Philosophy Compass, Volume 17, Issue 1, January 2022.

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

In this paper, I review two related lines of computational research: discovery of scientific knowledge and causal models of scientific phenomena. I also report research on quantitative process models that falls at the intersection of these two themes. This framework represents models as a set of interacting processes, each with associated differential equations that express influences among variables. Simulating such a quantitative process model produces trajectories for variables over time that one can compare to observations. Background knowledge about candidate processes (...) enables search through the space of model structures and associated parameters to find explanations of time-series data. I discuss the representation of such process models, their use for prediction and explanation, and their discovery through heuristic search, along with their interpretation as causal accounts of dynamic behavior. (shrink)

Historians of technology have provided important accounts of technological innovation, but they rarely employ concepts which permit a rigorous analysis ofinvention as a mental or cognitive process. This article seeks to address this theoretical lacuna by using concepts adapted from cognitive psychology to compare the mental processes of two telephone inventors, Alexander Graham Bell and Thomas Edison. Specifically, we suggest that invention may be seen as a process in which inventors combine ideas with objects, or what we call mental models (...) and mechanical representations. The strategies by which inventors generate and manipulate these mental models and mechanical representations are what we refer to as heuristics. Using these concepts to narrate the development of the telephone, this article shows how invention can be interpreted as being much more than simply a mysterious act of individual genius. (shrink)