With the development of information technology and the steep increase in information capacity in the 5G era, the flaws in human judgment are constantly amplified.

As a large amount of information is interspersed with a large amount of noise, decision makers need to establish a set of appropriate processing mechanisms to improve the correctness of decision making.

With the development of society, human evolution has evolved from materialization to virtualization. The connection with the physical object is getting farther and farther away, and we need more imagination and understanding to be able to adapt to it.

The virtualized world not only requires us to apply the paradigm of dimensionality, to learn to map the three-dimensional world to the higher dimensional thinking layer, but also to learn to apply the knowledge crystals formed by sublimation in the thinking layer to the real world.

In short: this is a workbook that guides us on how to implement dimensional descent.

Chapter 1

Judges around the world mostly have discretion in deciding cases.

After nine years of effort, the U.S. Congress passed the Sentencing Reform Act of 1984, which reduced the net disparity in sentencing that resulted from the contingent status of sentencing judges.

In 2005, mandatory sentencing guidelines were eliminated.

(The justice system has a bias toward the rule of man, and the influence of privileged thinking cannot be ignored)

This book reduces noise in decision-making by uncovering information buried in big data, the new trend of big data penetrating the leadership of behavioral economics.

Noise tells decision makers, "Know thyself." Being alert to flaws in cognition is more important than anything else. Providing a proper cognitive system, this book can help us make more accurate decisions in business, politics, and personal life.

Where there is judgment, there is noise, and noise and bias together lead to errors in decision-making judgment.

The difference between noise and bias:

Noise: irregular errors;

Bias: systematic errors.

(Deviation tends to be known, noise tends to be unknown.)

Noise is subdivided into horizontal noise, stable pattern noise, and contextual noise