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Often people will report “I was traveling on the highway and all the traffic came to a grinding halt for 10 minutes. Eventually, the traffic moved again, but even though I traveled a further 20 miles on the highway I didn’t see any sign of any accident or incident that could have caused the snarl up!”
Let’s understand how this can happen:
Assume that the driver in the “first car” (for the purposes of this illustration) who is driving at 100km/h is momentarily distracted and in reaction quickly touches his foot on the brake and slows his speed down from 100km/h to 90km/h.
This alarms the driver behind him, who immediately also puts his foot to the brake, and slows his car down to 80km/h.
This is even more alarming to the driver of the third car, who quickly brakes his speed down to 70km/h.
You can easily understand how 10 cars down the line, this reflexive feedback response through the line of drivers is going to cause the 10th vehicle to come to a complete stop! And once one car has stopped this will cause many behind him to come to a halt too.
By this time the driver of the first car has recovered his composure, and puts his foot back on the gas and begins to accelerate from 90km/h back to 100km/h.
A couple of seconds later, driver number two sees the gap between his car and the first one in front of him begin to widen, so he too begins to accelerate again.
Slowly but surely each car begins to accelerate until eventually, even the cars that have been brought to a standstill begin to move again and before long, they are all traveling at 100km/h looking out for the big accident or incident that caused the snarl-up and seeing nothing!
This is not an analogy to be taken too literally in the context of biotechnology but rather acts as a point of reference to explain some elements related to this process.
Latency
The analogy illustrates how there may be a delay between a perturbance and effect. In our experience, this can manifest in many ways:
- The link between the perturbance and the effect is not recognized – eg a seasonal or “one-off” event happens, everything seems to get back to normal operation but over time system performance deteriorates
- The initial effect may be recognized, but the subsequently continued system deterioration is not recognized or associated with the initial perturbance
- The perturbance is often subtle – such as low-level overloading of the system, and is not recognized as a disruptive event. So the gradual deterioration of system performance is often not linked to the cause
- A regular or constant perturbance is not recognized, so gradual deterioration is unexplained
What this means is that the link between cause and effect that our “Newtonian” mindset tends to seek is not made because it is not direct and obvious enough.
Usually what is experienced are sludge management “problems” and the need to periodically shut down digesters, desludge them, desludge ponds etc. And then start all over again.
Or an algae bloom, which dissipates, and seems a little worse next summer when it blooms again.
But the root cause of the problem, systemic disruption, and degradation is never recognized or addressed, (mainly because until the development of the BSP we didn’t know how to.)
Butterfly Effect
The highway analogy is a good illustration of the so-called “butterfly effect” which is often poorly understood.
An initial event – “a butterfly flapping its wings” – or in our case the first motorist lightly tapping his brakes, causes a series of feedback loops (the response of each successive driver behind him) which leads to a rapid, escalated or amplified response being propagated throughout the system that far exceeds the scale and direct effect of the original “flap of the butterfly’s wings”.
The “butterfly effect” does not propagate and amplify its effect through the “butterfly” growing bigger or the “flap” becoming stronger. It is all about the cumulative effect of lots of little events “at the margin” and it is propagated via feedback loops. The exponential growth due to compound interest on reinvested money is a good example.