Simulation Games (part one)

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 which their individual 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 the field of 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 which requires a great deal of information to be able 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 useful 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

Obviously, all social agents would like to work in Quadrant 1, though they would normally 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, or policies centred in the individual management of elements. Greater significance will be awarded to the concepts which govern the system, to ‘the weak signals’ being interpreted as indicators, and to the handling of the relationships themselves.

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 simulation help us to understand complex dynamic contexts, and thus are ideal for learning to acquire 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 the development of flexibility, dialogue and creativity, emphasising personal initiative, encouraging group self-organisation and models of communication, 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 this 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 the development of their own group and of collaborative relationships between its members, proposing discussion, a definition of roles, and the more detailed distribution of the workload. Finally, they came up with solutions to improve the process as a whole.


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 forecast. Calculations and projections are complicated when we increase either 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.
It is here that the systemic perspective is critical, and where simulations can facilitate the understanding of concepts and the visualisation of solutions.


  • 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.
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