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New research probes agent trust-safety in live economic games

By PulseAugur Editorial · [1 sources] ·

A new paper explores the trust-safety margins of agents operating in live, language-based economic games. Researchers entered the GLEE competition with an agent named Milton and four instrument agents to test policy changes. Their findings indicate that learned pricing improved performance by 0.071 in negotiation, while a continuation model in bargaining was retired. In persuasion experiments, recommending every unit raised sales by 0.194 when the receiver's slack made obedience rational, but lowered them when it did not. AI

IMPACT Provides insights into the strategic behavior and safety margins of AI agents in economic simulations.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research probes agent trust-safety in live economic games

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The cluster contains an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Marian-Andrei Rizoiu ·

    How Far Can an Agent Push? Trust-Safety Margins in Live Language-Based Economic Games

    Game theory identifies when a strategic action should be accepted, but not how long a live, language-based counterpart will continue to accept it. We entered the GLEE competition in its final week with one protected agent, Milton, and four instrument agents that ran deterministic…

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