arXiv:2606.29533v1 Announce Type: cross Abstract: We study the problem of forecasting for an arbitrary number of downstream agents with unknown objectives, each of whom best responds to the forecaster's predictions. We seek a single forecaster that guarantees sublinear swap regret for all downstream agents simultaneously. For two-dimensional outcome spaces, we give a polynomial time algorithm that guarantees $\tilde{O}(\sqrt{kT})$ swap regret for any downstream agent with $k$ actions. This improves over the previously known bound of $\tilde{O}(kT^{5/8})$ and avoids the exponential in $T$ runti
Forecasting algorithms are continually being refined as AI and distributed systems become more complex, necessitating better multi-agent coordination and prediction capabilities.
Improved multi-dimensional forecasting with sublinear swap regret for multiple agents is crucial for developing more robust and efficient AI systems, especially in dynamic and competitive environments.
The development of a polynomial-time algorithm offering significantly better performance (O(sqrt(kT)) vs O(kT^(5/8))) for forecasting in multi-agent settings improves the efficiency and reliability of AI agent interactions.
- · AI agents
- · Reinforcement learning researchers
- · Algorithmic trading platforms
- · Supply chain optimization
- · Inefficient multi-agent systems
- · Systems relying on slower forecasting models
More efficient and predictable interactions within complex AI agent systems.
Accelerated development of sophisticated AI applications requiring multi-agent coordination, such as autonomous systems or complex simulations.
Potential for new economic models based on optimized multi-agent forecasting, impacting markets and resource allocation.
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Read at arXiv cs.LG