Abstract
People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.
Highlights
People can perform remarkably well at causal reasoning tasks that prove to be extremely difficult for statistical methods and machine learning algorithms
This paper illustrates that the quantum inference model can account for data from three different causal reasoning experiments
The quantum model has previously been used to account for order effect data in a number of different inference tasks (Trueblood and Busemeyer, 2011) illustrating the generalizability of the model to a large range of phenomena
Summary
People can perform remarkably well at causal reasoning tasks that prove to be extremely difficult for statistical methods and machine learning algorithms. The quantum model is able to account for the medical inference data (Bergus et al, 1998) and a similar type of data from the domain of jury decision-making (Trueblood and Busemeyer, 2011) In these experiments, subjects read fictitious criminal cases and made a sequence of three judgments for each case: one before the presentation of any evidence, and two more judgments after presentations of evidence by a prosecutor and a defense. How likely is it that the baby’s mother has dark skin?”) are significantly lower than the probability judgments for diagnostic problems with weak alternative causes We describe the model in the framework of causal reasoning and demonstrate how it can account for the two findings
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