mathcal{USR}\\ \text{wins}&|&\text{doesn't counterattack} \end{array} \right),


In any round where the p+ϵp+\epsilon attack bribes are available, voting XX, whether followed by counterattacking or not, is a dominated strategy, so it can be excluded. Hence, we can calculate the payoff table for USR\mathcal{USR} given by appealing in round ii versus not counterattacking under the assumption that there are zero votes for XX, see Figure 5.

Figure 5: Payoffs for executing Counterattack 1 versus not counterattacking and voting YY, once the possibility of voters voting XX, a dominated strategy, has been eliminated.

Note the RiR_i^* in the payoff table, which are given by the partial appeal payments of Equation 1. These values implicitly depend on the yi,k;y_{i,k}; however, by our assumptions on our appeal fee models this dependence is continuous. Also note that we have 0RiRi0\leq R_i^*\leq R_i.

We begin with a lemma that explores the probability that at least one voter appeals in a given round. In all of the following lemmas we assume that voters satisfy * and that fBf_B is common knowledge to voters.

Lemma 7.9: Let iNi\in\mathbb{N}, il1i\leq l_{**}-1. Suppose that (Mi,Ri,c1,c2,c3,d,p)(M_i,R_i,c_1,c_2,c_3,d,p) are well-designed, and

Prob(Xin jthwinsround)>Rj1(Mj11)(d+p)+ϵ\text{Prob}\left(\begin{array}{ccc} X &|& \text{in }j\text{th} \\ \text{wins}&|&\text{round} \end{array} \right)> \frac{R_{j-1}}{(M_{j-1}-1)(d+p)+\epsilon}

holds for all jNj\in\mathbb{N} such that i<jli<j\leq l_{**}. Then, Pi(XC)>0P_i(X|C)>0, and there exists an equilibrium where each round ii voter USRk\mathcal{USR}_k triggers an appeal independently with probability yi,ky_{i,k} for some yi,k[0,1]y_{i,k}\in[0,1], with

Prob(Ki1)1Ri((Mi1)(d+p)+ϵ)Pi(XC).\text{Prob}(K_i\geq 1)\geq 1-\frac{R_i}{\left((M_i-1)(d+p)+\epsilon\right)P_i(X|C)}.

PROOF: Take USR\mathcal{USR} a voter in round ii. We calculate the expected returns from USR\mathcal{USR}’s perspective using the payoffs of Figure 5:

E[Do not counter-attack in ith round]E\left[\begin{array}{c} \text{Do not counter-} \\ \text{attack in }i\text{th round} \end{array} \right]


E[Counterattackin ith round]E\left[\begin{array}{c} \text{Counterattack} \\ \text{in }i\text{th round} \end{array} \right]
=Ri+Pi(XC)((Mi1)d+Mip+ϵ)+Pi(YC)p.= -R_i^*+P_i(X|C)((M_i-1)d+M_ip+\epsilon)+P_i(Y|C)p.

The system is structured so that a single counterattacker in the iith round is sufficient for the game to progress to the i+1i+1st round. Then

Pi(XC)=Prob(Xin i+1stwinsround).P_i(X|C)=\text{Prob}\left(\begin{array}{ccc} X &|& \text{in }i+1\text{st} \\ \text{wins}&|&\text{round} \end{array} \right).

In particular, our assumed lower bound for j=i+1j=i+1 gives us Pi(XC)>0P_i(X|C)>0.


Pi(XN)=Prob(progress toUSR does noti+1st roundcounterattack)P_i(X|N)=\text{Prob}\left(\begin{array}{ccc} \text{progress to}& | & \mathcal{USR}\text{ does not} \\ i+1\text{st round}&|& \text{counterattack}\end{array}\right)
Prob(Xin i+1stwinsround)\cdot \text{Prob}\left(\begin{array}{ccc}X & | & \text{in }i+1\text{st} \\ \text{wins}&|&\text{round}\end{array}\right)
=Prob(Ki1)Pi(XC)=\text{Prob}(K'_i\geq 1)P_i(X|C)

We define

f(yi,1,,yi,N)f(y_{i,1},\ldots, y_{i,N})
=E[Counterattackin ith round]E[Do not counter-attack in ith round].=E\left[\begin{array}{c} \text{Counterattack} \\ \text{in }i\text{th round} \end{array} \right]-E\left[\begin{array}{c} \text{Do not counter-} \\ \text{attack in }i\text{th round} \end{array} \right].

Then, if USR\mathcal{USR} believes that no other participant will counterattack, she estimates

f(0,,0)=E[Counterattackin ith round]E[Do not counter-attack in ith round]f(0,\ldots, 0)=E\left[\begin{array}{c} \text{Counterattack} \\ \text{in }i\text{th round} \end{array} \right]-E\left[\begin{array}{c} \text{Do not counter-} \\ \text{attack in }i\text{th round} \end{array} \right]
=Pi(XC)[((Mi1)(d+p)+ϵ)]Ri0,=P_i(X|C)\left[((M_{i}-1)(d+p)+\epsilon)\right]-R_{i}^*\geq 0,

where the last inequality uses

Pi(XC)=Prob(Xin i+1stwinsround)P_i(X|C)=\text{Prob}\left(\begin{array}{ccc} X &|& \text{in }i+1\text{st} \\ \text{wins}&|&\text{round} \end{array} \right)
Ri[((Mi1)(d+p)+ϵ)]Ri[((Mi1)(d+p)+ϵ)].\geq \frac{R_{i}}{\left[((M_{i}-1)(d+p)+\epsilon)\right]}\geq \frac{R^*_{i}}{\left[((M_{i}-1)(d+p)+\epsilon)\right]}.

Note that Pi(XC)P_i(X|C) is independent of yi,ky_{i,k} for all kk (though it depends on the yj,ky_{j,k}