Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires developing a model. This model represents the key characteristics or behaviours/functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.
The usage of simulation:
- Many contexts, such as simulation of technology for performance optimization, safety engineering, testing, training, education, and video games. Often, computer experiments are used to study simulation models.
- Scientific modeling of natural systems or human systems to gain insight of it’s functioning.
- To show the eventual real effects of alternative conditions and courses of action.
- When the real system cannot be engaged or accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist.
Effective methods to enhance learning about complex systems must include
- Tools to articulate and frame issues, elicit knowledge and beliefs, and create maps of feedback structure of an issue from that knowledge
- Formal models and simulation methods to assess the dynamics of those maps, test new policies and practice new skills
- Methods to improve scientific reasoning skills, strengthen group process and overcome defensive routines for individuals and teams
Learning is a feedback process. It is an iterative cycle of invention, observation, reflection and action. The feedback from the real world to the decision maker includes all forms of information, both quantitative and qualitative. PDCA cycle (Plan-Do-Check-Act) is the improvement process in Total Quality Management (TQM). Effective learning involves continuous experimentation in both virtual worlds and the real world with feedback.
Single loop learning
The single loop feedback as shown below describes the most basic type of learning. This loop is a classical negative feedback. Decision makers compare information about the state of the real world to various goals, perceive discrepancies between desired and actual states. They take actions that (they believe will) cause the real world to move towards the desired state.
Information feedback about the real world is not the only input to our decisions. Decisions are the result of applying a decision rule or policy to information about the world, as we perceive it. The policies are themselves conditioned by institutional structures, organizational strategies and cultural norms. The mental models of the real world we hold, govern these policies. Feedback from the real world can also cause changes in mental models. Mental models are widely discussed in psychology and philosophy. Such learning involves new articulations of our understanding, our reframing of a situation and leads to new goals and new decision rules, not just new decisions. The learning feedback operated in the context of existing decision rules, strategies, culture and institutions; that in turn are derived from prevailing mental models.
Our world is actively constructed – modelled- by our sensory and cognitive structures. Recent research shows that the neutral structures responsible for the ability to ‘see’ illusory contours such as the white triangle exist between the optic nerve and the areas of the brain responsible for processing visual information. Active modelling occurs well before sensory information reaches the areas of the brain responsible for conscious thought.
Double loop learning
In double loop learning, information feedback about the real world not only alters our decisions within the context of existing frames and decision rules but feeds back to alter our mental models. As our mental models change, we create different decision rules and change the strategy and structure of our organizations. The same information, filtered and processed by a different decision rule, now yields a different decision. The development of system thinking is a double loop learning process. In which we replace a reductionist, partial, narrow, short-term view of the world with a holistic, broad, long-term, dynamic view and then redesign our policies and institutions accordingly.
Barriers to learning
We face grave impediments to learning in complex systems such as a nation, firm or family. Every link in the feedback loops by which we might learn can be weakened or cut by a variety of structures. Some of these are physical or institutional features of the environment. The elements of dynamic complexity that reduce opportunities for controlled experimentation prevent us from learning the consequences of our actions, and distort the outcome feedback we do receive. Some are consequences of our culture group process and inquiry skills. Still others are fundamental bounds on human cognition, particularly the poor quality of our mental maps and our inability to make correct inferences about the dynamics of complex nonlinear systems.
What hinders our ability to understand the structure and dynamics of the complex system?
- Dynamic complexity, imperfect information about the state of the real world
- Confounding and ambiguous variables, poor scientific reasoning skills, defensive routines
- Barriers to effective group processes
- Implementation failure
- Misperceptions of feedback
Idealized learning loops
Effective learning involves continuous experimentation in both the virtual world and real world, with feedback from both informing development of the mental models, the formal models, and the design of experiments for the next iteration.
Requirements for successful learning in complex systems
- Learning does not require good mental models of the environment. All we require is the ability to generate new candidate decision rules sufficiently different from current procedures and the ability to recognize and reward those that improve performance. Selection of the best performing rules over time will lead to high performance without the need to understand how or why something works.
- Virtual worlds in which the decision makers can refresh decision making skills, conduct experiments, and play. They can be physical models, role-plays, or computer simulations. In systems with significant dynamic complexity, computer simulation will typically be needed.
Advantages of virtual worlds
They provide low-cost laboratories for learning. The virtual world allows time and space to be compressed or dilated. Actions can be repeated under the same or different conditions. One can stop the action to reflect. Decisions that are dangerous, infeasible, or unethical in the real system can be taken in the virtual world. Thus controlled experimentation becomes possible, and the time delays in the learning loop through the real world are dramatically reduced. In a virtual world, one can try strategies that one suspect will lead to poor performance or even simulated catastrophe. Often pushing a system into extreme conditions reveals more about its structure and dynamics than incremental adjustments to successful strategies. Virtual worlds are the only practical way to experience catastrophe in advance of the real thing. It provides high-quality outcome feedback. The degree of random variation in the virtual world can be controlled. It offers the learner greater control over strategy, leading to more consistency of decision-making, avoiding implementation failure and game playing.
Pitfalls of virtual worlds
Virtual worlds are effective when they engage people with the situation, but they do not guarantee that virtual worlds overcome the flaws in our mental models, scientific reasoning skills, and group process. Effective learning in virtual worlds will often require training for participants in scientific methods. The use of virtual worlds to stimulate learning in organizations often requires that the group spend time addressing their defensive behavior. Developing disciplined scientific reasoning and an open, trusting, environment with learning as its goal takes effort and practice.
Why simulation is essential?
In the real world, feedback is very slow and often rendered ineffective by dynamic complexity, time delays, inadequate and ambiguous feedback, poor reasoning skills, defensive reactions, and the costs of experimentation. Simulation is the only practical way to test hypotheses emerging from elicitation techniques and other problem structuring methods. The complexity of the cognitive maps produced in an elicitation workshop vastly exceeds our capacity to understand their implications. Qualitative maps are simply too ambiguous and too difficult to simulate mentally to provide much useful information on the adequacy of the model structure or guidance about the future development of the system or the effects of policies.
Recent advances in interactive modeling, tools for representation of feedback structure and simulation software, made it possible for anyone to engage in the mental modeling process.