4.12 Appendix 2: Limitations to Building a Crystal Ball
83
at least to the French mathematician Louis Bachelier (1870–1946). Bubbles and
crashes in stock markets are examples of undesired consequences of such herding
effects.
4.12.7 Randomness and Innovation
Randomness is a ubiquitous feature of socio-economic systems. However, even
though we would often like to reduce the risks it generates, it would be unwise to try
to eliminate randomness completely. Randomness is an important driver of creativity
and innovation, while predictability excludes positive surprises (“serendipity”) and
cultural evolution. We will later see that some important social mechanisms can only
evolve in the presence of randomness. While newly emerging behaviors are often
costly in the beginning (when they are in a minority position), the random coincidence or accumulation of such behaviors in the same neighborhood can enable their
success (such that new behaviors may eventually spread).
4.13 Appendix 3: Will New Technologies Make the World
Predictable?
How can we know that the processing power, data volumes, and complexity will
always grow according to the same mathematical laws, which I have assumed
above (in Fig. 4.1)? In fact, we can’t, but Moore’s law for the computational
processing power has been valid for many decades. A similar thing applies to the
curve for the stored data volumes. In future, quantum computers may potentially
change the game. They are based on a different computing architecture and concept.
For example, classical data encryption would become easily breakable, but new
encryption schemes would become available, too. Thus, could such a paradigm shift
in computing make optimal top-down control possible? I doubt it, because there are
also new technologies that will dramatically increase data production rates, such
as the Internet of Things.
59 Moreover, the production of hardware devices, both of
computers and communicating sensors, might be a limiting factor, as are data transmission rates. Finally, when computer power and data rates are increasing, this is
promoting complexity, too, as new devices and functionalities can be produced.
We must also realize that a small subsystem of our universe such as a quantum
computer can, by its very nature, not evaluate and simulate the entire world, including
its own state and temporal evolution. Simplifications will always be needed. Unfortunately, sheer data mining and machine learning are often quite bad at predicting future
changes such as paradigm shifts due to systemic instabilities or innovations, which
59 See http://www.industrytap.com/knowledge-doubling-every-12-months-soon-to-be-every-12hours/3950.
83
at least to the French mathematician Louis Bachelier (1870–1946). Bubbles and
crashes in stock markets are examples of undesired consequences of such herding
effects.
4.12.7 Randomness and Innovation
Randomness is a ubiquitous feature of socio-economic systems. However, even
though we would often like to reduce the risks it generates, it would be unwise to try
to eliminate randomness completely. Randomness is an important driver of creativity
and innovation, while predictability excludes positive surprises (“serendipity”) and
cultural evolution. We will later see that some important social mechanisms can only
evolve in the presence of randomness. While newly emerging behaviors are often
costly in the beginning (when they are in a minority position), the random coincidence or accumulation of such behaviors in the same neighborhood can enable their
success (such that new behaviors may eventually spread).
4.13 Appendix 3: Will New Technologies Make the World
Predictable?
How can we know that the processing power, data volumes, and complexity will
always grow according to the same mathematical laws, which I have assumed
above (in Fig. 4.1)? In fact, we can’t, but Moore’s law for the computational
processing power has been valid for many decades. A similar thing applies to the
curve for the stored data volumes. In future, quantum computers may potentially
change the game. They are based on a different computing architecture and concept.
For example, classical data encryption would become easily breakable, but new
encryption schemes would become available, too. Thus, could such a paradigm shift
in computing make optimal top-down control possible? I doubt it, because there are
also new technologies that will dramatically increase data production rates, such
as the Internet of Things.
59 Moreover, the production of hardware devices, both of
computers and communicating sensors, might be a limiting factor, as are data transmission rates. Finally, when computer power and data rates are increasing, this is
promoting complexity, too, as new devices and functionalities can be produced.
We must also realize that a small subsystem of our universe such as a quantum
computer can, by its very nature, not evaluate and simulate the entire world, including
its own state and temporal evolution. Simplifications will always be needed. Unfortunately, sheer data mining and machine learning are often quite bad at predicting future
changes such as paradigm shifts due to systemic instabilities or innovations, which
59 See http://www.industrytap.com/knowledge-doubling-every-12-months-soon-to-be-every-12hours/3950.