Abstract:
Sociotechnical systems are software-supported systems that involve multiple autonomous principals. Meaning is what matters to the principals in their decision making. Two communities, separately from each other, have talked about the importance of communication meaning: distributed systems and multiagent systems. Whereas the distributed systems community has largely given up the pursuit of meaning-based abstractions, the multiagent systems community has made grand strides in the area. In particular, via advances on languages for protocols and norms, work in multiagent systems resolves the apparently paradoxical question that has troubled distributed systems since its conception: How can you have programming abstractions that are general purpose but support decision making based on application-specific meaning? I discuss why the advances on meaning strike against conventional wisdom in systems, including in networks, distributed systems, and programming languages and why multiagent systems lies at the heart of the systems research.

Biography:
I am a Senior Lecturer (Associate Professor) in the School of Computing and Communications at Lancaster University. Through my work on multiagent systems, my interests and contributions span from AI to programming and software engineering and from services to networks and distributed systems.
My work addresses the engineering of sociotechnical systems, that is, systems involving interactions between autonomous principals. It stems from the question: What are good software representations for autonomy? It is founded in the insight: Good representations support decision making by users by capturing the meaning of their interactions.