Mechanism Design for Blockchain Order Books against Selfish Miners

Lingjie Duan; Yunshu Liu

REVIEW 3 major objections 4 minor 43 references

Mechanism Design for Blockchain Order Books against Selfish Miners

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper argues that a simple choice of block size — the largest index with buyer utility at least seller cost — bounds the price of anarchy by the quantity ratio and makes homogeneous-quantity trading (NFTs) socially optimal.

desk verdict Real mechanism-design idea and substantial analysis, but the PoA theorem relies on zero-fee equilibria that contradict the paper's own zero-fee rejection rule. read the letter →

arxiv 2501.12576 v1 pith:3GVLYUMU submitted 2025-01-22 cs.GT cs.HCcs.NI

Yunshu Liu , Lingjie Duan This is my paper · ORCID

classification cs.GTcs.HCcs.NI MSC 91A1091B2691A80

keywords blockchainorderbookpriceofanarchyselfishminersmechanismdesignadjustableblocksizemixed-strategyNashequilibriumsocialwelfareNFTtrading

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Blockchain order books let miners choose which buyer-seller pairs to match, and miners maximize fees, not social welfare. This paper tries to prove that this can drive social welfare arbitrarily far from optimal — price of anarchy infinite — and that a one-parameter fix restores performance: set the block size to the number of mutually profitable matches. If the analysis is right, a system designer who knows only the distribution of utilities, costs, and quantities can choose a block size so that the worst equilibrium is within a bounded factor of the optimum, and exactly optimal when every trade involves the same quantity, as in NFT markets. The paper also reports an Ethereum testbed implementation and dataset experiments supporting these bounds.

What carries the argument

The load-bearing object is the threshold block size $A_{\mathrm{th}}$, the number of buyer-seller pairs for which the buyer's utility is at least the seller's cost. It is paired with a two-stage game: in Stage I buyers and sellers choose fees (pure Nash equilibrium when $A \geq A_{\mathrm{th}}$, mixed-strategy equilibrium when $A < A_{\mathrm{th}}$), and in Stage II miners select the top-fee feasible matching under block size $A$. The PoA bounds follow from evaluating social welfare at these fee equilibria and comparing with the welfare-maximizing matching, using the fact that the threshold block size admits only profitable pairs into the first block.

What would settle it

A concrete check: in the Case 3 regime $R_{j+1} < C_1$, equations (99)-(100) set all fees to zero; since the model assumes miners reject zero-fee transactions, the resulting outcome has no trades, so the profile is not a Nash equilibrium unless a positive-fee equilibrium is constructed. Simulating this two-buyer two-seller regime under $A = A_{\mathrm{th}}$ would settle whether the PoA bound holds there.

Watch

Extended reading notes

Core claim

Under the proposed adjustable block size (ABS) mechanism, the system designer sets $A = A_{\mathrm{th}}$, where $A_{\mathrm{th}}$ is the largest $i$ with $R_i \geq C_i$ (or $\min\{K,N\}$ if all pairs are profitable). Theorem 3 states that for complete information the price of anarchy is at most $\bar{b}/\underline{b}$, where $\underline{b}$ and $\bar{b}$ are the lower and upper bounds on traded quantities, and exactly $1$ when $\underline{b} = \bar{b}$ (homogeneous quantities). Theorem 4 extends this to incomplete information: with i.i.d. values, using $A^* = \lfloor N(C(\eta) + N^{-\psi}) \rfloor$ gives the same bounds as $N \to \infty$. The social optimum is restored for homogeneous-quantity trading because the threshold block size excludes exactly the unprofitable pairs that a fee-maximizing miner would otherwise include to collect extra fees.

Load-bearing premise

The entire price-of-anarchy bound rests on the Stage I fee-setting equilibria in Propositions 1 and 2 being valid for every parameter regime; in the regime of Theorem 3 where $R_{j+1} < C_1$, the proposed all-zero fees contradict the assumption that miners reject zero-fee transactions, so no valid trade occurs unless another equilibrium is supplied.

Editorial extensions

If this is right

If Theorem 3 is correct, any blockchain order book that sets block size to $A_{\mathrm{th}}$ guarantees at least a $\underline{b}/\bar{b}$ fraction of optimal social welfare even with fully selfish miners.
For homogeneous-quantity markets such as NFT trading, the mechanism is exactly socially optimal, not merely approximately so.
If Theorem 4 is correct, the designer needs no individual bid/ask data — only the distributions — and the same guarantees hold asymptotically.
The mechanism changes only the block size (gas limit in Ethereum), so it can be deployed without altering consensus, matching, or fee rules.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's threshold rule assumes the fee-setting equilibrium in every regime; the zero-fee case of Theorem 3 ($R_{j+1} < C_1$) is the regime most likely to need a tie-breaking or minimum-fee rule in practice.
An implicit consequence is that the price-of-anarchy bound degrades linearly with quantity dispersion $\bar{b}/\underline{b}$, so the mechanism is most attractive in markets where order sizes are similar; for high dispersion, quantity-aware matching rules would be a natural next step.
The asymptotic result suggests a testable prediction: in large NFT-like markets, the observed social welfare ratio should approach 1 as the number of sellers grows, and the benchmark gap should persist.
The mechanism's robustness to non-selfish miners is only tested empirically at 20% cooperation; a natural extension is to characterize the PoA as a function of the fraction of protocol-following miners.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The core idea is genuinely good: set the block size to the number of profitable buyer-seller pairs, and a simple protocol parameter fixes the welfare disaster caused by myopic miners. The paper is the first analytical treatment of selfish miners in blockchain order books, and the infinite-PoA diagnosis is nicely constructed. The game-theoretic setup with PSNE and MSNE, plus the incomplete-information extension, is substantial. The experiments with real NFT and Bitcoin data are a useful sanity check, and the reported 3.7x improvement over the benchmark is plausible if the mechanism works.

The soft spot is load-bearing. Theorem 3's proof constructs fee profiles with zero fees on one or both sides in several cases (equations 93, 95, 99-100). But Section II-A3 explicitly states that miners reject zero-fee transactions. Under the paper's own rule, those profiles are not Nash equilibria: in Case 3 with all-zero fees, no trade occurs and welfare is zero, not the claimed social optimum. The PoA=1 and PoA≤b/b results are therefore not established as written. This is fixable—one can likely supply positive fees or relax the zero-fee assumption—but it cannot be ignored.

Other issues are smaller. Theorem 4 uses asymptotic normal approximations for order statistics without stating regularity conditions on the CDFs; the proof has some algebraic leaps around the variance terms. The implementation section demonstrates adjusting the gas limit on Ethereum, which shows feasibility of block-size changes, but it does not test the matching equilibrium itself. The citation pattern looks fair; self-citations are not a problem here. There is no circularity: the PoA bounds come from equilibrium analysis, not from fitting parameters to the welfare results.

Who should read this: anyone working on mechanism design for DEXs, NFT markets, or blockchain transaction-fee design. The paper deserves a careful referee despite the flaw, because the central idea is important and the error appears localized and repairable. My recommendation: send it to peer review, but require a corrected Theorem 3 with valid fee profiles (or an explicit relaxation of the zero-fee rejection rule) before acceptance.

Referee Report

3 major / 4 minor

Summary. The paper analyzes blockchain-based order books as a two-stage game in which buyers and sellers set transaction fees and a selfish miner matches transactions. It claims the first analytical quantification of the efficiency loss caused by selfish miners, proving an unbounded price of anarchy (PoA), and then proposes an adjustable block size mechanism. The main theoretical results are Theorem 3, which states that setting block size A = Ath gives PoA ≤ b/b in general and PoA = 1 for homogeneous quantities, and Theorem 4, which extends the bound to an incomplete-information limit with N → ∞. The paper also reports an Ethereum testbed implementation and experiments on NFT and Bitcoin order-book datasets, claiming social optimum for homogeneous-quantity trading and up to 3.7× welfare improvement over a benchmark.

Significance. The problem is relevant and the proposed mechanism is attractively simple: it only adjusts a protocol parameter and does not require changing the matching protocol. If the main theorems were fully correct, the paper would be a solid contribution to mechanism design for decentralized exchanges. The Ethereum implementation and the use of real datasets are genuine strengths, as is the attempt to handle mixed-strategy equilibria. However, the proof gaps discussed below directly affect the central claims, so the paper is not yet at the standard required for acceptance.

major comments (3)

[Section VII-D-1 (Theorem 3 proof), eqs. (92)-(100)] Theorem 3's proof constructs Stage I equilibria in which all fees on at least one side are zero: Case 2(b) sets f_sell,n = 0 for all sellers (eq. (93)), Case 2(c) sets f_buy,k = 0 for all buyers (eq. (95)), and Case 3 sets all fees to zero when R_{j+1} < C1 and C_{j+1} > R1 (eqs. (99)-(100)). This contradicts the model assumption in Section II-A3 that miners reject zero-fee transactions. In the all-zero regime of Case 3, the Stage II problem (16) selects no transaction at all, so the resulting social welfare is 0, not the claimed swopt in eq. (101). The same problem invalidates the assumed matching in Cases 2(b) and 2(c), because zero-fee buyers or sellers cannot be included in a miner's selection. Since these regimes are explicitly covered by Theorem 3, the claims PoA = 1 and PoA ≤ b/b are unsupported as written. The authors need to supply valid strictly-positive fee equilibria with a complete no-deviation check, or explicitly relax the zero-fee rejection assumption and recompute the welfare consequences.
[Section VII-D-2 (Theorem 4 proof), eqs. (125)-(151)] The proof of Theorem 4 relies on asymptotic approximations for which the required regularity conditions are not stated in the theorem. Equation (125) defines ρ = K/N and the subsequent order-statistics approximations (e.g., eqs. (130)-(131), (178)-(179), (183)-(186), (196)-(199)) assume that ρ is a fixed positive constant and that the relevant quantiles are interior points where the densities of R and C are positive and continuous. The theorem statement only says 'N → ∞' and does not state that K scales with N, nor does it state conditions such as C(η) + N^{-ψ} < 1 and C(η) - N^{-ψ} > 0 for all sufficiently large N. Without these assumptions, the normal approximations, the variance formulas, and the limits in eqs. (44)-(45) are not justified. The theorem should either include explicit distributional and scaling assumptions or prove the bounds by a different method that is robust to the unstated cases.
[Sections IV-B and VII-D-2 (Theorem 4)] Theorem 4 inherits the zero-fee equilibrium problem from Theorem 3 because its welfare computation evaluates the social welfare at the complete-information fee equilibria of Propositions 1 and 2 for the realized buyer and seller types. If those equilibria are not valid in the zero-fee regimes identified above, then the social-welfare lower bounds in the proof of Theorem 4 (e.g., eqs. (204)-(218)) are not grounded. Thus Theorem 4 cannot be accepted until the equilibrium issue raised in the first major comment is resolved.

minor comments (4)

[Section VII-C, proof of Theorem 2] In the high-block-size example, the text says the social optimum is to match R1 with C2, but the displayed inequalities imply that the optimum match is R1 with C1; the ratio should be (R1 - C1)/(4ϵ) rather than (R1 - C2)/(4ϵ). In addition, the limit in eq. (88) should be taken as d approaches (R1+R2-C1-C2)/2 from below, not from above. These are typos, but they should be corrected because they obscure the construction.
[Section II-A-2, Theorems 3 and 4] The notation b/b requires the lower bound b on trading quantities to be strictly positive. The model only states bk, qn ∈ [b,b] and does not explicitly assume 0 < b ≤ b; without this assumption, the bound PoA ≤ b/b is not well-defined. Please add the strict positivity assumption explicitly.
[Equations (35), (36) and Lemma 2.4] There is an inconsistency in the upper support of the mixed-strategy distributions: eq. (35c) and eq. (36c) use (⌈ min{...}/A⌉ - 1)d, while Lemma 2.4 and the derivation around eqs. (79)-(80) use ⌈ min{...}/A⌉ d. This discrepancy affects the explicit form of the CDF and should be reconciled.
[Proof of Theorem 1] The proof of Theorem 1 is only a short sketch and does not fully justify why the argmax in (24) must be a top-i fee set in the presence of constraint (5). Since the later analysis verifies candidate equilibria under this selection rule, I do not treat this as a blocking issue, but the proof should be expanded.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the PoA bounds are derived from equilibrium computations, not from fitting or from load-bearing self-citation.

full rationale

The paper's derivation chain is essentially self-contained and non-circular. The PoA bounds in The Theorems 3 and 4 are obtained by (i) solving the Stage II miner optimization (Theorem 1), (ii) characterizing Stage I fee-setting PSNE/MSNE (Propositions 1 and 2), and (iii) computing social welfare at those fee profiles. The block sizes Ath and A* are defined from the primitives, R, C, K, N, and the distributional equation N C(eta)=K(1-R(eta)), not fitted to the welfare outcomes. The homogeneous-quantity PoA=1 and heterogeneous PoA<=b/b follow from the explicit quantity bounds b<=min{bk,qn}<=b and the threshold structure of Ath, as shown in the inequalities around eqs. (102)-(116); they are not identities built into the definition of the mechanism. The few self-citations (e.g., [24] for the horizon assumption T>=min{N,K}/A, and [36] for dynamic matching) are background modeling assumptions and are not load-bearing for the PoA theorems. A separate correctness concern is that Theorem 3's equilibrium candidates with zero fees in eqs. (93), (95), and (99)-(100) conflict with the paper's own rule that miners reject zero-fee transactions (Section II-A3); this invalidates those NE claims in some parameter regimes, but it is an equilibrium-validity gap, not a circular reduction where the conclusion is assumed in the inputs. Therefore the circularity score is low.

Assumptions & free parameters 2 free parameters · 7 assumptions · 0 invented entities

No invented entities. The central results introduce no new physical or mathematical objects beyond a block-size choice rule. The main uncharged inputs are the behavioral assumptions about miners and the regularity assumptions on distributions.

free parameters (2)

psi (block-size redundancy exponent) = 0.85 in experiments
Algorithm 2 sets A*=floor(N(C(eta)+N^{-psi})); the theorem holds for any psi in (0,1), so it is a design knob rather than a fitted constant, but the experiments fix psi=0.85.
bid-to-utility/cost ratio 1.05 = 1.05
Experimental mapping from bid/ask prices to utilities/costs follows reference [39]; not used in theoretical bounds, only in simulations.

assumptions (7)

domain assumption Miners are myopic and maximize only current-block fee revenue; they do not strategize over future blocks.
Section II-A3, based on cited practice; used to derive Theorem 1 and all equilibrium analysis. If miners were forward-looking, equilibria change.
domain assumption Miners reject zero-fee transactions and break ties among equal-fee matches randomly.
Section II-A3; required for the PSNE/MSNE fee gaps and for the claim that high-fee buyers/sellers get matched.
domain assumption Buyers and sellers trade at the mid-price (R_k+C_n)/2 with quantity min{b_k,q_n}; fees are per transaction and independent of quantity.
Section II-B2; this makes fees pure transfers and simplifies miner objective to total fee count.
domain assumption Valuations, costs, and quantities are static and either public or drawn from known distributions.
Section II-B5 and IV-B; the incomplete-information mechanism relies on the designer knowing R(.), C(.), B(.), Q(.) exactly.
domain assumption R(.) and C(.) have positive densities and equation (42) has a unique interior solution eta.
Algorithm 2 and proof of Theorem 4 use central order statistics with normal approximations and require smoothness; not stated as a theorem hypothesis.
standard math Central order statistics are asymptotically normal under the assumed distributions.
Used in the proof of Theorem 4 to estimate the probability that the i-th buyer's value exceeds the i-th seller's cost; the paper cites a standard note but does not state the regularity conditions.
domain assumption T >= min{N,K}/A so that all transactions can eventually be recorded.
Footnote 2; needed to define delay cost and equilibrium payoffs.

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Cite this review

Pith. "Pith review of Mechanism Design for Blockchain Order Books against Selfish Miners." pith.science (2026). https://pith.science/paper/3GVLYUMU

@misc{pith2026250112576,
  author       = {Pith},
  title        = {Pith review of: Mechanism Design for Blockchain Order Books against Selfish Miners},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3GVLYUMU}},
  note         = {Machine review of arXiv:2501.12576}
}

read the original abstract

In blockchain-based order book systems, buyers and sellers trade assets, while it is miners to match them and include their transactions in the blockchain. It is found that many miners behave selfishly and myopically, prioritizing transactions with high fees and ignoring many desirable matches that could enhance social welfare. Existing blockchain mechanisms fail to address this issue by overlooking miners' selfish behaviors. To our best knowledge, this work presents the first analytical study to quantify and understand buyer and seller transaction fee choices and selfish miners' transaction matching strategies, proving an infinitely large price of anarchy (PoA) for social welfare loss. To mitigate this, we propose an adjustable block size mechanism that is easy to implement without altering the existing decentralized protocols and still allows buyers and sellers to freely decide transaction fees and miners to selfishly match. The analysis is challenging, as pure strategy Nash equilibria do not always exist, requiring the analysis of many buyers' or sellers' interactive mixed-strategy distributions. Moreover, the system designer may even lack information about each buyer's or seller's bid/ask prices and trading quantities. Nevertheless, our mechanism achieves a well-bounded PoA, and under the homogeneous-quantity trading for non-fungible tokens (NFT), it attains a PoA of 1 with no social welfare loss. We implement our mechanism on a local instance of Ethereum to demonstrate the feasibility of our approach. Experiments based on the realistic dataset demonstrate that our mechanism achieves social optimum for homogeneous-quantity trading like NFT. It can enhance social welfare up to 3.7 times compared to the existing order book benchmarks for heterogeneous-quantity trading of Bitcoin tokens. It exhibits robustness against random variations in buyers and sellers.

Figures

Figures reproduced from arXiv: 2501.12576 by the authors.

**Figure 2.** Buying utility and selling cost for high block size. [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Buying utility and selling cost for low block size. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

Figures from the paper (26 more)

**Figure 4.** Figure 4: The histogram of real dataset and fitted PDF. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: ABS implementation in Ethereum blockchain testbed. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: Mechanism comparison for homogeneous-quantity trading. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

**Figure 7.** Figure 7: Mechanism comparison for heterogeneous-quantity trading. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

**Figure 8.** Figure 8: Impact of the random number of users on performance ratio. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

**Figure 9.** Figure 9: Impact of block size limit on homogeneous-quantity trading. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

**Figure 10.** Figure 10: Impact of block size limit on heterogeneous-quantity trading. [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

**Figure 11.** Figure 11: Buyer’s utility and seller’s cost At Nash equilibrium of Stage I, users will set the transaction fee as in Table II. TABLE II: Stage I Equilibrium under C1 + 2ϵ = R2 < C2 = R1 − 2ϵ and A > 1 1 2 Buyer ϵ ϵ Seller ϵ ϵ For this example, the social welfare is sw = R1 + R2…

**Figure 12.** Figure 12: Buyer’s utility and seller’s cost Based on users’ fee equilibrium characterized by Propositions 1 and 2, miners must record all transactions in blockchain: 2 transactions in the first block and 2 transactions in the second block. Hence, the social welfare is sw = R1 +…

**Figure 13.** Figure 13: Buyer’s utility and seller’s cost when R1 < C1 possible match. Hence, the social optimum is zero. Any block size A also yields no match and corresponding social welfare is zero. Hence, any Ath achieves social optimum. 2) Case 2: As illustrated in [PITH_FULL_IMAGE:fig…

**Figure 14.** Figure 14: Buyer’s utility and seller’s cost when RK ≥ CN Hence, the social optimum is as follows: swopt = min X {K,N} n=1 (Rn − Cn). (89) We consider three subcases to show that Ath = min{K, N} achieves social optimum. a) If K = N, then under Ath = min{K, N}, the following stra…

**Figure 15.** Figure 15: Buyer’s utility and seller’s cost when Rj ≥ Cj and Rj+1 < Cj+1 Hence, the social optimum is as follows: swopt = X j i=1 (Ri − Ci). (98) Under Ath = j, the following strategy constitutes a NE: f buy,NE k =    0, if 1 ≤ k ≤ j and Rj+1 < C1, Pj i=1 1…

**Figure 16.** Figure 16: Buyer’s utility and seller’s cost when R1 < C1 2) Case 2: As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p038_16.png]

**Figure 17.** Figure 17: Buyer’s utility and seller’s cost when RK ≥ CN Hence, the social optimum is as follows: swopt = X n∈N X k∈K xkn min{bk, qn}(Rk − Cn) ≤ b min X {K,N} n=1 (Rn − Cn). (102) We consider three subcases to show the social welfare of setting block size as Ath = min{K, N}. a)…

**Figure 18.** Figure 18: Buyer’s utility and seller’s cost when Rj ≥ Cj and Rj+1 < Cj+1 Under Ath = k, the following strategy constitutes a NE: f buy k =    0, if 1 ≤ k ≤ j and Rj+1 < C1, Pj i=1 1(Rj+1 ≥ Ci) min{bj+1, qi}[Rj+1 − Ci ] 2 Pj i=1 1(Rj+1 ≥ Ci) + ϵ, if 1 ≤ k ≤ …

**Figure 19.** Figure 19: A = ⌊NC(η) + δN⌋ illustration. We illustrate how we set block size in [PITH_FULL_IMAGE:figures/full_fig_p041_19.png]

**Figure 20.** Figure 20: Three parts to estimate swopt . The detailed estimation is as follows: swopt = ⌊NC( X η)−δN⌋ i=1 P(Ri ≥ Ci)E[Ri − Ci |Ri ≥ Ci ] + ⌊NC X (η)+δN⌋ i=⌊NC(η)−δN⌋+1 P(Ri ≥ Ci)E[Ri − Ci |Ri ≥ Ci ] + min{ X ρN,N} i=⌊NC(η)+δN⌋+1 P(Ri ≥ Ci)E[Ri − Ci |Ri ≥ Ci ]. (127) Here we es…

**Figure 21.** Figure 21: Social welfare loss due to R2 and C2 [PITH_FULL_IMAGE:figures/full_fig_p046_21.png]

**Figure 22.** Figure 22: Social welfare loss due to R3 and C3. We can use such method to calculate all the social welfare loss due to Ri and Ci , where i = 2 to ⌊NC(η)+δN⌋. Hence the total expected social welfare loss from block 1 is swloss 1 = ⌊NC X (η)+δN⌋ i=2 swloss 1,i < ⌊NC X (η)+δN⌋ i=2…

**Figure 23.** Figure 23: Three parts to estimate swopt . As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p050_23.png]

**Figure 24.** Figure 24: Social welfare loss due to R2 and C2 [PITH_FULL_IMAGE:figures/full_fig_p055_24.png]

**Figure 25.** Figure 25: Social welfare loss due to R3 and C3 [PITH_FULL_IMAGE:figures/full_fig_p055_25.png]

**Figure 26.** Figure 26: Gas limit adjustment. 2) Running the Modified Ethereum Client: After modifying the gas limit, we run a local instance of the Ethereum blockchain using the modified Geth client in a Cygwin64 terminal. The steps to achieve this are outlined below: 1) Navigate to the Get…

**Figure 27.** Figure 27: Ethereum blockchain mining. cd /cygdrive/d/Program\ Files/go-ethereum-1.11.2.2 curl -X POST --data "{\"jsonrpc\":\"2.0\",\"method\":\"eth_ getBlockByNumber\",\"params\":[\"latest\",true],\"id\":1}" -H "Content-Type: application/json" http://localhost:8545 [PITH_FULL_…

**Figure 28.** Figure 28: displays the results of executing the code. The red rectangle in the figure highlights that the block’s gas limit is indeed set to 0x200b20 in hexadecimal, which is equivalent to 2,100,000 and matches the value we configured. This confirms that the ABS mechanism has b…

**Figure 29.** Figure 29: displays the results that miners successfully record the transactions in the blockchain. This completes the experiment [PITH_FULL_IMAGE:figures/full_fig_p064_29.png]

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Reference graph

Works this paper leans on

43 extracted references · 42 canonical work pages

[1]

What is non-fungible token (nft)? a short discussion about nft terms used in nft,

D. Ghelani, “What is non-fungible token (nft)? a short discussion about nft terms used in nft,” Authorea Preprints, 2022. 65

work page 2022
[2]

Market value,

“Market value,” https://www.binance.com/en/price/dydx-chain, 2024

work page 2024
[3]

Issue of decentralized order book,

“Issue of decentralized order book,” https://pintu.co.id/en/academy/post/what-is-decentralized-order-book# pros-and-cons-of-decentralized-order-book, 2023

work page 2023
[4]

Towards understanding and characterizing the arbitrage bot scam in the wild,

K. Li, S. Guan, and D. Lee, “Towards understanding and characterizing the arbitrage bot scam in the wild,” in Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems , ser. SIGMETRICS/PERFORMANCE ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 89–90. [Onlin...

work page arXiv 2024
[5]

Effect of miner incentive on the confirmation time of bitcoin transactions,

B. G. Gebraselase, B. E. Helvik, and Y . Jiang, “Effect of miner incentive on the confirmation time of bitcoin transactions,” in 2021 IEEE International Conference on Blockchain (Blockchain) . IEEE, 2021, pp. 521–529

work page 2021
[6]

Price slippage in matching,

“Price slippage in matching,” https://www.coingecko.com/learn/slippage-crypto, 2023

work page 2023
[7]

Dex matching issue,

“Dex matching issue,” https://101blockchains.com/defi-slippage-explained/, 2024

work page 2024
[8]

Bitcoin cash,

“Bitcoin cash,” https://crypto.marketswiki.com/index.php?title=Bitcoin cash, 2020

work page 2020

Show all 43 references

[9]

Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability,

P. Daian, S. Goldfeder, T. Kell, Y . Li, X. Zhao, I. Bentov, L. Breidenbach, and A. Juels, “Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability,” in 2020 IEEE symposium on security and privacy (SP) . IEEE, 2020, pp. 910–927

2020
[10]

Deep reinforcement learning for the optimal placement of cryptocurrency limit orders,

M. Schnaubelt, “Deep reinforcement learning for the optimal placement of cryptocurrency limit orders,” European Journal of Operational Research, vol. 296, no. 3, pp. 993–1006, 2022

2022
[11]

Building trust takes time: limits to arbitrage for blockchain-based assets,

N. Hautsch, C. Scheuch, and S. V oigt, “Building trust takes time: limits to arbitrage for blockchain-based assets,” Review of Finance , p. rfae004, 2024

2024
[12]

Sok: Preventing transaction reordering manipulations in decentralized finance,

L. Heimbach and R. Wattenhofer, “Sok: Preventing transaction reordering manipulations in decentralized finance,” in Proceedings of the 4th ACM Conference on Advances in Financial Technologies , 2022, pp. 47–60

2022
[13]

Detecting and quantifying wash trading on decentralized cryptocurrency exchanges,

F. Victor and A. M. Weintraud, “Detecting and quantifying wash trading on decentralized cryptocurrency exchanges,” in Proceedings of the Web Conference 2021 , 2021, pp. 23–32

2021
[14]

Architecture of automated crypto-finance agent,

A. Raheman, A. Kolonin, B. Goertzel, G. Hegyk ¨ozi, and I. Ansari, “Architecture of automated crypto-finance agent,” in 2021 International Symposium on Knowledge, Ontology, and Theory (KNOTH) . IEEE, 2021, pp. 10–14

2021
[15]

Bitcoin xt,

“Bitcoin xt,” https://en.bitcoin.it/wiki/Bitcoin XT, 2015

2015
[16]

Ethereum fee,

“Ethereum fee,” https://www.blocknative.com/blog/eip-1559-fees, 2021

2021
[17]

Securing proof-of-stake blockchain protocols,

W. Li, S. Andreina, J.-M. Bohli, and G. Karame, “Securing proof-of-stake blockchain protocols,” in Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2017 International Workshops, DPM 2017 and CBT 2017, Oslo, Norway, September 14-15, 2017, Proceedings...

2017
[18]

Transaction delay,

“Transaction delay,” https://www.blockchain.com/explorer/charts/n-transactions, 2025

2025
[19]

Measurement and analysis of the bitcoin networks: A view from mining pools,

C. Wang, X. Chu, and Y . Qin, “Measurement and analysis of the bitcoin networks: A view from mining pools,” in 2020 6th International Conference on Big Data Computing and Communications (BIGCOM) . IEEE, 2020, pp. 180–188

2020
[20]

Fee calculator,

“Fee calculator,” https://www.cashbackforex.com/tools/exchange-fees-calculator?s=blockchain-com-exchange, Jan 2025

2025
[21]

Chainlink introduction,

“Chainlink introduction,” https://www.globalxetfs.com/chainlink-the-basics/, 2023

2023
[22]

Serum introduction,

“Serum introduction,” https://medium.com/perpdex/serum-order-book-implementation-2fca9301f76e, 2022

2022
[23]

From mining to markets: The evolution of bitcoin transaction fees,

D. Easley, M. O’Hara, and S. Basu, “From mining to markets: The evolution of bitcoin transaction fees,” Journal of Financial Economics, vol. 134, no. 1, pp. 91–109, 2019

2019
[24]

A storage sustainability mechanism with heterogeneous miners in blockchain,

Y . Liu, S. Ke, Z. Fang, M. H. Cheung, W. Cai, and J. Huang, “A storage sustainability mechanism with heterogeneous miners in blockchain,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 12, pp. 3645–3659, 2022

2022
[25]

Bitcoin protocol,

“Bitcoin protocol,” https://github.com/Maguines/Bitcoin-v0.1, 2016

2016
[26]

Selfish & opaque transaction ordering in the bitcoin blockchain: the case for chain neutrality,

J. Messias, M. Alzayat, B. Chandrasekaran, K. P. Gummadi, P. Loiseau, and A. Mislove, “Selfish & opaque transaction ordering in the bitcoin blockchain: the case for chain neutrality,” in Proceedings of the 21st ACM Internet Measurement Conference , 2021, pp. 320–335

2021
[27]

Ethereum staking distribution,

“Ethereum staking distribution,” https://dune.com/hildobby/eth2-staking, 2024

2024
[28]

dydx order type,

“dydx order type,” https://docs.dydx.exchange/api integration-trading/order types, 2024

2024
[29]

The meaning of utility measurement,

A. A. Alchian, “The meaning of utility measurement,” The American Economic Review , vol. 43, no. 1, pp. 26–50, 1953

1953
[30]

Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods,

A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods,” Journal of Forecasting, vol. 37, no. 8, pp. 852–866, 2018

2018
[31]

Forecasting mid-price movement of bitcoin futures using machine learning,

E. Akyildirim, O. Cepni, S. Corbet, and G. S. Uddin, “Forecasting mid-price movement of bitcoin futures using machine learning,” Annals of Operations Research , vol. 330, no. 1, pp. 553–584, 2023

2023
[32]

Mid-price prediction based on machine learning methods with technical and quantitative indicators,

A. Ntakaris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Mid-price prediction based on machine learning methods with technical and quantitative indicators,” Plos one, vol. 15, no. 6, p. e0234107, 2020

2020
[33]

Transaction fee mechanism design for the ethereum blockchain: An economic analysis of eip-1559,

T. Roughgarden, “Transaction fee mechanism design for the ethereum blockchain: An economic analysis of eip-1559,” arXiv preprint arXiv:2012.00854, 2020

2012 arXiv
[34]

Multi-objective optimization for uav swarm-assisted iot with virtual antenna arrays,

J. Li, G. Sun, L. Duan, and Q. Wu, “Multi-objective optimization for uav swarm-assisted iot with virtual antenna arrays,” IEEE Transactions on Mobile Computing , 2023

2023
[35]

Average-case analysis of greedy matching for large-scale d2d resource sharing,

S. Gao, C. A. Courcoubetis, and L. Duan, “Average-case analysis of greedy matching for large-scale d2d resource sharing,” IEEE Transactions on Mobile Computing , vol. 23, no. 5, pp. 3707–3721, 2023

2023
[36]

Dynamic matching for ride-sharing with deadlines,

S. Gao, C. Courcoubetis, and L. Duan, “Dynamic matching for ride-sharing with deadlines,” in 2024 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) . IEEE, 2024, pp. 241–248. 66

2024
[37]

Nft trading dataset,

“Nft trading dataset,” https://www.kaggle.com/datasets/mathurinache/nft-history-sales?resource=download, 2022

2022
[38]

Bitcoin order book dataset,

“Bitcoin order book dataset,” https://www.kaggle.com/datasets/maxreis/df-order-book, 2020

2020
[39]

Best value or lowest bid? a quantitative perspective,

W.-d. Yu and K.-W. Wang, “Best value or lowest bid? a quantitative perspective,” Journal of Construction Engineering and Management , vol. 138, no. 1, pp. 128–134, 2012

2012
[40]

Blockchain order book protocol,

“Blockchain order book protocol,” https://b2broker.com/news/what-is-cryptocurrency-matching-engine/, 2023

2023
[41]

Ethereum clients,

“Ethereum clients,” https://chainstack.com/ethereum-clients-geth-and-erigon/, 2022

2022
[42]

Solutions to scalability of blockchain: A survey,

Q. Zhou, H. Huang, Z. Zheng, and J. Bian, “Solutions to scalability of blockchain: A survey,” Ieee Access , vol. 8, pp. 16 440–16 455, 2020

2020
[43]

Time and energy minimization communications based on collaborative beamforming for uav networks: A multi-objective optimization method,

G. Sun, J. Li, Y . Liu, S. Liang, and H. Kang, “Time and energy minimization communications based on collaborative beamforming for uav networks: A multi-objective optimization method,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 11, pp. 3555–3572, 2021

2021

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

What is non-fungible token (nft)? a short discussion about nft terms used in nft,

D. Ghelani, “What is non-fungible token (nft)? a short discussion about nft terms used in nft,” Authorea Preprints, 2022. 65

work page 2022

[2] [2]

Market value,

“Market value,” https://www.binance.com/en/price/dydx-chain, 2024

work page 2024

[3] [3]

Issue of decentralized order book,

“Issue of decentralized order book,” https://pintu.co.id/en/academy/post/what-is-decentralized-order-book# pros-and-cons-of-decentralized-order-book, 2023

work page 2023

[4] [4]

Towards understanding and characterizing the arbitrage bot scam in the wild,

K. Li, S. Guan, and D. Lee, “Towards understanding and characterizing the arbitrage bot scam in the wild,” in Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems , ser. SIGMETRICS/PERFORMANCE ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 89–90. [Onlin...

work page arXiv 2024

[5] [5]

Effect of miner incentive on the confirmation time of bitcoin transactions,

B. G. Gebraselase, B. E. Helvik, and Y . Jiang, “Effect of miner incentive on the confirmation time of bitcoin transactions,” in 2021 IEEE International Conference on Blockchain (Blockchain) . IEEE, 2021, pp. 521–529

work page 2021

[6] [6]

Price slippage in matching,

“Price slippage in matching,” https://www.coingecko.com/learn/slippage-crypto, 2023

work page 2023

[7] [7]

Dex matching issue,

“Dex matching issue,” https://101blockchains.com/defi-slippage-explained/, 2024

work page 2024

[8] [8]

Bitcoin cash,

“Bitcoin cash,” https://crypto.marketswiki.com/index.php?title=Bitcoin cash, 2020

work page 2020