Early gigs by these German electronic pioneers routinely took place in art galleries, emphasising the band's conceptual distance from American rock music.
However, this argument is not totally convincing, possibly because Long does not explain exactly what he means by "conceptual distance".
By referring to these different kinds of function we can reduce the conceptual distance between physical and non-physical uses.
However, deeper study shows that the conceptual "distance" a radical breakthrough travels is far shorter than would initially appear.
This inverse relationship between conceptual distance and design creativity is robust across different design problems on the platform.
The similarity analysis is based on the estimation of the conceptual distance between the terms in a query descriptor and the terms in the specified descriptors of various design models in a software repository.
Conceptual distance (cdist) refers to the path length between two terms; while in the original case paths between terms were defined with respect to whether a meaning was narrower or broader ([19]), later on the paths in a parent-child (is-a) relationship were considered [20] - that is why in the latter case cdist coincides with path.
For this reason, the "conceptual distance" between the hardware and the target system is higher in the case of the computer simulation than in the case of the robotic model: only in the first case does something else (i.e., a simulated agent) come between the hardware and the target system.
In Chapter 7, Long adds that while robotic and computer simulations are on a par with respect to being a representation of the target system, the "conceptual distance" between model and target is higher in computer simulations, because they represent both the environment and the behavior of the system while a robot's behavior "just happens".
Edge counting-based similarity methods [11], e.g., minimum number of edges separating concepts c 1 and c 2, use a metric for measuring the conceptual distance of c 1 and c 2; these methods are useful for specific applications with highly constrained taxonomies.
