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Predicting the behavior of human participants in strategic settings is an important problem in many domains. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant's cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art, which relies on expert-constructed features.

Neural Information Processing Systems http://nips.cc/

The potential for artificial intelligence to affect election results is a major public concern. Two new papers - with experiments conducted in four countries - demonstrate that chatbots powered by large language models (LLMs) are quite effective at political persuasion, moving opposition voters' preferences by 10 percentage points or more in many cases. The LLMs' persuasiveness comes not from being masters of psychological manipulation, but because they come up with so many claims supporting their arguments for candidates' policy positions. "LLMs can really move people's attitudes towards presidential candidates and policies, and they do it by providing many factual claims that support their side," said David Rand, a senior author on both papers. "But those claims aren't necessarily accurate - and even arguments built on accurate claims can still mislead by omission."

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