Sparks Interview — W. Brian Arthur: A Historical Transformation Is Taking Place, And We Are At The Beginning Stage
In this session of Sparks Interview, we are honoured to have the father of Complexity Economics, W. Brian Arthur, with us to share some of his views on on the blockchain will make the blockchain more important.
Moreover, banks don’t really know how to deal with it. Last year I talked to a mainstream international commercial bank. They are aware that blockchain has arrived but do not know what to do about it.
So, what happens next?
I think it’s inevitable that everything will be recorded in a certain way by a certain block. Blockchain will be a part of our every everyday life.
We are actually in the midst of a huge transformation. Banking, insurance, shipping, trading, oil, transportation, oil transportation, commercial banking, retail banking — all of these industries will have to accept blockchain, possibly including Bitcoin, but mainly blockchain and artificial intelligence. They are all in the midst of the change, and of course, they may be at the beginning of the transformation.
When I talk to people in these industries, they tell me that they will inevitably become a fintech company in 10 or 15 years, but they do not know how to achieve it.
Big companies believe that digitalization is inevitable, and they welcome such change. However, they do not know how to do it. It’s a bit like crossing the Red Sea. They can stop by the water and try to put their feet in, but they do not know if they can swim.
Therefore, this will be a significant and historical change.
Bitcoin emerged during the 2008 financial crisis. If the next financial crisis approaches, will it be another chance for Bitcoin?
If I go to the U.S., I think no one I know will be able to predict when the next financial crisis will happen. If they do not know what will happen, they will put their money in the stock market. No one can be sure if the stock market is going to crash. However, I don’t think this matter is a big deal.
What’s more important is that we are in an era, which every industry is facing historical transformations, be it in the U.S., Europe or China. Every traditional industry is changing and digitalizing. For example, the medical industry is beginning to pay attention to what we will be like in 15 years. On the other hand, in 10 to 15 years the transportation and logistics industry is likely to have trucks from China or Australia transporting goods across countries.
All of these changes are happening, and we are just at the beginning.
In complex systems, a classic example is the difficulties in predicting the weather. However, in the past 30 years, despite its difficulties, it is getting more accurate. What are your views on this?
The difficulties with weather prediction lie in that the weather system is highly nonlinear; even the fundamental physics behind it is chaotic.
If there are some small deviations at the beginning of the forecast, for example, a difference of two to three degrees Celsius in the temperature; it may mean that the weather would be very different after two weeks.
If we look at this problem from a more macro perspective, we will find that there is also a revolution in the field of meteorology: sensors, thermometers, and pressure gauges are everywhere. In 2000, there were not many of them. Now, we may have hundreds of thousands or even millions of such sensors. They are found all over the sea level, the Arctic and even in remote areas of China or Siberia.
All the sensors collect large amounts of data. Circa 2012, we all started talking about big data. Although there was a lot of data on hand at that time, we did not know what to do with them. In contrast, our computers and CPUs have become faster. We also have neuro networks and algorithms in deep learning. Therefore, the combination of more efficient data processing capacity and better algorithms can help us make better predictions. It will change everything, but it’s still imperfect.
We are at the frontier of a new field driven by sensors or technology, which brings us large amounts of data, faster computing speed, and better algorithms. It will change everything.
Since we can better predict the weather through the technological revolution you just mentioned, can we better predict the market, or have we already done it?
This is a $6.4 billion question, and my answer is definitely yes.
We are able to predict everything better than before, but our prediction can never be perfect. This is because if you have a good forecast, you will invest based on it, but in turn, that very action will change your forecast. So, I do think we are getting better at forecasting. Moreover, natural language processing has been used to read text. I think this prediction can be tested positively by cognitive tests. But I think it’s far from perfect.
For example, there is a man named Jamal who was murdered in Turkey, or in similar places such as Istanbul, Saudi Arabia, and James. This kind of unexpected event is likely to change everyone’s attitude towards Saudi Arabia, and oil prices may be affected. Such unpredictable events always happen.
And if you can predict well, so can others. This will also have an impact on the predicted results. I think all the predictions will get more accurate, including those health-related i.e. who will have a heart attack and so on. But it’s always imperfect.
You view the economic system as a complex system, but is it also a dynamical system? If it is a dynamical system, it means that it must have a dynamic equation. What do you think might be the most basic dynamic equation?
It’s a very good question, and also a very fundamental one. I may spend the next six months thinking about it after leaving here.
In order to regard the economy as a standard system, neoclassical economists tend to regard the economy as a balanced system. All the forces are balanced, just like the mutual forces in the spider web, which are ultimately balanced. So, they are liberals who propose equality. Therefore, the concept of mainstream economics is equilibrium. Of course, there may be some parts in the system that are off-equilibrium, but they will eventually go back to being in equilibrium. Just like if you touch a spider web, it will soon return to its original shape.
However, the team that developed complexity economics with me do not quite agree with this concept. We attempt to point out that the economy is not necessarily in equilibrium.
Therefore, if we regard the economy as unbalanced, we will not treat it as a machine, or as a spider web, or any kind of balanced system. We think of it as an ecosystem in which individuals have different beliefs and behaviours. You don’t always know what actions are done by whom, how good your technology will be, and how the government will respond.
In Silicon Valley, we have all kinds of ideas, but the government will only respond after three years. Similar things could happen in China as well.
As a result, in such an off-equilibrium system, you have limited knowledge and are always exploring an environment with many participants. And those participants are figuring hard on what to do too. Everyone seems to be exploring in the dark. Among them, there may be people who do well, or who can make money from others, and so on. Therefore, fundamentally speaking, to treat the economy as a complex system is to treat it as an ecosystem.
Back to your question, there are many dynamical systems that can be described by equations but not necessarily by algorithms. This is because algorithms are mainly derived from the equilibrium system. Hence, these equations can be easily expanded, just like whether the Lorentz equation is correct.
In economics, a lot of things are triggered together. If this happens, the Central Bank will step in and re-stabilize the currency.
Therefore, not all dynamical systems can be written as equations.
From the point of view of complexity economics, we tend to regard the economy as a calculation itself. It’s like an ecosystem with many players and different strategies. You can generally describe it as a calculation process with many different factors.
This calculation is very complicated for the whole economy. However, if we only make a model for a certain oil trade or for other transactions, it is likely to be done with an algorithm model.
Agent-based Models (ABM) is an important method in the studying of complex theory. If ABM cannot fully simulate the interaction of people in society, is it feasible to use it to predict the systemic risk in finance?
We ran some of the earliest ABMs. At the Santa Fe Institute, we built a model based on the 31 years prior stock market. In my experience, it is not difficult to build an agent for this model. The key is to find out who the subject is and who they represent (maybe banks or financial institutions, insurance companies). Every subject is a participant, which will then be calculated through the target object library. It may be Python or C ++.
Each becomes its own single unit, a programming unit, and many instructions of each unit may be different. If that happens, you can attempt to predict first and then bet.
Stefan Thurner has done some great research on the question you asked earlier — building a model that can help you predict systematic risk. Stefan Thurner has a paper that I think is very logical, but there are systematic risks. This is a very large subject model by the Austrian Academy.
It is important to note that systematic risk arises from the correlation between financial institutions. This is because banks may hold debt with each other. In other words, if your bank makes money initially, but another bank runs into problems; it may also cause your bank to get into trouble. The problems that these agents may encounter are collectively called systematic risks.
In this case, you can build a realistic model of financial institutions to find out how they are linked to one another. For example, when there is a problem in the operation of a bank, how are these risks transferred to other linked banks, or even further spread through the entire financial system?
This is what happened to the financial system in 2008. AIG, Lehman Brothers, and other big banks were facing difficulties, and the banks closely linked to them fell into trouble too. Then the whole financial system collapsed, and the U.S. government had to intervene to support the country’s financial system.
According to Stefan Thurner, some financial companies are very dangerous. Once they collapse in the system, it will lead to the collapse of many countries’ economies.
In complexity economics, the theory of increasing returns and path dependence shows that economic development is determined by the interaction of various economic forces and random “historical events”. Further, this result is not necessarily optimal and cannot be fully predicted. Therefore, what should we do from a researchers’ standpoint?
Let me give you an example. In 1500, Italy was one of the most advanced countries in the world. Of course, it was so in about 1400 as well. They had some of the best scientists in the world, like Galileo or similar researchers. The economic development of northern and southern Italy was similar. They had Naples in the South and Rome further south. There were also Florence, Siena, and Milan in the north.
It has been proven that in order to surpass or keep up with the more economically developed regions and in so doing become more prosperous, workers in the area will learn more skills, so as to attract companies and investment into this region. Historically, southern Italy had been developing well, and this was also the case for a period of time.
So, if the southern part of Italy developed first, it will develop better increasingly; and the same is true for the northern part, which was what happened historically. From the 1850s to 2000, the automobile industry and fashion industry spread all over northern Italy. As a result, Milan’s tourism and other sectors have also developed. These examples are consistent with your question. You can say that they are all dependent on history, various minor events, and even unique to the development of Italy. However, if a series of different things happened in the history of Italy, perhaps the development of southern Italy will dominate.
I mean if a region, company, or technology, has been leading in its development, it will gain more advantages on this basis and keep itself in the leading position. However, we can’t predict in advance the reason that caused this.
Also, such a result might not even be optimal.
The following article will be part of our “An Observation on Crypto Cycles” series. Stay tuned!
Originally published at https://www.datadriveninvestor.com on December 17, 2019.
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Translated by (via our WeChat Account): Xin Yue
Editor: Daphne Tan
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