Bias

Noise - Part II - Your Mind is a Measuring Instrument

Noise - Part II - Your Mind is a Measuring Instrument

Translations: RU

Interesting to learn how your mind makes judgments. Two types of judgments: predictive and evaluative. How to measure errors from bias and noise. How to measure the cost of noise. And how to deal with catastrophic consequences.

Drilling down the noise to three components: system noise, level noise, and pattern noise. How to measure (and compensate) the noises. Occasional noise as a part of pattern noise. How to compensate occasional noise: wisdom-of-crowds effect, crowd-within-the-one effect, second answer effect, and dialectical bootstrapping tool.

How the group work influences the noise. Informational cascade and social pressure cascade. How to deal with them. The effect of group polarization.

Noise - Part I - Finding Noise

Noise - Part I - Finding Noise

Translations: RU

Do you realize how seriously your organization is affected by bad decisions? There are two types of errors in human judgments: Bias and Noise. Bias is a very widely recognized problem, and you definitely heard about it. Noise is not so widely recognized. Yet, it affects MANY decisions in ALL the industries.

There is a huge Noise in government decisions. There is a huge Noise in commercial companies.

Noise can be made visible and can be reduced. It is harder to measure the noise in unique singular decisions, but it is still there and it still can be reduced.

Designing Data-Intensive Applications - Chapter 12 - The Future of Data Systems

Designing Data-Intensive Applications - Chapter 12 - The Future of Data Systems

Translations: RU
Earlier the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 12 is a summary of the book and a visionary view of the future. Data Integration. Overview of the ways we have to integrate data. Causality and why we need Total Order and Idempotency.