Adaptive Socio-technical Systems Research Articles

Information Technologies in Complex Socio-Technical Systems Based on Functional Variability: A Case Study on HVAC Maintenance Work Orders

Information technology (IT) systems are known to promote improvements in quality and productivity of the work environments of complex and adaptive socio-technical systems that span hardware, community and software aspects. Systems development lies in eliciting and specifying requirements. However, current requirements of elicitation techniques are limited to correctly understanding the complexity involved in socio-technical systems. Therefore, approaches based on Resilience Engineering can provide concepts and methods for a better understanding of socio-technical systems’ functioning. This study aims to increase the application of the Functional Resonance Analysis Method (FRAM) in the requirements elicitation process. Specifically, understanding variability and its role in enhancing the requirements elicitation and specification process for the design/redesign of IT systems in complex socio-technical systems deployed in building maintenance is the main goal. This study proposes the merging of human factors and ergonomics (HFE) and Resilience Engineering concepts with Software Engineering. A case study was performed with workers to produce requirements specifications for work order issuing activity. This case study indicates the usefulness of the proposed approach for the specification of functional requirements to redesign the IT system examined. FRAM enables inferences to be made from hidden or fuzzy situations that are often not expressed by system users or are not detected by the system designer.

An Agent-Based Approach to Artificial Stock Market Modeling

Abstract Consumer stock markets have long been a target of modeling efforts for the economic gains anticipatorily enabled by well-performing models. Aimed at identifying strategies capable of achieving desired returns, many modeling approaches have attempted to capture the innumerable and intricate complexities present within these adaptive socio-technical systems. Decreasingly constrained by available computation power, contemporary models have grown in sophistication to include several of the features present in de facto market systems. However, these models require extensive effort to dictate the variety of states, behaviors, and adaptations that entities of the system may exhibit. Mandating the development of complex formulas and an incredible number of situational considerations, traditional approaches to stock market modeling are intensive to architect and applicable to a limited range of scenarios. Further, these models commonly fail to incorporate external influences on the actions of investing parties. Employing an agent-based approach, independent and externally influenced entities are modeled to simulate market activity. Under the jurisdiction of assigned simple rules, agents of the system interact in complex and emergent ways without requiring macroscopic guiding equations. Successive trails are conducted using varying initialization values, enabling the determination of robust investment strategies performing well across a range of market scenarios.

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

The energy domain is still dominated by equilibrium models that underestimate both the dangers and opportunities related to climate change. In reality, climate and energy systems contain tipping points, feedback loops, and exponential developments. This paper describes how to create realistic energy transition management models: quantitative models that can discover profitable pathways from fossil fuels to renewable energy. We review the literature regarding agent-based economics, disruptive innovation, and transition management and determine the following requirements. Actors must be detailed, heterogeneous, interacting, learning, and strategizing. Technology should be represented as a detailed and heterogeneous portfolio that can develop in a bottom-up manner, using endogenous feedback loops. Assumptions about discount rates and the social cost of carbon should be configurable. The model should contain interactions between the global, national, local, and individual level. A review of modelling techniques shows that equilibrium models are unsuitable and that system dynamics and discrete event simulation are too limited. The agent-based approach is found to be uniquely suited for the complex adaptive sociotechnical systems that must be modelled. But the choice for agent-based models does not mean a rejection of other approaches because they can be accommodated within the agent-based framework. We conclude with practical guidelines.

Towards the Comprehensive Design of Energy Infrastructures

Energy infrastructures are increasingly perceived as complex, adaptive socio-technical systems. Their design has not kept up; it is still fragmented between an engineering and economic dimension. While economists focus on a market design that addresses potential market failures and imperfections, opportunistic behavior, and social objectives, engineers pay attention to infrastructure assets, a robust network topology, and control system design to handle flows and eventualities. These two logics may be complementary, but may also be at odds. Moreover, it is generally unclear what design choices in one dimension imply for the other. As such, we are ill-equipped to identify, interpret, and address the challenges stemming from technical innovations, e.g., the integration of renewable energy technologies, and institutional changes, e.g., liberalization or new forms of organization like cooperatives, which often have interrelated operational and market implications. In response, this paper proposes a more comprehensive design framework that bridges the engineering and economic perspectives on energy infrastructure design. To this end, it elaborates the different design perspectives and develops the means to relate design variables of both perspectives along several layers of abstraction: the form of infrastructure access of actors, the division of responsibilities among actors, and type of coordination between actors. The intention is that this way system and market design efforts can be better attuned to each other and we further our understanding and conceptualization of the interrelationship between the techno-operational and economic-institutional dimensions of energy infrastructures. The framework also aids in overseeing the broader institutional implications of technical developments (and vice versa) and stimulates awareness of lock-ins and path-dependencies in this regard.

IE = Industrial Evolution?

Background IE as a field studies systems that consist of a myriad of interconnected environmental, social, economic and technical systems. People, machines, organization, the bio-geo-chemical environment, infrastructures, markets, laws and habits form a complex adaptive socio-technical system, evolving through constant parallel and distributed interaction of heterogeneous social and technical elements. Understanding and shaping this evolution is essential if we are to rise to the challenges of climate change, resource scarcity, population growth and world peace. These system can be characterized as Complex Adaptive Systems, which John H. Holland [8] defines as:

Advancing the science of measurement of diagnostic errors in healthcare: the Safer Dx framework

Diagnostic errors are major contributors to harmful patient outcomes, yet they remain a relatively understudied and unmeasured area of patient safety. Although they are estimated to affect about 12 million...

Complex Adaptive Socio-Technical Systems The Role of Socio-Technical Networks in New Product Development

New product development (NPD) is challenging, as it involves mastering new technologies by coordinating appropriate resources efficiently and effectively. Established models of NPD focus on structuring, planning and ordering the innovation process but do not account for complexity and organizational responses and strategies in handling day-to-day resource allocation and decision-making. Our work is theoretically based on ideas from complexity science and NPD procedures. We build on and extend the work on Complex Adaptive System (CAS) and combine it with Socio-Technical System in an innovative approach to analyze, understand and manage NPD complexity by means of network analysis and performance metrics.This paper contributes to existing literature by introducing a framework of complex adaptive socio-technical systems (CASS). It is intended to (1) consider dependencies between product and individuals, (2) provide indicators to measure complexity, efficiency and effectiveness, (3) explain complex phenomena by identifying patterns, and (4) enable NPD governance.We demonstrate the applicability of our framework and metrics using comparative longitudinal case studies using novel and unique data from 4 years of meeting minutes and document exchange in the design phase of the NPD process of a large German automobile OEM. Results reveal new measures for addressing operational, social and technical complexity, providing new insights into complex dynamic behavior. From a managerial point of view, contributions of CASS are twofold: First, new possibilities for NPD governance emerge from performance indicators. Second, the extraction of dynamic socio-technical networks supports project-to-project learning.

Migrating Software Products to the Cloud

Cloud computing, as a disruptive innovation, has the potential to adversely affect companies. The effects can be particularly extreme for small and medium sized enterprises (SMEs). Subsequently, considerations of organisational resilience should be made when integrating with disruptive innovations like cloud computing. This paper reports of a longitudinal study investigating how a set of SME high-value software vendors are migrating their software products to the cloud. Adaptive socio-technical systems (ASTSs) concepts are used to provide a framework for understanding the adoption process. This study draws out a set of macro and micro themes relating to key phases of strategy development, the migration process and the impact on customer perceptions. From the findings, more systemic and holistic approaches are identified to address key tensions through the adoption life cycle while considering organisational resilience.

PDES-MAS: Distributed Simulation of Multi-agent Systems

Multi-agent systems (MAS) are increasingly being acknowledged as a modelling paradigm for capturing the dynamics of complex systems in a wide range of domains, from system biology to adaptive socio-technical system of systems. The execution of such MAS simulations on parallel machines is a challenging problem due to their dynamic, non-deterministic, data-centric behaviour and nature. These problems are exacerbated as the scale of such MAS models increases. PDES-MAS is a distributed simulation kernel developed specifically to support MAS models addressing the problems of partitioning, load balancing and interest management in an integrated, transparent and adaptive manner. This paper presents an overview of PDES-MAS and for the first time it provides a quantitative evaluation of the system.

Adaptive socio-technical systems: a requirements-based approach

A socio-technical system (STS) consists of an interplay of humans, organizations, and technical systems. STSs are heterogeneous, dynamic, unpredictable, and weakly controllable. Their operational environment changes unexpectedly, actors join and leave the system at will, actors fail to meet their objectives and under-perform, and dependencies on other actors are violated. To deal with such situations, we propose an architecture for STSs that makes an STS self-reconfigurable, i.e., capable of switching autonomously from one configuration to a better one. Our architecture performs a Monitor-Diagnose-Reconcile-Compensate cycle: it monitors actor behaviors and context changes, diagnoses failures and under-performance by checking whether monitored behavior is compliant with actors goals, finds a possible way to address the problem, and enacts compensation actions to reconcile actual and desired behavior. Compensation actions take into account the autonomy of participants in an STS, which cannot be controlled. Our architecture is requirements driven: we use extended Tropos goal models to diagnose failures as well as to identify alternative strategies to meet requirements. After presenting our conceptual architecture and the algorithms, it is founded upon; we describe a prototype implementation applied to a case study concerning smart-homes. We also provide experimental results that suggest that our architecture scales well as the size of the STS grows.

Micro incident analysis framework to assess safety and resilience in the operation of safe critical systems: A case study in a nuclear power plant

Safety in modern organizations, comprised of many nested levels with different types of coupling between them, must be managed by a control structure embedded in this adaptive sociotechnical system. The resilience of sociotechnical critical systems still relies on human ability to handle unexpected events adequately. In this study, based on a schematic view of a nuclear power plant control system composed of three structural system layers, planning, operation, and hardware, we present a framework to analyze micro incidents during nuclear power plant operation. The analysis show operators’ control actions used to solve small conflicts that arose at the operational layer, and how they marshaled the resources required for their action/cognition, i.e. the material, social, and cultural characteristics of the environment. The micro incident framework enables an anticipated view of operators’ control actions, providing processes for systemic analysis and critical thinking about the possibility that relatively small problematic situations in the loosely coupled system layers may lead to negative outcomes at some future time.

An agent-based service-oriented simulation suite for critical infrastructure behaviour analysis

Critical infrastructure systems are complex networks of adaptive socio-technical systems that provide the most fundamental requirements of the society. Their importance in the smooth conduct of the society has made their role more and more prominent. A failure in any of these important components of today's industrial society can well affect the lives of millions of people. It is not only their individual break down that raises serious concerns, but their mutual reliance (interdependency) is even more threatening. Although interdependency in these infrastructure systems provides many benefits for their operation, a failure in one can ripple down to the others and cause a catastrophic irremunerable event. In this paper, we have introduced a simulation suite for analysing the behaviour of interdependent critical infrastructure systems. The simulation suite focuses on the types of services that are provided by infrastructure components. Each infrastructure system component is modelled as an agent and its services as its behaviour. We believe that this simulation suite can assist researchers in better understanding the critical infrastructure behaviour and hence prevent catastrophic failures.

ENTERPRISE ARCHITECTURE: ENABLING INTEGRATION, AGILITY AND CHANGE

Three core imperatives are essential for modern businesses and organizations: seamless integration of customer and operational processes, agility, and the ability to change. These imperatives are relevant in view of successfully executing strategic choices, but all too often not satisfied. Businesses and organizations are complex adaptive socio-technical systems and can be viewed from two fundamentally different perspectives: the functional (black-box) perspective and the constructional (white-box) perspective. Management and governance of businesses and organizations regard the functional, black-box perspective, which is inherently ill-suited for addressing the imperatives mentioned. It will be argued that establishing system integration, agility and change requires a focus on the system's design, hence necessitates the constructional perspective. The concept of architecture is considered fundamental for operationalizing the constructional perspective. Next to the more familiar notion of technology architecture, the concepts of business, organizational and information architecture are formally introduced and elucidated. Various domains within these architectures will be highlighted, whereby the importance of coherence and consistency is stressed, especially in view of the ability to change. Collectively, the four architectures are labeled Enterprise Architecture. Finally, enterprise architecture will be positioned as a crucial means for linking strategy development and execution.

Quantitation of Meissner's corpuscles in hereditary neurologic disorders. Charcot-Marie-Tooth disease, Roussy-Levy syndrome, Dejerine-Sottas disease, hereditary sensory neuropathy, spinocerebellar degenerations, and hereditary spastic paraplegia.

Diagnostic errors are major contributors to harmful patient outcomes, yet they remain a relatively understudied and unmeasured area of patient safety. Although they are estimated to affect about 12 million Americans each year in ambulatory care settings alone, both the conceptual and pragmatic scientific foundation for their measurement is under-developed. Health care organizations do not have the tools and strategies to measure diagnostic safety and most have not integrated diagnostic error into their existing patient safety programs. Further progress toward reducing diagnostic errors will hinge on our ability to overcome measurement-related challenges. In order to lay a robust groundwork for measurement and monitoring techniques to ensure diagnostic safety, we recently developed a multifaceted framework to advance the science of measuring diagnostic errors (The Safer Dx framework). In this paper, we describe how the framework serves as a conceptual foundation for system-wide safety measurement, monitoring and improvement of diagnostic error. The framework accounts for the complex adaptive sociotechnical system in which diagnosis takes place (the structure), the distributed process dimensions in which diagnoses evolve beyond the doctor9s visit (the process) and the outcomes of a correct and timely “safe diagnosis” as well as patient and health care outcomes (the outcomes). We posit that the Safer Dx framework can be used by a variety of stakeholders including researchers, clinicians, health care organizations and policymakers, to stimulate both retrospective and more proactive measurement of diagnostic errors. The feedback and learning that would result will help develop subsequent interventions that lead to safer diagnosis, improved value of health care delivery and improved patient outcomes.