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Policy modularity: toward a science of socially-embedded system

Doing Science of Design: Artifacts-as-Phenomena My ongoing research takes an unconventional perspective on the role played by scientific methods in the field of computing. This is best understood by comparing it with the discover-then-validate model, where principles are discovered as a result of critical analysis and synthesis of the ideas of others. Science enters the research process after the innovation has already appeared and is used to validate and help iteratively improve the innovation through measurement-based hypothesis testing and comparisons. Discover-then-validate places great weight on the community to discover principles to validate. In the area of software engineering, it has long been known that clever innovations get adopted very slowly (Riddle, 1984; Shaw, 2001) and that an expensive and problematic entangling of research and practice is often necessary (Potts, 1993). The alternative that I am exploring, artifacts-as-phenomena, is to conduct scientific research into the artifacts of computing as if they were natural phenomena, incorporating into the resulting analyses data obtained from designers, administrators and users concerning their intentions and experiences. The goal is not to propose a new design methodology in the short term or even a set of useful heuristics. Instead, the goal is to develop a principled understanding of how software works.