The secret to human 'brilliance' that AI just can't match

Summary

Stanford GSB professor Douglas Guilbeault argues that human decision-making often relies on 'satisficing'—gathering just enough information to make a satisfactory prediction, rather than seeking optimal solutions. Experimental games show this strategy also applies to learning social rules, offering new insights into social learning and highlighting a key difference between humans and large language models (LLMs). LLMs assume that problems are solved by absorbing vast data and performing complex probability calculations, but humans have limited neurons and often lack sufficient data, so they use satisficing in uncertain situations. This finding underscores the subtle interaction between mind, behavior, and social context that AI may not replicate.