scholarly journals Studying behaviour change mechanisms under complexity

2020 ◽  
Author(s):  
Matti Toivo Juhani Heino ◽  
Keegan Phillip Knittle ◽  
Chris Noone ◽  
Fred Hasselman ◽  
Nelli Hankonen
Keyword(s):  
Complex Systems ◽  
Behaviour Change ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Systems Approach ◽  
Complex Dynamics ◽  
Behaviour Change Techniques ◽  
Behaviour Change Interventions ◽  
Complex Adaptive ◽  
Change Mechanisms

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It 1) outlines the complexity of behaviours and behaviour change interventions, 2) introduces readers to some key features of complex systems and how these relate to human behaviour change, and 3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e. interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for nonlinear time series analysis which is able to quantify complex dynamics. The supplemental website (https://git.io/Jffrm) provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.

scholarly journals Studying Behaviour Change Mechanisms under Complexity

2021 ◽  
Vol 11 (5) ◽  
pp. 77
Author(s):  
Matti T. J. Heino ◽  
Keegan Knittle ◽  
Chris Noone ◽  
Fred Hasselman ◽  
Nelli Hankonen
Keyword(s):  
Complex Systems ◽  
Behaviour Change ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Systems Approach ◽  
Linear Time ◽  
Behaviour Change Techniques ◽  
Behaviour Change Interventions ◽  
Complex Adaptive ◽  
Change Mechanisms

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.

scholarly journals A Transdisciplinary Complex Adaptive Systems (T-CAS) Approach to Developing a National School-Based Culture of Prevention for Health Improvement: the School Health Research Network (SHRN) in Wales

2018 ◽  
Vol 22 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Simon Murphy ◽  
Hannah Littlecott ◽  
Gillian Hewitt ◽  
Sarah MacDonald ◽  
Joan Roberts ◽  
...  
Keyword(s):  
Complex Adaptive Systems ◽  
Health Research ◽  
Adaptive Systems ◽  
School Health ◽  
Systems Approach ◽  
Research Network ◽  
Health Improvement ◽  
Policy And Practice ◽  
Complex Adaptive ◽  
Culture Of Prevention

AbstractThe paper reflects on a transdisciplinary complex adaptive systems (T-CAS) approach to the development of a school health research network (SHRN) in Wales for a national culture of prevention for health improvement in schools. A T-CAS approach focuses on key stages and activities within a continuous network cycle to facilitate systems level change. The theory highlights the importance of establishing transdisciplinary strategic partnerships to identify and develop opportunities for system reorientation. Investment in and the linking of resources develops the capacity for key social agents to take advantage of disruption points in the re-orientated system, and engagement activities develop the network to facilitate new social interactions and opportunities for transdisciplinary activities. A focus on transdisciplinary action research to co-produce interventions, generate research evidence and inform policy and practice is shown to play an important part in developing new normative processes that act to self-regulate the emerging system. Finally, the provision of reciprocal network benefits provides critical feedback loops that stabilise the emerging adaptive system and promote the network cycle. SHRN is shown to have embedded itself in the system by securing sustainability funding from health and education, a key role in national and regional planning and recruiting every eligible school to the network. It has begun to reorient the system to one of evidence generation (56 research studies co-produced) and opportunities for data-led practice at multiple levels. Further capacity development will be required to capitalise on these. The advantages of a complex systems approach to address barriers to change and the transferability of a T-CAS network approach across settings and cultures are highlighted.

Complex Adaptive Systems Thinking Approach to Enterprise Architecture

2016 ◽  
pp. 339-389
Author(s):  
Marc Rabaey
Keyword(s):  
Complex Systems ◽  
Systems Thinking ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Enterprise Architecture ◽  
Time Dynamic ◽  
Complex Adaptive ◽  
Unstable Environment ◽  
Systems Thinking Approach ◽  
High Degree

Complex systems interact with an environment where a high degree of uncertainty exists. To reduce uncertainty, enterprises (should) create intelligence. This chapter shows that intelligence has two purposes: first, to increase and to assess (thus to correct) existing knowledge, and second, to support decision making by reducing uncertainty. The chapter discusses complex adaptive systems. Enterprises are not only complex systems; they are also most of the time dynamic because they have to adapt their goals, means, and structure to survive in the fast evolving (and thus unstable) environment. Crucial for enterprises is to know the context/ecology in which they act and operate. The Cynefin framework makes the organization and/or its parts aware of the possible contexts of the organization and/or its parts: simple, complicated, complex, chaotic, or disordered. It is crucial for the success of implementing and using EA that EA is adapted to function in an environment of perpetual change. To realize this, the chapter proposes and elaborates a new concept of EA, namely Complex Adaptive Systems Thinking – Enterprise Architecture (CAST-EA).

Modularity and Complex Adaptive Systems

2008 ◽  
pp. 75-104
Author(s):  
David Cornforth ◽  
David G. Green
Keyword(s):  
Fault Tolerance ◽  
Complex Systems ◽  
Long Range ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Single Unit ◽  
Phase Changes ◽  
Critical Phase ◽  
Complex Adaptive ◽  
Different Levels

Modularity is ubiquitous in complex adaptive systems. Modules are clusters of components that interact with their environment as a single unit. They provide the most widespread means of coping with complexity, in both natural and artificial systems. When modules occur at several different levels, they form a hierarchy. The effects of modules and hierarchies can be understood using network theory, which makes predictions about certain properties of systems such as the effects of critical phase changes in connectivity. Modular and hierarchic structures simplify complex systems by reducing long-range connections, thus constraining groups of components to act as a single component. In both plants and animals, the organisation of development includes modules, such as branches and organs. In artificial systems, modularity is used to simplify design, provide fault tolerance, and solve difficult problems by decomposition.

scholarly journals Close-to-nature silviculture in eastern Quebec: Advances over the last decade

2021 ◽  
pp. 1-13
Author(s):  
Robert Schneider ◽  
Laurie Dupont-Leduc ◽  
Vincent Gauthray-Guyénet ◽  
Nicolas Cattaneo ◽  
LaraMelo ◽  
...  
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Stand Structure ◽  
Systems Approach ◽  
Natural Disturbances ◽  
Management Scenarios ◽  
Forest Management Planning ◽  
Forest Resilience ◽  
Complex Adaptive ◽  
Harvesting Systems

The increase in intensity of the harvesting of eastern Quebec’s forests has resulted in profound compositional changes at the stand level. The composition and structure of presettlement stands provide key benchmarks when implementing ecosystem-based management (EBM). A core principle of EBM is the emulation of natural disturbances, and it is hypothesized that forest resilience will be maintained. Managers have thus adapted some of their silvicultural activities to better mimic the main natural disturbances in eastern Quebec. These adaptations include using variable retention harvesting systems instead of clear-cuts and converting even-aged stands. Nevertheless, other close-to-nature silvicultural practices must be developed, as gaps between managed and unmanaged stands persist. Most importantly, there is a need to consider global change within EBM, which could be accomplished by prioritizing forest functions rather than composition or structure when establishing silvicultural objectives. Elements of the complex adaptive systems approach to increasing forest resilience can be incorporated into the larger-scale EBM approach. This could be done by considering the functional complementarity of species, forest function, and stand structure in forest management planning. These efforts must not be constrained, however, to allowable annual cut calculations, as these are not sufficiently sensitive to compare different management scenarios.

1. Complex systems

2014 ◽  
Author(s):  
John H. Holland
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Tropical Rainforest ◽  
Hierarchical Organization ◽  
Self Organization ◽  
Physical Systems ◽  
Complex Adaptive ◽  
Distinctive Property ◽  
Adaptive Interaction

What is complexity? A complex system, such as a tropical rainforest, is a tangled web of interactions and exhibits a distinctive property called ‘emergence’, roughly described by ‘the action of the whole is more than the sum of the actions of the parts’. This chapter explains that the interactions of interest are non-linear and thus hierarchical organization is closely tied to emergence. Complex systems explains several kinds of telltale behaviour: emergent behaviour, self-organization, chaotic behaviour, ‘fat-tailed behaviour’, and adaptive interaction. The field of complexity studies has split into two subfields that examine two different kinds of emergence: complex physical systems and complex adaptive systems.

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