Social Interactions in Post-design Phases in Product Development and Consumption: Computational Social Science Modeling - Principia Designae: Pre-Design, Design, and Post-Design - page 140

11.2 Theoretical Context
11.2.1 Co-evolution of Technologies and Consumer
Preferences
Dosi ( 1982 ) introduced the idea of viewing technology evolution as trajectories
through the space of possible designs, and movement along a trajectory as the result
of “normal problem-solving” and “progressive refinement” by producers as they
find ways to improve trade-offs in design variables. Saviotti ( 1996 ) presents a more
formal model of technological evolution through design space, where the space is
defined by dimensions for each technical and service characteristic associated with
a particular technology. 'Characteristics' are formalized as a vector of variables that
specify both a product's internal structure ('technical characteristics') or services
performed for its users ('service characteristics') (Saviotti and Metcalfe 1984 ) .
This “twin characteristics framework” is important for understanding both the
producer's values, which center on technical characteristics and associated learn-
ing, and the consumer's values, which center on the service characteristics. We
apply this method for modeling the space of possible designs and to specify the
position of particular product designs within that space. Gero ( 1990 ) is a further
elaboration of these ideas in the field of design science, specifically via Gero's
Function-Behavior-Structure (FBS) ontology for designs. 'Structure' in FBS cor-
responds to Saviotti's 'technical characteristics', while 'Function' corresponds to
Saviotti's 'service characteristics'. 'Behavior' provides the ontological linkage
between Structure and Function, and thus are often the focus of attention of product
designers in pre-design and design phases. Because our simulation does not include
agents actually performing design acts or making explicit design decision, we do
not explicitly include Behavior in our model of product designs.
Saviotti ( 1996 ) also proposes methods of analyzing population-level dynamics
in design space such as movement along trajectories and changes to the 'techno-
logical frontier'. The latter is related to the 'adjacent possible', a phrase coined by
Kauffman ( 1996 ) for the set of all the designs that are directly achievable from an
existing set of competences. Thus, the technological frontier is the limit of what is
producible with today's costs and capabilities, while the designs in the set of the
'adjacent possible' are decision alternatives for producers who choose to expand or
extend the frontier.
Dosi and Nelson ( 2010 ) provide a recent survey of the state of research on
technology trajectories and evolutionary processes that give rise to them. They
describe the supporting evidence as “ubiquitous”, adding that “trajectory-like pat-
terns of technological advance have been generally found so far whenever the
analyst bothered to plot over time the fundamental techno-economic features of
discrete artifacts or processes.” (p. 16) Technology trajectories are an example of
emergent phenomena (Gilbert 2002 ; Goldstein 1999 ; Holland 2000 ) in that they
arise from the collective action of individual agents but are not simple aggregations
of agent behaviors. As Dosi and Nelson ( 2010 ) describe, technology trajectories