\section{Discussion}
\label{sec:discussion}

In this study, we have presented a novel trust management framework leveraging \ac{sl} to navigate the complexities of trust in \ac{cpss} using indirect observations under conditions of uncertainty.
The results indicate that the suggested framework dynamically adapts trust scores, especially in environments where data is uncertain or incomplete.
% improving reliability without requiring human input.

The trust value is determined by categorizing indirect observations and developing subjective opinions based on the evidence.
\ac{sl} is a logical reasoning framework utilized in diverse areas to evaluate trust while accommodating uncertainty in opinions.
Our framework provides a trust assessment based on long-term aggregated data and helps identify short-term trends in trust.

Current research into trust assessment has primarily concentrated on the binary domain.
Our study broadens this scope to encompass the multinomial domain by including an extra dimension where observations are categorized as uncertain.
The cumulative fusion operator merges evidence-based opinions about the same proposition in \ac{sl}.
We present our approach to performing the cumulative fusion operator on multinomial opinions and provide proof of its validity. 

Including the label of uncertainty in expressing opinions offers an additional understanding of the system state.
When evaluating indirect observations affected by noise, it is advantageous to distinguish between violations and uncertain readings, as this enables a more detailed comprehension of trust.
This differentiation is critical for making informed decisions, particularly in environments where missing or ambiguous data can significantly affect the reliability of the system's performance assessment and should not be immediately categorized as a violation.

In the proposed trust management framework, we evaluate evidence observations from a single source.
However, future studies could investigate generating multiple viewpoints from extra classifiers and fusing them using \ac{sl} within the trust management framework.
Analyzing multiple opinions can improve the precision and dependability of trust evaluations by considering various viewpoints and decreasing reliance on a single classifier. 

All violations in the trust management framework have an equal effect on trust, though they are not identical.
For instance, if a minor software glitch causes a one-second delay in information processing, its impact on trust should differ considerably from a server crash without reboot.
A potential enhancement would be to allocate varying weights to different categories of violations within the trust management framework.

We also introduce the concept of trust calibration actions to address the issue of undertrust. Users can leverage their domain knowledge and provide a list of actions to calibrate trust under specified conditions. Trust calibration actions are not part of the framework but supplement its functionality by providing predefined suggestions to the user.
The trust management framework equally values each trust calibration action the user performs.
Assigning weights to different trust calibration actions according to their significance or impact may lead to a more tailored trust evaluation.