My Take: Thinking Fast and Slow

Book Cover

I first read this book during my freshman year of college. Since then, “Thinking Fast and Slow” (TFS) by Daniel Kahneman has been one of the most influential books on my way of thinking. If I had to summarize this book in four words it would be — psychology, economics, statistics, and philosophy — all of which I am deeply interested in. TFS has piqued my curiosity so much that it had a large influence on why I am pursuing higher education in the field of analytics.

Since TFS is super dense and I want to make sure I cover everything. I am going to write chapters that align with the five parts referenced in the book as best as I can.

“A recurrent theme of this book is that luck plays a large role in every story of success” (Kahneman 9).

From the get-go like Bill Bryson’s “A Short History of Nearly Everything” and Nassim Taleb’s “Black Swan”, Daniel Kahneman reiterates the idea that luck has a dramatic influence on life events whether we, as individuals, would like to acknowledge it or not.

Success = talent + luck
Great success = a little more talent + a lot of luck (Kahneman 177)

We are all in consensus that luck has played some part in where they are today. Where people disagree is how much? How much influence has luck played in getting you in the position you are today? For myself, I fall back to the often said Pareto’s principle. I believe luck had an 80% role in where I am today and where I will be in the future. The far lesser 20% is because of my own due diligence. I see life a lot like the game poker where I could make a lot of +EV plays but not see the results. Luck has a much larger presence than skill until an arduously large sample size (in poker, tens of thousands of hands). The downside in life is we do not have the privilege of a large sample size. Thus our theta is our decay. I am in no way saying do nothing. Heck, many of us dedicate our entire life to improving that 20% because that is what we have control over. I mean why not? If life is worth anything, maximize the chances of achieving what you consider success. “Maximize” is a very scandalous word in that sentence. Maximize your chances to the extent you feel that you also enjoyed the journey as fate may not play out in your favor even though you did everything mathematically optimal.

The premise of this book surrounds this concept that human brains have two systems of thought: System 1 and System 2.

“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control” (Kahneman 20).

“System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration” (Kahneman 20).

In short, reading this sentence is System 1, and solving 12 x 34 is System 2. These systems are important because many times we use System 1 which is based on heuristics and effortless actions in events we should probably be using System 2. This discrepancy causes humans to be rationally irrational.

A bat and ball cost $1.10.
The bat costs one dollar more than the ball.
How much does the ball cost? (Kahneman 44)

Like me, you most likely thought 10 cents. That is System 1 answering a System 2 problem. Take a minute to think about the problem and you will figure out that the bat is $1.05, and the ball is $0.05.

For our ancestors, this discrepancy is negligible and advantageous. Although you may have fallen for the trick question above, System 1 is quick and dang accurate more times than not. In the wild that quick dirty estimate is what kept us alive in the wild. However, we no longer need such instincts to survive but still do possess them which is leads to our irrationality.

Before I move forward, a quirky fact about System 1 is that

“System 1 cannot be turned off. If you are shown a word on the screen in a language you know, you will read it” (Kahneman 25).

So, be wary. Even the quick drawdown of previous Google searches is being read by any overseeing coworkers.

On the topic of involuntary actions, the quickness of System 1 is partly attributed to its continuous nature of involuntary associations.

“Psychologists think of ideas as nodes in a vast network, called associative memory, in which each idea is linked to many others… Only a few of the activated ideas will register in consciousness; most of the work of associative thinking is silent, hidden from our conscious selves” (Kahneman 52).

Although the neuroscience behind how our brain works is well above the scope of the book, TFS goes over psychological studies and concepts that have changed our macro view of human behavior — many conducted by Daniel Kahneman himself. The excerpt above is one of them.

My biggest takeaway surrounding human consciousness and associations is that what we think is influenced by past and present surroundings.

Both the priming effect and the ideomotor effect are altering our decisions. Like the anchoring effect which is mentioned in a later section, the only counter to these biases is to know they exist. This is generally true for all System 1 biases as we are not consciously aware of our observations. It is Catch-22. The moment we do start rationalizing our brain is now using System 2.

Like the study that showed 90% of drivers think they are better drivers than average. We all believe we are above the statistic.

“You do not believe that priming applies to you because they correspond to nothing in your subjective experience. But your subjective experience consists largely of the story that your System 2 tells itself about what is going on. Priming phenomena arise in System 1, and you have no conscious access to them (Kahneman 57).

“Intelligence is not only the ability to reason; it is also the ability to find relevant material in memory and to deploy attention when needed” (Kahneman 46).

Especially in the sphere of cyber security, people use data, information, and intelligence interchangeably. Even so, there is a considerable difference. Data is everything that is collected. Information is relevant data. Intelligence are conclusions drawn from analyzing said information. This corresponds with Daniel Kahneman’s definition of human intelligence. A large part of intelligence is our brain’s ability to navigate the right nodes to find relevant information to draw conclusions.

When I think of the mere-exposure effect the first thought that comes to my mind is the scene from a movie called “Focus” featuring Will Smith. For context, Will Smith’s character is at a rich businessman’s football box and decides to play the businessman in a game of high-stakes cards. After losing a large sum of money playing cards, he offers the businessman double or nothing if he can correctly guess a random player’s number on the football field. There are around 100 players. The bet is so favorable towards the businessman that he cannot refuse. From a truly random and mathematical standpoint the EV for Will Smith is -98%. Any amount he bets over the long run he should only get 2% of his money back. (Bet x 2 x 1%-Bet). However, this event was not truly random because of the exposure effect. Throughout the entire day Will Smith and his team inconspicuously drilled the rich businessman with the number “55” thus causing him to pick player “55”. Although a little overzealous about the true odds of this effect (Will Smith’s character said the businessman was going to pick ‘55’ 70% of the time making his EV 40% [Bet x 2 x 70%-Bet]), it is a fun scene that reminds me of the mere exposure effect.

Part I gave a basic introduction to System 1 and System 2 along with examples of their impact on a daily basis.

In the next chapter, I will be discussing Part II: Heuristics and Biases.

“The prominence of causal intuitions is a recurrent theme in this book because people are prone to apply causal thinking inappropriately, to situations that require statistical reasoning…System 2 can learn to think statistically, but few people receive the necessary training” (Kahneman 77).

Recently, I have gone down the rabbit hole of watching hours of YouTube political commentators, which I like to call “media-politicians”. These are people affluent in the political discussion but hold no political position. The most notable would-be Ben Shapiro (~Republican) and Cenk Uygur (~Democrat) albeit both are far winged in their political ideologies. Every year, there is this event called Politicon an annual event where popular media-politicians have a civil discussion with others who hold differing views. I say “discussion” over “debate” because, let us be honest, these commentators are not going to switch their political stance or the audiences. The crowd of people attending this event are already cemented in their worldview. “Debate” infers there is a winner and a loser. Discussion helps disseminate that connotation as I believe understanding other worldviews is an important process to become an independent thinker.

One observation about each political side is their adamant use of facts to back their claims because facts are hard to refute. Before I go further, let it be known I have no political agenda and do not care where you fall on the political spectrum. I made an interesting and, what I consider, mindful observation that is used by both political parties that I think all people should take into consideration.

In the 2018 Politicon, Charlie Kirk and Hasan Piker had a conversation on an assortment of political topics. In this discussion, both mentioned facts with very large statistical inaccuracies.

Let us begin with Charlie Kirk. To back one of his claims, Charlie Kirk mentioned the fact that the deadliest cities in America are governed by Democrats. (Click here to see his tweet of the list). Although factually valid, this fact does little to prove anything once one acknowledges the base rate that the majority of cities are governed by Democrats. The base rate is slow to come to our mind because of the specificity of the information provided which I will discuss further later in this post. Secondly, as referenced by Politifact, Charlie Kirk also implies that this heightened amount of violence is a result of Democratic leadership. Correlation does not mean causation. With something as complex as the inner workings of a city, assuming the murder rate in a city is because of the leadership’s political affiliation is a large reach.

Hasan Piker rebutted this claim by stating that the poorest counties in the US are conservative. To investigate, I found this Politifact about the claim which lists the ten poorest counties in America — all of which are Conservative. Looking at the data, I noticed that of the ten poorest counties the largest county had a population of 20,000 people with the majority under 10,000. The average US county population is 100,000.

“Extreme outcomes (both high and low) are more likely to be found in small than in large samples” (Kahneman 111).

This is a textbook example of the Law of Small Numbers which states that smaller samples sizes are more likely to deviate from the population and be the extremes on a distribution. These sparsely populated counties are likely the poorest counties due to coincidence from the lack of a meaningful sample size than because of political affiliation. Hasan Piker’s claim is also inconclusive.

To wrap it up, be mindful that political commentators are trying to push their narrative so do your due diligence on the questionable claims’ political commentators are blabbering.

“The availability heuristic, like other heuristics of judgement, substitutes one question for another: you wish to estimate the size of a category or the frequency of an event, but you report an impression of the ease with which instances come to mind” (Kahneman 130).

This anecdote has been a real eye-opener for me in understanding System 1 and bias within human judgement. The lapse in judgement especially holds true for events that fall within one of the three categories: salient, dramatic, and personal. In these categories, we tend to gauge the probability of the event happening with the vividness and ease at which the events come to our mind. Because the categorized events are easily able to be remembered by their exotic nature, this usually leads to an overestimation of their true occurrence.

Two examples of where this heuristic bias is prominent is in insurance and public funding. The tragic helicopter accident involving Kobe Bryant, his daughter, and numerous others was felt around the world.

“Application requests jumped by 50% on the Tuesday after Bryant’s death, 52% on Wednesday, and 55% on Thursday, while the volume of submitted applications increased by 58% on Sunday, Jan. 26 — the same day as the crash — and 61% on Monday. The spike subsided to normal levels within a week.”

For days after news broke of his death, the amount of life insurance inquiries skyrocketed. His death tapped into all three of the categories that are prone incur estimation bias: Salient — Celebrity, Dramatic — Helicopter Crash, Personal — People have watched his stardom on and off the court for years.

The basic premise of how insurance companies make money is through the premiums you pay for the ability to spread out your cost of risk into smaller increments. There is a misconception that insurance de-escalates risk. The amount of risk you incur does not change and is still prevalent. What you are doing is de-escalating the impact of incurring the entire cost of risk at once — for a premium.

The magic of insurance companies is that the customers are the one who spread the cost of risk into manageable increments for insurance companies. This is done through the Law of Large Numbers by aggregating bundles of low probabilities events. In simplest terms this is the equation insurances companies need to balance to maintain profitability: [(annual likelihood of event) x (users) < (premium) x (users)]. To side with caution, a probabilistic distribution can also be thrown on it to ensure they have adequate reserves. However, insurance companies must take into consideration what is being insured because not all events are independent (ex. natural disaster for a regional insurer). The premiums are gross profit. This can be extremely profitable through certain customers paying substantial premiums because of heuristic biases and our inability to understand small and large probabilities. During the period after Kobe’s death, insurance companies not only received a large increase of applicants but also had the opportunity to charge a larger premium because the applicant’s memory of Kobe’s death.

“People are inadequately sensitive to the difference between low and negligibly low probabilities” (Kahneman 140).

I will dive into this idea in Part IV, but let it be known that humans are really bad at estimating extremely small or large probabilities. When estimating the occurrence of an unlikely event our difference between 1/10,000 and 1/100,000 is negligible. Even though the difference is a factor of 10.

There needs to be better effort done by the public, media, and politicians to better allocate government resources.

“The availability cascade is a self-sustaining chain of events, which may start from media reports of a relatively minor event and lead up to public panic and large-scale government action” (Kahneman 142).

In short, the media finds an interesting and unflattering story. The public gets enraged about what is going on and demands Politicians to act even through what happened is niche and requires an over-allocation of governments resources. Politicians are forced to act to keep public support and society experiences negative EV.

“The most coherent stories are not necessarily the most probable, but they are plausible, and the notions of coherence, plausibility, and probability are easily confused by the unwary” (Kahneman 159).

Another bias in System 1 is how it uses sees coherence, plausibility, and probability interchangeably. Coherence is the flow of the story; plausibility is the possibly of the story being true; probability is the likelihood story happening. All of which have a degree of similarity that is not suitable when specificity is needed. This is much like the difference between accuracy and precision in statistics. Accuracy is the closeness of values to the truth while precision is the closeness of multiple measurements that could entirely be far from the truth.

I do not want to dive deeper into why our brains makes said mistakes because it is best just to read from the person who ran the experiments, Daniel Kahneman.

One interesting takeaway from the conducted experiments is that

“In the absence of competing intuition, logic prevails” (Kahneman 160).

Meaning when the information provided contains strictly a singular occurrence of coherence, plausibility, or probability we think rationally.

“The test of learning psychology is whether your understanding of situations you have encountered has changed, not whether you have learned a new fact. There is a deep gap between our thinking about statistics and our thinking about individual case” (Kahneman 174).

A parallel between psychology and statistics is the way we conduct ourselves in everyday life and what we learn in the classroom do not interlink. In real life, very often, we are completely obvious to our own bias even though they would be apparent from an observer.

“Regression to the mean: the more extreme the original score, the more regression we expect” (Kahneman 178).

“Whenever the correlation between two scores is imperfect, there will be regression to the mean” (Kahneman 181).

My current investing strategy is entirely influenced by the regression to the mean concept. Simply stated regression to the mean is the idea that the extreme winners or losers are extreme because of their absence or honeypot of luck rather than from true out/under performance. Over time, these extreme things will regress to their true performance.

Again, I am not a financial advisor, so do want you want with my narrative. Now onto my investing strategy. I believe this concept especially holds true for highly efficient markets, like the NYSE. Recently, I looked at the worst performers within the DOW. One company that stuck out to me was Exxon — a corporation that used to have the largest market cap in the world. Exxon’s prominence two decades ago could entirely be because of an excess amount of luck and has spent the last decade regressing to and below the mean. I do not believe Exxon’s lackluster performance in the last decade is due to a loss of its competitive edge but futile market trends on the oil industry. Thus, I am betting on Exxon to regress to the mean in the upcoming years.

To close,

“If your predictions are unbiased, you will never have the satisfying experience of correctly calling an extreme case” (Kahneman 192).

This is mostly due to the fact unbiased predictions take large consideration of the base rate and extreme outliers are likely due to factors that are out of estimable control (luck).

“A general limitation of the human mind is its imperfect ability to reconstruct past states of knowledge, or beliefs that have changed. Once you adopt a new view of the world (or any part of it), you immediately lose much of your ability to recall what you used to believe before your mind changed” (Kahneman 202)

There is no clearer example of this than in the fictional, dystopic novel 1984 by George Orwell. The main character of this book is a guy named Winston Smith. Winston Smith’s job is to rewrite history to conform with reality — to correct misnomers of government forecasting so postdated articles will align with what had and is happening. With these changes in manuscripts, people have no way to contradict their previous claims. What is said today has always been what was predicted in the past. As it goes, Oceania has always been in a war with Eastasia.

“CEOs do influence performance, but the effects are much smaller than a reading of the business press suggests” (Kahneman 205).

To put it bluntly, even the best CEOs have a plausible but relatively insignificant impact on the overall success of a company. If I had to choose between great macroeconomics conditions or a spectacular CEO, I would choose the economic conditions every time with one exception: if Jack Welch was the CEO. Here is a hypothesis I plan on delving further into at a later date but will formalize here, I believe as the size of a company increases (to a cap) the CEO’s influence diminishes. Large Fortune 500 companies are already highly efficient machines which is why they are in the situation they sit. Additionally, a large company’s CEO cannot watch over its tens of thousands of employees or even understand in detail what a quarter of its divisions do. On the other hand, with smaller and more nimble companies that perhaps may not be totally efficient, the CEO can identify and improve those processes as well as better understand the day-to-day operations. If an absolutely horrible CEO were to take reign of a Fortune 500 company (which happens a lot), it usually takes a hefty amount of time before the company starts to burn to the ground. There is only so much a CEO can change in a company that is already on autopilot. A smaller company has much more elasticity to the actions put forth by head honcho. Thus, I believe the talent of a CEO has a more direct impact on the successful or failure of a small firm over a large firm.

To give better perspective on the impact of a good CEO, I am going to refer to Daniel Kahneman’s example. If there is miraculously a 0.30 correlation between the success of a firm and the “goodness” of a CEO, the R-squared is .09 (.3⁰²). All else equal, this means a “good” CEO will outperform a “bad” CEO a measly 59% of times — which is a little better than flipping a coin.

“For some of our most important beliefs we have no evidence at all, except that people we love and trust hold these beliefs” (Kahneman 209).

I have many beliefs founded on my own logic rather than scientific backing that make me me. I will admit some of my beliefs may be completely ignorant. Hopefully through exposure I will have the opportunity to refine and alter those beliefs to gain a broadened understanding of the world.

My journey of identity building has recently just begun. Like most freshman, college was the first time I was not in my parents’ household. My first step on-campus was less a myriad of me and more a collage of my parents’ beliefs. Through my college experience I gained exposure to other schools of thought and critical thinking skills that have helped me gain a foothold on my own identity.

“For a large majority of fund managers, the selection of stocks is more like rolling dice than like playing poker. Typically, at least two out of every three mutual funds do not achieve their own benchmark in any given year” (Kahneman 215).

The biggest misconception about the stock market is that you need to be smart and well-informed to make money. To be honest, the people that know nothing about the market and infrequently check their portfolio usually make out the best in the long run. Benchmarks like the DOW 30 and S&P 500 are so hard to beat over time because they have minimal fees and automatically adjust to the market environment. Because of their passive nature, this combination allows them to be a fairly safe bet and extremely hard to beat over the long run. In fact, Warren Buffet was so confident about the efficiency of index funds that he challenged any hedge fund to a $1 million prop bet if any hedge fund could outperform the S&P 500 over a 10 year period; Someone accepted the challenge and likewise the Oracle of Omaha won. Index funds are designed in a way to get the cumulative gains of the biggest winners (like Apple and Amazon) and the losses of the biggest losers (like GE). Over time, the biggest winners outweigh the biggest losers leading to a net gain. As referenced in Part II, any unbiased model will be not able to predict the outliers (think Apple and Amazon). Index funds do not have to worry about picking those massive winners because they are a part of the large basket of holdings they own.

“In highly efficient markets, however, educated guesses are no more accurate than blind guesses.” (Kahneman 215).

There are multiple theories surrounding the efficiency of the stock market. These include strong, semi-strong, and weak form efficiency. Although there may be various inefficiencies at the market at different times, the crux of the stock market is generally very efficient which is why people that know a lot about the stock market could ultimately end up being losers while others that know nothing next to nothing about the market huge winners.

“Everything makes sense in hindsight, a fact that financial pundits exploit every evening as they offer convincing accounts of the day’s events. And we cannot suppress the powerful intuition that what makes sense in hindsight today was predictable yesterday. The illusion that we understand the past fosters overconfidence in our ability to predict the future” (Kahneman 218).

One of my biggest pet-peeves is when someone sees a capital vehicle go up an arduously large amount in value and then moan about not buying that asset a month earlier. They then go on to explain how it was inevitable that the particular asset was going to increase in value. The magic about hindsight is you get to see the result without noise to create a story that will conform to your narrative.

“An algorithm that is constructed on the back of an envelope is often good enough to compete with an optimally weighted formula, and certainly good enough to outdo expert judgement” (Kahneman 226).

With the explosive demand for data-scientists, businesses are looking to use algorithmic techniques to drive their company to the frontier of efficiency and innovation. Often, companies want to achieve this through implementing buzzwords to their operations like machine-learning, deep-learning, and artificial intelligence. However, before companies decide to commit on the large expenditures, I recommend they establish their basis. I have noticed that a lot of the work businesses are looking for in data scientists can be done by data analysts. The biggest misconception in analytics is that a model *must be* complex to be good. The power of a model as simple as linear regression should not be understated. These simple models can be implemented with considerable accuracy in a timely manner. It is when these simple models are not good enough that the buzzwords should be implemented.

“An options asymmetric nature makes them particularly valuable when environments become more risky. Because you have little to lose and much to gain, events that make outcomes more extreme are welcome” (Desai 44).

This is not only in reference to the financial derivative but also the act of choice. A common example of the use of options for high schoolers is the college admissions process. College seeking students use their early decision action on their first college choice to increase odds of acceptance knowing they are committed if accepted. (Some students also use game theory to maximize their choice like choosing an attainable reach school as their ED to increase odds of acceptance, but that is beyond my point). In the financial world, Mihir Desai mentions the iconic example of Fred Smith, the founder of FedEx, use of options.

“In FedEx’s early days FedEx maxed out their credit limit with their fuel supplier. They owed $24,000 and only had $5,000 in cash. Smith recognized that if he went bankrupt, he would get nothing — but that if he allowed another day to operate, he had the possibility of success. Thus, he sought after risk and volatility: the blackjack table” (Desai 44).

Fred Smith’s choice here is straightforward: bankruptcy from ill delivery or bankruptcy from Vegas with the chance of seeing another day. He chose the latter and ultimately prevailed.

The focal point of this chapter is the Prospect Theory.

Figure 1: Prospect Theory visualized (Kahneman 283).

The premise of the prospect theory includes the following:

· Evaluation is relative to a neutral reference point

· The principle of diminishing sensitivity applies to both sensory dimension and the evaluation of changes of wealth

· Loss aversion: losses loom larger than gains (Kahneman 282)

As illustrated in the figure above, the focal point is the center of the graph (0,0). This center is relative to the risk taker and circumstance. The principle of diminishing sensitivity is seen as the function tapers off as it moves further away from the reference point. Lastly, loss aversion is shown with the convexity of the function while the risk taker is in the red and concavity while in the black. The Prospect Theory helps better explain insurance. People are willing to make negative EV decisions for the sake of their sanity. Secondly, emotional duress or tranquility is a considerable factor of human decision making and results in the sigmoid shape of the Prospect Theory that emphasizes loss aversion. Unlike humans, and along with the benefit of the Law of Large Numbers, insurance companies are not susceptible to such psychological phenomenon. Firstly, the premiums are based on a mathematical model that are “generally” free of human biases. Albeit humans are very bad at understanding fat tailed events and many go broke because they forgot to include xxx in their model. Secondly, if the risk is properly distributed so too should the payouts. Thirdly, the executives do not really care if they go broke because they want to maximize a poorly designed corporate compensation structure by incurring massive unknown risk to juice up profits. It’s the stakeholders that front the cost: shareholders, government, and community. I joke but not really. The Great Financial Collapse is a prime example of irresponsible optionality with society fitting the bill.

“People attach values to gains and losses rather than to wealth, and the decision weights that they assign to outcomes are different from probabilities.”

Figure 2: Fourfold Pattern (Kahneman 317)

Essentially, people mathematically overpay for low and high probability events that result in true material loss for society. Prime examples are in the court of law with frivolous claims that over settle and class action lawsuits that under settle.

Part V is a very much parallel to the work of another Nobel Prize winner, Angus Deaton. In his book, “The Great Escape — health, wealth, and the origins of inequality”, he articulates very interesting ideas about life and well-being that I plan on collating Part V with at a later date.

“Memories are all we get to keep from our experiences of living, and the only perspective that we can adopt as we think our lives is therefore that of the remembering self” (Kahneman 381).

Works Cited

Desai, Mihir. The Wisdom of Finance: Discovering Humanity in the World of Risk and Return. Boston: Houghton Mifflin Harcourt, 2017.

Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2015.