Hello book friends, welcome to continue to be a guest in the little book boy's reading circle, today we continue to talk about the book, noise, yesterday we talked about how to eliminate noise in advance, this is mainly divided into two categories of methods, one is boosting, that is to say, we use the weakness of human nature, to achieve the right purpose, such as putting a fly pattern in the urinal or putting a small goal, then when men urinate, they are more inclined to aim at these things. This prevents urine from splashing. Another way to eliminate noise beforehand is to really overcome deviations. The biggest difficulty with this approach is how to recognize that a new problem is similar to the one we see elsewhere, and that the deviations that we have narrowed in one place are likely to appear elsewhere as well. It's very winding, to put it simply, these errors have not occurred before. For example, I know that in the sales office, you are oppressed by sales, and there will be a big deviation in the decision to buy a house, so I tell you, what he said is crazy, you don't want to buy it, go home and calm down for a night, and buy it the next day. Then you go buy it again.
Whether it is a post-correction or a pre-elimination elimination, what they all have in common is the avoidance of a certain deviation that is generally considered to exist. Such biases exist and can be recognized, but often we are not aware of their authenticity or even have no impact on our decision-making. For example, when your friend hears that you are working on a private placement, he wants to join the investment, but hesitates because he has already invested in other investments and is satisfied with it. He is eager to join your private equity project, but he doesn't know whether to sell his existing assets. This is a status quo bias – knowing that a better option is just around the corner, but not willing to give up what you already have. In this context, the authors suggest that we should abandon the afterthought bias detection method and instead guard against it in the decision-making process. However, most people don't see themselves as flawed, and that's where we need to bring in a third party – the decision observer. As a decision-maker, do you often wonder if your judgment is biased and noisy?The observer may be your best decision-making advisor. After all, it's much easier to let someone else find fault with it than to deny yourself. Charlie Munger, for example, is an excellent observer of decision-making, having worked with Warren Buffett for decades.
However, to ensure effectiveness, it is better for this observer to have no interest in you. If you pay him, it will be difficult for him to give a fair opinion. If the relationship between the two parties is strained, it is impossible for him to look at you objectively. Therefore, the best observer should not be troubled by personal feelings, and should not be right about things and people. This perspective can help you better examine your own decision-making process.
In some organizations, a freelance supervisor can be set up to act as an observer of decision-making. In addition to examining the content of the proposal, they pay special attention to the process of generating the proposal and the dynamics of the team. This will allow decision observers to better identify biases and noise that may affect the proposal process.
In practice, decision watchers can help us be more productive while avoiding bad decisions due to personal emotions and biases. Therefore, as a good leader, we should actively seek such assistance.
We all know that fingerprinting is seen as a reliable piece of evidence in the judicial system, but the authors reveal that there can also be noise interference in the process. As a result, even with a perfect alibi, the results of fingerprint identification can be incorrect. Perhaps, some suspects' fingerprints are omitted because the examiners do not strictly compare them.
This also shows that the correctness of judgment must be accompanied by the existence of noise, and the only way to eliminate noise is for us to admit its existence. We must not have any illusions that there is something perfect in the world. Therefore, we need to be skeptical of all results. Only in this way can we continuously eliminate errors and reduce noise through program optimization.
Fingerprint examiners should also be aware of this, and they should ignore the suspect information in the case as much as possible to avoid preconceived ideas. Their job should be as objective and neutral as possible to ensure the accuracy of fingerprinting.
In general, we should always be vigilant and be skeptical and reflective about each outcome, so that the impact of noise can be minimized to reach a fair verdict. First of all, we need to ensure the independence of the various departments, because only then can their decisions really work. It's not just as important as financial transactions and decision-making, but because it allows us to cut through the noise and improve accuracy. Just like a prep and a chef work in the same kitchen, but they are not in the same position, which means that their work is independent. In this way, we are able to avoid introducing too many distractions in the stir-frying process and thus improve the quality of the dish.
In addition, the author proposes an effective solution to the noise generated in the judgment process. That is, let different experts make their own assessments independently and without interfering with each other. This is an effective way to mitigate the effects of noise, as the results of the evaluations of different experts can be combined to eliminate some of them. For example, if 100 people each make the same judgment, then 90% of the noise can be eliminated, and if 400 people all make the same judgment, then 95% of the noise can be eliminated. The effect is so remarkable that it can almost eliminate noise.
In short, only by allowing different departments and different experts to make judgments independently and without interfering with each other can the noise in the decision-making process be effectively eliminated. This not only improves the accuracy of decision-making, but also reduces the impact of human factors on outcomes, thereby better serving the needs of society.
First, choose a better and more professional judge. These people usually have superior intelligence, exceptional insight, and a keen grasp of detail. If you can find such people, you can use their wisdom and experience to make your ** more accurate and effective.
Secondly, the results of multiple independent judgments are summarized and averaged. This approach is similar to "crowd testing in one", and by collecting opinions from a wide range of professionals, the accuracy of ** is further improved. Of course, there is also a certain error in such a **, but this error can be gradually reduced by many experiments and verifications.
This is exactly the case in the United States. The poll data does not exactly match the final results, which is inseparable from the problem of statistical methods. But some wise men are able to accurately ** results, and this is often because these wise men have wisdom and insight beyond ordinary people. However, if ordinary people want to reach the same height, they need to constantly train and Xi to improve their judgment.
In this regard, the authors propose three training methods, one of which is logical reasoning. Through continuous scrutiny and thinking, we can find the possibility of greater probability, so as to improve the accuracy of **.
First of all, chess and card training is like learning card Xi skills, and then through data analysis, accurately judge the future odds. This method is simple, but it has a very practical value.
Then, teamwork is the second method, which is to get more accurate results by aggregating the judgments of individual members. This is like the analyst index and active ** index in the financial industry, which are all manifestations of this idea.
As for the third method, the selection of elites requires a long process and requires a sufficient voice. This is actually a manifestation of the market mechanism, only those who look in the right direction can survive here, and those who look in the wrong direction will be eliminated.
Overall, all three methods have their own advantages and are all very efficient. For example, through the training of chess and cards, we can change the perspective of looking at things, so as to make more accurate **;By working as a team, we can avoid a lot of mistakes;And by selecting the elite, we are able to find the super ** and thus rewrite our ** results more quickly.
Overall, all three methods are very effective and very easy to apply in the real world. So, if you're looking to improve your abilities, consider these methods, practice and improve, and you'll be able to succeed!When we walk into the hospital, we are often asked by the doctor to do a series of tedious tests. In fact, this is the process of doctors using a variety of advanced equipment to eliminate noise interference and judge the condition more accurately. For example, when a person's blood sugar level rises to a certain level, then he may have diabetes, and the doctor does not need to make a judgment. This kind of automatic judgment by the device is extremely accurate, and this is one of the important development trends of medicine in the future.
As technology continues to advance, more and more medical devices will replace doctors' diagnoses. Even, some wearable devices collect our data bit by bit in my life, such as when I go to the hospital, my blood pressure tends to rise, but at home it doesn't. That's why this scene in a hospital often brings significant noise interference. In the future, the collection of a large amount of personal long-term data will gradually replace various medical judgments.
When it comes to performance reviews and recruitment, reducing noise is just as important. Tomorrow, we will continue to ** this question and see how we can eliminate subjectivity through scientific data analysis and better evaluate and recruit.