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u 3
u 1 = # edges
u 2 = Mean clustering coefficient
u 3 = max distance
in spring-electrical embedding
Fig. 11.2 Maximum spanning distance is the third of the three dimensions of utility, measured
on the same Product as shown in Fig. 11.1 . The normalized utility vector of this product is
(0.1, 0.39, 0.46)
In addition to these two views that are relevant to Consumers, we also characterize
Products in ways that are particularly relevant to Producers. This is an important
feature to our design because of the need to model plural interests, perceptions,
and value systems between Producers and Consumers. We have adapted the idea of
'production recipe' from (Auerswald et al. 2000 ) , where it was used in an agent-based
model of learning-by-doing on the shop floor. A production recipe is a vector of
characteristics that related to the production or assembly process, and therefore to the
costs and complexity of manufacturing and the challenges of learning through expe-
rience. We have defined the following eight-element vector for specifying recipes:
r 1
¼ Number of degree-1 nodes
r 2
¼ Number of degree-2 nodes
r 3
¼ Number of degree-3 nodes
r 4
¼ Number of degree-4 nodes
r 5
¼ Number of degree-5 nodes
r 6
¼ Log of number of cycles ¼ ln(c), rounded to nearest 0.5
r 7
¼ Length of longest chain
r 8
¼ Number of chains of degree-1 or degree-2 nodes
These are each normalized to a range of values between 0 and 6. There are
91 unique recipes for the 112 unique Products. The Hamming distance between any
two recipes is a measure of accessibility from one to the other through learning-by-
doing and also explicit design explorations.
The cost to manufacture a given design has two components. The first is material,
which is a simple function of the number of edges. The second is assembly cost, which
is a function of the recipe and the Producer's cumulative experience in each of the
dimensions of the recipe. With zero experience, the cost function rises as the square of
each recipe element value, summed across the recipe. Thus, initially most of the
designs are too expensive to manufacture, rendering them infeasible. With experience
the exponent of the cost function is reduced until it plateaus to yield a linear function of
each recipe element value.
 
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