[Submitted on 30 Jul 2026]

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Abstract:Distributed discrete-choice optimization in decentralized settings is often hard to explore and navigate: disentangling what other agents choose, how their choices are interdependent, and how they collectively reach a global objective quickly becomes intractable as the system scales. The major limitation is observability of the search process. Existing methods are largely centralized and offer limited support, visualizing only the final solution or providing algorithm backends over a fixed dataset, so how a solution is reached stays a black box. We present Argonaut, a lightweight, containerized optimization dashboard that enables interactive, visual exploration of the entire search process for multi-agent discrete-choice optimization in decentralized settings. Users upload datasets, construct agents and options, modify the decision space and its parameters on the fly, and run multiple algorithm backends to inspect how each configuration shapes local agent decisions and the resulting global objective. By uniting system construction, optimization, and analysis in one interactive loop, the first of its kind, Argonaut makes distributed discrete-choice optimization a human-in-the-loop process rather than a one-shot, black-box computation. We evaluate Argonaut on real-world household-electricity, shared-mobility, and sensor-data-exchange datasets scaling to 5600 agents and up to 1M solutions under brute force. Built on a this http URL interface with extensible Java and Python optimization backends, it maintains a typical runtime of 200 agents over 100 decision attributes in under 30 seconds.

Submission history

From: Srijoni Majumdar Dr [view email]
[v1] Thu, 30 Jul 2026 09:52:33 UTC (609 KB)