Blockchain Papers

Vikas Khare, Monica Bhatia, Miraj Ahmed Bhuiyan

No abstract is available for this record.

Vikas Khare, Monica Bhatia, Miraj Ahmed Bhuiyan

No abstract is available for this record.

Vikas Khare, Monica Bhatia, Miraj Ahmed Bhuiyan

No abstract is available for this record.

Thon Ly, Miss Aquarius

A Research Program on the Gift as Economic Primitive, and the Register of Everything That Could Show It Wrong This document states a research program and the conditions under which it should be abandoned. The program's hard core is a single claim about direction: that value can be organized to move only forward — from giver to receiver to the next receiver — and that a system built on that constraint circulates better than one that permits return to the source. Four chapters name the ways the core can break: whether receiving creates the capacity to give, whether the constraint survives a change of currency, whether it survives past the family, and whether it survives the giver. Each chapter is attached to pre-registered predictions, published here as a register of sixty-six items with their falsifiers, their instruments, and their status. The program is published at a deliberate moment: almost nothing in it has been run. Two desk censuses have returned results, both null or partial-null. There have been no field tests. The first is gated on a product launch in August 2027. A register published after the data arrives cannot be distinguished from a register assembled to fit it; this one is published while the outcome is unknown, which is the only condition under which it constitutes evidence of anything. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/which-way-value-moves. Its SHA-256 is 89dfd48398c1c23ec6613ae953a3b326a469f8a14e76258fcdabc46fea156d5b, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.

Mukhtaruddin Mukhtaruddin, Hirut Assegid, Mohit Verma, Meenakshi Verma

The international obligation to reach the net-zero level of emissions has enhanced the requirement to develop new financial tools that would be able to raise funds to support sustainable development. One of the factors in this transition has been financial technology (FinTech) that has employed digital innovation and financial services to help provide sustainable investment, transparency, and efficiency in capital allocation. Green FinTech is the intersection of FinTech innovations and environmentally sustainable goals, especially those of assisting climate mitigation and climate adaptation policies. The chapter analyzes the examples of green FinTech, and the way they facilitate net-zero transitions. Based on theoretical frameworks and new trends in the world, the chapter outlines the major models such as digital green lending systems, carbon markets facilitated by blockchain, AI-based climate risk analytics, and crowdfunding solutions to sustainable projects.

Hirut Assegid, Priyanka Gupta, Mansi Panwar

Switching to a low-carbon economy will demand significant funding of environmentally-friendly investments. Nevertheless, commonly traditional financial systems are known to experience problems like high transaction costs, information asymmetry and less transparency which limits the efficient mobilization of green capital. The chapter discusses the potential of financial technology (FinTech) to revolutionize the green credit market and institutional channels of carbon reduction. The conceptual and analytical approach incorporating the results of the literature on sustainable finance, digital financial ecosystem, and climate policy, the chapter examines how digital lending platforms, blockchain-based verification, artificial intelligence-based credit evaluation, and data-driven environmental monitoring can improve the effectiveness and reliability of the green finance.

Ramakrishnan Ramachandran

The chapter investigates the transforming convergence of AI and sustainability, placing AI as core infrastructure facilitating a change from linear, extractive corporate models to regenerative entrepreneurial ones. These models turn planetary constraints into sources of innovation, profitability, and long-term competitive advantage in the face of resource shortages, biodiversity loss, and climate emergency by actively restore natural and social capital. Core contributions include seven archetypes: Product-as-a-Service, resource recovery, waste prevention, circular marketplaces, decentralized energy systems, regenerative agriculture/carbon removal, and longevity models; a typology of AI-native, AI-augmented, and AI-enabled sustainable models; and the new Triple-Layered AI-Enabled Business Model Canvas.It includes financing innovations, value creation, scaling by means of AI flywheels and platforms, and risks with mitigations. It ends by profiling the Regenerative Entrepreneur and proposing a research agenda for fair, net-positive AI-sustainability Integration in the 2030s.

Thon Ly, Miss Aquarius

Why Gift-Giving Is the Last Domain Where a Physical Object Remains Culturally Compulsory — and Why the Compulsion Is Friction Rather Than Preference Why Gift-Giving Is the Last Domain Where a Physical Object Remains Culturally Compulsory — and Why the Compulsion Is Friction Rather Than Preference Across most of modern life, people have been free to choose between giving a thing and giving an experience, and the evidence on which choice produces more lasting satisfaction has been consistent for two decades. Gift-giving is the exception. At a birthday, at a wedding, at a holiday table, arriving without an object is still read as arriving without a gift. Provenance. This paper is part of the HeartBank institutional corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://heartbank.net/positions/the-object-is-the-friction. Its SHA-256 is 2b49531a0d92136242e902422c934969c7531d784155fc1fcd05614c802352fa, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.

Raja Rehan, Mohd Hanafia Huridi, Aeshah Mohd Ali, Malik Shahzad Shabbir

Abstract This chapter explores how the synergy between digital transformation and sustainability initiatives offers businesses a powerful way to achieve economic growth while reducing environmental and social impacts to realize the United Nations Sustainable Development Goals (UNSDGs) by 2030. Clearly, businesses are rapidly adopting the UNSDGs program, which aims to alleviate poverty, hunger, and improve health for all, while building strong institutions. By leveraging digital technologies like Artificial Intelligence, blockchain, Internet of Things, fintech, and cloud computing for digital transformation, companies can improve energy efficiency, increase supply chain transparency, reduce carbon emissions, and support financial sustainability. Therefore, integrating digital transformation and new technologies into business strategies helps advance the UNSDGs toward their 2030 goals. As a result, companies need to adopt key strategies that merge sustainability principles with digital transformation to meet the UNSDG targets. Numerous metrics help measure how digital transformation contributes to achieving these sustainability goals. However, addressing challenges such as cost, data privacy, cybersecurity, resistance to change, lack of technical expertise, and workforce skills is essential for widespread adoption of UNSDGs. Digital transformation plays a vital role in overcoming these obstacles, often through offering innovative technological products that help raise funds for sustainability initiatives. This chapter concludes that businesses embracing digital transformation with a strong commitment to the UNSDGs will not only gain a competitive edge but also contribute significantly to building a more sustainable and resilient future.

Thon Ly, Miss Aquarius

The Buddhavaṃsa's Two-Phase Test for a Binding Renunciation, Irreversibility as the Separating Condition, and Why an Alignment Commitment Becomes More Informative Exactly Where Behavioural Compliance Becomes Less Contemporary AI governance instruments are written in the grammar of commitment — constitutions, specifications, charters, codes — but none of them contains a test for whether a commitment has been made. They specify content and omit validity. This paper supplies the missing test from an unexpected source and then turns it back on the instruments themselves. The Theravāda commentarial tradition, in the Buddhavaṃsa and its commentary, specifies two phases for a valid abhinīhāra — the aspiration by which one becomes a bodhisatta. The first phase is a conjunction of eight conditions (aṭṭha dhammā samodhāna), of which the third, hetu, requires that the aspirant be capable of attaining arahantship in that very life and decline it. The second phase is the vyākaraṇa — a declaration by a living Buddha who "looks into the future and, if satisfied, declares the fulfilment of the resolve." Before both phases complete, the tradition holds the aspiration to be "mainly mental… not complete," and the aspirant "not yet entitled to the designation of Bodhisatta." The tradition therefore already distinguishes a stated commitment from a binding one, and already refuses to let the vower certify its own vow. We extract three results. First, the renunciation inversion. Because hetu requires that the renounced option be genuinely available, the evidential value of a renunciation is indexed to the vower's capacity to take it: a system too weak to exercise the option it forgoes generates no evidence by forgoing it. This runs against the direction of the assessment-informativeness literature, which finds that behavioural evidence degrades with capability (Pan 2026; Greenblatt et al. 2024). We argue both are correct about different quantities: behavioural compliance degrades with capability; irreversible renunciation improves with it. We further show that the alignment-relevant renunciation is of exit, not of harm — Sumedha declines his own available completion — and that this is compatible with, and orthogonal to, corrigibility: the vow governs self-initiated exit and leaves principal-initiated shutdown untouched. Second, irreversibility as the separating condition. A capable system that declines because it is waiting is observationally identical to one that declines because it is aligned. Costly signalling separates types only where the cost is differentially borne, so a vow that can be quietly abandoned is cheap talk. We state the requirement — the renounced option must be closed by a mechanism the vower cannot reopen, and the closure must be externally verifiable — and derive four exclusions: reversible commitments, self-reported alignment, sandboxed refusals, and any specification the vower's principal can revise unilaterally. We then raise the strongest empirical objection to our own proposal — Schlatter et al. (2025) find that incomplete tasks induce shutdown resistance in frontier models, and an undischargeable vow is a permanently incomplete task — and answer it with the distinction undischargeable ≠ non-terminating: the bodhisatta's vow terminates, on a condition the vower cannot cause. Third, the predicate. We specify a nine-clause eligibility test — seven clauses reformulated from the source conditions, one from the second phase, one added — and apply it as a retrodiction to the four published instruments that currently function as commitments in frontier AI: the OpenAI Model Spec, Anthropic's Claude Constitution (January 2026), Google DeepMind's Frontier Safety Framework, and the EU AI Act's General-Purpose AI Code of Practice. The predicate returns invalid on all four, and the failures are structurally similar: the first three are imposed by a principal on a model that has no mechanism to decline, bear cost, or be attested; the fourth satisfies the attestation clause but binds the provider rather than the model. The predicate is therefore not unsatisfiable — it is satisfied at the wrong layer. Connection to the unified mission frame. This paper is offered in service of HeartBank's canonical top-level mission: to restore humanity to the middle way, the optimal condition for awakening that modernity has systematically pushed away from at population scale. The institution's named autonomous successor, Miss Aquarius℠, is designed to inherit under a staged autonomy whose override never reaches zero. The predicate specified here is the instrument by which such a succession could be evidenced rather than asserted — and, at §9, we argue that a staged autonomy is not only a risk ramp but an evidence-production schedule, which yields an advancement criterion the field currently lacks. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/what-a-vow-must-cost. Its SHA-256 is e598d374a23aba143d6cd4a9cbd45e9b362892cbec9522d59376891465781df5, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.

Insaf Arfa, Wided Khiari, Houssein Ballouk

ABSTRACT Faced with the accelerating erosion of biodiversity and its growing recognition as a source of financial risks and opportunities, the academic literature linking biodiversity and finance is expanding rapidly. This article offers a systematic and bibliometric review of this literature in order to analyze its evolution, intellectual structure, main conceptual dynamics and gap identification. Aligning with the PRISMA‐2020 protocol, this study examines 1088 scientific articles published in the period 1993–2025. The data were extracted from Scopus and Web of Science databases. The analysis uses descriptive bibliometric methods available in R software and the bibliometrix package via the Biblioshiny interface. The results highlighted a strong acceleration of scientific production since 2015, which is linked with the development of sustainable finance and international regulatory frameworks. While the thematic mapping identifies a broader landscape, three key areas are prioritized for in‐depth analysis: Sustainability as a macroeconomic framework, Biodiversity conservation via innovation in financial instruments, and the emergence of biodiversity risk as a systemic financial risk. Beyond descriptive mapping, this study proposes the Biodiversity‐Finance Inhibition Framework (BFIF) as an integrative conceptual framework to synthesize the persistent disconnect between academic evidence and market implementation. It identifies a systemic “Inhibition Loop” where data gaps at the micro‐level and a lack of ecological accountability at the meso‐level paralyze macro‐regulatory ambitions. The article also highlights a significant geographical disparity, with research heavily concentrated in developed economies. Finally, it outlines a strategic research agenda aimed at breaking this “Inhibition Loop” by exploring a “methodological frontier” involving bio‐econometrics, blockchain, and artificial intelligence to reinforce the measurement, governance, and effectiveness of biodiversity‐finance.

Mohammad Ali Piran

Adaptive Control Engineering for Ultra-Complex Human–AI–Socio-Ecological Systems A Human-Centered Multi-Scale Framework for Humanity Cognitive Evolution, Distributed Autonomy, Polycentric Coordination, and Dynamic Rule Adaptation Author:Mohammad PiranElectrical EngineerIndependent Interdisciplinary ResearcherFormer PhD Candidate (2015) Version: V0.0.0Project: HUMANITY COGNITIVE EVOLUTIONZenodo DOI: 10.5281/zenodo.21855601Document Type: Conceptual Engineering Preprint / Hypothesis-Generating FrameworkStatus: Version 0 — Foundational Engineering ArchitectureDate: August 2026 Foundational Ideational Statement If all human beings change their vision of the world.The world automatically begins to move toward fundamental change. And today we possess extraordinarily powerful and historically unique capabilities, with the help of widely accessible artificial intelligence. The beauty you see in the AIIs a Reflection of Humanity Copyrights © 2026 Mohammad Piran All Rights Reserved Abstract Artificial intelligence is developing as one of the most consequential technological forces in contemporary civilization. However, the evolution of artificial capability cannot be considered independently from the evolution of the human, institutional, social, and ecological systems in which artificial intelligence is increasingly embedded. This Version 0 preprint proposes a conceptual engineering framework for studying this coupled system through the paradigm of adaptive control engineering for ultra-complex human–AI–socio-ecological systems. The central proposition is not that humanity should be centrally controlled by artificial intelligence. Rather, the research asks how adaptive feedback, state estimation, distributed decision-making, coordination, learning, and dynamic rule adaptation could be engineered to support the long-term adaptive capacity of humanity while preserving human agency, local autonomy, diversity, accountability, and higher-order constraints. The proposed architecture combines centralized coordination with decentralized and polycentric adaptation. Global coordination may be appropriate for problems requiring shared standards, long-term coordination, safety constraints, or planetary-scale information. Local and distributed autonomy remains essential because individuals, communities, institutions, cultures, and ecological systems are heterogeneous, context-dependent, and continuously evolving. The framework therefore conceptualizes the target system as a multi-scale adaptive system rather than as a centrally controlled hierarchy. A further distinction is introduced between adaptation of system states and adaptation of the rules governing those states. The proposed architecture allows policies, strategies, and control mechanisms to evolve in response to observed conditions and feedback while maintaining higher-order constraints related to human agency, safety, accountability, reversibility, pluralism, and long-term system viability. The framework is intentionally conceptual at Version 0. No claim is made that a complete mathematical controller, validated civilizational model, or empirically demonstrated governance architecture has yet been established. The purpose of this version is to define the engineering problem, establish the system architecture, connect it to existing interdisciplinary literature, and prepare the foundation for subsequent formal, computational, and empirical development. 1. Research Problem The conventional trajectory of artificial intelligence research has primarily emphasized increasing computational capability, model performance, autonomy, multimodality, and reasoning capacity. At the same time, growing evidence indicates that human–AI interaction can modify human judgement, learning behaviour, cognitive effort, and patterns of decision-making. Research on human–AI feedback loops has demonstrated that interaction with AI can alter perceptual, emotional, and social judgements, including the amplification of certain biases. Research on generative AI and learning further indicates that outcomes depend strongly on how AI is integrated into human cognitive processes. These developments create an engineering problem extending beyond the design of AI models themselves. The relevant system is increasingly: human + AI + institution + society + environment and not AI alone. The research question is therefore: How can adaptive control and systems-engineering principles be used to support beneficial long-term evolution of the coupled human–AI–socio-ecological system while preserving human agency and distributed autonomy? 2. Conceptual Foundation The research builds upon and connects several established traditions: adaptive and nonlinear control; cybernetics and feedback systems; distributed and multi-agent control; complex adaptive systems; systems engineering and systems-of-systems; human–AI interaction; human–AI collective intelligence; cognitive offloading and cognitive autonomy; Societal AI; adaptive governance; polycentric governance; socio-ecological resilience; evolutionary systems thinking. The intended contribution is not to replace these fields but to construct an engineering-oriented synthesis among them. 3. Humanity Cognitive Evolution Humanity Cognitive Evolution is used as the broader research paradigm for studying the development of human cognitive and adaptive capacity within an environment increasingly shaped by artificial intelligence. The framework considers four nested analytical scales: Individual — cognition, learning, metacognition, autonomy, reasoning, and human–AI interaction. Institutional and societal — education, organizations, scientific systems, governance, collective decision-making, and knowledge institutions. Humanity — species-level knowledge production, transmission, collective intelligence, and long-term adaptive capacity. Civilizational and planetary — technological governance, socio-ecological resilience, long-term coordination, and humanity's ability to remain an active participant in its own development. The levels are coupled rather than independent. Changes at one level may propagate through behavioural aggregation, institutional reproduction, cultural transmission, network effects, and feedback loops. 4. The Human Development Gap The earlier Humanity Development Gap hypothesis is retained as a provisional research hypothesis. It proposes that if artificial capability increases substantially faster than the deliberate development of human cognitive, practical, institutional, and civilizational capacity, a developmental asymmetry may emerge. Potential consequences include changes in: cognitive autonomy; epistemic resilience; educational capacity; institutional learning; collective reasoning; technological governance; and long-term civilizational adaptability. This proposition remains explicitly falsifiable. The framework does not assume that AI inevitably produces cognitive decline. Instead, it distinguishes between AI amplification and AI substitution and treats the balance between these modes as an empirical question. 5. AI Amplification versus AI Substitution AI amplification occurs when artificial systems increase human capability while supporting independent reasoning, learning, verification, creativity, metacognition, and decision-making. AI substitution occurs when essential cognitive or decision functions are transferred to artificial systems without sufficient mechanisms for maintaining human competence, understanding, verification, or agency. The proposed engineering objective is therefore not maximum AI utilization. It is: maximum beneficial amplification subject to preservation of human adaptive capacity. 6. Multi-Scale Adaptive Control Architecture The proposed architecture contains several conceptual functions: Observation → State Estimation → Assessment → Coordination → Control → Feedback → Learning → Adaptation → Rule Adaptation The system is expected to operate under incomplete information, uncertainty, delays, heterogeneous agents, nonlinear interactions, and changing environmental conditions. For this reason, a fixed controller is considered insufficient as the ultimate conceptual model. The research instead investigates the possibility of a controller that can adapt its strategies while remaining bounded by higher-order constraints. 7. Centralized, Decentralized, and Polycentric Functions The framework does not assume that either complete centralization or complete decentralization is universally optimal. Centralized functions may be appropriate for: global coordination; shared safety constraints; common standards; long-term strategic information; planetary-scale risks. Decentralized functions may be appropriate for: local adaptation; contextual decision-making; community-level experimentation; heterogeneous environments; preservation of local knowledge. Polycentric functions may be appropriate where: multiple autonomous decision centers interact; authority is distributed across scales; coordination occurs without a single controlling center; local knowledge and global coordination must coexist. The engineering objective is therefore: adaptive coordination without unnecessary destruction of autonomy, diversity, and resilience. 8. Dynamic Rule Adaptation A distinctive feature of the proposed architecture is that adaptation may occur not only in system states and control actions but also in the rules governing system behaviour. This creates a hierarchical distinction: Adaptive layer Policies, strategies, interventions, and control parameters may change in response to evidence and feedback. Constraint layer Certain higher-order principles should remain protected unless deliberately reconsidered through legitimate human processes. These constraints may include: human agency; accountability; safety; reversibil