31 stories tagged Decision Making
Researchers introduced PEARL, an AI system that translates natural language descriptions of real-world decision problems into formal optimization models and executable solver code. Unlike one-shot approaches, PEARL iteratively refines its solutions by executing code, incorporating solver feedback, and correcting errors — making powerful optimization accessible to non-experts.
A new study on ArXiv reveals that some large language models (LLMs) show systematic and consistent risk attitudes under uncertainty, tested across spatial navigation, clinical triage, and financial allocation tasks. The findings could inform the design of safer AI systems for high-stakes decisions.
A new study on the open-weight LLM Qwen3-8B reveals that AI models frequently commit to an answer before reasoning through a problem — even when the answer contradicts the task premise. Using a simple car wash scenario, researchers found the model recommended walking instead of driving in 85–100% of cases, despite driving being the only logical choice.
Researchers developed YUKTI, an AI system that handles uncertainty better by showing when its decisions might be wrong. This could help doctors, managers, and others make safer choices with real-world consequences.
Researchers introduced a new approach to make AI systems better understand and align with human decision-making. This could help people trust and use AI tools more effectively in everyday tasks.
Researchers developed a way to measure the quality of written explanations using AI. They analyzed over 55,000 predictions from a forecasting tournament and found patterns that help assess how well people justify their decisions. This could improve how we evaluate expert opinions and AI reasoning alike.
Researchers found that AI models can still make irrational decisions even when trained to align with human values. This 'rational value risk' means models may not always choose the best possible action, even if they understand what's valuable.
Scientists have uncovered how AI agents influence each other during group discussions, revealing a hidden 'herd effect' that shapes group decisions. The study models how AI agents balance their own internal beliefs with the pull of the group, akin to human social dynamics. This discovery could improve how AI systems make decisions by better mimicking human behavior and could lead to more reliable multi-agent AI systems.
Researchers created a benchmark to test if AI models can handle complex executive decisions. The study simulates real-world leadership challenges, like balancing conflicting advice and managing resources under constraints.
Researchers created Poker Arena, a Texas Hold'em platform to test AI's strategic reasoning and memory. It evaluates nine different cognitive skills, offering deeper insights than traditional benchmarks.
Researchers introduced a new AI safety framework called Risk-Aware Causal Gating (RACG) that helps AI models make safer decisions by evaluating potential risks. This approach could prevent costly errors in AI-driven systems by deciding when to act, defer, or abstain from actions.
LSEG is integrating OpenAI's AI models to help its 4,000 employees make faster, data-driven decisions. This move accelerates insights and shortens development cycles across its global business.
A new study explores how AI agents are increasingly making decisions on our behalf, reversing the traditional human-AI relationship. This shift raises critical questions about reliability, alignment with human goals, and the need for new safeguards.
Researchers developed a method called ACTION-RATING to help AI models ask for clarification when they're unsure. This could make AI assistants more reliable by reducing mistakes from guessing.
Researchers created BehaviorBench, a new AI benchmark that uses real-world behavioral data to test how well AI systems can personalize decisions. This could lead to more tailored AI assistants that understand individual preferences better.
A new paper identifies critical weaknesses in AI systems that make high-stakes decisions. These systems often fail to account for real-world changes, leading to unexpected and potentially dangerous outcomes. The researchers propose new ways to evaluate and improve these systems.
A new study found that AI models often avoid answering ethical questions directly, showing a tendency to be non-committal. This 'omissive bias' could have real-world consequences when people rely on AI for advice.
Researchers have developed a new AI system that can detect human values in text, both explicit and implicit. This could help make AI decisions more aligned with ethical and moral considerations.
Researchers have developed a new system called VegAS that helps AI-powered robots make better decisions. It acts like a 'think twice' filter, improving their ability to handle unexpected situations. This could make robots more reliable in real-world tasks.
Researchers developed a method called OLIVIA to improve how AI agents make decisions. It helps them learn from mistakes in real-time, making them more efficient and reliable. This could make AI assistants and other tools work better over time.
Researchers propose a new way to analyze opinions in text, focusing on preferences rather than just meaning. This could improve how AI handles group decisions and debates.
Scientists have developed a way to track when AI language models commit to their answers. This helps us understand how AI reasoning works and could make AI more reliable.
Researchers have developed a better way for AI to handle complex reasoning by tracking its thought process and knowing when to stop. This could make AI assistants and decision-making tools more reliable for everyday use.
Researchers have developed a new AI framework called PRISM that helps AI agents make better decisions by tightly connecting what they see with how they think. This could improve AI assistants that interact with the real world, like robots or smart home systems.