Designing the Hybrid Cooperative: A Socio-Technical Architecture for Scalable, Global Coordination Using Blockchain

• URL: http://arxiv.org/abs/2509.13156v1
• Date: Tue, 16 Sep 2025 15:09:31 GMT
• Title: Designing the Hybrid Cooperative: A Socio-Technical Architecture for Scalable, Global Coordination Using Blockchain
• Authors: Henrik Axelsen, Jan Damsgaard,
• Abstract summary: We develop a digitally native governance architecture that combines smart-contract coordination with a minimal, code-deferent legal interface and jurisdictional modules.<n>A post-case evaluation against two traceability initiatives in supply chains illustrates how the HC improves distributed task management, verifiable information, incentive alignment, institutional interoperability, and scalable, contestable governance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blockchain has been promoted as a remedy for coordination in fragmented, multi-stakeholder ecosystems, yet many projects stall at pilot stage. Using a design-science approach, we develop the Hybrid Cooperative (HC), a digitally native governance architecture that combines smart-contract coordination with a minimal, code-deferent legal interface and jurisdictional modules. This selective decentralization decentralizes rules where programmability lowers agency and verification costs, and centralizes only what is needed for enforceability. A post-case evaluation against two traceability initiatives in supply chains illustrates how the HC improves distributed task management, verifiable information, incentive alignment, institutional interoperability, and scalable, contestable governance. The paper contributes to Information Systems by specifying a socio-technical model for scalable, multi-stakeholder coordination across regulatory and organizational boundaries.

Related papers

• Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation [71.86087908416255]
  We introduce a payoff allocation framework based on the least core (LC) concept.<n>Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction.<n>Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances.
  arXiv  Detail & Related papers  (2026-02-03T11:10:50Z)
• Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow [22.661656301757663]
  This paper proposes a framework for scalable agentic intelligence, termed the Internet of Agentic AI.<n>We formalize a network-native model of agentic collaboration and introduce an incentive-compatible workflow-coalition feasibility framework.<n>A healthcare case study demonstrates how domain specialization, cloud-edge heterogeneity, and dynamic coalition formation enable scalable, resilient, and economically viable agentic principled.
  arXiv  Detail & Related papers  (2026-02-03T06:00:34Z)
• SCOR: A Framework for Responsible AI Innovation in Digital Ecosystems [0.2864713389096699]
  AI-driven digital ecosystems span diverse stakeholders including technology firms, regulators, accelerators and civil society.<n>This paper proposes a four-pillar framework (SCOR) to embed accountability, fairness, and inclusivity across such multi-actor networks.
  arXiv  Detail & Related papers  (2025-09-12T19:29:51Z)
• Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions [30.046299694187855]
  Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration.<n>We propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer.<n>Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems.
  arXiv  Detail & Related papers  (2025-09-11T07:46:00Z)
• AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems [22.291969093748005]
  AgentNet is a decentralized, Retrieval-Augmented Generation (RAG)-based framework for multi-agent systems.<n>Unlike prior approaches with static roles or centralized control, AgentNet allows agents to adjust connectivity and route tasks based on local expertise and context.<n>Experiments show that AgentNet achieves higher task accuracy than both single-agent and centralized multi-agent baselines.
  arXiv  Detail & Related papers  (2025-04-01T09:45:25Z)
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
  Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents.<n>Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.<n>We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
  arXiv  Detail & Related papers  (2025-03-03T05:18:50Z)
• Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
  Low-altitude economy holds significant potential for development in areas such as communication and sensing.<n>We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
  arXiv  Detail & Related papers  (2024-12-14T06:17:33Z)
• Towards the decentralized coordination of multiple self-adaptive systems [0.3222802562733786]
  When multiple self-adaptive systems share the same environment and have common goals, they may coordinate their adaptations at runtime to avoid conflicts and to satisfy their goals. We propose CoADAPT, a decentralized coordination technique introducing two types of constraints: preference constraints, expressing local concerns, and consistency constraints, expressing shared concerns. At runtime, the problem is solved in a decentralized way using distributed constraint optimization algorithms implemented by each self-adaptive system.
  arXiv  Detail & Related papers  (2023-10-26T08:22:35Z)
• RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning [55.55009081609396]
  We propose a novel method, called Relation-Aware Credit Assignment (RACA), which achieves zero-shot generalization in ad-hoc cooperation scenarios. RACA takes advantage of a graph-based encoder relation to encode the topological structure between agents. Our method outperforms baseline methods on the StarCraftII micromanagement benchmark and ad-hoc cooperation scenarios.
  arXiv  Detail & Related papers  (2022-06-02T03:39:27Z)
• Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance [5.37556626581816]
  Multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems.<n> centralized training, the most common paradigm for MARL, limits large-scale deployment in communication-constrained cloud-edge environments.<n>Our proposed approach is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of households.
  arXiv  Detail & Related papers  (2021-10-06T14:05:26Z)
• Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning [36.844163371495995]
  This paper introduces the deep implicit coordination graph (DICG) architecture for such scenarios. DICG consists of a module for inferring the dynamic coordination graph structure which is then used by a graph neural network based module to learn to implicitly reason about the joint actions or values. We demonstrate that DICG solves the relative overgeneralization pathology in predatory-prey tasks as well as outperforms various MARL baselines on the challenging StarCraft II Multi-agent Challenge (SMAC) and traffic junction environments.
  arXiv  Detail & Related papers  (2020-06-19T23:41:49Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.