Dealing with complex systems often requires one to think differently they are used to. If you have the time, the best introduction to systems thinking is Thinking in Systems by Donella Meadows:
If you are looking to dive right in here are the core concepts:
Source: 6 Fundamental Concepts of Systems Thinking - Lela Acaroglu
I am not going to rewrite the article above here since I want you, the reader, to go read Lela’s article and if it helps you, clap for it. At BlockScience our cadCAD modeling work almost always starts with System Mapping:
System maps help one to identify what concepts, constructs and stakeholders are relevant to your model and to begin to define their relationships to one another, as well as what your goals for the system, rather than just for the parts should be. When dealing with social and economic systems common mapping exercises include roles taxonomies which identity stakeholder groups, their possible actions, and the form their incentives or individual utilities might take. This is commonly in the workflow for an agent based model, and similar patterns appear in business model canvas exercises. The main difference here is that we often identify stakeholders whose objectives or incentives are in tension with one another.
A classical economics model of price discovery through the tension between actors providing supply and demand is an example of emergence, but at this point the dynamics are widely understood and accepted. However, minor variations to the assumptions can result in different outcomes. Introduction of elastic supply changes the emergent dynamics. Broadening further, systems with substitutable goods, or even transactions which require more than two actors to clear add to the complexity of the system and the emergent dynamics become unclear. It’s possible there is no equilibrium at all. I prefer to make analogies to ecologies because our stakeholder groups can be viewed a bit like species and the system properties like aspects of the environment that change as a result of the species behavior, i.e. a beaver damn could change the physical system in a way that positively effects some species and negatively affects others.
Ecological thinking is incredibly helpful for modeling complex systems because it moves us away from prediction and towards understanding dynamics: how the system changes as a result of both systemic natural laws which are inviable and agent behavior which cannot be controlled or even known completely. Furthermore, when designing economic systems for long term success it is critical to identify all stakeholder groups and ensure that none is in such a privileged position so as to extract value from and kill off the others. In ecological terms this would be a predator hunting its prey into extinction, resulting in the eventual death of the predator as well.
This pattern can also rear its ugly head when providers of capital exploit their position to extract large returns stifling the growth of the labor providing stakeholders, and undermining the value delivered to the consumer stakeholders. In the end the desire for more returns in the short term undermines the long term returns on the part of the capital provider.
An outline of systems archetypes is presented here:
For more educational material on systems thinking and systems innovation some extremely valuable education resources can be found at: