How do people use causal models of the world to support inference and decision-making? We investigate the role of causal thinking in human cognition and how people construct causal models from uncertain data. We show how people have an amazing capacity for flexible causal inference, but also how they adopt simplifying strategies to cope with complexity.
How do people gather and evaluate evidence to support their beliefs? We investigate how lay people and experts use evidence to assess their theories, exploring both strengths and shortcomings in human reasoning. We also seek to develop formal methods to improve the quality of reasoning in law, medicine, intelligence analysis, business and other applied domains.
How do people explore what-if questions? Counterfactual thinking is critical to many areas of human inference and decision-making, allowing us to imagine alternative worlds and infer what would have happened if we’d taken different actions. We investigate how people generate counterfactuals and how they use them to assign causality, blame and praise. We also explore the close link between causal and counterfactual reasoning.
How do people assign praise and blame? While people are adept at assigning responsibility in simple contexts, the complexity of social contexts presents various challenges. We often face the ‘problem of many hands’, where several agents – with diverse roles, intentions and knowledge – contribute to a joint outcome. We investigate how people assign blame in such situations, showing how they use causal models to make sense of social behaviour and to infer hidden states such as intent and foresight. We also examine how people attribute responsibility when both humans and AI systems combine to cause an adverse outcome.
26 Bedford Way
London, WC1H 0AP
020 7679 5389
firstname.lastname@example.org (Lab manager)