Policy Analysis evolved from Operations Research through Systems Analysis to policy analysis in problem-oriented work carried out at the RAND Corporation and other organizations.
The ACM-related bibliography had a common theme: the absence of any explicit reference to fundamental policy analysis. It appears that the models were invented (and/or evaluated) by scientists focused on their specific field of interest, attempting to address narrow technical concerns. The endeavours to address security policy questions excluded policy analysis; a security policy that aspires to be adopted in the WBSN domain is (or can be) an instance of a [public] policy concerning -what is to many- a global public good. The need for formalising security policies via policy analysis, in a manner similar to the utilisation of Mathematics in formalising ACM, is apparent.
The following figures depict the fundamentals of policy analysis (Walker, 2000). The study of policy analysis is highly recommended, as a detailed presentation of this is beyond the scope of this report.
Figure 1: The purposes of Policy Analysis (Mayer, et al., 2004; Walker, 2009)
Figure 2: The Styles of Policy Analysis (Mayer, et al., 2004; Walker, 2009)
Figure 3: Elements in the Policy Analysis Approach (Walker, 2000)
Figure 4: a) A conceptual system diagram b) A schematic presentation of the different methods (Franzteskaki & Walker, 2012)
Given the fact that the social media landscape is a new and dynamic territory, thus surrounded by uncertainty and consistently presented with numerous unintended consequences, two approaches to policy design are referenced in this report. Both approaches were developed at the RAND Corporation, where the key enabler of the Internet, packet switching, was conceived and where the first email was sent (note: email as a messaging tool was in itself an unintended consequence of its original purpose) and, where Rational-Choice Theory (as mentioned on page 8) was modelled and tested.
Assumption-Based Planning (ABP) treats the assumptions underlying a plan as similar to the axioms underlying a mathematical system and reduces the effect of unintended consequences during long-range planning by dealing with both known and unknown ”unknowns” (Dewar, et al., 1993; Dewar, 2002).
Figure 5: ABP Steps and Logical Dependence (Dewar, et al., 1993)
This type builds upon ABP to cater to inputs of predicted variability for use in short to middle range planning offering improved accuracy and precision of models (Walker, et al., 2001).
Figure 6: The Adaptive Policy Making Process (Walker, et al., 2001)