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Argonaut is an innovative interactive dashboard designed for visual exploration of distributed discrete-choice optimization in decentralized environments, addressing the challenges of observability and agent interdependence. By allowing users to upload datasets, construct agents, and dynamically modify decision parameters, Argonaut transforms the optimization process into an engaging, human-in-the-loop experience rather than a mere black-box computation. Evaluation across real-world datasets demonstrated Argonaut's capability to scale to 5600 agents and 1 million solutions efficiently, maintaining a runtime of under 30 seconds for complex configurations.
Argonaut turns the opaque process of distributed optimization into an interactive exploration, enabling real-time insights into agent decision-making and global objectives.
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 Node.js interface with extensible Java and Python optimization backends, it maintains a typical runtime of 200 agents over 100 decision attributes in under 30 seconds.