1 citations · 2 across the 8 of their papers we have counts for
9 papers
Artificial intelligences and human scientists exhibit complementary strengths in theory building
Ke Li, Spyros I. Zoumpoulis, Phanish Puranam +80
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social sci…
Why They Disagree: Decoding Differences in Opinions about AI Risk
Nghi Truong, Phanish Puranam, Özgecan Koçak
Identifying the reasons for disagreements between influential points of view on issues that affect the public can help produce informed policy responses, even if they do not bring…
Can LLMs Help Improve Analogical Reasoning For Strategic Decisions? Experimental Evidence from Humans and GPT-4
Phanish Puranam, Prothit Sen, Maciej Workiewicz
This study investigates whether large language models, specifically GPT4, can match human capabilities in analogical reasoning within strategic decision making contexts. Using a no…
How Generative AI Adoption Alters the Demand for Cognitive and Social Skills Within Roles: A Skill-Centric Analysis
Piyush Gulati, Arianna Marchetti, Victoria Sevcenko +1
A common view holds that generative AI (GenAI) automates cognitive tasks, reshaping roles to emphasize social skills over cognitive ones. Drawing on the framework we develop in thi…
Why Trust in AI May Be Inevitable
Nghi Truong, Phanish Puranam, Ilia Testlin
In human-AI interactions, explanation is widely seen as necessary for enabling trust in AI systems. We argue that trust, however, may be a pre-requisite because explanation is some…
Tool and Trainer? Experimental Evidence on Human Learning from Predictive AI in Cancer Diagnosis
Vivianna Fang He, Sihan Li, Phanish Puranam +1
Artificial intelligence (AI) is increasingly deployed to aid professional judgment, yet its implications for the development of human expertise remain unclear. We examine how the a…
Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving
Sanghyun Park, Boris Maciejovsky, Phanish Puranam
Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wis…
LLMs as mediators: Can they diagnose conflicts accurately?
Özgecan Koçak, Phanish Puranam, Afşar Yegin
Prior research indicates that to be able to mediate conflict, observers of disagreements between parties must be able to reliably distinguish the sources of their disagreement as s…
Learning what they think vs. learning what they do: The micro-foundations of vicarious learning
Sanghyun Park, Phanish Puranam
Vicarious learning is a vital component of organizational learning. We theorize and model two fundamental processes underlying vicarious learning: observation of actions (learning…