Agents Are Not Algorithms: The Tradeoffs of Decision-Time Reasoning in AI Trading
A new study compares agentic AI systems, which use large language models for real-time decisions, with traditional trading algorithms. Researchers find agentic AI improves decision quality but can miss fast-moving market opportunities due to slower reasoning, a tradeoff called the reasoning dividend versus deliberation tax. Combining agentic judgment for complex tasks with algorithmic support for routine ones helps match algorithm performance. The study uses a real-time market simulator and suggests further research on long-term impacts and scalability.
What it examines
This paper studies agentic AI systems in trading, focusing on real-time reasoning versus pre-set algorithms. Using a market simulator, the authors test how reasoning intensity and market speed affect trading decisions, aiming to understand the tradeoffs between decision quality and time costs in AI-driven trading.
What it concludes
The study finds that real-time reasoning improves some trading decisions but can slow agents down, especially in fast markets. Combining agentic judgment with pre-computed actions makes AI competitive. These insights can help design smarter trading systems and suggest future research on balancing reasoning and speed in AI markets.
Evidence objects
Agentic AI systems using large language models boost decision quality in fast-paced trading, but their real-time reasoning can cause delays, leading to missed opportunities and stuck inventory as markets move quickly.
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Researchers highlight a tradeoffdubbed the 'reasoning dividend' versus the 'deliberation tax'where agentic AI excels in complex tasks but risks losses if it spends too long deciding in volatile environments.
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By blending agentic judgment for complex decisions with algorithmic support for routine tasks, agentic systems can match deterministic algorithms, though questions remain about their long-term market impact and scalability.
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This paper uniquely examines tradeoffs between agentic AI systems and traditional algorithms in trading, introducing the novel concepts of 'decision-time reasoning,' 'reasoning dividend,' and 'deliberation tax.' Its fresh analysis of real-time versus pre-computed reasoning offers compelling insights, significantly advancing understanding of AI architecture design and performance in financial markets.
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Raw abstract and provenance
Agents Are Not Algorithms: The Tradeoffs of Decision-Time Reasoning in AI Trading · Ing-Haw Cheng · Maurice Granger · Justin Shi · Vasily Strela · Do you have a job
Source row: 132 · abstract type: snippet