Are there any models in cognitive psychology that study the belief in conspiracy theories through the lens of Bayesian decision theory?

For reference, in Bayesian decision theory a rational agent often behaves so as to minimize its expected (projected) loss. This expected loss is subjective and involves:

1. An estimated probability over a set of events (or possible explanations)
2. The loss the subject individually assigns to or perceives associated with a given event (or explanation)

Under this model a rational agent can make decisions as per:

$d^* = \underset{d}{\operatorname{argmin}} \mathrm{E}^\pi\left[L\left(\theta,d\right)| \text{D}\right]$

where we have:

• $L$ is the (subject's) loss function
• $\pi$ is the subject's posterior or prior beliefs over a set of parameters / events / explanations $\theta$
• $d$ is the decision the agent is trying to make
• $\text{D}$ is the observed data (e.g. available evidence to the subject)

I have often been intrigued about this connection since one could argue that if a subject assigns a high loss to a specific belief (e.g. a conspiracy theory that the subject is particularly afraid of), the subject may choose to believe it or at least behave as if it was true, even if there is little evidence to support it. Moreover, some subjects may render conclusions out of loss aversion or fear (which are mathematically equivalent to a negative form of utility in the expression above)

From a computational standpoint, the optimization (minimization) of the expected loss can be ill-conditioned if the probability $\pi$ collapses (little evidence supporting an explanation) but the assigned loss $L$ is large, which could lead different agents to believe, act and behave very differently depending on how they integrate, approximate and optimize the above expectation.

Note: I'm not familiar with the psychology of conspiracy theories, but I always wondered about this possible modeling connection.