Over the past two decades, Bayesian models have been used to explain behaviour in domains from intuitive physics and causal learning, to perception, motor control and language. Yet people produce clearly incorrect answers in response to even the simplest questions about probabilities. How can a supposedly Bayesian brain paradoxically reason so poorly with probabilities? Perhaps brains do not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, they could be approximating Bayesian inference through sampling: drawing samples from a distribution of likely hypotheses over time.
This promising approach has been used in existing work to explain biases in judgment. However, different algorithms have been used to explain different biases, and the existing data does not distinguish between sampling algorithms. The first aim of this action was to identify which sampling algorithm is used by the brain by collecting behavioural data on the sample generation process, and comparing it to a variety of sampling algorithms from computer science and statistics. The second aim was to show how the identified sampling algorithm can systematically generate classic probabilistic reasoning errors in individuals, with the goal of upending the longstanding consensus on these effects. Finally, the third aim was to investigate how the identified sampling algorithm provides a new perspective on group decision making biases and errors in financial decision making, and harness the algorithm to produce novel and effective ways for human and artificial experts to collaborate.
The conclusions of the action were that a self-consistent sampling approach can explain a wide range of behavioural data, including psychological time series, probabilistic reasoning errors, and financial forecasting. New ways for human and artificial agents to collaborate were identified.
