Systems Science (SYS)
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Important Information for Proposers
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A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 18-1), is effective for proposals submitted, or due, on or after January 29, 2018. Please be advised that, depending on the specified due date, the guidelines contained in NSF 18-1 may apply to proposals submitted in response to this funding opportunity.
The Systems Science (SYS) program supports fundamental research leading to a theoretical foundation for design and systems engineering. In particular, the Systems Science program seeks intellectual advances in which underlying theories (such as probability theory, decision theory, game theory, organizational sociology, behavioral economics or cognitive psychology) are integrated and abstracted to develop explanatory models for design and systems engineering in a general, domain-independent fashion. Ideally, the explanatory models, derived from the underlying theoretical foundations will lead to testable hypotheses. Based on collected evidence supporting or falsifying the hypotheses, new insights are gained allowing the explanatory models to be refined or updated.
Systems research that does not address the Engineering of Systems is out of scope. Domain-specific applications of the theoretical foundations are also out of scope. Research that focuses on domain-specific applications, but simultaneously advances our fundamental understanding of design and systems engineering will be considered for co-funding with other programs (see "Related Programs" below for examples). Such proposals should be submitted to the appropriate disciplinary program, with the System Science program identified as a secondary program.
Research topics of interest in SYS include, but are not limited to:
- Processes: Search Strategy, Guidance and Control
Design and systems engineering are processes consisting of a large number of synthesis and analysis steps in sequence or parallel, at gradually increasing levels of detail and accuracy. SYS supports research towards understanding the nature of this search process: How best to monitor, guide and control the search process? Which progress metrics to use? Which search strategy to use? How best to frame individual design decisions? How to determine appropriate abstractions for specification and analysis at each step in the process? To what extent should the expected downstream process steps be considered when contemplating the current search steps?
- Organizations: Decomposition, Communication and Incentivisation
Systems engineering and design processes are executed in an organizational context. Depending on the nature of the artifact being developed, different processes and corresponding organizational structures are best. This raises questions: How best to decompose problems and delegate the decomposed parts? What is the impact of incentive structures on design outcomes? How best to facilitate interactions and communication between experts with disparate backgrounds towards ideation and analysis in design? These are questions at the boundary of traditional engineering disciplines, so that rigorous advances are likely to require collaboration between engineering researchers and organizational sociologists.
- Modeling: Creation, Use and Assessment of Models
In design and systems engineering practice, models are ubiquitous. Models enable designers to communicate, to predict, and ultimately to explore the design space efficiently and effectively. But several questions remain: How to determine which modeling formalism is most appropriate? What are the cognitive models of modeling? How best to teach modeling to engineering students? How to facilitate the reuse and sharing of models? How to assess and characterize the accuracy and applicability of models?
- Research Methodology
An important challenge in design and systems engineering research is that it involves a normative aspect: the ultimate goal is to "improve" design. This raises the questions: Which metric should be used to express "goodness"? How to measure it rigorously? What constitutes acceptable evidence when comparing the "goodness" of different design methods?