An increasingly complex environment
We define a system as a set of interrelated and interdependent elements. We define systemic thinking as the discipline that allows us to visualise these interrelationships. Systemic thinking offers a holistic perspective because systems are qualitatively different from the sum of their parts (Kriz, 1998). The systems possess characteristics that their parts lack.
Systemic thinking attempts to understand these properties based on the system parts and their interactions.
During the conference he led at the University of Seville on 15 December 1998, Professor Jay W. Forrester defined system dynamics very simply when he said, ‘For the last 30 years, I’ve been developing a field known as system dynamics. System dynamics combine theory, method and philosophy to analyse the behaviour of systems. System dynamics use concepts from feedback control to organise information in a computer simulation model…; the resulting simulation reveals behaviour implications of the system represented by the model.’
Three concepts are thus basic to understanding system dynamics: the concept of the system itself, the model concept, and the computer simulation concept.
We define a complex system as one that requires a great deal of information to be described. Complex systems present a form of behaviour which may, in many cases, be exactly the opposite of what might intuitively be expected. Forrester calls this behaviour ‘counter-intuitive’. The intuition that presides over systems analysis is the result of the analysis of simple systems; therefore, the conclusions reached from the application to complex systems of this intuition may lead to results which are completely contrary to those that appear in reality (Rodríguez, 2000).
When we are faced with a complex environment, it may prove helpful to consider a simple yet powerful diagram developed by Bob Armstrong. This diagram consists of four quadrants using four constant concepts:
- Rational/Intuitive
- Calibrated/Non-calibrated (Quantitative/Qualitative)
- Few variables/Many variables
- One decider/Many deciders
Diagram by Armstrong/Hobson
All social agents would like to work in Quadrant 1, though they would usually be situated in Quadrants II and III. The greater the degree of environmental complexity and the number of interrelationships, the closer we get to Quadrant IV, where it will no longer be possible to apply simple or lineal policies centred on individual elements’ individual management. Greater significance will be awarded to the concepts which govern the system, to ‘the weak signals’ being interpreted as indicators, and to the relationships’ handling.
Understanding the complex environment
The concept of gaming/simulation embodies knowledge garnered from various scientific disciplines and attempts to make these complex realities understandable. Games and simulations help us understand complex dynamic contexts and thus are ideal for learning systemic skills. The gaming/simulation permits the breaking of rigid, rigorously hierarchical social forms of organisation by forming groups who are responsible for themselves. It permits flexibility, dialogue and creativity, emphasising personal initiative and encouraging group self-organisation and communication models based on systems competence (Kriz & Rizzi, 1998).
Paradigm from ‘The Gaming Discipline…’ (Duke, R., 1998)
Throughout history, there have been many studies that have demonstrated the suitability of the discipline of gaming/simulators in the understanding of systems. In his Doctorate thesis research, Willy Kriz analysed 125 people using a series of trials destined to reveal their knowledge, personality, interests, styles of interaction, and so forth. A few months earlier, some of these people had participated in a programme imparting training in systems competence, which was based on simulation and games. The difference between the two groups consisted that the latter group confronted risk and uncertain situations better, encouraged a more sustainable use of resources, and created more communications structures and more efficient teams. They were more interested in developing their group and collaborative relationships between its members, proposing discussion, defining roles, and the more detailed distribution of the workload. Finally, they came up with solutions to improve the process as a whole.
Conclusion
We understand complexity as the number of variables multiplied by the number of connections. In controlled environments, both are low, and situations can be optimised and forecasted. Calculations and projections are complicated when we increase the variables to be considered or the relationships and dependencies. In these cases, we work with hypotheses that allow us to visualise possible scenarios. Depending on the reality and how close it is to one of these planned scenarios, we will still be able to manage within a certain degree of certainty and security.
The real difficulty appears when the variables and connections are so numerous that it is impossible to project data or find explanations based on history.
Here, the systemic perspective is critical and where simulations can facilitate understanding concepts and the visualisation of solutions.
References
- Duke, R. (1998) The Gaming Discipline as perceived by the Policy and Organization Sciences. Gaming/Simulation: for policy development and Organizational Change. Tilburg University Press, pp.21-27
- Kriz, W (1998), Training of systems Competence with Gaming/Simulation in Gaming/Simulation: for policy development and Organizational Change. Tilburg University Press, ch. 39
- Kriz, W. & Rizzi, P. (1998) Simulación y juego para el desarrollo de los Recursos Humanos. Los Juegos de Simulación: Una Herramienta para la Formación. 25, 131-137
- Rodriguez, P (2000), Metodología Dinámica para el Análisis de Sistemas Sociales y Económicos. www.cacit.com
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