Social Interactions in Post-design Phases in Product Development and Consumption: Computational Social Science Modeling - Principia Designae: Pre-Design, Design, and Post-Design - page 160

innovation processes that span social and technical domains and diverse social
groups. The architecture of the simulation system demonstrates that it is feasible to
build rich computational models to study the simultaneous influence of several
factors at once, and across different social levels. This is especially important when
we are studying how agents react to and even generate novelty. In particular, our
agent architecture for Consumers includes a comparatively rich set of capabilities to
model situated cognition, both with artifacts and also with other agents. Also, our
artifact architecture for Product demonstrates that it is possible to design an
environment that is rich in possibilities without requiring the designer to (neces-
sarily) plan or explicitly design all those possibilities ahead of time. For example,
we chose three utility dimensions for Products based on the characteristics of
graphs (i.e. degree count, clustering coefficient, and longest span in an embedding).
But the simulation system could be greatly enriched if the utility dimensions were
open-ended and endogenously created and diffused by agents. This is an example of
the benefits of the computational approach.
Another significant benefit of computational modeling is the ability to run both
exploratory and controlled experiments that have demonstrable relevance to real-
world settings. The results presented for Setting 1—Producer Acting in Isolation—
are still exploratory at this stage. However we were able to identify three qualitative
distinctions between design trajectories. In future work, we aim to quantify and
measure these distinctions so that we can do more rigorous experiments involving
design trajectories as dependent variables.
The results presented in Setting 2—Consumers Acting in Isolation—take us
closer to controlled experiments and statistical hypothesis testing. We were able to
identify conditions where our Consumers were and were not able to endogenously
form clusters of values. Clustering has a significant effect on the diffusion and
adoption of innovation, both in the form clusters of early adopters and also clusters
of resistance to change.
Another contribution of our research is demonstrating how to measure and
evaluate changes in value systems in the context of innovation, both at the level
of an individual, in a group, and in a population. In this paper we have presented and
discussed three different analysis and representation methods—(1) design trajecto-
ries in the space of possible designs, (2) Value Space for populations of Consumers
and their ideal product vectors; and (3) Utility Space for populations of Consumers
(and Producers) to monitor and measure how their utility function changes over
time. One of our most significant lessons from this research so far is the value of
multiple simultaneous measurements and representations, because value systems is
inherently multi-level, multi-dimensional, and even pluralistic, even within an
individual agent.
With further experiments and results, we expect that our experiments will reveal
emergent patterns of organization that shape the Producer's design choices.
In particular, we believe that experiments will reveal self-reinforcing processes
(both direct and indirect) where early Consumer learning and preference formation
influences Consumer receptivity to new Products that have similar surface charac-
teristics or offer similar performance dimensions, and this in turn influences