A Fuzzy Implementation for the Socio-Cognitive Approach to Trust - Trust Theory: A Socio-Cognitive and Computational Model - page 307

Table 11.1 Internal and external factors for the automated medical system

Internal factors Ability or

Competence
beliefs

They concern the efficacy and efficiency of the
machine; its capability to successfully apply the right
procedure in the case of correct/proper use of it.
Possibly also its ability to recover from an
inappropriate use

Disposition or
Availability
beliefs

They are linked to the reliability of the machine, its
regular functioning, its ease of use; possibly, its
adaptability to new and unpredictable uses

Unharmfulness
beliefs

They concern the lack of internal/ intrinsic risks of the
machine: the dangers implied in the use of that
machine (for example side effects for the trustor's
health), the possibility of breaking and so on

External factors Opportunity

beliefs

Concerning the opportunity of using the machine,
independently of the machine itself, from the basic
condition to have the room for allocating the
machine to the possibility of optimal external
conditions in using it (regularity of electric power,
availability of an expert person in the house who
might support its use, etc.)

Danger beliefs They are connected with the absence of the systemic

risks and dangers external to the machine that could
harm the user: consider for example the risk for the
trustor's privacy: in fact we are supposing that the
machine is networked in an information net and the
data are also available to other people in the medical
structure

What are the meanings of our basic beliefs in the case of the doctor and in the case of the
automated medical system? For both the latter and former, the internal and external factors are
shown in Table 11.1 and 11.2.
Each of the above mentioned beliefs may be generated through different sources; such as: di-
rect experience, categorization, reasoning, and reputation. So, for example, ability/competence
beliefs about the doctor may be generated by the direct knowledge of a specific doctor, and/or
by the generalized knowledge about the class of doctors and so on.

11.9 Overview of the Implementation
An FCM is an additive fuzzy system with feedback; it is well suited to the representation of a
dynamic system with cause-effect relations. An FCM has several nodes, representing causal
concepts (belief sources, trust features and so on), and edges, representing the causal power
of a node over another one. The values of the nodes representing the belief sources and the
values of all the edges are assigned by a human; these values propagate in the FCM until a
stable state is reached; so the values of the other nodes (in particular the value of the node
named trustfulness) are computed.