Best self-help books and their review notes

Here is the list of books I want to read 


Presence .

Presence: Bringing Your Boldest Self to Your Biggest Challenges

In the mean time before I read this book,here are the titbits given by a reviewer  Dr Ali Binazir.

-- The cortisol-testosterone dual hormone hypothesis: you're most effective when you have high testosterone and low cortisol
-- We usually think that confidence leads to decisions and thoughts drive behavior. But a surprising amount of the time, it's the other way around: decisions create confidence and behavior creates thoughts.
-- 80% of all fibers from the vagus nerve go from the body to the brain, not the other way around. Body changes mind!
-- The more people use the word "I", the less powerful and sure of themselves they are likely to be.
-- "Ultimately, participants’ speaking rate had an inverse relationship with how powerful they felt. That is, the more slowly they read the sentences, the more powerful, confident, and effective they felt afterward." Speak slowly to feel powerful!
-- Hunched over posture of staring at smartphones ("iPosture") kills both your mood and your productivity.
-- Why new year’s resolutions don’t work
-- Loved the section on self-nudges: little, incremental ways to change our behavior for the better.
-- "The three most important things to understand about the self, particularly as it relates to presence. The self is: 1. Multifaceted, not singular. 2. Expressed and reflected through our thoughts, feelings, values, and behaviors. 3. Dynamic and flexible, not static and rigid."

Solve for Happy: Engineer Your Path to Joy

Think and grow rich

the upside of turbulence
http://agribusiness.purdue.edu/quarterly-review/book-review-the-upside-of-turbulence

Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts

Here are just some of the things I took away:
- Resist the urge to associate bad outcomes (Seahawks losing to the Patriots in the Super Bowl) with bad decisions (Pete Carroll's play call was backing by solid data). My concern is that as we make continued investments in data science and analytics, we will tend to use that data for "resulting" rather than supporting the quality of decisions, and we'll end up with many fewer aggressive or game-changing decisions.

- We can improve the way in which we collect and vet data, and that process may challenge some of our assumptions (one of my immediate reactions was that adopting this line of thinking actually addresses the closely held belief firewall that Matt Inman addresses in his "belief" comic

- Finding a peer group that can help you build a non-confrontational, non-threatening decision review team will improve your executive function and "network leadership" (which explains why there are CxO councils, nerd exchanges, and even why hackathons are popular -- they are immediate and safe spaces in which to share decisions ranging from corporate strategy to Javascript toolkit choice)

- Some decision paths have hysteresis - even if you end up at the same outcome, the path you take to get there may be different and therefore your valuation of the outcome is different. The example Duke dissects is winning $1,000 and then losing $900 of it back, versus losing $1,000 and winning $900 back -- you're likely to be happier you "only lost" $100 versus the outcome where you "only won" $100..

- We have to imagine the future impacts of our decisions, which involves scenario planning, careful consideration of risks and future inputs (information) we may or may not see, and some of that future-proofing involves changing our reward valuation such that we are able to break consistently bad or ill-informed decision making processes.

The Ideal Team Player: How to Recognize and Cultivate The Three Essential Virtues

H3 Leadership: Be Humble. Stay Hungry. Always Hustle(people smart).

How to be creative . Claudie shannon's book review  "How claudie shannon invented the information age"

The first one that I might speak of is the idea of simplification. Suppose that you are given a problem to solve, I don’t care what kind of a problem — a machine to design, or a physical theory to develop, or a mathematical theorem to prove, or something of that kind — probably a very powerful approach to this is to attempt to eliminate everything from the problem except the essentials; that is, cut it down to size. Almost every problem that you come across is befuddled with all kinds of extraneous data of one sort or another; and if you can bring this problem down into the main issues, you can see more clearly what you’re trying to do and perhaps find a solution. Now, in so doing, you may have stripped away the problem that you’re after. You may have simplified it to a point that it doesn’t even resemble the problem that you started with; but very often if you can solve this simple problem, you can add refinements to the solution of this until you get back to the solution of the one you started with.

A very similar device is seeking similar known problems. I think I could illustrate this schematically in this way. You have a problem P here and there is a solution S which you do not know yet perhaps over here. If you have experience in the field represented, that you are working in, you may perhaps know of a somewhat similar problem, call it P’, which has already been solved and which has a solution, S’, all you need to do — all you may have to do is find the analogy from P’ here to P and the same analogy from S’ to S in order to get back to the solution of the given problem. This is the reason why experience in a field is so important that if you are experienced in a field, you will know thousands of problems that have been solved. Your mental matrix will be filled with P’s and S’s unconnected here and you can find one which is tolerably close to the P that you are trying to solve and go over to the corresponding S’ in order to go back to the S you’re after. It seems to be much easier to make two small jumps than the one big jump in any kind of mental thinking.

Another approach for a given problem is to try to restate it in just as many different forms as you can. Change the words. Change the viewpoint. Look at it from every possible angle. After you’ve done that, you can try to look at it from several angles at the same time and perhaps you can get an insight into the real basic issues of the problem, so that you can correlate the important factors and come out with the solution. It’s difficult really to do this, but it is important that you do. If you don’t, it is very easy to get into ruts of mental thinking. You start with a problem here and you go around a circle here and if you could only get over to this point, perhaps you would see your way clear; but you can’t break loose from certain mental blocks which are holding you in certain ways of looking at a problem. That is the reason why very frequently someone who is quite green to a problem will sometimes come in and look at it and find the solution like that, while you have been laboring for months over it. You’ve got set into some ruts here of mental thinking and someone else comes in and sees it from a fresh viewpoint.

Another mental gimmick for aid in research work, I think, is the idea of generalization. This is very powerful in mathematical research. The typical mathematical theory developed in the following way to prove a very isolated, special result, particular theorem — someone always will come along and start generalization it. He will leave it where it was in two dimensions before he will do it in N dimensions; or if it was in some kind of algebra, he will work in a general algebraic field; if it was in the field of real numbers, he will change it to a general algebraic field or something of that sort. This is actually quite easy to do if you only remember to do it. If the minute you’ve found an answer to something, the next thing to do is to ask yourself if you can generalize this anymore — can I make the same, make a broader statement which includes more — there, I think, in terms of engineering, the same thing should be kept in mind. As you see, if somebody comes along with a clever way of doing something, one should ask oneself “Can I apply the same principle in more general ways? Can I use this same clever idea represented here to solve a larger class of problems? Is there any place else that I can use this particular thing?”

Next one I might mention is the idea of structural analysis of a problem.Suppose you have your problem here and a solution here. You may have too big a jump to take. What you can try to do is to break down that jump into a large number of small jumps. If this were a set of mathematical axioms and this were a theorem or conclusion that you were trying to prove, it might be too much for me try to prove this thing in one fell swoop. But perhaps I can visualize a number of subsidiary theorems or propositions such that if I could prove those, in turn I would eventually arrive at this solution. In other words, I set up some path through this domain with a set of subsidiary solutions, 1, 2, 3, 4, and so on, and attempt to prove this on the basis of that and then this one the basis of these which I have proved until eventually I arrive at the path S. Many proofs in mathematics have been actually found by extremely roundabout processes. A man starts to prove this theorem and he finds that he wanders all over the map. He starts off and prove a good many results which don’t seem to be leading anywhere and then eventually ends up by the back door on the solution of the given problem; and very often when that’s done, when you’ve found your solution, it may be very easy to simplify; that is, to see at one stage that you may have short-cutted across here and you could see that you might have short-cutted across there. The same thing is true in design work. If you can design a way of doing something which is obviously clumsy and cumbersome, uses too much equipment; but after you’ve really got something you can get a grip on, something you can hang on to, you can start cutting out components and seeing some parts were really superfluous. You really didn’t need them in the first place.

Now one other thing I would like to bring out which I run across quite frequently in mathematical work is the idea of inversion of the problem.You are trying to obtain the solution S on the basis of the premises P and then you can’t do it. Well, turn the problem over supposing that S were the given proposition, the given axioms, or the given numbers in the problem and what you are trying to obtain is P. Just imagine that that were the case. Then you will find that it is relatively easy to solve the problem in that direction. You find a fairly direct route. If so, it’s often possible to invent it in small batches. In other words, you’ve got a path marked out here — there you got relays you sent this way. You can see how to invert these things in small stages and perhaps three or four only difficult steps in the proof.

Now I think the same thing can happen in design work. Sometimes I have had the experience of designing computing machines of various sorts in which I wanted to compute certain numbers out of certain given quantities. This happened to be a machine that played the game of nim and it turned out that it seemed to be quite difficult. If took quite a number of relays to do this particular calculation although it could be done. But then I got the idea that if I inverted the problem, it would have been very easy to do — if the given and required results had been interchanged; and that idea led to a way of doing it which was far simpler than the first design. The way of doing it was doing it by feedback; that is, you start with the required result and run it back until — run it through its value until it matches the given input. So the machine itself was worked backward putting range S over the numbers until it had the number that you actually had and, at that point, until it reached the number such that P shows you the correct way. Well, now the solution for this philosophy which is probably very boring to most of you. I’d like now to show you this machine which I brought along and go into one or two of the problems which were connected with the design of that because I think they illustrate some of these things I’ve been talking about.

In order to see this, you’ll have to come up around it; so, I wonder whether you will all come up around the table now.

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This piece was the by-product of wor

Nate Silver's signal and the noise book

https://www.lesswrong.com/posts/rGj2K8vu5qQCTWCar/some-highlights-from-nate-silver-s-the-signal-and-the-noise

As a part of my work for MIRI on the "Can we know what to do about AI?" project, I read Nate Silver's book The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. I compiled a list of the takeaway points that I found most relevant to the project. I think that they might be of independent interest to the Less Wrong community, and so am posting them here.

Because I've paraphrased Silver rather than quoting him, and because the summary is long, there may be places where I've inadvertently misrepresented Silver. A reader who's especially interested in a point should check the original text. 

Main Points

• The deluge of data available in the modern world has exacerbated the problem of people perceiving patterns where none exist, and overfitting predictive models to past data.

• Because of the risk of overfitting a model to past data, using a simple model can give more accurate results than using a refined model does.

• A major reason that predictions fail is that predictors often don't take model uncertainty into account. Looking at a situation from multiple different angles can be a guard against failure to give adequate weight to model uncertainty. 

• Average different perspectives often yields better predictive results than using a single perspective.

• Humans have a very strong tendency toward being overconfident when making predictions.

• People make better predictions in domains where they have tight feedback loops to use to test their hypotheses.

• Sometimes people's failure to make good predictions is the result of perverse incentives. 

Chapter Summaries

Introduction

Increased access to information can do more harm than good. This is because the more information is available, the easier it is for people to cherry-pick information that supports their pre-existing positions, or to perceive patterns where there are none.
The invention of the printing press may have given rise to religious wars on account of facilitating the development of ideological agendas.

Chapter 1: The failure to predict the 2008 housing bubble and recession

• There was an issue of people failing to take into account model uncertainty. In particular, people shouldn't have taken the forecasted 0.12% default rate of mortgage securities at face value. This rate corresponded to the rating agencies giving mortgage securities AAA ratings, which are usually reserved only the world's most solvent governments and best-run businesses.
• Some of the actors involved failed to look at the situation from many different angles. For example, the fact that the increase in housing prices wasn't driven by a change in fundamentals seems to have been overlooked by some people.
• Each individual factor that contributed to the housing bubble, and to the recession, seems like a common occurrence (e.g. perverse incentives, inadequate regulation, ignoring of tail risk, and irrational behavior coming from consumers). The severity of the situation seems to have come from the factors all being present simultaneously (by chance). Any individual factor would ordinarily be offset by other safeguards built into our social institutions.

Chapter 2: Political Predictions

• Political pundits and political experts usually don't do much better than chance when forecasting political events, and usually do worse than crude statistical models.
• Averaging individual experts' forecasts gives better forecasts than the forecasts of the average individual, with the effect size being about 15-20%.
• There are some experts who do make predictions that are substantially more accurate than chance.
• The experts who do better tend to be multidisciplinary, pursue multiple approaches to forecasting at the same time, be willing to change their minds, offer probabilistic predictions, and rely more on observation than on theory.
• Making definitive predictions that fall into a pre-existing narrative is associated with political partisanship. It's negatively correlated with making accurate predictions, but positively correlated with getting media attention. So the most visible people may make systematically worse predictions than less visible people.
• The failure to predict the fall of the Soviet Union seems to have arisen from a failure to integrate multiple perspectives. There were some people who were aware of Gorbachev's progressiveness and other people who recognized the dysfunctionality of the Soviet Union's economy, but these groups were largely nonoverlapping.
• Nate Silver integrates poll data, historical track record of poll data, information about the economy and information about the demographics of states, in order to make predictions about political elections.
• There's an organization called the Cook Political Report that has a very impressive track record of making accurate predictions about how political elections will go.

Chapter 3: Baseball predictions

• Baseball statistics constitute a very rich collection of data, and people who aspire to predict how well players will play in the future  have rapid feedback loops that allow them to repeatedly test the validity of their hypotheses.
• A simple model of how the performance of a baseball player varies with age outperformed a much more complicated model that attempted to form a more nuanced picture of how performance varies with age by dividing players into different classes. This may have been because the latter model was over-fitted to the existing data.

Chapter 4: Weather Predictions

• Weather forecasters have access to a large amount of data, which offers them rapid feedback loops that allow them to repeatedly test their hypotheses.
• The method of predicting what would happen under certain initial conditions for many different examples of initial conditions and then averaging over the results is tantalizing. It suggests the possibility of reducing uncertainty in situations that seem hopelessly complicated to analyze, by averaging over the predictions made under different assumptions.
• It's impressive that the weather experts are well calibrated.
• Local news networks sacrifice accuracy and honesty to optimize for viewer satisfaction.
• The integrated use of computer models and human judgment calls does notably better than computer models alone.
• The human input is getting better over time.
• Hurricane Katrina wasn't appropriately addressed because the local government didn't listen to the weather forecasters early enough, and the local people didn't take the hurricane warning sufficiently seriously.

Chapter 5: Earthquake predictions:

• The Gutenberg-Richter law predicts the frequency of earthquakes of a given magnitude in a given location. One can use the frequency of earthquakes of a given magnitude to predict the frequency of earthquakes of a higher magnitude (even without having many data points).
• Efforts to build models that offer more precise predictions than the Gutenberg-Richter law does have been unsuccessful, apparently owing to overfitting existing data, and have generally done worse than the Gutenberg-Richter law.

Chapter 6: 

• Communicating a prediction of the median case without giving a confidence interval can be very pernicious, because outcomes can be highly sensitive to error.
• Economists have a poor track record of predicting GDP growth. There's so much data pertaining to factors that might drive GDP growth that it's easy to perceive patterns that aren't real.
• The economy is always changing, and often past patterns don't predict the future patterns.
• Prediction markets for GDP growth might yield better predictions than economists' forecasts do. But existing prediction markets aren't very good.

Chapter 7: Disease Outbreaks

• Predictions can be self-fulfilling (e.g. in election primaries races) or self-canceling (e.g. when disease outbreaks are predicted, measures can be taken to prevent them, which can nullify the prediction).

Chapter 8: Bayes' Theorem

• When gauging the strength of a prediction, it's important to view the inside view in the context of the outside view. For example, most medical studies that claim 95% confidence aren't replicable, so one shouldn't take the 95% confidence figures at face value.

Chapter 9: Chess computers

• Our use of prediction heuristics makes us vulnerable to opponents who are aware of the heuristics that we're using and who can therefore act in unexpected ways that we're not prepared for.

Chapter 10: Poker

• Elite poker players use Bayesian reasoning to estimate the probability of a hand based on the cards on the table, contingent on opponents' behavior.
• Elite poker players also additional information, such as the fact that women tend to play more conservatively than men do, in order to refine their predictions about what cards the opponent has
• Often the 80%/20% rule applies to getting good at predictions relative to what's in principle possible. A relatively small amount of effort can result in large improvements. In competitive contexts such as poker, serious players have all already put this amount of effort in, so beating them requires further effort. But in arenas such as election results predictions, where not many people are trying hard, it's possible to do a lot better than most people do with relatively little effort.

Chapter 11: The stock market

• It's difficult to distinguish signal from noise when attempting to make predictions about the stock market.
• There are some systematic patterns in the stock market. For example, between 1965 and 1975, rises in stock prices one day were correlated with rises in stock prices the next day. But such patterns are rapidly exploited once people recognize them, and disappear.
• It's not so hard to predict a stock market bubble. One can look at the average price to earnings ratio across all stocks, and when it's sufficiently high, that's a signal that there's a bubble.
• It's hard to predict when a bubble is about to pop.
• Most investors are relatively shortsighted. This is especially the case because most investors are investing other people's money rather than their own.
• There are incentives not to short-sell stocks too much, both cultural and legal. This may give rise to a market inefficiency.
• An 1970 investment of $10k in S&P 500 would have yielded $63k in profit in 2009, but if one adopted the strategy of pulling money out when the market dropped by 25% and putting it back in when it had recovered to 90% of its earlier price, the profit would only be $18k. Many investors behave in the latter fashion.

Chapter 12: Climate change

• There's a lot of uncertainty around climate change predictions: there's uncertainty about the climate models, uncertainty about the initial conditions, and uncertainty about society's ability to adapt.
• There may be global cooling coming from sulfer emissions
• The amount of uncertainty can easily justify focus on mitigating climate change, because the risk of the problem being worse than expected entails more potential negative consequences than the consequences in the median case.
• A simple regression analysis looking at the correlation between CO2 levels and temperature may give a better predictive model than more sophisticated climate models.

Chapter 13: Terrorism

• Governments often prepare for terrorist attacks, but often prepare for the wrong kinds of terrorist attacks, unaware of bigger threats.
• The September 11th scenario hadn't been considered and rejected, but rather, hadn't been considered at all.
• If one looks at number of terrorist attacks as a function of their magnitude, they seem to obey a power law.
• There are some reasons to be concerned about the possibility of a nuclear weapon terrorist attack, or bioterrorism, in the United States. Such an attack could kill over a million people