A Model Centric Framework and Approach for Complex Systems Policy

Abstract

Twenty-first century systems engineering is no longerdocument-centric; instead, it is model-centric. Model-centric systems engineering helps reduce ambiguity, increase clarity, and increase the analytics of the resulting complex systems. However, complex systems are governed by organizational policies that are still document-centric. Such policies are difficult to analyze, and gaps in policy can lead to major deficiencies in the resulting complex systems. This article introduces a framework for policy content modeling (PCM) and analysis. The framework represents the conceptual view and is supported by a step-by-step approach to achieve complete policy modeling and analysis. This approach was used with the intention of identifying and analyzing gaps in policy content and calculating policy toxicity, which negatively affects the resulting system. This framework and approach was also applied to Veterans Affairs (VA) and university policies. The VA PCM is conducted to discover toxicity in the policies and University policies modeling is done to graphically represent an undocumented policy and toxicity in the policy implementation. Index Terms-Complex systems policy, modeling, model-based systems engineering (MBSE), model centric systems engineering, policy, policy analysis. I. INTRODUCTION C OMPLEX engineered systems are manifestations of requirements derived from customer wants and needs. In addition to satisfying customer requirements, engineered systems must also adhere to policies, regulations, or standards established by the government, organization, technology, or interfaces [1], [2]. Public or corporate policies are typically documents that use natural-language, such as English to explain different organizational structures, definitions, rules, and regulations of conduct that dictate the resulting system [3]. Gaps in these policies can influence the outcome of the resulting system [4], [5]. Natural-language documents are not completely digital, and thus, they cannot be automatically analyzed or updated [3]. Another issue is that policies are not static entities; rather they are, dynamic, complex interdependent mazes of natural-language documents designed to accomplish a specific mission [6]. Most public, academic, and healthcare policies tend to be nonmachine-readable natural-language documents, which Manuscript

A Systems Approach for Solving Inter-Policy Gaps in Dynamic Spectrum Access-Based Wireless Rural Broadband Networks

“…From MBSE, other tools have emerged, such as policy content modeling and digital twins. Policy content modeling [21] can be applied to develop a machine-readable policy, which can be exported to XML and deployed in simulation tools for testing conformance between proposed policies and of flexible spectrum utilization. This policy model can be treated as a Digital Twin [22] to improve understanding of the emergent and dynamic aspects of this engineered system.…”

“…Definition Reference systems thinking ability to see interrelationships between parts and the whole [11] iceberg model structure used to uncover the underlying structure that contributes to an event [19] system of systems (SoS) collection of independent systems that provide enhanced capabilities when combined [17], [18] model-based systems engineering (MBSE) the use of formal models as the sole source of truth in a system life cycle [20] policy content modeling process for converting text-based policy documents to simulations [21] digital twin a simulation version of a real-world system to test new policies [22] Systems engineering tools have been used to improve the design of systems by encouraging a model-centric approach. The primary advantage of these tools is in early identification of potential weaknesses in complex systems, where cascading failures can be difficult to predict [23].…”

“…One of the challenges with analyzing policy gaps is that policies are articulated in documents, rather than models. In [21] authors demonstrate an approach for conducting policy content modeling to create a machinereadable digitized policy model to analyze gaps and quantify the negative impacts of gaps. A promising approach is to estimate the policy toxicity of the gaps to prioritize efforts to resolve these issues.…”

“…The policy toxicity ranged from 0.13 to 1 across various documents, where high numbers indicate higher numbers of gaps in general as well as higher numbers of important (or more highly weighted) gaps. This approach can structure efforts to revise policies by prioritizing gaps that could have the biggest impact if they are resolved [21]. MBSE has begun to be slowly adopted in the wireless community.…”

Converting natural language policy article into MBSE model

“…Public and corporate policies are often governed by a collection of complex and sometimes intricate written documents that explain structures, definitions, roles, and responsibilities [2]. Although information technology has advanced to support storing the files electronically using Microsoft Word and Adobe Acrobat, to deal with the overlapping and coupling within the policies, MBSE methodologies and approaches are assessed and utilized to analyze this complex system [4].…”