Where do hypotheses come from?

Here‘s the pre-print of our latest work (mostly done by Ishita Dasgupta) on a rational model of hypotheses generation. The main idea is to define hypotheses generation as a Markov chain Monte Carlo algorithm that locally traverses the space of hypotheses with only a finite sample size. It turns out that this algorithm can explain many “biases” of hypotheses generation and evaluation such as super- and subadditivity, anchoring, the crowd within-effect, and the dud alternative effect, to name just a few. We also test the predictions of this model within two experiments.


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