The Cobra Effect - Deepstash

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The Cobra Effect

Most of us have a simple, cause-to-effect relationship with our surroundings and the events that unfold in our lives. We try to solve problems using a linear-thinking model, resulting in inconstant consequences. This is known as The Cobra Effect.

Linear thinking assumes that there will be no unpredictable side-effects and nothing unusual or unexpected will happen, which may or may not be the case.

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Origin of The Cobra Effect

Origin of The Cobra Effect

The term ‘Cobra Effect’ originates from Colonial India, which was under the rule of the Britishers. To tackle the problem of the growing number of cobras, the British government announced a bounty on every dead cobra. Enterprising locals started breeding cobras and kept on claiming the bounty reward. When the Britishers realized this and stopped the reward, the snakes were set free, increasing the population in the city.

This anecdote revealed that a linear, logical solution could also make the problem worse.

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Understanding Dynamic Systems

A dynamic system has two types of feedback loops:

  • Positive Feedback: This feedback loop keeps the coveted effect in progress.
  • Balancing Feedback: Also called negative feedback, this loop keeps the system in a balanced state.

Instead of assuming that a dynamic system is a chain of linear events, we need to step back and look at the big picture and try to understand the complex feedback interactions.

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Applying Mental Models

It is impossible to accurately predict all potentialities in any proposed decision, as the amount of information we have is almost always inadequate.

Mental Models are cognitive constructs in our brain, which like an algorithm, can help us minimize any unwanted consequences while making a choice.

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Second-Level Thinking

Second-level thinking is a form of mental model that makes us map out the complex implications of the various decisions under consideration.

This helps us take into account the limitations of our minds and separate the signal from the noise.

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