A survey of contemporary philosophical and scientific literatures reveals that different authors employ the term ‘heuristic’ in ways that deviate from, and are sometimes inconsistent with, one another. Given its widespread use in philosophy and cognitive science generally, it is striking that there appears to be little concern for a clear account of what phenomena heuristics pick out or refer to. In response, I consider several accounts of ‘heuristic’, and I draw a number of distinctions between different sorts of heuristics (...) in order to make sense of various research programmes. I then develop a working characterization of ‘heuristic’ which is shown to be coherent and robust enough to serve as a kind within philosophy and cognitive science broadly. My intent is not to pursue a unified account of ‘heuristic’, but to highlight some features of certain kinds of heuristics that are important for theorizing about cognition in order to proceed with a science of the mind/brain. 1 Why We Need an Appropriate Characterization of ‘Heuristic’2 Inadequate Definitions2.1 A positive attempt2.2 Heuristics versus guaranteed correct outcomes3 A Taxonomy of Heuristics3.1 Stimulus-driven versus heuristic3.2 Perceptual versus cognitive heuristics3.3 Computational versus cognitive heuristics3.4 Methodological versus inferential heuristics3.5 Summary of distinctions4 Characterizing Cognitive Heuristics4.1 Heuristics as rules of thumb4.2 Beyond rules of thumb: exploiting information5 Concluding Remarks. (shrink)

This paper explores the treatment of intelligent agents as innovations. Past writings in the area of intelligent agents focus on the technical merits and internal workings of agent-based solutions. By adopting a perspective on agents from an innovations point of view, a new and novel description of agents is put forth in terms of their degrees of innovativeness, competitive implications, and perceived characteristics. To facilitate this description, a series of innovation-based theoretical models are utilized as a lens of analysis, namely (...) Kleinschmidt and Cooper’s (J Prod Innovation Manage 8:240–251, 1991) market and technological newness map, Abernathy and Clark’s (Res Policy 14:3–22, 1985) competitive implications framework, and Moore and Benbasat’s (Inf Syst Res 2:192–222, 1991) list of perceived innovating characteristics. Together, these models provide a theoretical foundation by which to describe intelligent agents, yielding new insights and perceptions on this relatively new form of software application. (shrink)