To address this, I decided to shift my perspective and just write. Turn on the hose and let all the crap flow out; eventually, as the wise say, the good stuff will come too, or so I hope. Here it goes!

Recently I stumbled upon an essay/lecture by Richard Hamming (more about him below) entitled “You and Your Research” while going through the Stripe Press website (I highly recommend Stripe Press for those of you interested in the future of progress). You can also find the whole talk by Hamming here:

https://calhoun.nps.edu/bitstream/handle/10945/37504/Hamming_YouAndYourResearch_1986.pdf?sequence=1&isAllowed=y

Part I: Who was Richard Hamming?

Richard W. Hamming, was an American mathematician & computer scientist, best known for his works in computer engineering and telecommunications. He made significant contributions to the Manhattan Project & later joined the Bell Labs, where he created a family of mathematical error-correcting codes, called ‘Hamming codes.’ Hamming codes opened an entirely new field of study & solved important problems in telecommunications & computer science. However, on top of making impactful scientific contributions, Hamming observed and researched the characteristics, traits, and habits of exceptional scientists who had meaningfully contributed to the advancement of science. In this article, I attempt to unpack his talk:

Part II: ‘Is It A Matter Of Luck?’

It turns out, according to Hamming, that the ‘major objection by people against striving to do great things is the belief that it is all a matter of luck’, when history in fact shows us that ‘luck favors the prepared mind’ (Louise Pasteur) and ‘fortune favors the bold’!

For instance, Hamming explains that Einstein started thinking about ‘what would light look like if he went at the velocity of light’ as early as 12 years of age, leading to his later discovery of the ‘special relativity.’ Its true that others had also started working on this theory, but Einstein had prepared himself long ago. Edison also claimed that ‘genius is 99% perspiration and 1% inspiration.’ What I found particularly inspiring was Hamming’s observation that many great people struggled early on; Newton was not exceptional until well into his undergrad days, and Einstein was not a great student by his society’s standards. This reminds me of that memorable quote from the movie, The Imitation Game, on the life of Alan Turing:

“Sometimes it is the people who no one imagines anything of, who do the things that no one can imagine”

Going back to ‘you and your research’, Hamming asserts that an important property to have in doing great things, is ‘the belief you can do important things.’ Its striking that most scientists he surveyed spent most of their time working on things ‘they believe are not important and unlikely to lead to important things.’ Therefore, it is the confidence (or courage) in yourself, which is an essential property!

“The difference between having a vision and not having a vision is almost everything”

Part III: ‘The Institute for Advanced Study at Princeton Has Ruined More Great Scientists Than Any Other Place Has Created’ ~ Richard Hamming

On the importance of environment and working on the right problems, Hamming observed that while those who worked with their doors open got less work done -due to distractions- compared to those who closed their office doors, they tend to work on the right problems. And whether there is a cause-and-effect relationship there, Hamming hypothesizes that “the open mind leads to the open door, and the open door tends to lead to the open mind'.”

“the conditions you tend to want are seldom the best ones for you—the interaction with harsh reality tends to push you into significant discoveries which otherwise you would never have thought about while doing pure research in a vacuum of your private interests”

Part IV: ‘Drive'

Hamming notes that one of his colleagues at Bell Labs, John Tukey (the prolific American mathematicians best known for the development of fast Fourier Transform algorithm), was so incredibly knowledgeable that he was surprised to find out they were of the same age. It was his drive and strong work ethics, it turns out, that set Tukey apart.

“intellectual investment is like compound interest: the more you do, the more you learn how to do, so the more you can do, etc.”

Hamming suggests that in order to be a thought leader and accomplish great things, take the time [regularly and frequently] to ask the big questions, and not stay immersed in the sea of details, where many are stuck.

Part V: ‘Tolerating Ambiguity’

There is no doubt that great people can tolerate ambiguity. You should be able to ‘believe your organization & field of research is the best there is, but also that there is much room for improvement.’ Too much of either and you will become blind to chances of significant improvements, or crippled by doubt.

“I have not the faintest idea of how to teach the tolerance of ambiguity, both belief and disbelief at the same time, but great people do it all the time.”

Part VI: ‘Do Your Job In Such A Fashion That Others Can Build On Top Of It’

We often think that we must make ourselves so indispensable in our jobs that no one could possibly replace us. While there is merit in that idea, Hamming argues that if we desire recognition then others must be able to use our results, adopt, adapt, extend, and elaborate them, and in the process give credit where its due. It is then, that we can get promoted and move on to bigger and better things.

“Do you want to be a reformer of the trivia of your old organization or a creator of the new organization?”

Part VII: ‘Selling’

According to Hamming, as a scientist you must master 3 things to sell new ideas:

1. Giving formal presentations,

2. Producing written reports, and

3. Mastering the art of informal presentations as they happen to occur.

Contrary to popular belief, good ideas do not simply win out automatically and MUST be well presented.

Hamming suggests that scientists must adopt the habit of privately critiquing all presentations and ask the opinion of others. The gentle art of educating your bosses is an important part of the job of those who wish to rise to the top. As is expected, we may encounter superiors who are less competent than us sometimes in our career - the clever would not complain and utilize the art of selling and the psychology of persuasion to surpass them.

Let us end with a quote from Socrates:

“The unexamined life is not worth living.”

XOXO

Z

You see, I was never really entirely sure about what my dreams were. Since the age of 5, I dreamt of becoming a physician: save lives, open heart (or brain) surgeries, The Purpose, greater good, status, savior, God! I wanted all those things.

And then…it all came crashing down when I discovered The Universe.

The space, far above every hospital’s altitude level, with all its glories, adrenaline rush, the epic courage, The Purpose, greater good, status, savior, God! I was going to become a NASA astronaut!

Four (4) degrees later, $200k in student loans, expensive flight training, a boatload of random side projects, and I am neither a physician nor an astronaut. The rationale: physicians are to be replaced with robots and astronauts don’t get paid very much, with the added risk of early termination.

After 2 years of solitude and meditation (sponsored by the Pandemic), I realized, I can achieve all those dreams and much more by becoming an entrepreneur. Finally!

And dear reader, once I figure out how to effectively build and sell decentralized digital health solutions in order to vastly improve the state of human health, it is all over for you.

XOXO

Z

I have heard the concept of Game Theory being thrown around a lot throughout the years, but I never truly understood the implications of it.

As it turns out, this theoretical framework can be applied to a number of different fields, from political science, to psychology, military, business and economics. What I would like to understand and write about, however, is how does Game Theory apply to the blockchain technology and decentralized systems.

To start, lets try to define Game Theory in a simple language:

A game has players, where the outcome of the game depends on the strategy each player follows. Depending on the outcome of the game, each player receives a payout. The information available at any given point in a sequential game is referred to as information set, and equilibrium is when the players have made their decisions and an outcome is reached.

Now that we are familiar with the basic terms commonly used in Game Theory, lets look at how it works:

It is important to note that Game Theory makes several assumptions: first that the ‘players’ interacting in the ‘game’ are rational, and that each player’s goal is to maximize her payout. There are many different types of game theories, however, cooperative and non-cooperative are the most common. The cooperative game theory focuses on how coalitions interact when payouts are known, looks at how groups are formed and how payouts are distributed to the players. On the other hand, the non-cooperative game theory looks at how rational agents or players interact with each other to achieve the maximum payout. A very simple example of non-cooperative game is rock-paper-scissors.

One of the most well-known examples of the game theory is prisoner’s dilemma:

The prisoner’s dilemma: popular example of 2-player game and a useful tool for strategic decision making. Two prisoners of a crime are interrogated separately, and neither knows of the other’s plans, in other words, they cannot cooperate/collude. Each prison has 2 strategic options, with a total of 4 outcomes: each prison can either remain silent, or blame the other prison. The consequence of such actions is that if both prisoners remain silent, they both get 1 year prison term; if one blames the other, they will go free and the other would get 5 years and if both blame each other, they will each get 3 years. The prisoner’s dilemma shows that when each individual pursues his or her own self-interest, the outcome is worse for both of them, than if they had cooperated.

BLOCKCHAINS AND DECENTRALIZED SYSTEMS

Now, lets look at the applications of the game theory in blockchains. In centralized systems, cooperation that leads to the greater good is often enforced by a central authority such as the governments, institutions, orgs etc. In the case of decentralized blockchain technology and peer-to-peer networks, alternative incentive models need to be at play in order to promote positive outcomes.

As an example, Bitcoin’s proof of work (PoW) consensus mechanism utilizes economic incentive by intentionally making mining difficult and inefficient. In PoW systems, it typically pays more to be a good cooperative actor than a malicious one.

On the other hand, Ethereum blockchain’s proof of stake (PoS) consensus mechanism, combines reward with punishment. Nodes have a ‘skin in the game’ and are required to stake their own money to become validators. A node’s bad behavior will directly lead to them losing their crypto stake and node privileges.

On the other hand, as these decentralized networks grow, a coordinated attack becomes less and less likely.

In conclusion, game theory helps us understand the incentivization of consensus models and build trustless decentralized systems. It is, therefore, integral to the security and ingenuity of blockchains.