Cognitive Bias and Belief Revision
This work addresses the impact of cognitive biases on belief revision processes, which is relevant for AI systems modeling human-like reasoning, though it appears incremental as it applies known biases to existing methods.
The paper formalizes three cognitive biases (confirmation, framing, anchoring) within belief revision frameworks and applies them to three established methods, investigating their reliability in truth tracking through computer simulations.
In this paper we formalise three types of cognitive bias within the framework of belief revision: confirmation bias, framing bias, and anchoring bias. We interpret them generally, as restrictions on the process of iterated revision, and we apply them to three well-known belief revision methods: conditioning, lexicographic revision, and minimal revision. We investigate the reliability of biased belief revision methods in truth tracking. We also run computer simulations to assess the performance of biased belief revision in random scenarios.