Paradox predictor
In the age of information technology, analysts and even predictors have begun to appear. Their task is to talk about what will happen after a while, what to look for and what trends to expect in the foreseeable future. The article would like to analyze a little different types of predictions.
A well-known business publication is preparing a number on trends for next year. Identifies key issues, polls experts. Expert opinions are divided equally from the most pessimistic to the most optimistic scenario, the most cautious make neutral predictions.
Such moments cause me embarrassment. If we are talking about predicting the growth of Apple shares, then in a year we will see only one outcome: the cost will either fall, or increase, or remain the same within a certain corridor. This means that, at a minimum, ⅔ “experts” are mistaken right now in their forecasts and “experts” cannot be by definition. However, this happens very often.
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50% is the probability of a blonde meeting a live dinosaur on a city street. She will either meet him or not. So you can evaluate the logic of some experts, i.e. guessed / did not guess. I note that 50% is quite a large percentage of the probability of a forecast, therefore, with a large number of opinions, you will always find someone who guesses that he doesn’t say anything about his real analytical skills.
That is, you need to learn to distinguish expert opinion from the banal guessing.
The maze principle says “always turn right”. In other words, always hold the same opinion. After all, if you sit on the bank of the river for a long time, the corpse of the enemy will pass by.
This principle is well demonstrated by the most influential economist in the world, Nouriel Roubini, who predicted the 2008 world crisis. The whole point is that starting from the beginning of the 90s, Nuriel repeatedly predicted the collapse of economies, emerging markets and economic crises. Given the stable “instability” of the world economy and geopolitical processes, anyone can predict a financial crisis in such conditions.
In such a situation it is very important not only to predict the very fact of the occurrence of an event, how many to identify the true reasons for which this event occurs. And to do this when your "predictions" other "experts" will twist at the temple in front of you.
A causal relationship is a relationship between phenomena in which one phenomenon, called a cause, under certain conditions, gives rise to another phenomenon, called a consequence.
One of the main problems of predictions is that very often cause and effect are confused, or nothing is known about the real causes.
For example, when a banking crisis occurred in Ukraine, citizens called for a large amount of consumer loans, which depleted banks' cash reserves, were called the cause. At the same time, the real reason for the cash deficit was that the banks had placed deposits for 30 years at 30% (!) Per annum and handed out loans without confirming the borrower's solvency. Thus, the moment when deposit rates increased to 30% per annum and was the very moment when the experts had to guess about the coming financial crisis. Oh, yes, it was still possible to read the Western press, which wrote about the impending crisis at the time when we massively recruited loans.
Another predictor trick is to make predictions based on historical events. Again, not understanding cause-effect relationships. This leads to wrong conclusions.
For example, yesterday someone wrote to me in the comments on facebook that Windows Phone is waiting for a failure, because I quote “I have an android and an iPhone, enough for development”. The objective situation is that even if Windows Phone is waiting for a failure, it is clearly not for this reason. The presence of successful Android and iOS also does not say anything about the future situation. After all, you can immediately remember Kodak, Blackberry and the same Nokia. With this simple example, I wanted to show that the analysis of cause-effect relationships is a more laborious task than it might seem at first glance.
I rarely read success stories, because in 99.99% of cases they are nothing more than a successful combination of well-known factors and conditions. I also try not to read the biographies of great people, moreover, I consider them destructive for the development of analytical skills. I will not give specific names, but in the biography of each person you can find something that presumably influenced his success (although in reality this could be wrong). The main problem is that reading the biography (usually in a positive way), you can find a lot of fictitious cause-and-effect relationships, and not recognize the real reasons for success. Moreover, sometimes even the person himself cannot say with certainty what exactly caused the success.
But I love reading failure stories. Not to set yourself up for a possible file or to convince yourself that “this is normal” (as is customary in a startup party). No, my goal is to identify not so much the moment when it became clear to everyone that everything would fly into the air, and the earliest moment when it was already possible to recognize an impending threat .
When I read the opinions of experts, I try to pay attention not to conclusions (they should always be done by myself), but to the reasoning chain “cause” - “effect 1” - “effect 2” and how great the probability of this chain is. Of all the chains, you need to choose the one with the highest probability. And, of course, do not forget about the "black swans".
So what's the paradox of the soothsayer? It lies in the fact that the longer the prediction period, the easier it is to make an accurate prediction. But to predict what will happen in a month is almost impossible.
Project management is, first of all, an assessment of possible risks and how they may affect the course of a project. Here, analysis of cause-and-effect relationships is indispensable. It is very important at the early stages to identify possible threats and model various scenarios. The later these problems come out, the worse it will be for you.
And yet, analyze and draw your own conclusions, and do not be fooled by the opinions of the “experts”.
Thanks for attention!
50/50
A well-known business publication is preparing a number on trends for next year. Identifies key issues, polls experts. Expert opinions are divided equally from the most pessimistic to the most optimistic scenario, the most cautious make neutral predictions.
Such moments cause me embarrassment. If we are talking about predicting the growth of Apple shares, then in a year we will see only one outcome: the cost will either fall, or increase, or remain the same within a certain corridor. This means that, at a minimum, ⅔ “experts” are mistaken right now in their forecasts and “experts” cannot be by definition. However, this happens very often.
')
50% is the probability of a blonde meeting a live dinosaur on a city street. She will either meet him or not. So you can evaluate the logic of some experts, i.e. guessed / did not guess. I note that 50% is quite a large percentage of the probability of a forecast, therefore, with a large number of opinions, you will always find someone who guesses that he doesn’t say anything about his real analytical skills.
That is, you need to learn to distinguish expert opinion from the banal guessing.
Stable “instability”
The maze principle says “always turn right”. In other words, always hold the same opinion. After all, if you sit on the bank of the river for a long time, the corpse of the enemy will pass by.
This principle is well demonstrated by the most influential economist in the world, Nouriel Roubini, who predicted the 2008 world crisis. The whole point is that starting from the beginning of the 90s, Nuriel repeatedly predicted the collapse of economies, emerging markets and economic crises. Given the stable “instability” of the world economy and geopolitical processes, anyone can predict a financial crisis in such conditions.
In such a situation it is very important not only to predict the very fact of the occurrence of an event, how many to identify the true reasons for which this event occurs. And to do this when your "predictions" other "experts" will twist at the temple in front of you.
Causal relationships
A causal relationship is a relationship between phenomena in which one phenomenon, called a cause, under certain conditions, gives rise to another phenomenon, called a consequence.
One of the main problems of predictions is that very often cause and effect are confused, or nothing is known about the real causes.
For example, when a banking crisis occurred in Ukraine, citizens called for a large amount of consumer loans, which depleted banks' cash reserves, were called the cause. At the same time, the real reason for the cash deficit was that the banks had placed deposits for 30 years at 30% (!) Per annum and handed out loans without confirming the borrower's solvency. Thus, the moment when deposit rates increased to 30% per annum and was the very moment when the experts had to guess about the coming financial crisis. Oh, yes, it was still possible to read the Western press, which wrote about the impending crisis at the time when we massively recruited loans.
Cyclic processes
Another predictor trick is to make predictions based on historical events. Again, not understanding cause-effect relationships. This leads to wrong conclusions.
For example, yesterday someone wrote to me in the comments on facebook that Windows Phone is waiting for a failure, because I quote “I have an android and an iPhone, enough for development”. The objective situation is that even if Windows Phone is waiting for a failure, it is clearly not for this reason. The presence of successful Android and iOS also does not say anything about the future situation. After all, you can immediately remember Kodak, Blackberry and the same Nokia. With this simple example, I wanted to show that the analysis of cause-effect relationships is a more laborious task than it might seem at first glance.
Success stories and failures
I rarely read success stories, because in 99.99% of cases they are nothing more than a successful combination of well-known factors and conditions. I also try not to read the biographies of great people, moreover, I consider them destructive for the development of analytical skills. I will not give specific names, but in the biography of each person you can find something that presumably influenced his success (although in reality this could be wrong). The main problem is that reading the biography (usually in a positive way), you can find a lot of fictitious cause-and-effect relationships, and not recognize the real reasons for success. Moreover, sometimes even the person himself cannot say with certainty what exactly caused the success.
But I love reading failure stories. Not to set yourself up for a possible file or to convince yourself that “this is normal” (as is customary in a startup party). No, my goal is to identify not so much the moment when it became clear to everyone that everything would fly into the air, and the earliest moment when it was already possible to recognize an impending threat .
When I read the opinions of experts, I try to pay attention not to conclusions (they should always be done by myself), but to the reasoning chain “cause” - “effect 1” - “effect 2” and how great the probability of this chain is. Of all the chains, you need to choose the one with the highest probability. And, of course, do not forget about the "black swans".
So what's the paradox of the soothsayer? It lies in the fact that the longer the prediction period, the easier it is to make an accurate prediction. But to predict what will happen in a month is almost impossible.
How can this help me?
Project management is, first of all, an assessment of possible risks and how they may affect the course of a project. Here, analysis of cause-and-effect relationships is indispensable. It is very important at the early stages to identify possible threats and model various scenarios. The later these problems come out, the worse it will be for you.
And yet, analyze and draw your own conclusions, and do not be fooled by the opinions of the “experts”.
Thanks for attention!
Source: https://habr.com/ru/post/209306/
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