Part II Starting a Systems Thinking Consulting Firm: What have we Learned, What are we learning!
In the previous article, I wrote about the beginnings of our consulting firm and our learnings from modeling real-world systems. This article will talk about the importance of making models usable and the ethics of modeling & consulting work.
People often ask the question “What do you do? What is DESTA all about?”. I don’t have a simple, straightforward answer to that question, even after several attempts at making a standard efficient response template. Partly the problem is that the field of systems thinking and system dynamics is inherently creative and iterative. There are different ways of doing it depending on what you are doing it for. Every time we work on an assignment, we refine our process of applying systems thinking. So we don’t have standard templates for doing things. But we are comfortable with that because our intention behind adapting the process is to make sure that the result is fit-for-purpose, i.e. the outputs are usable and useful for the stakeholders. I can say that we would be highly disappointed if we make the most amazing models and we remain the only ones admiring them. Hence, to become a successful practitioner, one needs to learn the art of making models usable and useful for stakeholders.
Developing Usable Models
People often wish to have a model which can potentially keep answering all questions that come to the minds of all stakeholders. This is exactly the way a systems model should never be built. One model should never aim to answer all questions (Ford 2010). What one would end up doing in such a situation is developing a highly complex and detailed model. Such a model is no less complicated to use than the real-world complexity that we are trying to grapple with. Something in the category of what is shown in the image below as being the “mother of all models”. Such models take an enormous amount of time/effort to be developed but don't always provide a higher utility/value. Beyond a threshold, overworking a model decreases its utility purely due to its size and difficulty in putting it to use.
A model should not lead to increased confusion or lack of confidence from its end-user. It certainly can lead to an understanding that no one intervention can solve all problems and thus no one solution is right. But this does not mean that you provide a highly complex model to an end-user, who then runs in loops to identify what the model is communicating. One must always remember that models are simplifications of reality and thus they can never capture reality in totality (Sterman, 2002). Many of our clients are stuck in this conundrum. They want to have a supermodel, which has never been developed before, that must be able to answer as many questions as possible. Such a model could end up having thousands of scenarios and hundreds of levers, but then one will need to write a manual on how to operate such a model. Forget interpreting and making sense of the results.
The job of a systems thinker must be to help decision-makers make a choice, the outcome of which is consistent with the future direction they wish to pursue. The job of a systems model is not to help people in executing those decisions. No model can capture complete real-world complexity to the extent that it can prescribe an operational pathway for executing a decision. Models help us understand the multiple future possibilities under various “what if” questions, and then through a learning process help us make better choices and decisions. The “how-to” part of the decision is outside the model. The model provides us with a policy testbed that we can use to test our assumptions and improve our decisions. It is a low-cost experimental laboratory, where real-world decisions could be implemented and its future results evaluated in a safe fail manner, with no real-world consequences.
A systems model must always communicate a story and how the story could evolve, over time, if a set of decisions are taken. What are the current aims of people and how we could help them meet these aims through the use of models? The story is nothing but a set of causal relationships that our model captures and simulates. For example, the above images show a set of feedback loops depicting the interplays between livestock, income, land, fodder, and charcoal. These help the end-user to understand the story of the landscape and the model then becomes a vehicle to have systemic conversations with stakeholders. But it is not always necessary for the end-user to see all those complicated diagrams and model equations. These can stay with the modeler to meet the standards of creating a scientific and robust model. A simplified plausible story, representing the reality, should be good enough to gain people’s trust in the model and thereby get them interested to see the outcomes. Models that have high utility are those than can communicate the essence of the story and what unintended consequences their current actions could generate over time. One must have a minimalist approach towards communicating the model story and insights. Sharing too much information does not always result in high levels of engagement. However, the models should always maintain the highest level of scientific rigor and be open for scrutiny, and end-users should have the option of understanding and questioning all the intricacies of the model, should and when they wish to. System Dynamics models are white-box models, open for all to understand and examine.
Models must be made accessible to people (Electronic Oracle, 1971). If one wants to have a recurring business then the models (simplified interfaces, stories, graphics) need to stay with people who can benefit from using them. This way we always have the possibility of people coming back with questions or ideas on how the model learning could be applied in the real world and how model improvements could make them more useful.
Interpreting Models
Model results need to be relevant to end-users and stakeholders. They should be able to relate to the scenarios and the indicators used for projecting the scenarios. Choosing indicators for demonstrating results is another full-blown task in itself. Something that clients spend the least time upon. Without having the right indicators, we always run the risk of miscommunicating results or ignoring the counterintuitive outcomes. It’s like driving a car without a dashboard or flying an airplane with a cockpit having irrelevant indicators.
Ethics of Modeling and Consulting
While publishing models, sharing of credits and Intellectual Property Rights are important and sensitive aspects. We think it is beneficial if an agreement on these is reached in the initial stages of collaboration. This improves the quality of work and the joy of collaboration. Of course, it also facilitates a hassle-free and productive publication process. We have benefited a lot by sharing our models, even work-in-progress versions, with experts, coalition partners, reviewers, and colleagues. Fearing that someone may steal your model is a legitimate fear but the returns on improving the model through sharing are also high. We are learning to be cautious but at the same time open about sharing our models.
Co-creation of knowledge is a sentimental business. One has to be very careful in understanding and respecting the sentiments of the partners, clients, and co-workers. A key ethic in a consulting firm is respecting the sentiments of others. This involves maintaining the confidentiality and fair intellectual property rights. There have been instances where we had to negotiate for fair intellectual property rights because we wanted the models to be published. Such efforts must be treated as investments into making your models accessible to people.
We have learned that legally binding a relationship through a contractual obligation serves compliance but does not guarantee commitment in the long run. Contracts could be used to avoid unnecessary competition between collaborators eg. having non-competing clauses. But having a non-competing clause does not always guarantee collaboration. In our experience, those relationships have flourished the most where there has been no mention of competition clauses. This is also a partial reflection of the fact that both parties were more interested in collaboration and never saw each other as a competitor. Thus, we think the absence of such clauses in a contract is a very positive sign. While the presence of such clauses is a sign that this might end up being a transactional business. There is a fine line between a collaboration and a consulting arrangement, which goes a long way in determining how the relationship evolves. We are learning to understand these nuances better. Our experience is teaching us that being good in the technical aspects of systems thinking and modeling is not sufficient if you have to survive in a commercial environment. One has to combine business and technical skills. Which in some sense is a fusion of the art, of doing business, and the systems science of modeling.
By Mihir Mathur.
This is part II of the article series. In the next part, I reflect upon what we think are the key tenets for becoming a successful systems thinker and modeler.
