As humans, robots, and software agents increasingly share safety-critical environments, coordination must move from static task allocation to managing uncertain commitments. Existing frameworks fall short: they either assume rigid, static teams or learn opaque joint policies that are hard to adapt and difficult to integrate with human decision-makers. To overcome these limitations, we propose Risk-Aware Option Clearing (ROC), a unifying coordination mechanism in which agents expose options (temporally extended skills) paired with risk summaries that predict outcome distributions. A central clearinghouse then assigns tasks by optimizing risk-adjusted mission utility under deadlines and safety constraints. ROC is a family of mechanisms, ranging from deployments where the clearinghouse learns outcome models from data to ones that consume full distributional predictions from agents. By treating risk-aware options as the basic coordination unit, ROC sketches a scalable, transparent infrastructure for integrating heterogeneous agents into future mixed human--agent societies and outlines a research agenda for such risk-aware clearing layers.

翻译：随着人类、机器人和软件代理越来越多地在安全关键环境中协同工作，协调方式必须从静态的任务分配转向管理不确定的承诺。现有框架存在不足：它们要么假设刚性的静态团队，要么学习难以适应且难以与人类决策者整合的不透明联合策略。为克服这些局限，我们提出风险感知选项清算机制（ROC），这是一种统一的协调机制，其中代理提供选项（时间扩展技能）并附带预测结果分布的风险摘要。中央清算中心随后通过优化截止时间和安全约束下的风险调整任务效用进行任务分配。ROC是一系列机制的集合，涵盖从清算中心从数据中学习结果模型到直接使用代理提供的完整分布预测等不同部署方式。通过将风险感知选项作为基本协调单元，ROC勾勒出将异构代理整合到未来人机混合社会的可扩展透明基础设施，并概述了这类风险感知清算层的研究议程。