Tuesday, 27 January 2009
Epistemic Landscape Models of Cognitive Labor
Michael Weisberg, University of Pennsylvania
12:05 pm, 817R Cathedral of Learning

::: photos

Abstract: A single scientist's discovery can open up a new research domain, but such a domain is vast and the discovery hardly constrains the next plausible research steps that might be taken in this domain. This is because in most research areas, there are neither singular goals nor well-defined research trajectories in advance. Goals and trajectories evolve as research is done. Despite this, the research community in most fields converges on approaches that yield significant results, and finds ways to integrate the knowledge of its various members.

I believe that this convergence and coordination is partially explained by science's social structure.  Scientists are not lone agents, cut off from the outside world, responding only to information generated in their own laboratories. Rather, they make decisions about what to investigate by integrating what they discover for themselves with what they learn from others. They also take into account external factors such as grants, prizes, and prestige. These sources of feedback lead scientists to coordinate and divide their resources among differing approaches to the research domain. But th4/28/09p Kitcher has called this fact about scientific communities the division of cognitive labor.

The division of cognitive labor is one of the most striking features of modern scientific communities and has been argued to be a key component in their epistemic success.  But there are theoretical questions concerning cognitive labor that can be addressed with computational models: What different types of divisions of cognitive labor are possible? How effective are these divisions for achieving scientific goals? Are there tradeoffs among these divisions?  What kinds of individual motivations can lead to these divisions? How do restrictions of information and resources affect these choices and the division which is an outcome of the choices? What kinds of incentives or structural features might the scientific community adopt to achieve better divisions of cognitive labor?

Answering these questions is the goal of work on new models of cognitive labor.  In this talk, I will discuss the epistemic landscape approach, which I developed with my student Ryan Muldoon.