Executable Modeling for System of Systems Architecting: An Artificial Life Framework

There is a diversity of frameworks and methodologies for enabling architecture developments. Static representation frameworks provide a standardized way to communicate the architecture to stakeholders, but do not provide means to analyze the system states and system behavior. Therefore, there is a need to convert static representation frameworks to executable models. The aim of this paper is to present Artificial Life approaches as a methodology for understanding behavior of System of Systems. For this, an Artificial Life based framework for modeling System of Systems is presented. The framework comprises cognitive architectures embedded in multi-agent models. Financial markets are selected as an analysis domain to demonstrate the framework since they are a good example of self-organizing systems that are nonproprietary and exhibit emergence on a grand scale. From the Artificial Life Framework trader-based architectures are formulated as models to analyze system level behavior. The Artificial Life based framework provides a flexible way of modeling sub-systems of System of Systems and it captures the adaptive and emergent behavior of the system

Towards reframing professional expert support

The paper addresses practical ways of reconfiguring professional expertise in development practice in moving away from the expert as a technocrat. Two projects associated with managing natural resource dilemmas suggest an alternative way of framing intervention involving professional experts providing a more appropriate collaborative learning space for development practice. The paper describes the heuristic devices generated by each project as helpful in bringing out dialectic tensions between practice and understanding, and between systems of interest and situations of interest (or situated problems). Firstly, SLIM (social learning for the integrated management and sustainable use of water at catchment scale) - a European Framework Programme 5 project - exemplifies social learning as a measure of sustainable development. The heuristic illustrates the dependence of sustainability on changes in practice and understanding amongst professionals and other stakeholders as part of concerted - rather than merely individual or even collective - action. Secondly, ECOSENSUS (Electronic/Ecological Collaborative Sensemaking Support System) - a Guyana focused intervention involving several UK universities in collaboration with the University of Guyana and Amerindian community representatives from the North Rupununi wetlands - builds on the SLIM heuristic in supporting the development of practice. Additionally, the ECOSENSUS heuristic provides conceptual space for the interaction between conceptual constructs of distributed stakeholders (that is, systems thinking) including those with professional expertise, and the actual context of intervention (the situated problem). Both SLIM and ECOSENSUS provide heuristics for process-orientated management enabling more meaningful and purposeful interaction between professional/ technical experts and other stakeholders, as an alternative to conventional project-orientated management intervention. An alternative framing may help to steer practice away from the apoliticised comforting linearity of professionalised systematic project management towards more constructive systemic endeavours involving multiple stakeholders

Application of the Andersen Health System Utilization Framework in the Investigation of the use of Traditional Medicine in Kumasi, Ghana

There is a gap in theoretically-based-research on the use of Traditional Medicine (TM) in Low and Middle-Income Countries (LMIC). The Andersen Health System Utilization (AHU) framework was used to explore the factors associated with TM use among chronically ill patients seeking care from the Komfo Anokye Teaching Hospital (KATH), Ghana, West Africa. Two research questions allowed a focused application of the AHU model. The first research question sought to identify the need, predisposing, and enabling factors associated with TM use. The second research question sought to examine the relationship between TM and perceived health status. Multinomial logistic regression and instrumental variable (IV) Tobit regression analyses were used to address the research questions. Applying the AHU framework, predisposing factors were identified as significant predictors of TM use, including marital status, the use of TM by family/friends, and favorable beliefs regarding TM. The presence of comorbidities – a need factor – was also found to be associated with TM usage. However, in contrast to the AHU framework, enabling factors were not associated with TM use among the study population. Additionally, the study did not find an association between TM use and perceived health status. This study\u27s results contribute to the general understanding of the use of TM for preventive and curative purposes in LMIC

The stability of a graph partition: A dynamics-based framework for community detection

Recent years have seen a surge of interest in the analysis of complex networks, facilitated by the availability of relational data and the increasingly powerful computational resources that can be employed for their analysis. Naturally, the study of real-world systems leads to highly complex networks and a current challenge is to extract intelligible, simplified descriptions from the network in terms of relevant subgraphs, which can provide insight into the structure and function of the overall system. Sparked by seminal work by Newman and Girvan, an interesting line of research has been devoted to investigating modular community structure in networks, revitalising the classic problem of graph partitioning. However, modular or community structure in networks has notoriously evaded rigorous definition. The most accepted notion of community is perhaps that of a group of elements which exhibit a stronger level of interaction within themselves than with the elements outside the community. This concept has resulted in a plethora of computational methods and heuristics for community detection. Nevertheless a firm theoretical understanding of most of these methods, in terms of how they operate and what they are supposed to detect, is still lacking to date. Here, we will develop a dynamical perspective towards community detection enabling us to define a measure named the stability of a graph partition. It will be shown that a number of previously ad-hoc defined heuristics for community detection can be seen as particular cases of our method providing us with a dynamic reinterpretation of those measures. Our dynamics-based approach thus serves as a unifying framework to gain a deeper understanding of different aspects and problems associated with community detection and allows us to propose new dynamically-inspired criteria for community structure.Comment: 3 figures; published as book chapte