aiqbench analysis
This is a theoretical chapter, not an empirical result. It argues that multi-agent systems and normative-systems research, which have long modelled institutions, are underused in today's agentic AI, and it questions the "compression-as-intelligence" view of intelligence. Readers working on agent architectures may find the framing useful, but the chapter offers conceptual guidance rather than benchmarks or deployable methods.
Anchoring Distributed Agency: Institutional Metaphors for Designing Large-Scale AI and AI Techniques for Running Institutions
arXiv:1803.03407v3 Announce Type: replace Abstract: Artificial Intelligence (AI) started out with the ambition to reproduce the human mind, but as soon as the sheer scale of that ambition became apparent, it retreated into either studying specialized intelligent behaviours or proposing overarching architectural concepts for interfacing specialized intelligent components---conceived of as agents acting through organizational structures. The agent-based modelling paradigm proved to have interesting applications beyond the original domain, supporting the understanding, simulation, and the assessment of behaviour of socio-economic and legal structures on an aggregate level. Looking at contemporary AI, whereas text completion provides a successful example of specialized behaviour, surprisingly little of the rich foundation of multi-agent systems (MAS) and normative systems research is currently reflected in Agentic AI systems. Addressing this gap, the chapter examines cross-cutting conceptualisations, modelling problems, and design challenges shared by large-scale distributed AI and institutional systems. Three main contributions can be highlighted. First, by tracing the historical trajectory of AI, the chapter identifies an \textit{ecological} perspective on intelligence, examining both the value and limits of the \textit{compression-as-intelligence} hypothesis. Second, it shows how moving between internal cognitive mechanisms and external manifestations of agency supports a functional separation of concerns for both individual and collective agency. Third, it argues that \textit{epistemological}, \textit{normative}, and \textit{value pluralisms} are necessary conditions for sustaining the ecological character of intelligence. Furthermore, we indicate how AI techniques can support institutions by modelling social roles, simulating normative interactions, detecting and diagnosing failures, and supporting procedures of review and revision.
Abstract:Artificial Intelligence (AI) started out with the ambition to reproduce the human mind, but as soon as the sheer scale of that ambition became apparent, it retreated into either studying specialized intelligent behaviours or proposing overarching architectural concepts for interfacing specialized intelligent components---conceived of as agents acting through organizational structures. The agent-based modelling paradigm proved to have interesting applications beyond the original domain, supporting the understanding, simulation, and the assessment of behaviour of socio-economic and legal structures on an aggregate level. Looking at contemporary AI, whereas text completion provides a successful example of specialized behaviour, surprisingly little of the rich foundation of multi-agent systems (MAS) and normative systems research is currently reflected in Agentic AI systems. Addressing this gap, the chapter examines cross-cutting conceptualisations, modelling problems, and design challenges shared by large-scale distributed AI and institutional systems. Three main contributions can be highlighted. First, by tracing the historical trajectory of AI, the chapter identifies an \textit{ecological} perspective on intelligence, examining both the value and limits of the \textit{compression-as-intelligence} hypothesis. Second, it shows how moving between internal cognitive mechanisms and external manifestations of agency supports a functional separation of concerns for both individual and collective agency. Third, it argues that \textit{epistemological}, \textit{normative}, and \textit{value pluralisms} are necessary conditions for sustaining the ecological character of intelligence. Furthermore, we indicate how AI techniques can support institutions by modelling social roles, simulating normative interactions, detecting and diagnosing failures, and supporting procedures of review and revision.
invited chapter. major update since the first version in 2018, before camera-ready version
Artificial Intelligence (cs.AI)
arXiv:1803.03407 [cs.AI]
(or arXiv:1803.03407v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.1803.03407 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 346 words
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