THE SYSTEMS-BASED EVOLUTIONARY LEARNING LABORATORY FRAMEWORK
This section describes a comprehensive systems thinking approach, embedded in a cyclic Evolutionary Learning Laboratory (ELLab) framework that is designed to deal effectively with complex issues in a variety of contexts.
The ELLab is an innovative systems-based framework – one that differs from other systems frameworks and approaches.
Innovation is embedded throughout the concept, design and application of the ELLab framework. Research and documentation of systems thinking abound in the literature and research journals.
However, its practical implementation and tracking of discernible impact is at best unclear, disjointed and highly variable.
The ELLab recognises these deficiencies in the practical application of systems thinking and provides a framework and implementation pathway to bridge the gap between science, research and the need to make a difference in the varied economic and socio-political environments in which decision makers, managers and the broader community grapple with the complexities of the real world.
The ELLab recognises the multi-dimensional nature of the broad environmental parameters and creates a practical approach to the engagement with stakeholders, identification of issues and the analysis and synthesis of the issues. Above all, the ELLab recognises that the impact of research is of little significance if the science is right but the application, analysis and route of impact are poorly executed.
In the ELLab, which is both virtual (a way of thinking; a concept) and real (individuals coming together to work for consensus), all stakeholders involved develop a deep understanding of the system, a shared vision and skills for systemic continuous adaption, innovation and improvement.
The ELLab consists of a unique seven step iterative process of thinking and acting in which the participants engage in well-defined activities, creating a systemic framework and environment where policy makers, managers, local facilitators, members of the community and researchers collaborate and learn together in an ‘experimental laboratory’ – to understand and address complex multidimensional and multi-stakeholder problems of common interest in a systemic way.
The ultimate goal is to achieve coherent actions directed towards sustainable outcomes.
The process of establishing an ELLab is a unique ‘methodology’ to collaboratively integrate and use existing and future knowledge to help manage complex issues. It starts at the ‘Fourth level of thinking’ with an issues workshop (step 1) and a series of forums with specialist groups to gather the mental models of all stakeholders involved in the issue under consideration, their perceptions of how the system works, what they regard as barriers to success and drivers of the system and possible strategies (solutions) to overcome these problems.
This is followed by implementing the ‘Third level of thinking’ through follow-up capacity building (step 2) sessions during which the participants (all stakeholders) learn how to integrate the various mental models into a systems structure (step 3).
The Vensim software program is a valuable tool for the development of a systems model (Causal Loop Diagram) of the issue under consideration. This learning step is of particular importance in order for all involved to take ‘ownership’ of the systems model.
Once completed, the participants move to the ‘Second level of thinking’ by interpreting and exploring the model for patterns, how different components of the model are interconnected and what feedback loops, reinforcing loops and balancing loops exist. This step aims to assist relevant stakeholders to develop an understanding of their interdependencies and the role and responsibility of each stakeholder group in the entire system.
The main barriers and drivers of the system are discussed in more detail, which provides the stakeholders with an opportunity to develop a deeper understanding of the implications of coordinated actions, strategies and policies.
Overall, this process provides all stakeholders with a better understanding of each other’s mental models and the development of a shared understanding of the issue(s) under consideration.
The interpretation leads to the identification of leverage points for systemic intervention (step 4).
Leverage points are places within a complex system (e.g. an economy, a living body, a city, an ecosystem) ‘where a small shift in one thing can produce big changes in everything.’
Senge also refers to leverage points as the ‘right places in a system where small, well-focused actions can sometimes produce significant, enduring improvements’.
Identification of leverage points greatly assists the devising of systemic interventions (finding systems based solutions) that will contribute to the achievement of goals or solving problems in the system under consideration.
The outcomes are used to develop a refined systems model, which at the same time forms an Integrated Systemic Master Plan (step 5), with systemically defined goals and strategies (systemic interventions).
In order to operationalise the master plan, Bayesian Belief Network (BBN) modelling is used to determine the requirements for implementation of the management strategies; the factors that could affect the expected outcomes; and the order in which activities should be carried out to ensure cost-effectiveness and to maximize impact.
The process of developing good policies and investment decisions is based on the best knowledge (scientific data and information, experiential knowledge, expert opinions) that is available at any point in time.
The systems model can be used to test the possible outcomes of different systemic interventions by observing what will happen to the system as a whole when a particular strategy or combination of strategies is implemented. That is before any time or money is invested in the actual implementation thereof.
Once the systemic interventions have been identified and an operational plan has been developed, the next step for the people responsible for the different areas of management is to implement the strategies and/or policies (step 6) that will create the biggest impact. Targets are determined and monitoring programs are implemented to measure and/or observe the outcomes of the strategies and policies.
In many cases it only requires an adjustment of existing monitoring programs to comply with the targets set within the ELLab process (e.g. to include factors to be measured that were used in the construction of the Bayesian Management Model).
Because no systems model can ever be completely ‘correct’ in a complex and uncertain world and unintended consequences always occur, the only way to
manage complexity is by reflecting (step 7) at regular intervals on the outcomes of the actions and decisions that have been taken to determine how successful or unsuccessful the interventions are and to identify unintended consequences and new barriers that were previously unforeseen.
In summary, the ELLab framework is generic and is designed to deal with any complex issue, regardless of its context (e.g. from large organizations and natural or social systems, to a dysfunctional family or a small business that is not profitable) or discipline area (e.g. business, health, engineering, education, marketing, development, environmental management and so on). The following sections provide further elaboration on and demonstration of various successful applications of the ELLab framework in solving complex problems in a variety of contexts