pith. sign in

arxiv: 2605.17425 · v1 · pith:R73VZHWHnew · submitted 2026-05-17 · 💱 q-fin.GN · econ.GN· q-fin.EC· q-fin.TR

The Viability of Blockchain Markets under Discrete Clearing and Paid Priority

Pith reviewed 2026-05-19 22:47 UTC · model grok-4.3

classification 💱 q-fin.GN econ.GNq-fin.ECq-fin.TR
keywords blockchain marketsdiscrete clearingpaid priorityendogenous selectionprice discoveryliquidity impairmentadverse selectionmarket viability
0
0 comments X

The pith

Blockchain markets with discrete clearing and paid priority undermine their own viability by selecting only high-valuation traders.

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

This paper constructs a model of blockchain-based markets that clear at fixed intervals and sequence trades by the priority fees traders pay. It demonstrates that this setup causes endogenous selection: traders participate only when their valuation justifies the expected fees and costs, and the minimum valuation required rises with greater competition. As a result, price discovery suffers, prices become biased, and liquidity providers face greater adverse selection because trading clusters among aggressive participants in each discrete round. Longer block times, intended to bolster consensus security, actually strengthen these distortions and can drive markets to close down.

Core claim

Blockchains clear at discrete intervals called block time, and transactions are executed sequentially according to priority fees paid by traders who compete for queue position. Paid-priority ordering induces endogenous selection, where only traders with sufficiently high valuations participate. The participation cutoff rises with competition, which intensifies with lower information costs or higher liquidity demand. This hinders price discovery and biases prices. It also impairs liquidity as the cutoff concentrates trading among aggressive traders and increases adverse selection that liquidity suppliers absorb in a single clearing round. Although longer block times enhance consensus security

What carries the argument

The participation cutoff arising from traders' rational comparison of private valuations to expected priority fees and information costs, which rises with competition intensity.

If this is right

  • Price discovery is hindered and prices become biased.
  • Liquidity is impaired because trading concentrates among aggressive traders, increasing adverse selection for liquidity suppliers.
  • Longer block times, while enhancing consensus security, amplify the distortions and can lead to market shutdown.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Markets might function better if blockchains adopted non-priority-based ordering like first-come-first-served or random allocation to reduce selection effects.
  • These issues could explain observed inefficiencies in current decentralized exchanges operating on blockchains.
  • Protocol upgrades shortening block times might improve market viability but at the potential cost of reduced security.

Load-bearing premise

Traders rationally compare their private valuations against the expected priority fees and information costs when deciding whether to participate.

What would settle it

Data from blockchain markets showing whether participation drops and prices bias more as priority competition increases or block times lengthen, or if markets close under those conditions.

Figures

Figures reproduced from arXiv: 2605.17425 by Agostino Capponi, \'Alvaro Cartea, Fay\c{c}al Drissi.

Figure 1
Figure 1. Figure 1: Left panel: Average absolute trading volume as a function of queue position in the block for transactions in multiple Uniswap v3 pools; 0 corresponds to first position and 1 to last position. The transactions are between 1 January 2023 and 31 December 2023 in 15 different Uniswap v3 pools with multiple transactions in at least 10 different blocks. For each pool, the trading volumes are normalized by the st… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of priority fees as a function of timing within the blockchain slot. Informed traders are typically associated with higher priority fees, as shown empirically in Capponi et al. (2023b) and theoretically in our model below. The figure shows that they tend to submit their transactions very close to the end of the blockchain slot. The data is from EthPandaOps and it includes 107 transactions obse… view at source ↗
Figure 3
Figure 3. Figure 3: Scatter plots of the slippage and the price impact of 2.622 million LT transactions against the approximations (A2) and (A3). The transactions are between 1 January 2023 and 31 December 2023 in 38 different Uniswap v3 pools. For each pool, the slippages and price impacts are scaled between [0, 1] for buy orders and [−1, 0] for sell orders. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_3.png] view at source ↗
read the original abstract

This paper develops a model to evaluate the viability of blockchain markets as the sole venue for price formation. Blockchains clear at discrete intervals called block time, and transactions are executed sequentially according to priority fees paid by traders who compete for queue position. We show that these features undermine the viability of markets. Paid-priority ordering induces endogenous selection, where only traders with sufficiently high valuations participate. The participation cutoff rises with competition, which intensifies with lower information costs or higher liquidity demand. This hinders price discovery and biases prices. It also impairs liquidity: the cutoff concentrates trading among aggressive traders and increases adverse selection that liquidity suppliers absorb in a single clearing round. Although longer block times enhance consensus security, they amplify these effects and can cause markets to shut down.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper develops a theoretical model of blockchain markets that clear at discrete block-time intervals with transactions ordered by paid priority fees. It claims these features induce endogenous trader selection, where only participants with sufficiently high private valuations enter the market. The resulting participation cutoff rises with competition intensity (driven by lower information costs or higher liquidity demand), which in turn impairs price discovery, biases transaction prices, concentrates trading among aggressive traders, amplifies adverse selection for liquidity suppliers in each clearing round, and can cause markets to shut down even as longer block times improve consensus security.

Significance. If the derivations and equilibrium analysis hold, the paper offers a valuable contribution to the intersection of market microstructure and blockchain economics. It supplies a coherent causal mechanism linking protocol-level design choices (discrete clearing and fee-based priority) to participation thresholds and liquidity outcomes, which could inform both exchange design and regulatory discussions around decentralized trading venues. The explicit treatment of endogenous selection and its feedback onto adverse selection in a single-round clearing is a strength.

minor comments (3)
  1. The abstract condenses the causal chain into a single paragraph; expanding the description of how the participation cutoff is derived from traders' rational comparison of valuations, fees, and information costs would improve accessibility without lengthening the paper substantially.
  2. In the model section, clarify the exact functional forms used for trader valuations and the information-cost distribution so that readers can verify the comparative-statics results on the cutoff's response to competition parameters.
  3. Figure or table presenting the equilibrium cutoff as a function of block time, information cost, and liquidity demand would make the shutdown threshold result more transparent and easier to compare with the analytical expressions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive evaluation of the manuscript and for recommending minor revision. The report accurately summarizes our core findings on endogenous selection in blockchain markets. We address the referee summary below and will incorporate minor clarifications in the revised version.

read point-by-point responses
  1. Referee: REFEREE SUMMARY: The paper develops a theoretical model of blockchain markets that clear at discrete block-time intervals with transactions ordered by paid priority fees. It claims these features induce endogenous trader selection, where only participants with sufficiently high private valuations enter the market. The resulting participation cutoff rises with competition intensity (driven by lower information costs or higher liquidity demand), which in turn impairs price discovery, biases transaction prices, concentrates trading among aggressive traders, amplifies adverse selection for liquidity suppliers in each clearing round, and can cause markets to shut down even as longer block times improve consensus security.

    Authors: We appreciate the referee's precise restatement of the model's implications. The derivations in Sections 3 and 4 establish the participation cutoff and its comparative statics with respect to information costs and liquidity demand. The single-round adverse selection effect follows directly from the concentration of aggressive traders at the cutoff. We will add a short clarifying paragraph in the introduction to emphasize that longer block times improve security but exacerbate the viability issues, as already shown in Proposition 5. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper constructs a theoretical model of trader participation under discrete block clearing and paid-priority fees, deriving endogenous selection and rising cutoffs directly from rational valuation comparisons against expected fees and information costs. No step reduces by construction to a fitted parameter, self-referential definition, or load-bearing self-citation; the viability-undermining conclusions follow from the stated equilibrium conditions on participation and adverse selection without importing unverified uniqueness theorems or renaming external patterns. The derivation remains self-contained against the model's own assumptions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The model rests on standard rational-trader assumptions applied to blockchain-specific features; no explicit free parameters or invented entities are named in the abstract.

axioms (1)
  • domain assumption Traders decide participation by comparing private valuations to priority fees and information costs in a competitive queue.
    This assumption directly generates the endogenous selection and rising cutoff described in the abstract.

pith-pipeline@v0.9.0 · 5670 in / 1211 out tokens · 40240 ms · 2026-05-19T22:47:13.664256+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

62 extracted references · 62 canonical work pages · 1 internal anchor

  1. [1]

    Angeris, Guillermo, Alex Evans, and Tarun Chitra, 2021, Replicating market makers,arXiv preprint arXiv:2103.14769

  2. [2]

    Aoyagi, Jun, and Yuki Ito, 2021, Coexisting exchange platforms: Limit order books and automated market makers

  3. [3]

    Auer, Raphael, Jon Frost, Leonardo Gambacorta, Cyril Monnet, Tara Rice, and Hyun Song Shin, 2022, Central bank digital currencies: motives, economic implications, and the re- search frontier,Annual review of economics14, 697–721

  4. [4]

    Baldauf, Markus, and Joshua Mollner, 2020, High-frequency trading and market perfor- mance,The Journal of Finance75, 1495–1526

  5. [5]

    Barbon, Andrea, and Angelo Ranaldo, 2021, On the quality of cryptocurrency markets: Centralized versus decentralized exchanges,arXiv preprint arXiv:2112.07386

  6. [6]

    Barut, Yasar, and Dan Kovenock, 1998, The symmetric multiple prize all-pay auction with complete information,European Journal of Political Economy14, 627–644

  7. [7]

    Biais, Bruno, 1993, Price formation and equilibrium liquidity in fragmented and centralized markets,The Journal of Finance48, 157–185

  8. [8]

    Biais, Bruno, Pierre Hillion, and Chester Spatt, 1999, Price discovery and learning during the preopening period in the paris bourse,Journal of Political Economy107, 1218–1248

  9. [9]

    Biais, Bruno, David Martimort, and Jean-Charles Rochet, 2000, Competing mechanisms in a common value environment,Econometrica68, 799–837

  10. [10]

    Budish, Eric, Peter Cramton, and John Shim, 2015, The high-frequency trading arms race: Frequent batch auctions as a market design response,The Quarterly Journal of Economics 130, 1547–1621

  11. [11]

    Capponi, Agostino, 2024, Maximal extractable value and allocative inefficiencies in public blockchains,Available at SSRN 4931619

  12. [12]

    Capponi, Agostino, and Ruizhe Jia, 2021, The adoption of blockchain-based decentralized exchanges,arXiv preprint arXiv:2103.08842

  13. [13]

    Capponi, Agostino, Ruizhe Jia, and Shihao Yu, 2024, Price discovery on decentralized ex- changes,Available at SSRN 4236993

  14. [14]

    Cardozo, Pamela, Andrés Fernández, Jerzy Jiang, and Felipe Rojas, 2024, On cross-border crypto flows . 67 Cartea, Álvaro, Fayçal Drissi, and Marcello Monga, 2023, Predictable losses of liquidity provision in constant function markets and concentrated liquidity markets,Applied Math- ematical Finance30, 69–93. Cartea, Álvaro, Fayçal Drissi, and Marcello Mon...

  15. [15]

    Cong, Lin William, and Zhiguo He, 2019, Blockchain disruption and smart contracts,The Review of Financial Studies32, 1754–1797

  16. [16]

    Cong, Lin William, Xiang Hui, Catherine Tucker, and Luofeng Zhou, 2023, Scaling smart contracts via layer-2 technologies: Some empirical evidence,Management Science69, 7306–7316

  17. [17]

    De Frutos, M Ángeles, and Carolina Manzano, 2002, Risk aversion, transparency, and market performance,The Journal of Finance57, 959–984

    Cong, Lin William, Ye Li, and Neng Wang, 2021, Tokenomics: Dynamic adoption and valuation,The Review of Financial Studies34, 1105–1155. De Frutos, M Ángeles, and Carolina Manzano, 2002, Risk aversion, transparency, and market performance,The Journal of Finance57, 959–984

  18. [18]

    Drissi, Fayçal, Xuchen Wu, and Sebastian Jaimungal, 2025, Equilibrium liquidity and risk offsetting in decentralised markets,arXiv preprint arXiv:2512.19838. 68

  19. [19]

    Foster, F Douglas, and S Viswanathan, 1996, Strategic trading when agents forecast the forecasts of others,The Journal of Finance51, 1437–1478

  20. [20]

    Fung, Ben Siu-cheong, and Hanna Halaburda, 2016, Central bank digital currencies: a frame- work for assessing why and how, Technical report, Bank of Canada Staff Discussion Paper

  21. [21]

    Garman, Mark B, 1976, Market microstructure,Journal of financial Economics3, 257–275

  22. [22]

    Glosten, Lawrence R, 1994, Is the electronic open limit order book inevitable?,The Journal of Finance49, 1127–1161

  23. [23]

    Glosten, Lawrence R, and Paul R Milgrom, 1985, Bid, ask and transaction prices in a specialist market with heterogeneously informed traders,Journal of Financial Economics 14, 71–100

  24. [24]

    Grossman, Sanford J, and Joseph E Stiglitz, 1980, On the impossibility of informationally efficient markets,The American economic review70, 393–408

  25. [25]

    Harvey, Campbell R, 2016, Cryptofinance,Available at SSRN 2438299

  26. [26]

    Harvey, Campbell R, 2021,DeFi and the Future of Finance(John Wiley & Sons)

  27. [27]

    Harvey, Campbell R, and Daniel Rabetti, 2024, International business and decentralized finance,Journal of International Business Studies1–24

  28. [28]

    Harvey, Campbell R, Fahad Saleh, and Ruslan Sverchkov, 2025, An economic model of the l1-l2 interaction,Available at SSRN 5163823

  29. [29]

    Hasbrouck, Joel, Thomas J Rivera, and Fahad Saleh, 2022, The need for fees at a dex: How increases in fees can increase dex trading volume,Available at SSRN 4192925

  30. [30]

    Hasbrouck, Joel, Thomas J Rivera, and Fahad Saleh, 2023, An economic model of a decen- tralized exchange with concentrated liquidity,Available at SSRN 4529513. 69

  31. [31]

    He, Xue Dong, Chen Yang, and Yutian Zhou, 2025, Arbitrage on decentralized exchanges, arXiv preprint arXiv:2507.08302

  32. [32]

    Heimbach, Lioba, Vabuk Pahari, and Eric Schertenleib, 2024, Non-atomic arbitrage in de- centralized finance, in2024 IEEE Symposium on Security and Privacy (SP), 3866–3884, IEEE

  33. [33]

    Heines, Roger, Christian Dick, Christian Pohle, and Reinhard Jung, 2021, The tokenization of everything: Towards a framework for understanding the potentials of tokenized assets., PACIS40

  34. [34]

    Hendershott, Terrence, and Albert J Menkveld, 2014, Price pressures,Journal of Financial economics114, 405–423

  35. [35]

    Hillman, Arye L, and John G Riley, 1989, Politically contestable rents and transfers,Eco- nomics & Politics1, 17–39

  36. [36]

    Ho, Thomas, and Hans R Stoll, 1981, Optimal dealer pricing under transactions and return uncertainty,Journal of Financial economics9, 47–73

  37. [37]

    Ho, Thomas SY, and Hans R Stoll, 1983, The dynamics of dealer markets under competition, The Journal of Finance38, 1053–1074

  38. [38]

    Holden, Craig W, and Avanidhar Subrahmanyam, 1992, Long-lived private information and imperfect competition,The Journal of Finance47, 247–270

  39. [39]

    Hub, BIS Innovation, 2023, Project mariana: Cross-border exchange of wholesale cbdcs using automated market-makers

  40. [40]

    John, Kose, Leonid Kogan, and Fahad Saleh, 2023, Smart contracts and decentralized fi- nance,Annual Review of Financial Economics15, 523–542

  41. [41]

    John, Kose, Barnabé Monnot, Peter Mueller, Fahad Saleh, and Caspar Schwarz-Schilling, 2025, Economics of ethereum,Journal of Corporate Finance91, 102718. 70

  42. [42]

    John, Kose, Thomas J Rivera, and Fahad Saleh, 2020, Proof-of-work versus proof-of-stake: A comparative economic analysis,Available at SSRN 3750467

  43. [43]

    Klein, Olga, Roman Kozhan, Ganesh Viswanath-Natraj, and Junxuan Wang, 2023, Informed liquidity provision on decentralized exchanges,Available at SSRN 4642411

  44. [44]

    Klose, Bettina, and Dan Kovenock, 2015, The all-pay auction with complete information and identity-dependent externalities,Economic Theory59, 1–19

  45. [45]

    S., 1985, Continuous auctions and insider trading,Econometrica53, 1315–1335

    Kyle, A. S., 1985, Continuous auctions and insider trading,Econometrica53, 1315–1335

  46. [46]

    Kyle, Albert S, 1989, Informed speculation with imperfect competition,The Review of Eco- nomic Studies56, 317–355

  47. [47]

    Lazear, Edward P, and Sherwin Rosen, 1981, Rank-order tournaments as optimum labor contracts,Journal of political Economy89, 841–864

  48. [48]

    Lehar, Alfred, and Christine Parlour, 2025, Decentralized exchange: The uniswap automated market maker,The Journal of Finance80, 321–374

  49. [49]

    Liu, Yulin, Yuxuan Lu, Kartik Nayak, Fan Zhang, Luyao Zhang, and Yinhong Zhao, 2022, Empirical analysis of eip-1559: Transaction fees, waiting times, and consensus security, inProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2099–2113

  50. [50]

    Madhavan, Ananth, 1992, Trading mechanisms in securities markets,the Journal of Finance 47, 607–641

  51. [51]

    Malinova, Katya, and Andreas Park, 2024, Learning from defi: Would automated market makers improve equity trading?,Available at SSRN 4531670

  52. [52]

    Medrano, Luis Angel, and Xavier Vives, 2001, Strategic behavior and price discovery,RAND Journal of Economics221–248. 71

  53. [53]

    Milionis, Jason, Ciamac C Moallemi, Tim Roughgarden, and Anthony Lee Zhang, 2022, Automated market making and loss-versus-rebalancing,arXiv preprint arXiv:2208.06046

  54. [54]

    Mizrach, Bruce, and Nathaniel Yoshida, 2025, The marginal effects of ethereum network mev transaction re-ordering,arXiv preprint arXiv:2508.04003

  55. [55]

    O’Hara, Maureen, 1998,Market microstructure theory(John Wiley & Sons)

    Moldovanu, Benny, and Aner Sela, 2001, The optimal allocation of prizes in contests,Amer- ican Economic Review91, 542–558. O’Hara, Maureen, 1998,Market microstructure theory(John Wiley & Sons)

  56. [56]

    Park, Andreas, 2023, The conceptual flaws of decentralized automated market making,Man- agement Science69, 6731–6751

  57. [57]

    Petryk, Mariia, Christoph Müller-Bloch, Jungpil Hahn, Hanna Halaburda, Ola Henfridsson, Daniel Obermeier, and Youngjin Yoo, 2025, Promises and perils of decentralization in the blockchain age

  58. [58]

    7805, Draft

    Thiery, Thomas, Francesco D’Amato, Julian Ma, Barnabé Monnot, Terence Tsao, Ja- cob Kaufmann, and Jihoon Song, 2024, EIP-7805: Fork-choice enforced inclusion lists (FOCIL), Ethereum Improvement Proposals, no. 7805, Draft. Available:https://eips. ethereum.org/EIPS/eip-7805

  59. [59]

    VanBommel, Jos, andPeterHoffmann, 2011, Transparencyandendingtimesofcallauctions: a comparison of euronext and xetra, Technical report, Luxembourg School of Finance, University of Luxembourg

  60. [60]

    Verrecchia, RobertE,1982, Informationacquisitioninanoisyrationalexpectationseconomy, Econometrica: Journal of the Econometric Society1415–1430. Vujičić, Dejan, Dijana Jagodić, and Siniša Ranđić, 2018, Blockchain technology, bitcoin, 72 and ethereum: A brief overview, in2018 17th international symposium infoteh-jahorina (infoteh), 1–6, IEEE

  61. [61]

    Wadhwa, Sarisht, Julian Ma, Thomas Thiery, Barnabe Monnot, Luca Zanolini, Fan Zhang, and Kartik Nayak, 2025, Aucil: An inclusion list design for rational parties,Cryptology ePrint Archive

  62. [62]

    Wang, Shuzheng, Yue Huang, Wenqin Zhang, Yuming Huang, Xuechao Wang, and Jing Tang, 2025, Private order flows and builder bidding dynamics: The road to monopoly in ethereum’s block building market, inProceedings of the ACM on Web Conference 2025, 2144–2157. 73