Workshops & conferences.

Examines financial system architecture, stability, and central banking issues under the theme "Fragmentation, Systemic Risk, and the Future of Central Banking."

Covers theory and applications of complex systems, including networks, self-organization, and collective dynamics.

A conference on information processing in complex self-organizing systems, spanning swarm intelligence, agent-based modeling, and self-organizing cyber-physical systems.

ADCOM 2027 solicits papers on architectures, coordination protocols, infrastructure and security frameworks for distributed, agent-driven computing ecosystems including centralized/decentralized agent networks and the Internet of Agents.

Covers unintended consequences of macroprudential policy, including distributional effects and changes in financial intermediation, as an annual workshop.

Covers sandboxing, privacy, trust establishment without central control, and rigorous evaluation needed to make internet-scale networks of autonomous, delegating AI agents trustworthy in practice.

The symposium covers engineering, measurement, and governance of trustworthy, accountable agentic AI systems across their full lifecycle, including auditing, standards alignment, and human oversight.

Research submissions treat internet-scale networks of communicating, adapting AI agents—rather than single models—as the unit of analysis, spanning representation/discovery, learning dynamics over agent graphs, and coordination, governance and safety at population scale.

An academic–central bank conference on financial stability and macroprudential policy, with 2026 focus areas including regulatory complexity, banking sector competitiveness, and the role of AI in banking and finance.

How intelligent agents can reason, act, and adapt safely and securely when operating autonomously in open-ended real-world environments, covering safety, security, multi-agent coordination, and governance.

The workshop examines the design, training, evaluation, deployment, and oversight lifecycle of meta-agents that coordinate or manage swarms of subordinate AI agents and, in some cases, humans.

Defines the abstractions, memory hierarchies, scheduling policies, and governance mechanisms needed for a shared operating-system layer supporting large-scale agentic AI deployments.

A workshop bridging micro-scale mechanism design and market-level economic analysis to map the economic consequences of machine learning's growing capabilities and propose interventions for long-term sustainability.

MASO 2026 addresses the socio-technical, operational, and governance challenges of deploying multi-agent and agentic AI systems within private enterprise and public-sector organizations.

How autonomous agents should act within human and institutional norms in multi-agent ecosystems, covering trust, transparency, accountability, and collective intelligence design for societal welfare.