Systems can be decomposed in many ways. Depending on various aspects the resulting decomposition can look different. If they are divided in terms of technology, you could end up with different compartments or layers classified by the underlying technology. A system can be broken down by structure or function. Compartmental models are a good example. A system can be broken down based on the domain. That particularly helps when a group of people and roles are required to work around the system. Makes it easier for different groups of people to focus on different areas of the system. Hiring, research, experiments all become more manageable.
Another heuristic way of decomposing a system is by identifying the atomics parts of the system. What I mean by that is these sub-units cannot be broken down any further and the behaviour of these units are fundamentally fixed. Either by definition or by some kind of universal laws. By doing this we can then express the system as a combination of these units.
This opens up to thinking about 2 aspects. How can we put these units together in different ways to reach different outcomes. Secondly, how the inherent behaviour of these atomic units can be changed. Has anything changed (new invention, better technology etc.) that can modify the behavoiur of these atomic units. Sometimes it can be easier to solve the problem on the atomic level rather than on the system level.
In the beginning of the 20th century, industrial revolution was beginning to kick business activity into pace. The economy began to gain momentum from the newfound productivity from all the machinery. That created a shift from organizations that were until then more family-run to enterprises. This trend spread across the globe. In the second half of the century, air travel became more and more prevalent. Globalization began. It became easier to do business across borders just like it became easier to travel. This led to the strengthening of conglomerates and monoliths of the corporate world. They had access to resources and capital that enabled them to expand globally.
In the last 20 years, we can see a similar trend that is fuelled by the internet. Internet companies were fragmented after the dot-com bubble. But they were in a position of advantage to get a sense of the scale of the internet early on. Those companies went on to become internet giants. Newer technology like the blockchain and trends like higher social media adoption is kickstarting a new wave in business cycles.
In the coming 20 years businesses will become more remote and decentralized. They can be owned, managed and operated in a distributed manner that was not possible before. Now the technology exists. Incentives to build on such platforms are limited but expanding. The true potential of decentralized businesses is still not known. We are in a state of early adopters moving to the peak of the hype.
The Dunning-Kruger Effect is a bias where people think that they are smarter than they actually are. A person with low ability is more likely to overestimate themselves. This is due to their lack of perception of the task at hand and lack of previous experience. Both leading them to mis-calibrate there judgement. If a person has tried to shoot a basket a 1,000 times, they are more likely to judge their own skills as compared to someone who has tried 10 times.
Neural networks during learning also exhibit some similar behaviour. When a neural network is undergoing training by use a dataset, it optimize it’s own parameters to create a best guess for the problem at hand. However, if there are not enough data to learn from the network will more often fail to generalize the problem at hand. This would mean that it has failed to get an “average” of the problem at hand forcing it to misread real world data.
Everyone is susceptible to this.
Study conducted. Tests on group of students. People who scored less overestimated while competent people underestimated. This is common when we set out to learn something new. Initially with little knowledge we seem to believe that we know a lot more than what we actually do. But as we progress and see the big picture as to how hard it is or how vast the topic is we tend to doubt ourselves. Psychologists call this the valley of despair.
This slope is similar to the hype cycle. Even on a large scale, especially in the realms of new technology adoption, a large group of people tend to follow these steps.
AdNauseam is a free browser extension that tries to trick advertising networks by messing with your browsing data. AdNauseam works locally, and itself doesn’t send the data out to any other services. It comes with 3 opt-in features which includes hide ads, clock ads and block malware.
What it basically does is to randomly click on ads on behalf of it’s users creating a some what balanced mix of browsing history that leads advertising trackers astray. This is known as a strategy of obfuscation. This process reduces the value of the aggregated data from the user. The second order effects include polluting the data collected as whole by these services. Imagine even if 1% of users use such a service, it can have profound effects on search results and targeted ads. They could effectively end up showing irrelevant ads to its users leading to a lower conversion rate.
In a way, this directly attacks the incentive in the whole advertising business. The reason why a company would use social companies like Google, Facebook and Twitter to run their ads is because of 2 reasons. Firstly, users spent a lot of time on these platforms and secondly, these platforms have an understanding on what each user is interested in. But if software’s like this reduce the value of those platforms, it will force companies to question such platforms. AdNauseam is a start to a new era of privacy focused applications.
Privacy as a Service
AdNauseam – White Paper
Green Bonds are used to raise money for environment and climate related projects. The financial instrument makes use of the debt capital markets to fund green initiatives. Governments and corporates are issuing green bonds to bring in capital. The World Bank is a major issuer of green bonds. As per a report from PwC, it is expected that 2021 will see the largest ever issuance of green bonds.
Green bonds are issued according to directives set by the Climate Bonds Initiative (CBI). The CBI is an international investor-focused not for profit organization that has set the certification and assurance standards for green bonds. This helps assure investors that the proceeds are being used for the right projects.
Could this system be built over a decentralized platform. There are 2 parts to this system. The part where money is raised based on a renting activity. And the second part, where the money is used to fund projects that help the environment. A blockchain could help bring access to a larger market for raising capital. Anyone could chip in to such an initiative. The blockchain would inherently secure the transaction and keep track of where the money is routed to.
Dapp is an app built on decentralized technology. Typical apps are built by organizations or single entities even if they are open-source. Dapps don’t have owners and are free from censorships. The app itself runs on a decentralized network making it almost impossible to take it down. Blockchain being the most popular of these kind of decentralized chains is a catalyst for such apps.
The application logic lives on the blockchain and would execute the same way irrespective of the environment. This gives dapps a more deterministic way of working. Dapps offer privacy to developers and is resistant to censorships. However, they are computationally a nightmare. To be efficient every node in the chain would have to execute it. It will be hard to maintain such an app and develop coherent user experiences where it is spread across multiple nodes.
Coinbase has launched an app store of sorts for dapps. Brave, a web browser, has built a web browser that focuses on privacy and information control. Instead of relying on the traditional advertising model, it uses consumer attention as it’s form of “currency”. Users can earn their attention token by using their web browser. Compound is an automated interest rate protocol that can be used to develop financial dapps. Compound is a platform that allows you to lend out your crypto assets and borrowers to borrow a loan against a collateral. The blockchain ensure the security of the lending activity itself.
A deep learning network contains multiple learning nodes separated as layers and interconnected both within and across different layers to create a network. Typically deep learning models are trained by activating all nodes for each training input. Another way to train the network is to sparsely activate the network for each input with the help of a Switch Transformer. This would mean that only a subset of all the nodes would be active and the subset would vary depending on the input. Sparsely activated networks would have a constant computational cost despite the size of the whole network. The key feature of sparse activation is that it enable the different parts of the network to specialize in different kinds of inputs and problems. More like how the brain is. Our brain have different regions that are responsible for different cognitive functions. However, this also brings new challenges like load balancing. To avoid over training of some parts of the network and vice-versa.
Google has trained language models consisting of 1.6 Trillion parameters using this technique. The nearest model in this area was that of GPT-3, which consisted of 175 Billion parameters. This gives an idea of the leverage these models have.
Content is abundant. Thanks to the internet which has created an economy of abundance. Curating content is becoming a prominent niche in the creator economy. The realization that most people don’t have time. And curating is an easy, low barrier to entry way to save time for other people, i.e. your audience. Saving time for others is a form of value that you can exchange. You don’t even have to be an expert in a field to curate. Spending some time on related websites and forums can help you identify what is part of the trend. Stumbling upon content that is “interesting” becomes more easy that way. The tricky part is to identify if that meets the minimum-value-proposition, whether it is engaging and whether it should be curated or not.
Getting it out to the world has never been easier. A lot of platforms exist that could help you kickstart a newsletter. Platforms like Curated are focused on curation as it’s main form of content for newsletters.
Saving pieces of content and capturing information and data is almost free thanks to technology. Hoarding information in the form of bookmark managers that are not tended to and overflowing to do lists are more common. Simply because we are exposed to more content than we have time for. In such a world, our attention becomes the new currency for social transactions (liking a post, sharing a tweet etc.) on the web.
This is a bias that leads to a person taking credit for positive outcomes and blaming external factors for negative outcomes. In other words, anything that we do that we perceive is good or leads to a good outcome we tend to positively reinforce it with self-serving bias. The hidden problem is whether the action is actually good for us in the long term, whether it aligns with our big-picture and that of the civilization and that of society. Assessing each action for its won merits and demerits prior to the outcome can be more helpful so that the outcome is not affecting our judgement.
A concept that is tied to this bias is the locus of control. It is the degree to which a person is affected by their own beliefs vs. external influence. In everyday life this translates into when a person is thinking for himself/herself, it is said that the person has an internal locus of control. Studies show that people develop a more internal locus of control with age.
Perfect information is a concept related to game theory where every player in the “game” is fully informed of all prior actions. An example of a game that allows for perfect information is Chess. Every move made right from the start of the game is visible to both players.
Applying this to a market, perfect information is when all market participants are aware of the current market prices, their own utility and own cost functions at any point of time. Complete information would mean that all strategies, cost functions, steps of all players are common knowledge throughout the gameplay. Both far from reality, but this concept has been used to solve different forms of games.
The assumption of rationality of buyers is a big one in the case of imperfect information. A rational buyer would not buy anything of lower economic value in exchange for money. However with imperfect information and in most cases also incomplete information it is hard to assess the value of a purchase say in the stock market. In a way, the decision of a “sell” or “buy” is dependent on the decision made by others as it would affect the price of the stock.
Perfect information for all market participants would still not be enough to assume everyone will behave rationally. Even if all information is available, they might not be credible. The future can change and can render the information unusable. Buyers will operate on a layer of personal biases nevertheless. Not to confuse this with complete information. Knowing all possible market scenarios and moves of market participants , although impossible with current technology, will not lead to rational decisions. Even with perfect and complete information we tend to compensate for that in our own way.