PID control and hype curve

Source: http://ch.ce.nihon-u.ac.jp/kako/PC_HTML/Lect/pt10/10_9_cmt.html

• The curves for PID control and hype cycle are similar, aren’t they?

• When something is increasing rapidly, it is easy to misinterpret it as continuing to increase all the time

• However, if attention is attracted by the increase and positive feedback is running that increases further due to that attention, this is often too much of abubble

• Conversely, when it is decreasing rapidly, it is often misinterpreted as decreasing all the way down to zero.

• When “fear,” for example, is created in response to a decrease, and the raw feedback that fear creates further decrease is running, this also goes too far.

• The two are the same as curves

• Behavior of systems with built-in “going too far” mechanisms

• tokoroten

  • In the case of PID, they overshoot based on the difference from the target value, but in the case of hype cycle, the target value (future market size) is uncertain, so those who estimate large overshoot, and as the target value becomes clearer, the overshooters refrain from investing and converge. I sense a difference in the handling of target values.
  • n>I see, it overshoots because the target value is unclear and the target value prediction function has a social proof term “because everyone is mentioning it”.
  • Combo with the stock market. There are a lot of “I want to raise the stock price, so I’m going into blockchain.
  • However, in the process from R&D to commercialization, investment is made because a lot of results have been achieved and development potential is expected, which is a form of investment for the incremental (differential) results.

• The difference from PID control is that in PID control, the target value is given, whereas in hype cycle, it is unknown.

• I understood that overshoot occurs when people who are not confident in their estimation of P principle of social proof place weight on control at D, “I am positive because others judge it to be positive”.

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