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System Dynamics

About sixty years ago, Professor Jay Forrester, who passed away last week, worked with General Electric’s household appliances team to seek to break the recurring, infernal business cycle they were confronted with. Whilst the economy and industry were blamed, it turned out that issues were essentially self-inflicted. GE was creating its own business cycle through misguided supply chain management and poorly-timed hiring and firing.

A couple of lessons could be drawn from his experience at GE and other assignments in the industrial space:

  • Individuals tend to have a poor intuitive understanding of stock and flow dynamics

  • Linearity is instinctively assumed whereas a non-linear behavior tends to be the rule

  • Feedback loops tend to be underappreciated

  • As systems are too narrowly defined in scope and time, endogenous factors tend to be wrongly treated as exogenous

According to Prof. Forrester, there are no “unexpected consequences”, only consequences; no “surprise shocks”, only shocks. Everything is connected. Everything drives everything as part of a system, of which the complexity almost gives it a life of its own.

To incorporate these findings in a new tool designed to improve decision making processes, JW Forrester created “System Dynamics”, a subject which became a core pillar of the M.I.T. Sloan School of Management where he taught and where I studied it a few decades later. Since no human mind could deal with the complexity of many systems, he contributed to the development of computer simulation models. His objective was to build the capabilities to simulate the non-linear behavior of complex systems using stocks, flows, internal feedback loops and time delays. He pushed it to simulate cities in “Urban Dynamics” where he demonstrated, for example, that the government subsidization of low-cost housing in inner cities designed to deal with the decline of American cities in the 60’s led to more unemployment and poverty.

Here are some of the practical lessons from System Dynamics for day-to-day decision-making processes:

  • Cause and effect are not necessarily closely related in time and space

  • Due to feedback loops, obvious solutions or policies implemented with a view to optimizing a system tend to be at best neutralized by the system itself(1) and at worst counterproductive

  • Certain policies can be effective, but their levers are pulled in the wrong direction in the vast majority of the cases

  • Ineffective change tends to lead to vicious circles as more of the wrong medicine is used, which ultimately leads to a damaging loss of system credibility

Let us illustrate these pitfalls with a critical look at the Fed since the 2008 financial crisis. Its unconventional monetary policies proved ineffective when benchmarked against expectations due to a poor appreciation of feedback loops.(2) Instead of reconsidering its approach, the Fed doubled down, like a pilot frantically pulling the airplane’s nose up when stalling; Whilst monetary policies could have worked if used in a more effective way, they have lost significant credibility in the eyes of economic actors. This loss in confidence associated with growing income inequality has contributed to a rise in populism which influenced the presidential elections in November, eight years after the launch of the first quantitative easing program. Now, the President-elect appears to be keen to bring some change to the Fed’s leadership. Was that the Fed’s master plan? I don’t think so.

Complexity can paradoxically represent a convenient excuse for simplified decision-making processes, including in business. It also provides a natural hedge for potentially unattractive bets since it is almost impossible to link a bad outcome to a bad decision. Still, being aware of human beings’ limitations when it comes to complex systems and using the lessons learned through system dynamics to challenge decisions and policies may bring humble decision-makers closer to achieving their objectives—to the extent that they care about more than the short term implications.

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