Systems thinking and adult cognitive development
Table of Contents:
In today’s increasingly complex world, there is an imperative to work constantly to develop one’s skills, self-understanding, and other personal resources. This requires human capital — knowledge, creativity, innovativeness, and an ability to solve ill-defined, complex problems - as well as communication skills and a rich social network. All individuals function as part of larger systems, and what they can achieve is largely determined by the opportunities and constraints presented by those systems. The ability to take appropriate action, the ability to solve problems and cognitive development as a whole is dependent on the systems of which we are part. This chapter discusses scientific systems approaches and their links to research on adult thinking. Our aim is to work towards a deeper understanding of the development of adult thinking.
The purpose of systems sciences is to model and understand different types of systems and the dynamics of the changes, feedback loops, and interactions happening within these systems. Kauffman (1995, p. 24) points out that life is not to be located in its parts, but in the collective emergent properties of the whole they create, that is, the whole is more than the sum of its parts. The concept of system thus refers to a holistic entity composed of and dependent on a series of interconnected and interacting parts. Systems may be physical, biological, abstract, social, or human. Systems thinking is not a uniform, fully integrated field of study, but rather a conceptual frame of reference. Its foundations lie in different systems theories that take a comprehensive and multidisciplinary view on exploring phenomena.
Systems thinking research is basically aimed at understanding phenomena in the systemic context and at applying that understanding to problem solving and learning, but it is more than that. Systems thinking has been described as a framework (Buckle Henning & Chen, 2012), a range of techniques or methods (Ackoff, Addison, & Carey, 2010; Checkland, 1999; Checkland & Poulter, 2006; Jackson, 2003; Stowell & Welch, 2012), and as a way of developing cognitive skills and abilities (Melia, 2012; Senge, 2006, p. 10). Furthermore, systemic understanding may be described in terms of systems intelligence (Hamalainen, Jones, & Saarinen, 2014). In its broadest sense, systems thinking is seen as a philosophy or worldview (Capra, 2005; Capra & Luisi, 2014), or as an ethical code, identity, and a collective membership of a wider school of thought (Buckle Henning, Wilm-shurst, & Yearworth, 2012).
In the systems framework the development of thinking has been described as a double loop learning process in which a narrow, short-sighted, and firmly established worldview evolves into a forward-looking, flexible, and dynamic view that recognises and acknowledges the bigger picture (Sterman, 2000, pp. 3—40). While learning that does not change mental models described as single loop learning, the deeper process of double loop learning has the potential to change the individual’s or the community’s thought models or actions (Argyris, 1977; Sterman, 2000, pp. 3—40)'. This kind of learning that changes mental models is often called conceptual change (Limon & Mason, 2002; see also Chapter 8). In studies of adult learning, these processes have been described by the concept of transformative learning (Mezirow, 1991). Critical reflection and the transformation of thought models are embraced by all major modern theories of learning, which in this sense come quite close to the latest modes of systems theories. Systems thinking is rarely adopted as an explicit starting point in learning research, but learning research shares many basic premises in common with systems thinking. For instance:
Systems thinking has addressed learning mainly through its focus on management and organisation research. Senge, known for his concept of the learning organisation, says that the development of systems thinking starts from changing mental models and worldviews by means of awareness and questioning (Goleman & Senge, 2014; Senge, 1990, 2006).
Towards systems theories
The roots of systems thinking lie in physics and the natural sciences. Ludvig von Bertalanffy, commonly acknowledged as the founder of the systems movement, was the first scholar to develop the concept outside the discipline of physics. Talcott Parsons, then, introduced systems thinking into sociology (e.g., Parsons, 1951), using system as an analytical tool for understanding social structures and the way they work. By the end of the 1950s, systemic thought had spread to almost all scientific disciplines. To coordinate the extremely heterogeneous field, the Society for General Systems Research was established in 1954 (Boulding, 1988, p. 33; Von Bertalanffy, 1972, p. 28).
Von Bertalanffy (e.g., 1950, 1968) advanced the concept of open system as a counterpart to the closed systems models that had been developed in the field of physics. Open systems are adaptive to their environment through feedback loops and strive to maintain a steady state. Von Bertalanffy was followed by several other scholars who rejected the former mechanistic view in favour of the organic nature of systems. One of the main trends in the American branch of systems thinking was system dynamics. This concept was developed by Forrester (e.g., 1968), who began to apply the insights of electrical engineering to analysing the behaviour of human and other systems. System dynamics thinking is where people live in a network of feedback structures (economic, political and ecological) whose properties are seen as the determinants of most problems (Bloomfield, 1986, p. viii). Direct or indirect applications of open systems thinking have produced many variations in the field (Stähle, 1998, pp. 29-54).
In the 1960s a new systems thinking trend began to evolve that was not grounded in the open systems perspective, but which turned the focus to the unpredictable and chaotic behaviour of systems (instead of the steady state) and towards the unpredictable dynamics of systems (instead of feedback processes). This new viewpoint, which later became known as the “science of chaos” and/or “complexity research”, evolved from the work of numerous scholars in different fields (Stähle, 2008).
These trends led to a new research approach known as complexity theory (CT) or complex adaptive systems (CAS) theory (Holland, 1995; Kauffman, 1993; Mitchell, 2009; Poutanen & Stähle, 2014). Although the two terms have been used interchangeably, CAS is possibly a more coherent strand of study, whereas “complexity theory” refers in general sense to the use of approaches and concepts derived from the study of complex systems. Complexity refers to phenomena like non-linear relationships, systemic interaction, boundary problems, emergence, and adaptation (Cilliers, 2011; Poutanen & Stähle, 2014). The CT and CAS perspectives have been applied in many studies from organisational and management studies to public policy, health, communication, and engineering research (ibid.). CT originates in the natural sciences, but there is now a growing trend to study social organisations as CAS. Complex systems, such as the human brain, organisations or markets, are capable of adapting and responding to environmental changes and exhibiting self-organising, emergent patterns of behaviour (Poutanen, Soliman, & Stähle, 2016; Poutanen & Stähle, 2014; Stähle & Äberg, 2015). Along with CT research, there has also been growing interest in innovation ecosystems, which refer to complexity and to the multifaceted co-creation of innovations and the role of virtual innovation platforms (Karakas, 2009).