Forum
What is DR@W Forum?
DR@W Forum is an interdisciplinary discussion series which focuses on theoretical and empirical research about decision making.
The usual structure of the forum is a 30 - 45 minute introduction of the topic/working paper, with ample additional time for discussion.
The audience prefers discussing work-in-progress topics as opposed to finished papers. We meet on Thursdays between 2:30 and 3:45pm during term time, with streaming via Zoom. Contact John Taylor (John.Taylor[at]wbs.ac.uk) if you would like to suggest a speaker for a future event. Notifications of upcoming DR@W Forum events along with other decision research related activities can be obtained by registering with the moderated Behaviour Spotlight email list.
Note that several talks during the 2024/25 academic year are being hosted and orgnanised by the Economics department. This is indicated in the calendar entries. These talks will all take place in the Social Studies building. If you require further details regarding these sessions, please contact Matthew Ridley (Matthew.Ridley[at]Warwick.ac.uk) in the Economics department.
DR@W Forum: Neil Bramley (Edinburgh)
Humans form rich causal models of the world that support prediction, explanation, planning and control. While Bayesian methods help formalize how such representations can be learned from data, they are only tractable in the simplest cases. Thus, a key question is how bounded human learners succeed in the face of the world’s formidable complexity. I will discuss a project aimed at unravelling this mystery. We investigate how people learn about probabilistic causal systems by performing interventions (actions that perturb a system of interest, like pushing a button, taking a medicine, or implementing a policy). Across a line of studies and extensive model comparison, I show that people adjust their causal representations in a piecemeal fashion, making small local changes rather than more extensive “Kuhnian” revisions. I formalize this with a model inspired by algorithms for approximating Bayesian inference, and use this model to explain how bounded learners can find high probability hypotheses even in complex learning domains. If there is time, I’ll mention several ways we are extending these ideas this to capture other aspects of human learning.
Key paper: https://psycnet.apa.org/record/2017-08702-001
Recent extension to concept learning: https://www.sciencedirect.com/science/article/pii/S0010028522000421
And incorporation of bootstrapping: https://www.nature.com/articles/s41562-023-01719-1