In a world where perfect information supposedly rules across the board, uncertainty certainly poses a challenge to mainstream economists. While some of the tenets of such assumption have been already addressed – via the theory of information asymmetries and the development of the rational expectations school, for example, uncertainty still poses critical questions.
For starters, uncertainty should not be confused with risk. The latter in a nutshell can be quantified using probability theory. Based on existing data and previous behavior, we could
say predict there is a 75 percent chance investments in the stock market can yield a 25 percent reward in say 5 years. This is not the case for uncertainty as here the outcome is entirely unknown. In other words, we have no idea what is going to
Arriving in Berlin from Africa via Frankfurt proved to be a nightmare this time around. While the flight and connections were almost perfect, the same cannot be said about my luggage. I checked in one bag at the point of origin and asked the airline agent to confirm my bag will indeed show up in Berlin while ensuring the bag tag had the TXL symbol (for Berlin’s Tegel airport) and the right flight number printed on it. Both checks yielded positive results.
Twelve hours later I found myself waiting for my smallish suitcase at Tegel. Bags are unloaded in batches. I oversaw all three of them. The conveyor belt then stopped, telling me my bag did not board the plane in Frankfurt. Or maybe at the point of origin. No idea.
As far as I know, there are at least two lost and found windows in Tegel.
My passport seems to profess a deep love for visa stamps. Every time the possibility of travel to another country arises, I can hear its excitement of filling yet another passport page with a brand new and (maybe) shiny visa stamp. The more, the merrier – although blank pages to host additional stamps are becoming scarce, yet again.
Finding an index for all sort of things is one of the traits of our data age. Yes, there is a passport-power index that ranks countries according to visa-free travel. For 2018, Japan and Singapore shared top honors followed by Germany and Denmark. The usual suspects sat comfortably in the basement: Pakistan, Somalia, Syria, Iraq, and Afghanistan. My passport is part of the middle-class having recently risen in the ranks thanks to the addition of the Schengen
A few months ago, as I was finishing a paper on blockchain technology, I received an unexpected comment on Artificial Intelligence (AI from here on in) from one of the peer reviewers. While addressing the overall topic of innovation in the 21st Century, I mentioned in passing the revival of both AI and Machine Learning (ML, not be confused with Marxism-Leninism) as a good example. The reviewer requested the deletion of one of the two terms as, in his book, they were exactly the same. Not so fast, was my prompt reply. In the end, both survived the overall peer review.
Looking at the history of AI helps shed some light on these concepts. While the AI term was coined in the 1950s, the work of Alan Turing, limited by the use of analog/mechanical computers, can be seen as its launching pad. Digital
According to latest estimates, global Internet penetration was close to 54 percent by the end of 2017. That is roughly 4 billion people. Figures for the number of unique cell phone users show that 5 billion people have access to the technology.
Armed with this numbers, I asked a business acquaintance who is a blockchain enthusiast and practitioner if the most popular blockchain platforms could effectively cater to all those users. Answer: “Not at this moment. But do not worry, we are working on it.”
The reason for this stems from the scalability constraints the most reputed blockchain platforms face. As I see, the scalability issue is related to three factors: 1.
A silent but intense competition seems to be taking place when it comes to defining blockchain technology. A Google search for the question “What is blockchain” yields over 120 million possible results. This number includes thousands of guides, videos, FAQs and other “educational” material on the subject. A shining example is a video depicting a blockchain expert trying to explain the technology to a 5-year-old kid. Really?
One common trait of all these resources is the lack of agreement on a single and straightforward definition of a blockchain. So take your pick. But, as mentioned in a previous post, this is probably not that relevant. After all, many people use mobile phones on an hourly basis and have no idea how they work. They do not need to, nor do they seem to care about it. The
In the short and medium term, technology and inequality seemed to be positively correlated. In the long term, however, things are not as clear-cut. With the right policies and democratic institutions in place, technology could become a catalyst to reduce income and wealth inequality. Historical evidence from last century clearly supports this claim. Will digital technologies of the 21st Century follow the same path?
The long-term is still quite a few years away for digital technologies such as AI and blockchains. In this post, I will look at the world of Bitcoin and explore its links to income and wealth inequality. I will assume the Bitcoin network is a country on its own with defined financial ties to the rest of the world mostly via crypto exchanges and miners.
Last May, the total
In a previous post, I pushed the idea that mining is part of the real sector of the blockchain economy. Unlike financial speculation, mining requires investment in hardware, electricity, space, human resources, etc. This also applies to small miners who undoubtedly will have to defray a lower investment amount but who can join a mining pool to share mining revenues. Also, miners face intense competition which in turn is a reflection of the high level of profitability in the sector.
Mining calculators seem to proliferate in the web. Such sites offer potential mining investors a rough idea of how much they can make on a daily and/or monthly basis given the current price of the crypto being mined and the hashing the investors is willing to purchase. For example, I am told that if I buy Bitcoin
It has already been three months since I last checked the ICO scene. At the time, I suggested ICOs were probably slowing down. New data seems to confirm this but all points to other trends not detected before. Figure 1 presents the latest data
ending on 31 May. 159 ICOs were successfully completed between March and May raising over 4 billion dollars. The data includes the Telegram wh.ich collected over 1.7 billion dollars in spite of not holding a public ICO phase. Telegram can indeed be seen as a statistical “outlier” making last April the most successful month ever. Note that the number of successful ICOs did not increase overall. March matched February with 57 ICO but was much more skinny regarding resources. April and May are fatter but do not exceed the number of ICOs of the previous
Most cryptocurrencies are now over 60% down from their December 2017 peak. While prices are still quite volatile, the trend for the last five months is decidedly downwards. While some still expect a recovery to the glorious days of last year, others see overvaluation all around accompanied by a financial bubble about to burst. Comparisons to the good old dot-com boom and subsequent crash of 20 years ago are cited as empirical evidence of what is coming.
Indeed, the current blockchain boom has similarities with the previous one. But there are also some fundamental differences springing from by the very nature of what we can call the blockchain economy. The first one is the marriage between technology and finance. Not that in the past the financial sector refused to use new technologies.