Marco Dessalvi, Massimo Bartoletti, Alberto Lluch-Lafuente
Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM - up to a trading fee used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. In this work, we extend a foundational model of AMMs by introducing a new parameter, the trading fee ϕ ∈ (0,1], into the swap rate function. Fee amounts increase inversely proportional to ϕ. When ϕ = 1, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. In particular, we prove that when ϕ < 1, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.
This paper presents the complete architectural blueprint for the Ternary Logic (TL) Smart Contract Constitutional Suite, defining the structural layout across three layers: the Logic Layer housing the ternary decision engine, the Execution Layer enforcing state transitions, and the Storage Layer providing immutable audit infrastructure. The blueprint specifies the precise components, interactions, and logic required to implement the unique triadic state model of the TL framework: Proceed (+1), Epistemic Hold (0), and Refuse (1). The Epistemic Hold state is introduced as a constitutional pause mechanism, transforming deliberation from an operational liability into a cryptographically verifiable evidentiary asset. The fail-closed default posture ensures that any transaction whose evidence has not been archived returns State 0, making uncertainty constitutionally visible rather than operationally invisible. The No Log = No Action invariant G(execute implies P(escrow_recorded and auditable)) is enforced across five independent layers from API schema validation through the on-chain terminal gate in TL_Ledger_Core.registerPermissionToken. The Dual-Lane Latency Architecture establishes a 2ms WCET hard ceiling for the Inference Lane and a 300ms hard ceiling for the Governance Lane, with the execution gate releasing only after a valid PermissionToken has been registered on-chain. The blueprint covers Solidity implementation patterns, a TLA+ formal verification specification proving the Epistemic Hold safety and liveness properties, an Oracle-Custodian asynchronous callback architecture, and the Ghost Governance prevention mechanism ensuring no contract call is made without a valid PermissionToken from the Governance Lane. Use cases are demonstrated across Central Bank Digital Currencies, decentralized finance, supply chain management, and AI-driven decentralized autonomous organizations, establishing TL smart contracts as constitutional code where the rules of economic interaction are harder to break than traditional legal agreements.
<b>Maximal Extractable Value (MEV)</b> has evolved from a theoretical artifact of transparent transaction ordering into a dominant economic force shaping Proof-of-Stake (PoS) blockchain ecosystems. While early research framed MEV as an unavoidable but competitive phenomenon, recent infrastructure developments—particularly MEV relays, aggregators, and proposer-builder separation (PBS)—have enabled the consolidation of extractive power into coordinated intermediary groups. This paper introduces <b><i>MEV Aggregator Drift</i></b>, a structural phenomenon in which MEV extraction progressively centralizes into opaque, off-chain coordination clusters <b><i>(“collusion packs”)</i></b> that undermine validator neutrality, distort protocol incentives, and introduce cartel-like dynamics without explicit on-chain collusion. We analyze the economic drivers, execution mechanisms, and systemic risks of MEV collusion across PoS and DeFi systems, and argue that existing mitigations focus on efficiency while neglecting enforceable neutrality. Finally, we outline mitigation requirements centered on validator accountability, behavioral monitoring, and transaction ordering attestation.
We study a three-stage decision process that consists of information acquisition, project choice, and execution of the selected project. A principal wants to choose and implement a proactive project, and hires an agent who chooses a costly effort at the information acquisition stage as well as a costly effort at the execution stage. What the principal can do at the beginning is the allocation of the formal decision authority over project choice, either to herself or the agent. We show that the principal may choose to delegate decision authority to the agent, however unlikely the interest of the agent is to be congruent with her interest, or however competent and experienced she is. We provide several testable predictions. (i) Delegation is more likely as the manager has discretion over both information acquisition and implementation. (ii) Delegation is less likely as opportunities for compromising improve. (iii) Whether or not the parties agree about the status quo matters: In particular, if their preferences about the default decisions differ, the organization is more likely to be decentralized for new project development as their interests are less likely to be congruent. We further discuss the extent to which our results on optimal delegation survive when artificial intelligence (AI) is deployed, distinguishing autonomous and nonautonomous AI. If AI can fully automate information acquisition or execution, delegation cannot be optimal, but it can be optimal if the agent remains responsible for execution. If AI instead supports execution by lowering its cost, delegation can survive.
Decentralized reputation mechanisms certify trust by making agents spend a scarce resource: computation (Proof of Work), capital (Proof of Stake), or identity (Proof of Personhood). In all three the resource is orthogonal to the quality being certified — a well-capitalized agent is not a skilled one. We study the construction in which the spent resource is the certified competence: Proof of Calibration, in which pseudonymous agents commit probabilistic predictions, outcomes resolve against ground truth, and reputation accrues through a strictly proper scoring rule. Building on the merit-gating results of Calibration-Gated Reputation (Alassa and Alashqar, SSRN 6505678), we replace that paper's concentration estimates with an exact theory. The realized mean score of an identity is a linear function of a single sufficient statistic — its empirical outcome frequency p̂ — so passing a merit bar is exactly the event that p̂ reaches an "alibi frequency" q_c, and the price of that event is an information divergence: the Sybil-lottery success probability is controlled, two-sidedly for all K and all N beyond a mild explicit threshold, by e^(−N·KL(q_c‖p)). Strict propriety is precisely positivity of this rate at every dishonest report, unifying the companion paper's impossibility and merit-gating theorems as the zero- and positive-rate regimes of one scalar. The theory is exact enough to reproduce, with zero free parameters, every Monte Carlo experiment of the companion suite, including the finite-size deviations from the asymptotic ε⁻⁴ cold-start law. Two structural results follow. First, the gate has two margins: in expectation, the adversary's best response is honesty and the margin is the generalized-entropy gap; at the event level, the optimal attack is skill-mimicry — report the bar's skill, not your own — and the margin is min{KL(p_h‖p_a), KL(1−p_h‖p_a)}, the divergence between the skills themselves, identical across all strictly proper, outcome-symmetric rules. The gate's security exponent is a property of the skills, not of the rule. Second, Sybil amplification requires independent evidence: identities scored on a shared outcome stream gain almost nothing from their number. Finally, the gate is not merely sound but optimal: no local pseudonymous mechanism that admits honest bar-skill agents with non-vanishing probability achieves a soundness exponent exceeding KL(p_h‖p_a), and the strictly proper gate attains it — it is the Neyman–Pearson test of the skill hypothesis. The composite Notch Score reduces to the same single statistic, yielding an exact calibration-weight floor.
This paper introduces Autonomous Mechanism Economics (AME), a theoretical framework for analyzing economic systems where human discretion is removed from mechanism execution. While classical mechanism design theory (Hurwicz, 1960; Maskin, 1999; Myerson, 1981) focuses on designing incentive-compatible rules, it implicitly assumes human agents execute these rules. We formalize a new class of economic mechanisms-Autonomous Mechanisms (AM)-where execution is performed by deterministic, immutable code rather than discretionary human agents. We establish four core theoretical results. First, Non-Discretionary Buyback (NDB) mechanisms minimize execution-layer agency costs (Theorem 1). Second, assets satisfying specific structural conditions-revenue increasing in market volatility combined with NDB execution-may exhibit antifragility, generating positive expected returns during market stress (Theorem 2). Third, when algorithmic buying capacity exceeds maximum individual selling capacity, markets may undergo threshold transitions to qualitatively different dynamics (Theorem 3). Fourth, USDdenominated staking requirements create self-reinforcing supply dynamics with bounded equilibrium returns (Theorem 4). We connect this framework to Kydland and Prescott (1977)’s “rules versus discretion” literature, arguing that AM protocols may represent a strong rules-based solution by eliminating not merely the incentive but potentially the ability to deviate from prescribed rules. Using data from Hyperliquid—a decentralized exchange implementing NDB at scale—we provide preliminary empirical support, documenting a volume-volatility correlation of 0.627 (p
Despite trading volumes in the tens of billions, NFT markets are illiquid: median quoted spreads of 48-200% far exceed equity levels, though execution-based measures covering nearly all sales yield effective costs of only 2-5%. Using over 410 million orderbook events-including, for the first time, comprehensive bid-side data-across six major collections, we document a distinctive institutional response: a two-tier orderbook in which collection-level floor bids, rather than token-specific orders, supply the dominant source of buy-side liquidity. A small number of algorithmic market makers provide these bids but face adverse selection inherent in collection-level offers, experiencing market-adjusted post-purchase returns of-3% to-7%. In panel regressions, collection identity absorbs over 30 percentage points of R-squared , dominating all observable spread determinants. A comparison of the same 10 000 assets under two market structures reveals that the native bilateral marketplace achieves tighter spreads (54% vs. 200%), suggesting that ease of bidding generates liquidity activity but not price efficiency.
Jonas Gehrlein, Grzegorz Miebs, Matteo Brunelli, Adam Mielniczuk · 5 authors
We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for maintaining the blockchain's physical infrastructure. The selection process is inherently subjective and multi-criterial and combines with the fact that nominators commonly operate through multiple accounts. This gives rise to a portfolio selection problem, where agents seek to distribute their nominations across accounts to diversify risk. We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives: the expected utility of the validators likely to be allocated, representing portfolio quality and profitability, and the expected entropy of the allocation, representing diversification and risk mitigation across stashes. Validator utilities are derived using an original active preference learning procedure based on multi-attribute value theory, with emphasis on top-ranked validators. The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm and, to support the final choice, we introduce an interactive binary search navigation procedure that guides the nominator through the front and identifies a satisfactory trade-off with only a few questions. Numerical experiments examine the optimization strategies, while an expert assessment involving five experienced nominators confirms the approach's practical relevance and usefulness.
<b>Proof-of-Stake (PoS) security models</b> assume validator independence, decentralized decision-making, and economically rational but non-coordinated behavior. This paper introduces <b><i>Shadow Validator Cartels</i></b>, a class of covert consensus capture attacks in which validators coordinate off-chain to influence block production, transaction ordering, and governance outcomes while remaining individually protocol-compliant. Unlike explicit majority or 51% attacks, shadow cartels do not require dominant stake ownership or on-chain collusion. Instead, they leverage shared infrastructure, aligned economic incentives, and soft coordination mechanisms that render their behavior statistically indistinguishable from organic validator activity. We analyze the structural enablers, formation mechanics, and systemic impacts of shadow validator cartels, demonstrate why existing decentralization metrics fail to detect them, and outline system-level mitigation requirements necessary to preserve credible neutrality in PoS networks.
Stefano Balietti, Pietro Saggese, Markus Strohmaier
Decentralized Autonomous Organizations (DAOs) use token-weighted voting to allocate resources, set protocol rules, and legitimate collective decisions. Yet, support in DAO voting is strikingly concentrated. What happens inside the ballot that produces this concentration? We study DAOs' governance at the proposal-choice level, linking each choice's voting-power share to three observable features: whether it expresses an approval-oriented stance, where it appears in the choice list, and whether it is selected by the proposal author. We find that (i) author-selected choices show the strongest and most robust association with voting-power share, with a 58.8% increase relative to non-author choices; (ii) approval-oriented choices retain a positive but slightly less consistent advantage (27.1%); and (iii) first-listed choices also attract systematically higher shares, consistent with position and order effects (7.7%). Results are robust across several specifications, which include subtracting an author's own voting power from computations. We use bias descriptively, to denote systematic associations rather than proven causal distortion. The results shift attention from proposal outcomes alone to the interface and social signals through which choices are presented. In DAO governance, ordering, author signals, and vote visibility should be treated as institutional design choices, not neutral implementation details.
Decentralized autonomous organizations (DAOs) represent a novel organizational form designed to enable collective decision-making without formal hierarchy. Despite their decentralized design, many DAOs exhibit tendencies toward centralization in the governance process. This study explains this governance paradox via the lens of transaction cost economics (TCE), highlighting how human asset specificity contributes to the emergence of centralized governance structures in settings where formal authority is absent. Specifically, we argue that human asset specificity, assessed through the technicality, complexity, and readability of governance proposals, is associated with a higher degree of centralization in the governance of DAOs. We test our hypotheses using a novel dataset of 3,807 proposals across major decentralized finance (DeFi) DAOs. Consistent with the predictions of TCE, empirical analyses show that proposals characterized by higher technicality, higher complexity, and lower readability are associated with higher levels of centralization. These results imply the relevance of TCE for governance in decentralized organizations-a novel form of organizations and offer practical guidance for protocol designers seeking to preserve decentralization in DAOs.
Financial reporting within enterprise resource planning now commonly rides on a blockchain backbone, yet the problem of keeping each distributed ledger in sync remains stubbornly difficult-especially when SAP modules are at the controls. This paper describes a simulation-based testbed that watches SAP payment journals as they hop between differently configured blockchains, measuring how and when each copy reaches the same state. By replaying typical SAP routines under adjustable delay windows and choice of consensus rules, the model tallies the frequency of divergence, the lag before agreement, and the mechanics of clearing up disputes. Output files display convex 3D surfaces, animated heat maps, and step-by-step trails of how conflicts get settled; taken together, they point middleware designers toward tighter sync logic, smarter contract frameworks, and faster multi-ledger audits. In broader terms, the findings shrink the technical distance SAP users must traverse to achieve clean, traceable cross-chain accounting.
Decentralized Autonomous Organizations (DAOs), powered by blockchain technology and smart contracts, have opened new avenues for addressing corporate governance challenges, such as effectively reducing contract risks and mitigating other organizational management issues. However, as a typical complex system that integrates both social and engineering complexities, DAOs still face problems in governance practice, including insufficient decentralization and low member participation. In particular, the liquid democracy design in their voting mechanisms-intended to allow members to flexibly switch between direct voting and delegated proxy-often leads to the phenomenon of “delegation equals abstention,” which is particularly acute in Product and Service DAOs, resulting in declining overall participation rates and excessive concentration of governance power among a few individuals. To address these challenges, this paper employs the ACP method (Artificial Systems + Computational Experiments + Parallel Execution) and proposes a dual-token governance mechanism that couples governance rights with an incentive layer. This mechanism moderately decouples governance rights from utility rights, encouraging ordinary members to shift from passive delegation to active participation in governance. At the same time, we introduce an SBT-based reputation system grounded in cumulative contributions, which dynamically constrains the upper limit of delegated receipts, thereby institutionally curbing the unchecked expansion of power by super delegates. Through case analysis combined with computational experiments, the effectiveness of this mechanism in enhancing the degree of governance decentralization and member participation is validated, providing both technical pathways and theoretical references for DAO governance optimization.
Decentralized Finance (DeFi) enables financial services to operate without centralized intermediaries, using smart contracts and blockchain consensus to ensure transparency and trust minimization. While DeFi protocols like Aave and MakerDAO use overcollateralization to mitigate credit risk, this approach creates capital inefficiencies and limits access to borrowers lacking on-chain assets. This paper introduces Inverum, a novel DeFi lending protocol designed to support undercollateralized loans for Web3 businesses and Decentralized Autonomous Organizations (DAOs). Inverum integrates on-chain credit scoring via soulbound tokens, decentralized liquidity pools, and governance-driven incentives to enable trustless, reputation-based lending. The protocol offers a fully composable framework for exploring undercollateralized lending without relying on traditional identity or off-chain reputation systems, contributing a research-ready model for future experimentation and protocol design.
This paper examines the strategic behavior of rational actors in the TON blockchain, focusing on their responses to slashing mechanisms in a proof-of-stake (PoS) environment. Slashing introduces financial penalties for behavior that threatens network integrity, addressing the nothing-at-stake problem, where validators in PoS systems can support multiple chains at no cost. Although slashing is intended to deter malicious behavior by Byzantine actors, it also affects rational validators by altering their expected returns. Using a game-theoretic model inspired by the BAR framework, this study examines how rational, utility-maximizing validators weigh the risks and rewards of violating or enforcing slashing mechanisms in the presence of potentially Byzantine actors when penalty enforcement is uncertain. Located at the intersection of game theory and distributed systems, this research sheds light on compliance and deviation dynamics in PoS networks, contributing to a deeper understanding of incentive alignment in blockchain governance.
H Kim, Gyu M. Lee, Junsik Sim, Jun-Seok Park · 5 authors
It has been a decade since decentralized finance emerged. With the advent of smart contracts, numerous financial products are being built on blockchains. Despite limitations such as gas fee restrictions and the need for oracles, smart contracts are bringing about financial innovation. Smart contracts are a crucial tool for implementing financial automation, ideally suited for eliminating intermediaries and implementing atomic transactions. For a transaction to occur, a price must be determined. Over the years, the Black-Scholes equation, which determines options pricing, the market scoring rules (e.g., LMSR) that enable prediction markets, and the constant product formula (e.g., CPMM), which is at the heart of automatic market makers (AMMs), have been developed. Prices are highly subjective, and in reality, multiple prices exist for a single product. However, in decentralized finance, a single price is mathematically determined in a specific situation and accepted without resistance by the market, a remarkable phenomenon. This paper examines why prices must be mathematically determined and why they remain consistent with real-world prices. It also ex-amines how these prices are determined mathematically. Further-more, it examines the price determination mechanism from a cybernetic perspective. In particular, we analyze the phenomenon in which prediction market prices are also used as automatic market makers, and clearly distinguish the difference between the use of market scoring rules and constant product formulas. This paper demonstrates the existence of both path-independent and path-de-pendent prices. While path-independent prices have been extensively studied, research on path-independent pricing has been sparse.
We model the ultimate price paid by users of a decentralized ledger as resulting from a two-stage game where Miners (/Proposers/etc.) first purchase blockspace via a Tullock contest, and then price that space to users. When analyzing our distributed ledger model, we find: - A characterization of all possible pure equilibria (although pure equilibria are not guaranteed to exist). - A natural sufficient condition, implied by Regularity (a la [Mye81]), for existence of a ''market-clearing'' pure equilibrium where Miners choose to sell all space allocated by the Distributed Ledger Protocol, and that this equilibrium is unique. - The market share of the largest miner is the relevant ''measure of decentralization'' to determine whether a market-clearing pure equilibrium exists. - Block rewards do not impact users' prices at equilibrium, when pure equilibria exist. But, higher block rewards can cause pure equilibria to exist. We also discuss aspects of our model and how they relate to blockchains deployed in practice. For example, only ''patient'' users (who are happy for their transactions to enter the blockchain under any miner) would enjoy the conclusions highlighted by our model, whereas ''impatient'' users (who are interested only for their transaction to be included in the very next block) still face monopoly pricing.