Where does knowledge come from? How does "certainty" come to be? What role do social, psychological and institutional practices play in these processes? To what degree is knowledge and practice shared and what is the landscape or ecology of knowledge transmission?
These weighty questions were the central focus of the inaugural meeting of the Metaknowledge Research Network, held in Pacific Grove, CA from August 19th – August 23rd. Organized by the CI's Knowledge Lab, the network brought together a broad array of domain experts from around the country.
Leaders in the fields of human genetics, sociology, mathematics, history, evolutionary biology, English literature and psychology, from institutions such as Princeton, Stanford, Harvard, UCLA, University of Chicago, and Northwestern gathered to frame the Knowledge Lab project research agenda for the coming year.
In his opening remarks, CI fellow and Knowledge Lab Director James Evans said that organizing such a diverse network of researchers was no accident.
About a third of the network "come from a computational modeling background who are interested in identifying and modeling knowledge generation and transmission processes," Evans said. The remaining two thirds of the network "take the long view of science and scholarship" and bring their expertise into meaningful conversations on topics that are of interest to them and to the network as a whole.
From those conversations, ten new research projects emerged and were added to Knowledge Lab’s agenda.
At the conference, participants discussed projects in areas such as Machine Science, Cognitive and Evolutionary Foundations of Science, The Representation of Knowledge, and The Lives of Concepts. Specific questions include:
“How do scientific conventions influence creativity and innovation?”
Exploring if and when conventions limit scientific creativity and forestall innovation, and how to stimulate creativity and foster innovation in convention-rich environments. This project will lead to the development of a powerful new method for modeling scientific reasoning and for identifying knowledge paradigms and promising, unexplored terrain.
“What are the biases and preferences that inform scientists’ theory selection as they study the Big Questions?”
A tool kit for identifying research traditions and exposing the influence of particular biases and preferences in real time. This project will provide new insight into the nature of science as a complex system by exposing the action of various non-empirical processes in shaping the evolving landscape of scientific attention.
“What makes someone a great scientist or inventor?”
An exploration of how individuals differ in their research risk-taking, while trying to settle questions about the origins of great scientific achievement and the existence of different “styles” of scientific genius. If indeed there are particular career trajectories and research styles associated with high achieving scientists, then this work could be used to identify and invest in these individuals more efficiently than current funding and granting systems, which are biased toward conservative individuals and incremental styles.
Other Knowledge Lab researchers will study how evolutionary processes and selection pressures influence the foundations of science, or how humans and machines actually collaborate in scientific investigation.
“I’m truly thrilled by what we were able to accomplish this week”, said Jacob Foster, one of the network’s founding members.