Blockchain Papers

Andrea Caravaggio, Silvia Leoni

Abstract The management of infectious diseases increasingly relies on innovative but costly pharmaceutical treatments, raising complex trade-offs between epidemiological containment, fiscal sustainability, and institutional coordination. We develop a spatially structured agent-based model in which decentralized health authorities allocate treatment under local budget constraints while infection spreads across a two-dimensional lattice through neighborhood spillovers. Within each location, treatment intensity is chosen endogenously, interacting with local GDP dynamics and pricing conditions. Simulation results reveal that purely decentralized optimization mitigates but does not reverse infection growth within policy-relevant horizons, generating persistent spatial heterogeneity in both epidemiological and economic outcomes. We then introduce bounded spatial policy interaction, showing that partial coordination substantially improves containment but may increase the persistence of fiscal engagement. Extending the model to heterogeneous and time-varying pricing, we find that price discrimination amplifies medium-run infection and fiscal pressure under decentralization. However, when surplus revenues finance endogenous R&D, treatment efficacy improves over time, generating a feedback mechanism in which innovation mitigates long-run epidemiological and economic losses. Our findings highlight the critical interplay between spatial structure, decentralized decision-making, pricing design, and innovation incentives in shaping epidemic outcomes. Effective management of high-cost treatments requires not only medical efficacy but also institutional coordination and carefully designed market mechanisms.

Sebastian Müller, Isabel Amigo, Alexandre Reiffers-Masson, Santiago Ruano-Rincón

In directed acyclic graph (DAG)-based distributed ledgers, unreferenced blocks (tips) form the backlog of a distributed queueing system. Each new block creates one tip and attempts to remove up to $k$ existing tips by referencing them. With heterogeneous propagation delays, these service decisions are made from delayed local information, so nodes may disagree on the backlog and some reference attempts are wasted. We study a continuous-time Poisson model with bounded heterogeneous delays and uniform tip selection. We prove that the embedded tip-configuration chain is irreducible, aperiodic, and positive Harris recurrent, and hence admits a unique stationary regime. The observer and local tip-pool sizes have stationary exponential moments, converge to their stationary limits, and satisfy almost-sure ergodic averages. We also derive a Little-type identity relating the stationary mean observer tip count to the mean time until a typical block is first referenced. Simulations are included as qualitative illustrations of the effects of delay variability and issuance heterogeneity.

Yiguang Pan, Xiaomei Deng

The rise of blockchain has led to discussions on new governance models and the cooperation of multiple participants. Due to the cognitive defects of the blockchain protocol in terms of intelligent contracts and decentralized autonomous organizations (DAOs), it is often unclear as to how to make decisions about the evolution of blockchain applications. Many autonomous organizations, with the support of network technologies such as blockchain, blindly absorb members and expand the scale of the capital pool, while ignoring the cost advantage of traditional autonomous organizations based on social relations and mutual supervision to fight information asymmetry. In this context, this study analyzes the evolutionary trend of autonomous organizations and their members’ strategies under different policy environments. To this end, under the digital economy background, based on game theory, the evolutionary dynamics method, and the form of the mutual insurance organization, this study constructs an evolutionary dynamics model of distributed autonomous organizations. The results show that blind expansion without review aggravates the overall risk pool’s moral hazard, in the context of mutual insurance. Organizational strategies, such as risk pool splits, can effectively improve the risk pool’s operating performance and establish a benign competition elimination mechanism. Driven by cooperation efficiency and split supervision based on homogeneous clustering, the comprehensive application of the market elimination mechanism can effectively combat moral hazards, restrain the adverse effects of member flow, expand the living space of small‐ and medium‐sized insurance organizations, curb the emergence of a large‐scale monopoly risk pool, and improve market vitality. These conclusions and suggestions also apply to autonomous organizations based on social relations and mutual supervision. The results offer specific decision‐making guidance and suggestions for the government, insurance companies, and risk management.

Mattia Bongini, Massimo Fornasier

In recent years, numerous studies have focused on the mathematical modeling of social dynamics, with self-organization, i.e., the autonomous pattern formation, as the main driving concept. Usually, first or second order models are employed to reproduce, at least qualitatively, certain global patterns (such as bird flocking, milling schools of fish or queue formations in pedestrian flows, just to mention a few). It is, however, common experience that self-organization does not always spontaneously occur in a society. In this review chapter we aim to describe the limitations of decentralized controls in restoring certain desired configurations and to address the question of whether it is possible to externally and parsimoniously influence the dynamics to reach a given outcome. More specifically, we address the issue of finding the sparsest control strategy for finite agent-based models in order to lead the dynamics optimally towards a desired pattern.