Decision Making for Inconsistent Expert Judgments Using Negative Probabilities
This work addresses decision-making challenges in fields like AI or statistics where expert judgments are inconsistent, but it is incremental as it focuses on a specific example without broad validation.
The paper tackled the problem of inconsistent expert judgments by comparing Bayesian, quantum-like, and negative probabilities approaches on a simple random-variable example, finding that negative probabilities had greater normative power than the other two methods.
In this paper we provide a simple random-variable example of inconsistent information, and analyze it using three different approaches: Bayesian, quantum-like, and negative probabilities. We then show that, at least for this particular example, both the Bayesian and the quantum-like approaches have less normative power than the negative probabilities one.