Το αυξανόμενο ενδιαφέρον για εναλλακτικές επενδύσεις που προσφέρουν διαφοροποίηση και υψηλότερες αποδόσεις, καθοδηγείται από τους περιορισμούς των παραδοσιακών χρηματοπιστωτικών αγορών και τις εξελισσόμενες ανάγκες των σύγχρονων επενδυτών. Στην παρούσα διδακτορική διατριβή διερευνώνται οι μηχανισμοί μεταβολής των τιμών δύο τομέων εναλλακτικών επενδύσεων: των επενδύσεων με κριτήρια Περιβάλλοντος, Κοινωνίας και Διακυβέρνησης (Environmental, Social, and Governance – ESG), με έμφαση στις εισηγμένες εταιρείες που επιδεικνύουν περιβαλλοντική υπευθυνότητα, και των ψηφιακών περιουσιακών στοιχείων που βασίζονται στην τεχνολογία Blockchain, μέσα από ένα ενοποιημένο μεθοδολογικό πλαίσιο. Το πρώτο μέρος της διατριβής εξετάζει τις χρηματοοικονομικές επιπτώσεις της Εταιρικής Περιβαλλοντικής Υπευθυνότητας (Corporate Environmental Responsibility – CER) στις επιχειρήσεις του δείκτη S&P 500 κατά τη διάρκεια δεκαπέντε ετών. Αξιολογείται η επίδραση της περιβαλλοντικής επίδοσης στην αποτίμηση της αγοράς μέσω δεικτών προσαρμοσμένων στον κίνδυνο, οι οποίοι βασίζονται στο υπόδειγμα CAPM και στην υπόθεση της αποτελεσματικής αγοράς. Η ανάλυση, η οποία στηρίζεται σε τεχνικές παλινδρόμησης δεδομένων πάνελ, όπως οι εκτιμήσεις OLS και 3SLS, εισάγει την έννοια του «Πράσινου Premium» — ενός μετρήσιμου αντισταθμίσματος μεταξύ περιβαλλοντικής υπευθυνότητας και αποδόσεων των επενδυτών. Τα αποτελέσματα δείχνουν ότι, ενώ η CER συσχετίζεται θετικά με λειτουργικούς δείκτες, όπως οι πωλήσεις και τα κέρδη, η χρηματιστηριακή απόδοση παραμένει κατώτερη, γεγονός που υποδηλώνει ένα διαρκές μειονέκτημα για τις επιχειρήσεις με περιβαλλοντικό προσανατολισμό στα μάτια των επενδυτών. Το δεύτερο μέρος της διατριβής επικεντρώνεται στη χρηματοοικονομική διάσταση του Blockchain, παρέχοντας μια ολοκληρωμένη ανάλυση της συμπεριφοράς τιμών των Μη Εναλλάξιμων Διακριτικών (Non-Fungible Tokens – NFTs) μέσω σύγχρονων μεθόδων μηχανικής μάθησης. Δεδομένα που αντλήθηκαν απευθείας από πλατφόρμες Blockchain και μετασχηματίστηκαν μέσω προηγμένων τεχνικών μηχανικής χαρακτηριστικών, χρησιμοποιούνται για την αξιολόγηση της προβλεπτικής ικανότητας των μοντέλων Random Forest, XGBoost και Πολυεπίπεδου Αντιληπτή (Multilayer Perceptron). Το τελικό σύνολο δεδομένων προέκυψε από μια προηγμένη διαδικασία μετασχηματισμού χαρακτηριστικών, εμπλουτισμένη με σύνθεση χαρακτηριστικών βάσει εξειδικευμένης γνώσης και Deep Feature Synthesis, αναδιαμορφωμένο μέσω Ανάλυσης Κύριων Συνιστωσών (Principal Component Analysis – PCA) και βελτιστοποιημένο μέσω τεχνικών επιλογής χαρακτηριστικών για βέλτιστη ερμηνευσιμότητα και μείωση διαστασιμότητας. Από τα μοντέλα που εξετάστηκαν, το XGBoost παρουσίασε τη μεγαλύτερη ακρίβεια πρόβλεψης, με τα ιστορικά δεδομένα τιμών να αποτελούν τον πιο καθοριστικό παράγοντα πρόβλεψης. Συνολικά, τα δύο μέρη της διατριβής συμβάλλουν στη διεύρυνση της βιβλιογραφίας σχετικά με τις μη παραδοσιακές κατηγορίες επενδυτικών στοιχείων, μέσω της εφαρμογής ισχυρών αναλυτικών μεθόδων σε διαφορετικά επενδυτικά πεδία. Τα αποτελέσματα παρέχουν εξειδικευμένες γνώσεις σχετικά με τους παράγοντες που διαμορφώνουν την αξία των περιουσιακών στοιχείων και θεμελιώνουν ένα μεταβιβάσιμο μεθοδολογικό πλαίσιο για την πρόβλεψη τιμών περιουσιακών στοιχείων σε αναδυόμενες και ετερογενείς αγορές.
This addendum supplements the original AIFTB whitepaper (Méndez Viera, 2026) to document the expansion of the US Liberty instrument ecosystem in response to the enactment of the Guiding and Establishing National Innovation for U.S. Stablecoins Act of 2025 (the “GENIUS Act”). The paper introduces USLP (US Liberty Peace), a precious metals-backed non-fungible token security designed to fund the Peace & Prosperity Dividend initiative, and analyzes the regulatory positioning of the complete US Liberty portfolio — USLC, USLP, USLS, USLD, and USLG — across dual jurisdictional frameworks: the GENIUS Act for payment stablecoins and SEC securities regulation for NFT-classified instruments. The GENIUS Act’s strict 1:1 reserve mandate, limited to specified high-quality liquid assets, precludes the inclusion of precious metals in a payment stablecoin’s reserve structure — a constraint that necessitated the creation of USLP as a structurally distinct instrument under a separate regulatory classification. The analysis demonstrates that the AIFTB patent architecture (U.S. Patent No. 12,548,029 B2) provides a unified compliance and fraud detection infrastructure operating across both regulatory regimes, including real-time dual-pass AI fraud detection, autonomous smart contract intervention, and embedded behavioral risk scoring formulas (Radicalization Risk Score, Trust Integrity Score, Validation Confidence Score, Financial Stability Score) applied with equal rigor to all instruments in the portfolio. The paper further addresses the GENIUS Act’s naming restrictions, yield prohibition, and implementation timeline, and evaluates the competitive implications of the Act’s compliance bar for the Autonomous Asset-Backed Securities (AABS) market category. The author argues that the resulting ecosystem constitutes the first vertically integrated AABS platform designed to operate within and across the post-GENIUS Act regulatory landscape, unified by a single patented AI-driven architecture — a dual-framework bridge that no identified competitor has replicated.
Tokenization of real-world assets (RWAs)—the representation of off-chain assets on a digital ledger—has gained momentum across money market funds, government bonds, gold, and private credit. It bridges traditional finance and on-chain markets while continuing to rely on traditional infrastructure for custody, legal enforcement, and price discovery. Tokenization promises efficiency gains in issuance, trading, and settlement and may facilitate secondary-market liquidity by making claims on otherwise illiquid assets more transferable. We distinguish three categories: tokenized liquid assets (e.g., gold and equities), money-like claims (e.g., stablecoins and tokenized deposits), and tokenized illiquid assets (e.g., loans and private credit). Tokenization of liquid assets integrates traditional markets with decentralized finance and reallocates liquidity across venues. Tokenization of illiquid assets, by contrast, facilitates secondary-market trading opportunities, but whether it creates meaningful liquidity depends on market design, investor participation, valuation quality, and asset opacity. This liquidity transformation inherits incentive problems familiar from banking and securitization while introducing new economic and operational risks related to custody, redemption design, and oracles. We argue that the trading speed of a tokenized claim should match the speed at which the underlying asset can be traded, valued, or redeemed—the speed-matching principle that organizes our policy framework. We propose a policy framework that ties regulatory requirements to the economic role tokens play and the speed and liquidity of their underlying markets rather than to the underlying technology, prioritizing clear legal foundations, credible redemption mechanisms, robust custody and audit standards, and sound oracle governance.
Smart contracts have revolutionized finance by enabling decentralized applications without intermediaries, yet their widespread adoption has exposed significant gaps in understanding their practical implications and unresolved challenges. Unlike existing works that primarily focus on theoretical overviews, this paper employs a rigorous empirical methodology to bridge the gap between research and real-world operations. By combining insights from user and developer communities, practical experiments on testnets and mainnets, and a comprehensive analysis of prior studies, this work uncovers underexplored applications, highlights discrepancies between theoretical models and actual behaviors, and identifies emerging security, privacy, and social challenges. The paper first provides structural insights into the surveyed contents and then introduces critical security, privacy, and social considerations. The first category of surveyed contents includes well-documented applications such as automated market makers (AMMs), non-fungible tokens (NFTs), and flash loans. Unlike existing works, this study offers unified explanations that integrate fragmented information while presenting experimental findings and practical proposals. The second category covers under-explored applications like NFT vouchers for real-world assets, wrapped NFTs, and reversible transactions. By offering actionable insights and usage guidelines, this study distinguishes itself by addressing the nuanced, practical realities of smart contract applications, equipping researchers, developers, and users with the knowledge needed to navigate the evolving world of smart contracts effectively.
Lending in decentralized finance (DeFi) relies on collateral and efficient liquidations to manage credit risk. The permissionless and pseudonymous nature of public blockchains precludes reputation-based lending in DeFi and renders liabilities effectively non-recourse. Frictions in collateral liquidations increase the risk of bad debt and may ultimately lead to protocol defaults and losses for liquidity providers. This paper studies liquidation dynamics in the Aave V2 Main Market on Ethereum using block-level data covering 46 months and more than 54 000 borrower positions. While most undercollateralized debt is liquidated almost instantaneously, a non-trivial share of positions remains open for extended periods. Using a state model to distinguish healthy, viable for liquidation, and stale borrower positions, this paper quantifies transition probabilities and identifies factors associated with liquidation success. Logistic regression results show that liquidation size, lower network transaction fees, and relative profitability are associated with the probability of liquidation success in the subsequent block. At the same time, oracle price distortions and asset price volatility are associated with lower liquidation likelihood, consistent with heightened execution risk. The findings provide new high-frequency evidence on liquidation frictions in a large and mature DeFi lending market. The results contribute to the understanding of the microstructure of DeFi liquidations and credit risk in decentralized lending protocols.
Ethereum is a decentralized blockchain platform that allows developers to deploy and run smart contracts, which are self-executing programs responsible for handling digital transactions without intermediaries. ERC standards define how these smart contracts are expected to behave in the Ethereum ecosystem. When these rules are not implemented correctly, contracts may contain security weaknesses that can lead to financial loss or unexpected behavior. For this reason, verifying whether a contract complies with ERC requirements is an important task during the development process. However, compliance verification is still often performed manually, which makes the process slow and dependent on expert knowledge. Most existing static analysis tools mainly detect predefined vulnerability patterns, but they may miss behavioral deviations from Ethereum Request for Comments (ERC) specifications that are not explicitly encoded as patterns. In this study, we present a multi-agent LLM framework designed to automate ERC compliance auditing. The system extracts contract-specific code fragments and evaluates them using multiple independent Large Language Model agents. Their outputs are aggregated through a confidence-weighted mechanism that aims to stabilize the final decision. Experiments on ERC-20, ERC721, and ERC-1155 contracts show that the multi-agent configuration improves recall and reduces false negatives compared to single-agent auditing.
This paper investigates the extent to which cognitive heuristics, social influence, and digitally-mediated sentiment drive the extreme volatility of cryptocurrency, Decentralised Finance (DeFi), and Non-Fungible Token (NFT) markets, and the degree to which these dynamics deviate from the Efficient Market Hypothesis. Using an integrative narrative review and a synthesis of empirical evidence from 2014–2025, the paper develops the Integrated Digital Asset Behavioural Model (IDABM), a four-variable framework relating market stability to social velocity (Sv ), heuristic load (Hl ), platform gamma (Pγ ), and liquidity leverage (Ll ). The analysis draws on demographic and sentiment data, case evidence from the 2022 Terra/ Luna and FTX collapses, and a comparative cross-asset bias taxonomy. The findings indicate that digital asset markets constitute a pure sentiment environment in which the absence of conventional valuation anchors produces heuristic dominance and structurally amplified herding behaviour. The paper concludes that effective regulation must shift from informational disclosure toward behavioural guardrails — including algorithmic accountability, regulation of gamified trading interfaces, and behavioural literacy requirements.
Aim: This study examines whether and how the disposition effect shapes Ethereum investors’ selling decisions. It asks whether investors are more likely to realize gains than losses, whether this asymmetry strengthens during high-volatility periods, and whether it weakens around major protocol upgrades, including the Merge, Shapella, and Dencun. Methodology: The study builds a high-frequency address-day panel for 2020–2024 using public on-chain data and labeled centralized-exchange deposit clusters as conservative proxies for sell decisions. Rolling cost bases are reconstructed under FIFO and value-weighted rules, and unrealized gains and losses are linked to realized sales through discrete-time logit and Cox hazard models. The design also includes event windows and robustness checks. Findings: The framework is designed to identify three mechanisms: asymmetric realization of gains over losses, stronger gain realization under high volatility, and attenuation around major protocol-upgrade events. Implications: The study offers a transparent design for analyzing behavioral bias in crypto-asset markets with verifiable blockchain data. It is relevant to exchanges, regulators, and market designers concerned with investor behavior and risk management. Originality/value: The article extends behavioral finance to Ethereum by using public ledger data rather than brokerage records and by integrating behavioral bias, volatility regimes, and protocol events in one framework.
This study examines how funding rate regimes affect the downside risk contribution of cryptocurrencies in equity portfolios. The research focuses on Bitcoin and Ethereum as the two largest cryptocurrency markets and evaluates whether downside risk differs across market conditions defined by perpetual futures funding rates. The objective is to connect cryptocurrency derivatives market conditions with portfolio downside risk assessment, as earlier literature has mainly examined these topics separately. The empirical analysis uses daily data from 1.1.2020 to 31.12.2025, and the data consists of returns for MSCI World Index, Bitcoin, Ethereum and perpetual futures funding rates. Research portfolios are constructed by adding cryptocurrency allocations of 5%, 10%, 15% and 20% to the equity benchmark portfolio. Downside risk is evaluated using historical Value-at-Risk, historical Expected Shortfall and Maximum drawdown. Downside risk is examined both over the full sample and separately across funding rate regimes. Funding rate regimes are classified into low, neutral and high conditions. The results suggest that adding cryptocurrency exposure increases downside risk relative to the equity benchmark across all portfolio groups. Funding rate regimes reveal meaningful differences in these results, but the effects differ between assets. Bitcoin portfolios show the clearest and most consistent regime dependence, with the most severe downside risk in the low funding regime and the mildest downside risk in the high funding regime. Ethereum portfolios show weaker and less stable regime separation. The results provide some indication that high funding conditions in Ethereum may be associated with less frequent but more severe tail losses. Specifically, high ETH funding environments appear to be associated with relatively mild Value-at-Risk results but more severe Expected Shortfall outcomes. Mixed Bitcoin-Ethereum portfolios produce strong statistical separation between regimes, with neutral funding conditions consistently associated with the mildest downside risk results. The findings suggest that derivatives market conditions may provide useful information when assessing downside risk of cryptocurrencies in equity portfolios. Funding rate conditioning appears particularly informative for Bitcoin downside risk assessment, while Ethereum results suggest that funding conditions may affect the structure of tail losses differently across market environments. The study contributes to existing literature by introducing a regime-conditional framework for evaluating downside risk under changing cryptocurrency derivatives market conditions.
Jun Young Byun, Yosep Na, Daehyun Kim, Hyun Ho Jeon · 6 authors
This paper examines whether on-chain factors derived from Ethereum blockchain data contain pricing information beyond established cryptocurrency risk factors. We construct 27 on-chain factors across four dimensions (network activity, scale-adjusted activity, valuation ratios, and token distribution) for 122 Ethereum-based tokens from July 2020 to August 2025, and evaluate them against 26 benchmark factors spanning size, momentum, volume, and volatility using a double-selection LASSO framework, complemented by portfolio sorts and three-factor regressions. Eight on-chain factors are significant in the cross-sectional pricing test, with the strongest evidence concentrated in scale-adjusted activity and token distribution. Transaction count to network value is the only factor that remains significant in the cross-sectional pricing test, portfolio sorts, and three-factor regressions. In contrast, valuation ratios based on market-to-realized values do not survive as independent sources of abnormal return once momentum is taken into account, reflecting the mechanical overlap between recent price appreciation and slowly adjusting realized values. Token-distribution factors, particularly small-holder share and centralized exchange share, generate the most robust abnormal returns and remain economically meaningful under equal-weighted construction and conservative transaction-cost assumptions. Subperiod analysis further reveals a change in on-chain pricing power: scale-adjusted activity factors are stronger earlier in the sample, whereas distribution-based factors become more important over time. Overall, the results show that blockchain-native information, especially holder distribution, captures a distinct dimension of cryptocurrency asset pricing.
Digitalisation of finance led to the creation of a digital financial economy, where digital assets such as cryptocurrencies, decentralized financial assets, non-fungible tokens, stablecoins, etc. were traded. In this study, machine learning and deep learning techniques, including ARIMA, FB Prophet, LSTM, and BiLSTM, have been used to forecast the prices of digital assets. In this study, Bitcoin, Ethereum, Uniswap, Aave, ApeCoin, and Decentraland tokens have been categorized into three groups, and the prediction models have been trained using the tokens' closing prices. The authors find that NFTs have been underestimated and that DeFi assets have greater growth potential. Whereas cryptocurrencies have been traded more and shown greater volatility than other asset classes. BiLSTM achieves the best results, with higher accuracy in price prediction. Here, it has been seen that ApeCoin, Decentraland, and Bitcoin are more stable than other assets. Thus, for an optimised portfolio and additional savings, it is necessary to provide a proper asset mix.
Do vulnerabilities in Decentralized Finance (DeFi) destabilize traditional short-term funding markets? While the prevailing ``Contagion Hypothesis'' posits that stablecoin reserve liquidations may transmit distress to traditional markets through fire-sale pressure, we document a short-horizon ``Flight-to-Quality'' pattern in the opposite direction. In the wake of major DeFi exploits, spreads on 3-month AA-rated commercial paper (CP) tend to narrow rather than widen. We interpret this pattern as consistent with a ``liquidity-recycling'' channel: capital leaving DeFi may be re-intermediated into traditional cash-management markets, with regulatory segmentation under SEC Rule 2a-7 making prime-eligible paper a plausible marginal destination. Because we do not directly observe daily fund-level routing into prime money market funds, this mechanism is inferred from pricing patterns and monthly holdings evidence rather than directly identified. The result is specific to exploit-driven operational shocks, this U.S. CP spread, and short event windows.
We develop a model of aberrant behavior by Bitcoin miners and test it with a new 2017-2025 dataset.Miners' rewards, comprised partly of user fees, exhibit variability across blocks of transactions.When large reward disparities exist between adjacent blocks, miners have incentives to attempt alternative versions of prior blocks and claim other miners' rewards for themselves.Regression analysis shows that fee differentials are associated with these attacks and longer waiting times between blocks.These patterns imply potential destabilization of the Bitcoin blockchain as future mining rewards become more volatile due to gradual withdrawal of fixed block subsidies.
Bitcoin is the largest digital asset class by market capitalisation, yet the overwhelming majority of its circulating supply remains economically idle. As of early 2026, approximately 19.8 million BTC have been mined, worth roughly $1.7 to $2.0 trillion at prevailing prices, distributed across cold wallets, exchange-traded fund (ETF) custody structures, and corporate treasuries. Less than 1% of circulating BTC participates in decentralised finance (DeFi) protocols, compared with an estimated 10 to 15% of Ethereum’s supply deployed in DeFi applications and approximately 28 to 30% when Ethereum’s native proof-of-stake staking is included, a network-security participation mechanism that has no direct equivalent at Bitcoin’s base layer. This paper argues that the persistence of this dormancy is not primarily a regulatory problem but an architectural one: the absence of programmable, trust-minimised financial infrastructure capable of deploying BTC productively at scale without requiring holders to relinquish effective control. I define Bitcoin activation as the deployment of previously idle BTC into productive financial uses, including lending, staking, liquidity provision, and restaking, through mechanisms that are trust-minimised, auditable on-chain, and preserve holder sovereignty over the underlying asset. The paper identifies three primary pools of idle Bitcoin, quantifies the scale of capital inactivity, examines the institutional and technical constraints that sustain it, and evaluates the emergence of Bitcoin decentralised finance (BTCFi) as a credible architectural response. Total value locked in BTCFi protocols grew from approximately $307 million to $6.5 billion in 2024, representing over 2,000% increase, driven largely by Babylon Protocol’s native staking infrastructure. I situate this growth within the broader institutional trajectory of Bitcoin’s adoption as a reserve asset and argue that BTCFi constitutes necessary infrastructure for the next phase of the Bitcoin network’s financial and security evolution. I identify open research questions regarding minimum viable institutional infrastructure, regulatory classification of on-chain BTC yield, and systemic risk in large-scale activation scenarios.
This study aimed to develop a conceptual model for pricing digital assets by integrating behavioral finance perspectives and identifying psychological and social factors influencing investors’ decision-making in decentralized markets. A qualitative grounded theory approach was adopted. The study involved 15 experts in digital currencies, blockchain, and behavioral finance selected through purposive sampling until theoretical saturation was achieved. Data were collected via semi-structured interviews and textual content analysis. Open, axial, and selective coding were applied to build the theoretical framework. Reliability was confirmed using quality control indices such as Krippendorff’s alpha, Holsti coefficient, Scott’s Pi, and Cohen’s Kappa, all indicating high inter-coder agreement. The resulting model captured multiple determinants of digital asset pricing. Causal factors included emotional and psychological behaviors (e.g., fear of missing out, fear and greed), the influence of news and media, and social association effects. Contextual factors encompassed uncertainty, ambiguity, and market volatility. Strategic factors such as market trust and credibility, investors’ knowledge and awareness, and reference points were identified. Core conditions included regulatory and legal environments, technological infrastructure, and macroeconomic conditions. Consequences involved enhanced market transparency, analysts’ and advisors’ influence, institutional and retail investor interactions, and the impact of past experiences on risk-taking. The proposed behavioral finance-driven model demonstrates that digital asset pricing extends beyond classical economic frameworks, heavily shaped by investor psychology and external information dynamics. The findings can guide investors toward more rational strategies and support policymakers in creating effective regulations and safer decentralized financial ecosystems.
Disasters and pandemics have adverse effects on both lives and economies, requiring timely and adequate funding for relief efforts. However, traditional donation systems often face challenges such as funding delays and public distrust. This paper proposed Funding Blocks (FunB)s, a decentralized donation software built on the Tezos blockchain (TzBlockchain). It ensures transparency, accountability, and security in a trustless environment. Smart contracts powered by the Tezos network’s proof-of-stake consensus algorithm facilitate automatic tamper-proof execution of donation transactions. This helps in eliminating intermediaries and reducing administrative costs. The platform’s decentralized nature enhances scalability and resilience, enabling swift response to global calamities. It offers a user-friendly interface for direct contributions, incorporating mechanisms to verify and validate charitable organizations. It also provides real-time tracking of funds, ensuring transparent visibility to donors. By leveraging blockchain technology, FunBs addresses funding challenges, accelerates response times, and enhances the efficiency of disaster relief efforts. This model contributes to creating a sustainable and resilient funding ecosystem that empowers individuals and organizations to make secure and transparent contributions during crises
Jan 1, 2026·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Blockchain, originally developed to solve the double-spending problem in digital currencies like Bitcoin, has evolved into a foundational technology with broad applications across public and private sectors.Its key features-immutability, decentralized trust, and cryptographic security-enable authenticated data sharing without the need for a central authority.This is particularly valuable in systems like supply chains, where participants may not know or trust each other.Smart contracts further enhance blockchain's utility by automating agreements through code, reducing uncertainty and fostering trust among stakeholders.The rise of the decentralized web, combined with emerging technologies like IoT, AI, and AR/VR, signals a wave of disruptive innovation whose full impact is yet to be seen.Given the rapid pace of development, academic research is essential to understand and guide blockchain's evolution.Conferences are especially important for timely knowledge dissemination, as they can keep up with the fast-moving nature of the field better than traditional journals.This mini-track builds on a series of successful sessions from HICSS conferences (HICSS-51 through HICSS-58), which have focused on blockchain's impact in areas such as fintech, transformation, and innovation.Over the years, it has served as a valuable forum for exploring blockchain technology and its implications for process improvement and innovation.For the current edition, six accepted papers contribute to expanding the academic understanding and supporting broader adoption of blockchain solutions.The first paper, "Playing Strategic Games in The Open Network (TON): Analyzing the Robustness of Proof-of-Stake Slashing Incentives", by Sascha Hgele, analyzes how rational validators in the TON blockchain respond to slashing penalties in a proof-of-stake system.Using a game-theoretic model, it reveals that when penalty enforcement is uncertain, validators strategically weigh risks and rewards, which impacts