Quantum Models of Human Causal Reasoning

Abstract

Throughout our lives, we are constantly faced with a variety of causal reasoning problems. A challenge for cognitive modelers is developing a comprehensive framework for modeling causal reasoning across different types of tasks and levels of causal complexity. Causal graphical models, based on Bayes’ calculus, have perhaps been the most successful at explaining and predicting judgments of causal attribution. However, some recent empirical studies have reported violations of the predictions of these models, such as the local Markov condition. In this chapter, the authors suggest an alternative approach to modeling human causal reasoning using quantum Bayes nets. They show that their approach can account for a variety of behavioral phenomena including order effects, violations of the local Markov condition, anti-discounting behavior, and reciprocity.