Cascades, Leaps, and Strawmen: How Explanations Evolve

Explanations are social, and when people try to explain something, they usually seek input from others. We present a simple theory of how people use the explanations they encounter as both aids to sense-making and clues to the broader landscape of possible explanations. The computational challenge of coming up with novel explanations draws people to imitate appealing ones (information cascades); this draw increases as less appealing alternatives become more distant (the ``strawman'' effect). Conversely, pairs of divergent high-quality explanations can lead to merging and syncretism, and pairs of low-quality explanations to long-leap exploration. We then use a transmission-chain experiment to test, and confirm, these predictions, although, intriguingly, we find little evidence that the imitation that good explanations can trigger actually leads to better outcomes. Our work provides new insight into how collective exploration can be promoted, or stalled, both by what has been found, and by implicit information about what is yet to be discovered.