This paper introduces Automated Institutional Discovery (AID), a novel computational framework that conceptualizes economic institutional design as a high-dimensional combinatorial search problem. Traditional institutional design relies heavily on human intuition, historical evolution, or analytically constrained mechanism design, which often fails in complex, adaptive multi-agent environments. AID transcends these limitations by framing institutions as tuples i = (r_1, r_2, ..., r_K) within an expansive institutional space and utilizing advanced search and optimization algorithms to discover configurations that maximize global objective functions F(i). By combining multi-agent simulation modeling with metaheuristic search strategies, AID evaluates allocative efficiency, incentive compatibility, resilience, and distributional equity without requiring empirical laboratory experiments. The framework establishes a paradigm shift from manual rule-making to automated machine discovery, offering robust applications for digital economies, decentralized finance, and economic governance.
This paper develops a Quantum-Institutional Automated Negotiation (QIAN) algorithm as an intelligent decision support system for carbon credit markets, contributing to quantum game theory applications in automated negotiation and institutional decision-making. We extend the EisertâWilkensâLewenstein (EWL) framework by introducing an Institutional Filter Function Ί_C that maps continuous quantum strategiesâphase shifts and superpositionsâonto finite, legally viable contract archetypes. This filter models regulatory, political, and organizational constraints that collapse the infinite quantum strategy space into a tractable finite set, enabling computationally efficient decision support. We prove convergence of the automated negotiation algorithm to a Pareto-superior Nash Equilibrium and demonstrate, through Monte Carlo simulation with literature-calibrated parameters, that the collapsed quantum equilibrium yields a mean joint utility uplift of 13.5% over classical cooperation (95% CI: 9.8%â17.3%, p < 0.001), with the upper bound reaching 17.3% and 26.8% of simulations achieving uplifts in the 15â30% range. The framework maps directly to blockchain-based smart contracts, providing a deployable mechanism for sustainable carbon markets that aligns with SDG 13 (Climate Action) and SDG 17 (Partnerships). This work advances quantum game theory from abstract formalism to computational institutional design, offering a novel decision support approach for negotiation analysis under real-world constraints.
In this paper, we develop an open-economy macroeconomic model of a Proof-of-Stake network to analyze nominal token-price dynamics and the systemic effects of speculative capital. We first consider a network populated solely by active utility users, who finance network activity through a steady exogenous inflow of fiat currency. We prove the existence of a unique, globally asymptotically stable steady-state equilibrium with a well-defined nominal token price and derive a closed-form expression for the network's relaxation time. Calibrating the model using parameters representative of the current Ethereum network, we estimate a relaxation half-life of approximately 46 years. This extreme macroeconomic inertia implies that the token price may remain persistently displaced from its evolving steady-state benchmark, producing sustained price overshooting as the network adjusts to changing fundamentals. We then introduce an Investor class to examine the effects of passive and active speculative capital. We show that passive institutional staking compresses the native staking yield and creates a structural imbalance that systematically raises the nominal token price while shifting consensus ownership away from active utility users. Active speculative capital has a qualitatively different effect. In response to capital shocks, the Consumer class's rigid preference for fiat-denominated consumption generates an endogenous constant-value strategy. This mechanism shifts staked-token ownership from the Investor class toward active utility users, with potentially favorable implications for consensus decentralization.
Human societies, economic markets, and digital systems face a fundamental coordination prob- lem: how self-interested agents cooperate in the allocation and use of scarce resources. Across these domains, contention necessitates identity systems that inform coordination mechanisms and govern resource allocation. However, existing literature typically treats identity establishment and coordination as separate problems. Institutional economics often assumes resource identity as an exogenous feature of the environment, while distributed consensus algorithms focus on coordina- tion under the assumption that resource identity is already known and agreed upon. This separation limits our understanding of how identity architectures shape the cost, efficiency, and stability of cooperation. 1 Using game-theoretic modeling and agent-based simulations, this study employs an isomorphic framework linking distributed systems and social institutions to analyze resource contention and coordination. We deploy a Cryptographic Content-Addressed Version-Aware Distributed Mutual Exclusion architecture, supported by an open-source implementation, to model resource identity allocation among concurrent processes. Simulations involving up to 10,000 agents are executed on the developed computational platform to evaluate how decentralized resource identity formation influences coordination costs. By tracking these interactions, the model measures how the structural method used to establish resource identity affects the cost, efficiency, and stability of cooperation among anonymous self-interested agents. The simulations indicate that cryptographically derived resource identities can achieve Nash- Implementable incentive compatibility, enabling cooperative outcomes without requiring a coor- dinating authority beyond a ledger that functions as a passive institutional record whose state may be modified only through resource-specific operations. These computational findings demonstrate a broader coordination paradigm in which identity emerges endogenously from the originating environment or substrate in which the resource is created. When treated as an active institutional design choice, such identity architectures may reduce reliance on external consensus among intelli- gent agents by shifting coordination toward resource-centered state recognition maintained through a non-strategic ledger. This framework endows scarce resources in contention with intelligence through the electronic capability of endogenous, unique self-identification and ledger registration, enabling autonomous self-allocation to contending network agentsâa mechanism that can transfer likely directly onto applications within the social sciences where allocation is consensus decided through an intelligent third party agent between independent contending agents. In attempt to provide resource itself the self-identification capability and participation in distribution to agents queueing for allocation, we see we are able to overcome computational cost in macro-structures. More generally, the results suggest that endogenous resource identity provides a framework for optimizing collective action and resolving contention across both digital platforms and broader social institutions. Keywords: resource identity, institutional coordination, cryptographic hashing, consensus protocols, game theory, agent-based modeling, mechanism design, distributed mutual exclusion, computational social science. 1 This paper is an independent writing sample submitted for graduate admission to the University of Chicago MACSS programme. It investigates the intersection of institutional economics, collective action theory, and distributed systems architecture. However, this research is not done for University of Chicago. The paper was not written specifically for this application; it reflects an independent research project undertaken in preparation for graduate study in computational social science. Research on isomorphic modelling for distibuted locking in computer networks: Cryptographic Content-Addressed Version-Aware Distributed Mutual Exclusion and corresponding codebase are available via GitHub (https://github.com/bpriyal/distcodelock/blob/main/README.md) and Zenodo (https://zenodo.org/ records/19634046).
Proof-of-Stake DAG-BFT consensus faces a trilemma between sybil resistance, reward fairness, and plutocracy. Existing protocols prioritize liveness over fair stake-based selection, driving longitudinal centralization. FairWave is a dual-channel DAG-BFT protocol that separates anchor selection from reward distribution. The selection channel is super-linear in stake, guaranteeing Sybil gain < 1 for K > 1; the reward channel is sub-linear via square-root stake normalization. DAG-derived uptime and latency metrics eliminate external oracles,and lagged reputation breaks circular dependency between selection outcomes and weights. Evaluated through approximately 550,000 Monte Carlo rounds against eight baselines, FairWave shows Gini 0.140 (vs. Pure-PoS 0.490, monotone HHI reduction from 0.039 to 0.020 over 50,000 epochs, and optimal Sybil split K * = 1. Safety follows unconditionally from the 2f + 1 commit rule; the liveness model predicts monotone degradation from 94.0% at b = 0.20 to 74.0% at b = 1/3, consistent with the architectural expectation of no discontinuous cliff.
This is an opinion paper concerning the decentralized epistemology, conceptualized as a branch of social epistemology and the philosophy of science that studies how knowledge, scientific truth, and intellectual consensus can be generated, validated, and distributed through networks of autonomous agents in the absence of a central authority (âipse dixitâ), hierarchical institutions, or trusted intermediaries. While classical epistemology focuses on the cognitive processes of the individual subject or the legitimation of knowledge by centralized institutions (such as universities, traditional peer-reviewed journals, and academies), decentralized epistemology analyzes the emergent properties of distributed systems. It combines game theory, computer science and facilitating technology (blockchain technology and distributed ledgers), and incentive mechanisms.
Linear contracts are ubiquitous in practice, yet optimal contract theory often prescribes complex, nonlinear structures. We provide a distributional robustness justification for linear contracts. We study a principal-agent problem where the agent exerts costly effort across multiple tasks, generating a stochastic signal upon which the principal conditions payment. The principal faces distributional ambiguity: she knows the expected signal for each effort level, but not the full distribution. She seeks a contract maximizing her worst-case payoff over all distributions consistent with this partial knowledge. Our main result shows that linear contracts are optimal for such a principal. For any contract, there exists a linear contract achieving weakly higher worst-case payoff. The proof introduces the concavification approach built around the notion of self-inducing actions; these are actions where an affine contract simultaneously induces the action as optimal and supports the concave envelope of payments from above. We show that self-inducing actions always exist as maximizers of the gap between the concave envelope and agent's cost function. We extend these results to multi-party settings. In common agency with multiple principals, we show that affine contracts improve all principals' worst-case payoffs. In team production with multiple agents, we establish a complementary necessity result: if any agent's contract is non-affine, the unique ex-post robust equilibrium is zero effort. Finally, we show that homogeneous utility and cost functions yield tractable characterizations, enabling closed-form approximation ratios and a sharp boundary between computational tractability results.
P Praveen Kumar, Dudimetla Pravalika, B. S. Dileep Kumar, Gattu Akshitha · 5 authors
The rapid advancement of blockchain technology has introduced new possibilities for secure digital ownership and transparent fundraising through Non-Fungible Tokens (NFTs).However, most existing charity platforms remain centralized, limiting transparency, accountability, and verifiable proof of donations.Donors often lack visibility into how funds are utilized, while reliance on intermediaries increases risks such as data manipulation, reduced auditability, and decreased trust.To address these issues, this work proposes a decentralized charity auction framework that leverages blockchain technology and NFT-based asset representation.The system is developed using the Django web framework integrated with Web3 infrastructure and smart contracts.In this model, each auction item is tokenized as a unique NFT, ensuring authenticity, traceability, and immutable ownership.The platform allows users to act as donors or auctioneers, enabling participation in NFT-based charity auctions.Users can place bids or contribute funds, with all transactions securely recorded on a blockchain ledger.At the end of each auction, NFT ownership is automatically transferred to the highest bidder or contributor, providing verifiable proof of participation.By removing intermediaries and incorporating a transparent, incentive-driven mechanism, the proposed system enhances donor trust and engagement.It ensures tamper-proof record-keeping and clear fund flow, strengthening accountability within charitable ecosystems.This framework demonstrates a scalable and efficient approach to modern fundraising, showcasing the potential of blockchain and NFTs in improving trust and transparency in charity applications.
The article provides theoretical-game model of voting in decentralized autonomous organization, where honest participants and evil agents meaning harm to the system strategically interact. To restrain harmful behavior mechanism of reputation tokens, i.e. intangible assets accumulated for conforming voting and lost in case of inactivity or confronting decision. The access to voting is given only when the minimum reputation threshold is exceeded. The model shows the introduction of reputation can transform one-step dilemma of participation into dynamic game. The key result is identification of two principle types of balance: mixing one, when evil agents behave like honest for a certain period of time in order to accumulate influence for the future attack and separating one, when they reveal their type quickly and are expelled from the system. Analysis shows that mechanism effectiveness depends drastically on its parameters (amount of rewards and fines) and informational structure: complete information of agents about proposal value can raise effectiveness of goal-oriented attacks. On the basis of this analysis recommendations were provided for designing sustainable systems, including the necessity to combine reputation with other mechanisms (quorum, delegation) and adjust parameters with regard to the share of evil agents.
Open access
Advanced Research in Systems and Signal Processing
We present two new proofs of the GibbardâSatterthwaite theorem, the foundational result in social choice theory establishing that every surjective, strategy-proof social choice function on three or more alternatives is dictatorial. Both proofs share a common engineâthe Mutual Exclusion of Influence (a six-line theorem showing that two voters cannot both control the same alternative pair at a shared profile while ranking the pair differently)âbut diverge in how they derive dictatorship from this principle. The first proof is purely combinatorial: mutual exclusion combined with a transition sequence identifies a uniquely decisive voter without constructing a classical pivotal voter. The second proof is information-theoretic: under the uniform distribution on preference profiles, strategy-proofness yields an exact identity relating conditional outcome entropy to option-set size. The zero-overlap theoremâa measure-theoretic consequence of mutual exclusionâforces influence entropy to concentrate entirely in a single voter, characterizing dictatorship as the unique entropy profile (log |X|, 0, âŠ, 0) compatible with strategy-proofness and surjectivity. To our knowledge, the second proof is the first to establish the GibbardâSatterthwaite theorem via Shannon-type information-theoretic quantities. The Mutual Exclusion Theorem itself is new and replaces the pivotal-voter construction across all four established proof routes with a single structural principle. Both proofs connect to the Adversarial Aggregation Channel (AAC) framework, in which the influence entropy corresponds to adversarial sub-channel capacity and the mutual exclusion principle instantiates a channel-capacity conservation law.
This technical disclosure describes advanced defence mechanisms for smart contracts including temporal displacement patterns, quantum-inspired uncertainty principles, and recursive trap architectures. The disclosed techniques create unpredictable defensive behaviours that resist analysis and exploitation. Temporal patterns introduce time-based variations in contract behaviour, quantum-inspired mechanisms create measurement-dependent state changes, and recursive patterns enable self-modifying defence structures. This document is published as a defensive publication to establish prior art and prevent third parties from obtaining patent protection for similar approaches.
Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emergesâmeasurable resistance manifesting as deadlock, thrashing, communication overhead, or outright conflict. This paper derives a formal framework for analyzing coordination friction from a single axiom: actions affecting agents require authorization from those agents in proportion to stakes. From this axiom of consent, we establish the kernel triple (alpha, sigma, epsilon)âalignment, stake, and entropyâas candidate sufficient statistics for any resource-allocation configuration. We propose a friction functional whose comparative statics encode three structural predictions: friction increases in stakes, increases in entropy, and decreases in alignment. The Replicator-Optimization Mechanism governs evolutionary selection over coordination strategies: configurations generating less friction persist longer, establishing consent-respecting arrangements as dynamical attractors rather than normative ideals. We develop formal definitions for resource consent, coordination legitimacy, and friction-aware allocation, plus machine-checked Lean 4 proofs of the core comparative-statics. Illustrative applications to cryptocurrency governance and political legitimacy show the same architecture spanning domains. v3.0.0 (2026-07-11): Matches arXiv v3 (94pp). The MARL empirical appendix has been split out into a standalone companion paper; total-variation legitimacy remark added (proved), reconciling the level-form dynamics with the total-variation measurement form; α-domain fixes; hedging pass throughout.
Decentralized finance (DeFi) agents automate multi-transaction workflows such as swapping, lending, and vault management, but they also create process-level risk. A run can consist of individually valid calls while still becoming economically unsafe because an intermediate step leaves latent authority, weakens execution constraints, or accepts unverified external evidence. Existing defenses are often mismatched to this process-level risk. Off-chain preflight checks alone cannot protect against runtime deviations from the intended plan, and coarse on-chain allowlists are too weak to express the call-level intent that matters in DeFi. We present CheckpointAgent, a workflow-security architecture for checkpointed DeFi-agent execution. It composes manifest commitments, smart-account policy guards, post-state predicates, and attestation-gated advancement to constrain a run step by step and tie checkpoint advancement to verifiable evidence. Rather than judging safety only after a workflow finishes, CheckpointAgent checks whether each step remains consistent with the intended workflow and stops execution when the required conditions no longer hold. In the author-curated 27-scenario local-chain suite, the strongest evaluated setting preserves all 5 benign runs and prevents unsafe completion in all 22 adversarial runs, stopping them either through on-chain enforcement or through trusted-attestation advancement under the configured attester assumption. Under explicit trust assumptions and within the measured workflows and snapshots, checkpointed execution can materially reduce process-level risk without modifying target protocols.
We study the epistemic efficiency of decentralized prediction markets under autonomous agentic liquidity. We introduce the information-incentive gap (G) â the discrepancy between ground truth and the market-implied probability â and establish, via ItĂŽ's calculus and exact solution of the resulting moment ODE, exponential convergence of its second moment together with an explicit upper bound for the gap of order O(Ï/λâââ). A two-level empirical study on information-driven event categories (Politics, Economics, Finance, Crypto Markets), drawing on approximately 40 million time-series records collected over the study period, is consistent with the model: (i) platform-level analysis of N=100 resolved binary events per platform shows the mean gap decreasing from GÂŻ=0.517 at Tâ168 h to GÂŻ=0.229 at Tâ30 min for Kalshi, and from 0.583 to 0.002 for Polymarket, with an empirical convergence rate λempâ1.4Ă10â6 sâ1; (ii) a paired cross-platform comparison of N=34 matched event groups shows that Polymarket exhibits a lower mean gap than Kalshi (mean ÎG=0.27 at Tâ6 h; Polymarket leads in 85% of pairs), consistent with the theoretical dependence of convergence speed on liquidity-driven λ. Monte Carlo simulation (N=50000 paths) confirms a >276Ă reduction in convergence latency and a 109Ă improvement in the Information Efficiency Ratio (IER) compared to the human-centric baseline.
A key scientific question underlying the blockchain ecosystem is to what extent the core security properties of the protocols hold when assuming rational validators in the presence of capable economic attackers. To what degree and at what cost can these systems be disrupted? In this thesis, I analyze the underlying economic security properties of three of the most fundamental decentralization consensus algorithms: proof of work (PoW), proof of stake (PoS), and oracle information aggregation. In Chapter 2 of this work, I counter a prominent narrative that PoW is inherently flawed in an environment in which double-spend attacks are possible. By considering counterattacks, I recover PoW robustness against reorganization attacks through a game-theoretic model. In particular, I consider hashrate markets as a potential vector of attack and show that PoW remains robust in this case. In Chapter 3 of this work, I show novel chain reorganization and finality-delay attacks on the PoS mechanism of Ethereum. These attacks are deviations from the âhonestâ staking strategy, and I show that for participants staking a substantial percentage of the networkâs staked assets, these attacks can be cheap and destructive to the network. In Chapter 4 of this work, I design an incentive mechanism for the information aggregation of noisy signals that is highly resilient to bribery. I establish the asymptotic strength and limitations of this mechanism against various classes of bribery including an attacker able to condition bribes on individual reports and on the outcome of the information aggregation. I achieve strong protection even in the latter case. To do this, I assume the presence of a source of truth (SoT) that is prohibitively expensive for typical use but can be invoked infrequently. This robustness to bribes is achieved even while in equilibrium there is no invocation of the SoT.
Open access
Blockchain Technology Applications and Security
Game Theory and Applications
Advanced Research in Systems and Signal Processing
The finite 1-bullet silent duel is considered, involving two duelists who shoot with exponentially-convex accuracy through a uniformly quantized time. The duel is a symmetric matrix game whose optimal value is 0, and each of the duelists has the same optimal behavior, whether it is in pure or mixed strategies. The actual beginning is never optimal in the duel. Apart from the very end of the duel, the conditions for the optimal time moment existence are found. Numerical experiments confirm that the optimality can be manipulated by changing the accuracy factor that scales the payoffs. The results are applicable in systems under limited or censored communication with uncertainty, latency, and lucrative delayed actions. Some examples of such set-ups are time-sensitive information release (privacy and censorship), queueing and load balancing (information science and telecommunication systems), and block proposal timing for decentralized consensus protocols (in Proof-of-Work and Proof-of-Stake).