SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens

Selecting a change and evaluating its impact on the performance of a complex adaptive health care delivery system

Complexity science suggests that our current health care delivery system acts as a complex adaptive system (CAS). Such systems represent a dynamic and flexible network of individuals who can coevolve with their ever changing environment. The CAS performance fluctuates and its members’ interactions continuously change over time in response to the stress generated by its surrounding environment. This paper will review the challenges of intervening and introducing a planned change into a complex adaptive health care delivery system. We explore the role of the “reflective adaptive process” in developing delivery interventions and suggest different evaluation methodologies to study the impact of such interventions on the performance of the entire system. We finally describe the implementation of a new program, the Aging Brain Care Medical Home as a case study of our proposed evaluation process

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today’s systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today’s systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing selfadaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Peer ReviewedPostprint (author's final draft

The everyday practice of supporting health system development: learning from how an externally-led intervention was implemented in Mozambique.

Health system strengthening (HSS) has often been undertaken by global health actors working through vertical programmes. However, experience has shown the challenges of this approach, and the need to recognize health systems as open complex adaptive systems-which in turn has implications for the design and implementation approach of more 'horizontal' HSS interventions. From 2009 to 2016, the Doris Duke Charitable Foundation supported the African Health Initiative, establishing Population Health Implementation and Training partnerships in five African countries (Ghana, Mozambique, Rwanda, Tanzania and Zambia). Each partnership was designed as a large-scale, long-term, complex health system strengthening intervention, at a primary care or district level-and in each country the intervention was adapted to suit that specific health systems context. In Mozambique, the Population Health Implementation and Training partnership sought to strengthen integrated health systems management at district and provincial levels (through a variety of capacity-development intervention activities, including in-service training and mentoring); to improve the quality of routine data and develop appropriate tools to facilitate decision-making for provincial and district managers; and to build capacity to design and conduct innovative operations research in order to guide integration and system-strengthening efforts. The success of this intervention, as assessed by outcome measures, has been reported elsewhere. In this paper, the implementation practice of this horizontal HSS intervention is assessed, focusing on the key features of how implementation occurred and the implementation approach. A case study focusing on HSS implementation practice was conducted by external researchers from 2014 to 2017. The importance of an accompanying implementation research approach is emphasized-especially for HSS interventions where the 'complex adaptive system' (complex and constantly changing context) forces constant adaptations to the intervention design and approach

Pathology and failure in the design and implementation of adaptive management

The conceptual underpinnings for adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex ecological systems as a result non-linear interactions among components and emergence, yet management decisions must still be made. The strength of adaptive management is in the recognition and confrontation of such uncertainty. Rather than ignore uncertainty, or use it to preclude management actions, adaptive management can foster resilience and flexibility to cope with an uncertain future, and develop safe to fail management approaches that acknowledge inevitable changes and surprises. Since its initial introduction, adaptive management has been hailed as a solution to endless trial and error approaches to complex natural resource management challenges. However, its implementation has failed more often than not. It does not produce easy answers, and it is appropriate in only a subset of natural resource management problems. Clearly adaptive management has great potential when applied appropriately. Just as clearly adaptive management has seemingly failed to live up to its high expectations. Why? We outline nine pathologies and challenges that can lead to failure in adaptive management programs. We focus on general sources of failures in adaptive management, so that others can avoid these pitfalls in the future. Adaptive management can be a powerful and beneficial tool when applied correctly to appropriate management problems; the challenge is to keep the concept of adaptive management from being hijacked for inappropriate use

A Realist Evaluation of the Sustainability of Disease Surveillance Intervention Outcomes in Sub-Saharan Africa

In recent years, the use of Information and Communication Technologies (ICTs) and to improve disease surveillance has been on the increase. This is in line with the notion that ICTs improve timeliness, availability and quality of public health data. Sub-Saharan Africa (SSA) is burdened with increasing health challenges and poor health infrastructure. Thus, an explosion of ICT-based health surveillance interventions to curb these challenges. However, despite the implementation of these interventions, important questions around the effectiveness and sustainability still remain. This study proposes a realist evaluation of disease surveillance intervention outcomes from a sustainability perspective to uncover what works, for whom, under what conditions and why? We also discuss how the complex adaptive systems theory and affordance theory provide a lens for investigating this phenomenon. The results of this study will contribute to the evidence based movement for Information Systems (IS) research and practice in SSA

The challenges facing sustainable and adaptive groundwater management in South Africa

Long-term population growth and economic development are placing ever-increasing pressure on South Africa’s freshwater supply. On the basis of the current climate change predictions, which often entail uncertain consequences for aquifer systems and the associated groundwater goods and services, it is expected that the stress on water will increase even further. Currently, South Africa’s groundwater governance regime does not provide the capacity to assure effective and sustainable resource regulation and allocation. To date, the management of groundwater is hampered by a variety of uncertainties, such as global climate change and socio-economic growth, as well as ineffective governance structures affecting resource use, regulation, protection and the implementation of alternative strategies needed to achieve sustainable management. This paper presents the results of a qualitative assessment of interviews conducted with experts in South Africa. Four key challenges are identified to the development of adaptive and sustainable groundwater management and the successful implementation of current water legislation in South Africa. These are: the undervaluation of groundwater importance and significance; the need for expertise and information at all scales; the centralisation of power; and the disregard of ecosystems and the associated goods and services. As a means to tackle these challenges, it has been assumed that the concept of adaptive water management represents a suitable approach to governing groundwater resources, by taking into account complex system linkages between hydrogeological, political, socio-economic and environmental domains. Supporting principles, such as tools for cooperation, participation and information networks, have been developed to facilitate the implementation of adaptive water management approaches and hence to achieve institutional change in the political arena of groundwater management.Keywords: groundwater, South Africa, ecosystem services, adaptive water management, qualitative assessmen

Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms