Cooperation as Black Box: Conceptual Fluctuation and Diagnostic Tools for Misalignment in MAS

📅 2025-06-28
📈 Citations: 0
✨ Influential: 0
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🤖 AI Summary
In multi-agent systems, conflation of “cooperation” and “coordination,” coupled with moralized misinterpretations, induces semantic ambiguity and normative projection during design—leading to meaning-level misalignment. This paper introduces the “Misalignment Mosaic” diagnostic framework, which systematically identifies latent semantic deviations across four dimensions: terminological inconsistency, concept-to-code decay, moralization of cooperation, and interpretive ambiguity. Innovatively treating “meaning itself” as a source of misalignment, the framework employs the Rabbit-Duck illusion as an analogy to expose the perspective-dependence of behavioral interpretation. As a qualitative tool, it generalizes to other overloaded concepts—including alignment and autonomy—and successfully deconstructs the black-boxing of cooperation. The framework provides an actionable, concept-level diagnostic methodology for multi-agent systems, significantly enhancing semantic consistency and interpretability.

Technology Category

Multiagent Systems: Coordination and CollaborationCognitive Modeling & Cognitive Systems: Agent ArchitecturesIntelligent Robots: Multi-Robot Systems

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsResponsible Web: Machine-in-the-loop, human agency and autonomySearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
📝 Abstract
Misalignment in multi-agent systems (MAS) is often treated as a technical failure; yet many such failures originate upstream, during the conceptual design phase, where semantic ambiguity and normative projection take place. This paper identifies a foundational source of interpretive misalignment in MAS: the systemic conflation of cooperation and coordination, and the moral overreading that follows. Using the Rabbit-Duck illusion, we illustrate how perspective-dependent readings of agent behavior can create epistemic instability. To address this, we introduce the Misalignment Mosaic, a diagnostic framework for diagnosing meaning-level misalignment in MAS. It comprises four components: 1. Terminological Inconsistency, 2. Concept-to-Code Decay, 3. Morality as Cooperation, and 4. Interpretive Ambiguity. The Mosaic enables researchers to examine how misalignment arises not only through policy or reward structures but also through language, framing, and design assumptions. While this paper focuses on the specific ambiguity between coordination and cooperation, the Mosaic generalizes to other overloaded concepts in MAS, such as alignment, autonomy, and trust. Rather than define cooperation once and for all, we offer a framework to diagnose meaning itself as a source of misalignment.
Problem

Research questions and friction points this paper is trying to address.

Addresses conceptual misalignment in multi-agent systems design
Identifies confusion between cooperation and coordination in MAS
Proposes diagnostic framework for meaning-level misalignment
Innovation

Methods, ideas, or system contributions that make the work stand out.

Diagnostic framework for MAS misalignment
Misalignment Mosaic with four components
Addresses language and design assumptions
S
Shayak Nandi
Grinnell College, Grinnell, IA 50112
F
Fernanda M. Eliott
Grinnell College, Grinnell, IA 50112