There is much we can learn from the development of qualitative relationships models though once we begin to ask questions like how long, how much, when, etc., a qualitative most is not likely to be of much use. The following video demonstrates how, in a very simple goal-seeking structure with delay, depending on the delay, it can be almost impossible to intuit the implications of the interactions with any level of accuracy. The difficulty arises essentially from operating with outdated data. See also Archetypes.
The simple savings account is used to demonstrate the nature of a reinforcing loop. Change the initial amount and interest rate and run the model to see the implications of changing these values.
This model represents an elaboration of the Savings Account model to investigate the implications associated with intending to save money for retirement so an amount may be withdrawn monthly for living expenses.
Investigations into the relationships responsible for the success and failure of nations. This investigation was prompted after reading numerous references on the subject and perceiving that *Why Nations Fail: The Origins of Power, Prosperity, and Poverty* by Acemoglu and Robinson seem to make a great deal of sense.
Inventory Model v2.0 adds production and order lead time. As well as the ability to keep track if parts are on order for a production run. See also: Inventory Model v3.0.
OK, we have a problem. Yet, do we really know what the problem is? More often than not we look at the symptoms, consider them the problem and attempt to fix them. This actually dooms us to failure because they're only symptoms.
Faced with a performance gap the two most obvious responses are to work harder or work smarter. There are trade offs associated with each, some obvious, some not so obvious.
Derived from Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement by Repenning and Sterman.
A small change in one variable can have a marked impact on multiple variables. Run the model (with height=0) and consider the output. What happens if you change to height=5. Run the model to find out. Was the change what you expected?
Purpose: Employ the Mono Lake model adapted from "Modeling the Environment" by Andrew Ford as a basis for developing a set of guidelines to support asynchronous multi-user model development.
Mono Lake is an ancient inland sea on the eastern side of the Sierra Nevada. From a policy point of view Mono Lake is the story of how a handful of people began a campaign to save a dying lake.
10.06.04 v1.0 created and documented by Bellinger. Initial problem with the reference mode has been fixed.