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Transcript
Rob Johnson:
I’m here today with Dr. Long Chen, the Director of the Luohan Academy and who’s been with me as a guest once before. Today we’re here to talk about a fascinating new report that the Luohan Academy has put up in conjunction with many famous economists including Mike Spence, Bengt Holmström, Patrick Bolton, Chris Pissarides, Eric Maskin, and numerous others.
At any rate, Dr. Chen, thank you for joining me. The name of your report, Understanding Big Data: Data Calculus in the Digital Era came out in February. It’s available on your website. In my own reading, it’s an extraordinary report. I would encourage all kinds of people who think they know what’s going on with big data and its influence on society. Everybody will learn from reading this reports. It’s very fresh, very deep, very thoughtful. So congratulations, but please tell me, what inspired you to bring this report to life and then what did you find in doing so?
Chen Long:
Well, thank you very much Rob for your kind words. I think a lot of people, we understand data plays a crucial role in the digital age. In the meantime, people are increasing worry about their privacy. There are several readings that had motivated us to write this up. People are worried about privacy, and they feel their losing control of their privacy, and they want to control it. On other hand that we see, there’s a lot of power and a lot of digital benefits out of using the data. So, we need a good way to understand what is privacy anyway, and what is the nature of data.
Let me start. I think about a couple of years ago at Luohan Academy’s annual meeting, we had this debate about the privacy-related issues. So, we have some guests from Europe and their opinion is that people’s privacy were somewhat invaded on the platforms because they had no choice. So, that actually motivate us to study what people would do if they do have choice, and there’s such a thing called privacy paradox.
Basically, it’s really globally in every country you can observe that when you ask them, do they worry about privacy, and almost all of them, most of them will say yes, globally, in any country including China. Then they turn around, published a lot of stuff about themselves, a lot of information, passing information. It seems they look like, they don’t care much at all. So, this is paradox.
Some people claim that, that’s because they have no choice. That’s why you are on Facebook. All your friends on that, you cannot just take yourself off of it. So, you’re forced to do it. Then, that is strange if you think about that there’s billions of people who actually do exactly the same thing. They worry about privacy, but then they share a lot of information.
So, it comes down to what is privacy and what is information? What is the benefit of information and data? Unless we put things together, we’re only able to understand what’s going on. It’s possible that we might even hurt the users in the name of protection because there seems a clear need for them to share information.
Another thing that motivated us to write this report with some of the best information economist is that actually, nowadays we call this thing data. In old days, we call them information. So, information economics in the sense is the data economics in this age. It’s really that very similar. So, we’d be thinking about the value of information for a long time, and so many economists had been answered in this report. They won Nobel Prize because of their deep thought about the role of information.
For a very, very long time, the efforts in theory and practice is to overcome the obstacles to plot the information, obstacles of asymmetric information. So, we’ve been promoting, spreading information for a very long time because we realize that’s one of the crucial thing that separates human being from animals. Then somehow, now we care so much about the downside about spreading information, personal information.
Of course, we care about privacy but we need to understand the value of information and data. So in a sense, we have to borrow the old lessons. The principles we have learned by so many economists who have spent so much time and so many institutional arrangements that are made to overcome the instrumental information. We’re trying to bring all those things together to help us understand the nature of data and privacy.
So, that sometimes reminds me of a parable. I think this came from India. It’s called the Elephant and the Blind Men. What it says is that once upon a time, there were I think five, six blind men. For the first time they were led to an elephant, and people ask them what does elephant look like. One of them touched the trunk and that feel like elephant is a trunk. Another guy touched the ear of the elephant and feel like the elephant is really a fan, a big fan that can wave around. Another one touched the body of elephant and he feel the elephant is really a wall. Of course, in a sense that they’re all right, but unless you put things together, you really won’t be able to understand what is an elephant.
In a sense, I think data is like that. So, that motivated us and it’s being an interesting journey. So, we were working together to try to understand what is the nature of data and what is the nature of privacy, and how should we deal with it to take the best leverage on the strengths of digital technology and data, but in the meantime we can protect privacy and other stuff well.
Rob Johnson:
Well, I’m curious about one dimension. When you talk about say the person on Facebook who puts things on there, is the problem that they don’t know what would be done with that information? In other words, if I’m tailoring what I want to put on my page, if I know everything you’re going to be doing with it, I can exercise my own judgment. If I think the only people seeing this are my 12 best friends from high school, but in fact all kinds of other people, and agencies, and others are seeing it, then perhaps I’m not conscious of protecting my own privacy.
I guess, where do you want the locus of responsibility to be? This should be with the person who joins a system or puts their data on, but knowing where it’s going to go? Should it be, how do I say it? Something imposed from above like by a government agency or whatever about what I call the rules of fair play? How do you see that architecture working?
Chen Long:
So, I think we firstly we probably needs to remind us that there has been a long tradition that we share personal information in the public. I give you one example is the yellow page. In the United States for a long time, for more than 100 years being most cities and towns, it can get the name, address, and telephone number from every family in a town. So we are very, very used to share information because I think for a long time, we realize that it’s very crucial for everybody to be connected to the society. You have to trust the society.
Another angle I can think about is the economic activity. A lot of time when we think about economic activity, we think about trades with those collaborations. Actually, beneath that economic activity is the information flow. This exchange of information. We have to know each other. A producer has to understand the preference of the customer to serve them well. This is a basic. I have to give the supply information such that they can give me the service, provide me stuff.
So really beneath any economic activities, so long as it is beyond two people, more than two people, then we have to exchange information. So that I think has been going on for a long time. That’s why I think as a society, for people, we are used to exchanging information. Always been doing that, while making a lot of arrangement to spread the information.
Of course, this other side is a privacy. It comes down to what you feel comfortable or you do not feel comfortable. In most circumstances that we do feel comfortable, but sometimes it might be abused in some way. So I think that’s why if we go back to the 1970s when the FIPPS, it’s the Fair Information, Practice Standard when it came out in the United States. Essentially, it asked how to make good use of information. So if that information is being used by some other people, some other institution, but somehow it sets some boundary. For example, we have to acknowledge them. It have to get some of the approval in general. You set boundaries, but otherwise I think exchanging information is essential and we all would be used to it for a long, long time.
Rob Johnson:
You do a very nice job really in the report of tracing essentially the history of economic thought about information with HIAC, Ronald Coase, famous for the Coase Theorem, to people like Stigwood, Sokoloff, and Spence who shared a Nobel Prize focused on asymmetric information and many others. You also talked about something you called the three Vs, velocity, variety, and volume of information. Can you describe a little bit how you came to frame it with the three Vs, how does that encompass what you’re trying to illuminate?
Chen Long:
We’ve been trying to promote the flow, the exchange of information as we just mentioned. So those information economists, they spend a lot of time think about how to overcome the problems of blocking the information. What’s special in the digital age is really make the data digital, so that can be easily produced, exchanged, at very, very little cost. So, that’s why we see an explosion of the data and information ever since the digital technology for the half century ago.
Now, if we want to describe what’s the nature, it’s still information. It’s still data. So, what changed? I think that’s the three Vs were trying to use to describe it. One is the volume. That’s the big data, the size. The amount of data we are using. Then the second is the variety. So, there’s so much more information that can be recorded, observed, recorded, and can be exchanged because they are all digitized. So, that’s the variety.
I think the last thing which is really, really crucial but people underestimate its power is the velocity. The speed of producing information is instant. So that means, we have a lot more instant ability than before. One example may be is let’s think about is the car. It’s the new car, that’s the Tesla. In other country, in China, it’s coming up.
If you simplify the explosion of the information, now if you have a thousand cars. If you try it without driver, so it used the AI. It drives by itself. So, amount of information it can produce a day is somewhat equivalent to one year’s search amount of information in China, so on Baidu, which is China’s version of the Google. So there’s really explosion of information, and we use those information instantly. That’s the velocity side.
So you can see, it’s the variety. It’s amount of information volume, and the instant timely nature of using that. That distinguish this age from industrial age.
Rob Johnson:
I remember as I’m listening to, I was inspired at one time when Ben Bernanke was the chairman of the fed. There was a group that included Google, credit card companies, and so forth because he was saying, “You have to make decisions about the course, the trajectory of the macro economy but the data from the US employment reports and so forth is compiled and consult something like 45 to 60 days after the time of what was being measured.”
So the credit card companies and the search engines and so forth, started looking to see and they said we could replicate that awareness with great confidence, 72 hours after it happens. We can put something on your desk for the Federal Reserve Board. So instead of waiting for six or eight weeks, you waited for three days to feel like you understood what the state of macro economic vitality was, whether you were ahead of curve, behind the curve, what have you.
They also could do things that gave you a much more textured sense of region rather than national aggregation by a zip code, or a county, or congressional district. They could see the variations. They could see by sector. I just remember listening to Bernanke talking one evening about, he felt like … The way I think he described it was like, “We went from the era of radio to the era of television. All of a sudden I could see the economy, whereas I was just dreaming and guessing with long lag times beforehand.”
Obviously to him, that was a great benefit. I didn’t see any weigh in, which the fed was snooping on an individual’s credit card or come to see what they were buying. They were holding the aggregate information together to have a much more sensitive and timely understanding. I mentioned there are thousands of applications like that, and the data just like you describe with Tesla, the data is getting faster, and better, more variations.
Rob Johnson:
I find this fascinating potential and perhaps if people understand your report, they might be more conscious of what the boundary should be, so that they don’t eliminate this potential. On the other hand, maybe officials become more mindful of where the danger zones are for violating personal privacy. How do you say? You’re allowing us with this report and the findings to go on a trajectory where we can get more from data and be less scared. I think that’s very productive. That’s a very constructive thing to offer to society.
Chen Long:
Thanks so much for your kind words. Let me follow up what you said. Precisely, so you mentioned the financial risk. That is something we’re really seeing this in China. So in the financial sector, sometimes you talk about the credit risk. So essentially if we think about a bank, it lends the money. Then the bank, you see is static in the sense that whether the borrower will return the money back, you won’t be able to see much after at least after several amount of days. Nowadays, there’s so much more real time daily data when the economy has been digitized. So actually, it can track credit risk daily. So, that really changes the definition of the risk management.
Traditionally, we think about, “Wow, we have to understand the probability of default and the loss given default, since those kind of concept.” For those kind of static, you’re assuming there’s stable distribution. Then, there’s nothing else you can do it. So, you’re going to have some reserve to prepare for that. How about you can know much more real time what’s going on the risk? You can actually change your risk. You can change your risk exposure. Change your product. So, that will change the whole dynamic of those things. So I’m pretty sure, Bernanke would be much more happier he had those tools.
That’s precisely during the COVID-19 in early 2020, actually in February 2020. At Luohan Academy, we actually use the big data to construct the Daily Economic Activity Index across China. So, that covers more than 300 regions. So, we actually can track the economic activity daily. Overall, it’s very precise. Then, we extended that globally to more than I think 190 country and regions. So why do we do that? Because many people can experience.
Now if you want to recover back to work, now should we speed up the recovery or should contain the Covid first? If you restart slowly, then people get hurt financially. Economy get hurt really bad. If you speed up to quickly, then the COVID-19 is coming back. So, we have to strike some balance because they are really going up the directions.
Now, you need some kind of measurement to see. We know every day how the Covid is spreading because we know how many people get a portion had died, so confirmed cases, but then we do not know enough about how economy is suffering daily. That combination is something in my experience and ask what path we … We call this the pandemic economy because we have to combine the two side to make the right decision.
The first thing I think I want to remind is there’s something we feel interesting in the report. So as I already mentioned, there has been a very long tradition for the human being, societies, to spread information sharing. Now, we care much more about privacy. So what exactly is the trade off? I mentioned that this privacy paradox. So people in every country worry about privacy but in the meantime, they share a lot of information. So, what’s going on?
Now, sometimes people say, “Well, maybe people in China do not care about this much about privacy, that’s why their digital spending is going up.” China is leading the world in the e-commerce for example. It’s e-commerce accounts for about 27% of the retail sales in China, and China’s economy leads the world. China has transformed itself into a mobile-based country.
Sometimes, people suspect that maybe Chinese do not care about privacy. Well, they do. Actually, we had a survey. It’s more than 90-some percent of the people, so they really worry about privacy. So, human being are the same. Now the question is, what exactly the nature, the thing that they care about? Do they really care about, if they do, why do they share personal data?
Sometimes then, it comes down to another argument that says that maybe that’s because they have no choice. Just like if you are in Facebook or maybe in China if they are on WeChat or Alipay, it’s a big app. Then if you don’t use it, that’s really a trouble. So, that’s why I think it’s one of the largest scale. It’s a big data on big data.
One of large scale test to see what people would do if they do have choice. So in this particular setting on Alipay, so we have more than 800 million users. So, it’s a big app. In a sense, that it don’t have a choice. Your life would be inconvenient if you don’t use it. Then on Alipay and like many apps these days, we have something called mini programs, which is the temporary version of the app on Alipay. So, it’s app on app.
For those apps, you can see that we have hundreds of thousands of those apps and some of them are very young new apps, some of them are very mature, big service providers. So each of them, when Alipay prop up to the potential users, they have to ask their approval. They have to tell them which kind of information are you willing to share. When you share, you can get this kind of service. It seems like that.
It’s like a restaurant. Once you use, that’s order in the restaurant. You are there. Then, you have to tell me some of your information. So, we have to connect to you so to know you are really going to pay the money later, things like that. So, are you willing to do this? If you do not, then you just pay cash. That’s okay, but then some people might want to do it.
So, there’s a lot of apps. There’s huge variety of those apps. So, they verify their necessity. So you can choose not to use it because some are kind of trivial. Some of them are essential. They’ll also verify the information sensitivity. Some of them ask for you some kind of your financial information, your credit score, your address, more private things.
Some of them ask very general. It’s just your name, your nickname, things like that. They verify the necessity by the sensitivity of information. Then on the other hand, we have people verify their risk aversion. How much trust they want to place on those apps. So, that becomes one of the largest scale of the study on how people, when they do have choice, how they trade off?
So, we have some interesting findings. For example, we find that about three quarters of the people. When they first time pop up, they are going to accept those service. They don’t really care as much about whether it’s a new service or some big names they trust, or some new apps, never tried. Three quarters of people.
Then later, there are certain regret because about 0.1% of the people will delete the app, so the information later. So, that means they don’t really regret. Fascinatingly, this is very consistent with what we observe in Europe, United States, globally. There was this privacy index study. That conclusion is also about three quarters of people. They are willing to accept the data. They’re pragmatic regarding how much they want to share the personal information. So, that’s very consistent.
Also about in both United States, Canada, and Europe about 0.1 or 0.2% people delete. They log off. They refuse to use those apps later. You can see that, that means people don’t really regret. We also find that when people use, they have more digital experience. So sometimes we suspect that, that’s because they don’t know. They could be cheated, abused. Then, we actually find that people when they have more data experience, they actually are more open to use them. So that really, the bottom line is this.
We actually did a separate academic study with my friend, Wei Xiong, and his student from Princeton, and with a couple of fellows at the Luohan Academy. What we find is that the people who are more worried about, concerned about privacy are precisely the type of people they use statistics very small. So what do that mean then? What is the nature of the privacy paradox?
Really, it’s just that there’s two kinds of the welfare related to the data from customers’ point of view. One is that they want their privacy protected. The other one is that they want to share some information to get better services. So, we have to satisfy both needs. That means to deal with the issue of privacy is not locked on data because if you lock on data, you refuse to exchange your information. You don’t get the service out of the society, out of a lot of economic activities. So the right approach then is to encourage the data exchange, but in the meantime, we protect the privacy in this process. So, that’s the one thing we learned.
So, that naturally propped us to ask the question. There must be a lot of the benefits in sharing information. So, what’s the value of data anyway, for us to share? So, we also look at the empiric evidence or summarize going back to the literature of the information economics. Essentially, we can say that there’s three type of benefits from sharing information.
One is the connectivity. So nowadays, you can really connect to people from very far away. For example, traditionally, any small shop. Their customers come from around within 10 kilometers. Once farther, there’s no trade between them. That’s called gravity model. It’s like the longer the distances, smaller gravity. Then nowadays on a typical trading platform, I think in the United States maybe Amazon, or eBay, in China, Alibaba.
On Alibaba’s platform, the average distance between the buyer and sellers is about 1,000 kilometers. So, that gravity model is essentially strong. That really reminds us the power of market. So, the power of market is to let more people to participate so that we have specialization or improve the productivity, et cetera. Then, if you do not have information, then this will not happen. So connectivity, means opportunity.
So nowadays, there’s lot of on Alibaba’s platform, we have more than 10 million SMEs. They are serving hundreds, millions of people. So, for those entrepreneurs of SMEs, half of them are women. So, they would never had opportunity without the digital platforms and without some kind of exchange of information. So, that means opportunities.
Now, we also try to ask some questions. What happens if sometimes we said, “Okay, you can connect but you don’t need much of my information.” Now, another interesting thing is you see on the platform nowadays, you actually for the customers, it’s very hard for them to choose. For example on Alibaba’s Taobao, you have more than a billion items, commodities. How are you going to browse through them? It’s impossible. So, you need recommendations.
A natural question is what happens if those recommendations do not contain any personal preference? We do not know anything about it. So we did a natural experiment here. So, we shut down a huge number of users when doing the recommendations. We shut down their personal preference. So the recommendation is based on the industry level kind of data. It’s extended data. You can see that those recommendations quickly converge to the top 1% of the brands. So, the more general standard like what we are used to in the industrial age.
Then actually the sales, the browsing just drop by 80%. So, it’s shocking because customers do not find those recommendation interesting, especially those SMEs. The small brands, they were hurt most. So you can see, that’s the power. By the way, that’s the part of the connectivity and the value of the based on personal information. Those help us to be smarter as we know.
Then finally, it builds a trust system. On the typical platform, you can have hundreds, millions of the users. You can have the tens, millions of merchants. Every part of the service of the products and including those SMEs, they’ll be rated by the customers. By doing that, that builds the trust system as if they have hundreds, millions of people, they’re seeing each other directly.
That really overcome the traditional lemon problem that Akerlof worry about because there’s no information. There’s no trust. There’s no market. There’s no exchange. There’s no economic activity. You can see those. Let me stop here for now. Maybe if you ask me questions.
Rob Johnson:
These are many interesting things here. I think about the fact that I use search and if the search knows that I’m 64 years old, I just took up surfing. I’m a beginner. All these kind of things, it will help me buy a better life jacket or the kind of surf board that can support my height and weight, and all these kind of things.
How would I say it? It would take a lot of going to stores and asking questions, a lot of time to learn the same thing that can be distilled very quickly. So I can see how by revealing myself, I can be helped by these services. So, I guess the nature of what you want to keep private, you have to be conscious of when you’re playing with these powerful tools, but there’s huge advances. That’s just a reflection. If you seemed more nuanced to it, please [inaudible 00:35:45].
The other piece that I think is also very interesting and you were alluding to the structure of markets. Does this information power, this aggregating power and everything else foster highly concentrated large monopolies who then can be monopsonies or monopolous and extract wealth in ways that a more competitive marketplace couldn’t? Is there something that says, “All kinds of small firms can participate because they can reach so many people over a broader footprint beyond a thousand kilometers, and now they can play at scale through the access to these platforms and that creates a more competitive marketplace”?
It used to be the guy down the street who knows that you can afford a car, can raise the price on your groceries. Now, if you know you can order and have them delivered from nine different places, there’s a competition there. So, I’m just curious where the balance is on this question between the power that leads to monopoly platforms and extracting either from the suppliers or from the buyers, and on the other side facilitating a more competitive marketplace?
Chen Long:
So you see, if we only discuss this theoretically, we can discuss this for a very long time without a conclusion because they all sound right. Then, so that’s why we have to respect reality, empirical evidence. Then that, we can see that really varies by country and by industry. So, we have to acknowledge this. We don’t pretend this, one way has to be true.
Now, let me give you some examples, for example in China. So in China, you see, Alibaba is the typical example. Alibaba, I think about five years ago, it’s still accounts for about 80% of e-commerce market because it has the first mover advantage. Then, it has come down to about 50%. It’s going down very steadily. So, that’s one example.
Another is what I’m used to is the Alipay. So, it’s a mobile payment. Alipay was the first to because of e-commerce, it took off first in China. It has more than again, 80% of the market share, but it’s now lower than 50%. Think about another one player is the TikTok. People in United States are very familiar now. TikTok, it took off very quickly. Another one is called the Pinduoduo. It’s e-commerce. It actually took this company only about four years, accumulate more than 400 million new users.
So, what we really observed really is that your powers. Presumably, the dominant power is fading quite fast. If we look at the history of the internet to nowadays, really, I think whoever claim it’s a big data is actually dying very fast in general. There are few examples. So my point is that I think the competition is dynamic. A lot of the advantage are transient.
New technologies are coming out. If we ask how come the big data doesn’t necessarily leaves you winner takes all because really if we simplify, data is really one dimension of the business model. So for the way you compete, you’re competing based on your products, your business model, how much you can satisfy your customers. So, that combined with a lot of new technology coming up, so it’s very dynamic.
Also, another interesting things about the data is that I think the power of data is there’s limit. For example, there are studies actually that found that the advantage of it do not last more than let’s say half a year. So, it’s not like if you have a thousand years of data, you can then know the human being so well. You can serve the customer about it. Actually, you do not. What really start is that you have a new product. People like it. So, you have some kind of data to understand how much they like it, how much you can improve your product to serve them even better, so that can take off.
Then, there’s a limited how long of history of data you’re going to use this. Normally, it’s no more than one year. So, the bottom line here is that if we want to understand how data or big data is contributing to the competition, we have to acknowledge that it is getting one more important. We have to make good use of it to understand your customer, but that doesn’t mean you’re going to be dominant because there’s numerous examples globally to show the upstage.
That you seemingly to have the first mover advantage, you presumably view some data. Somehow it doesn’t really leads to dominance for a long time. I also acknowledge that because it is one dimension of the competition, then it is getting important. So, it is possible in some industry, it can become more concentrated, but in many industries is definitely not the case.
Finally, I think especially on platforms because you have to look at what kind of platform it is. Now, if it is a platform that connects the suppliers with the customers, the platform actually has a lot of incentive to promote that connection to use the data to empower the demand side and supply side including the SMEs. So, that’s why I think we really need to, especially in the digital age, we really need to promote that everybody to participate and to get the benefits of the sharing of data, especially for the SMEs to get that benefits.
When we think about the digital divide, one of the big divide is information divide. So we really have to empower the smaller average Joes, the small companies, startups to get them to digitize the business infrastructure, such that a new startup that consists of only several people, they can serve the customers a thousand kilometers away. It can prosper. I think it has the both side of this.
I don’t think that hypothesis of winners takes all is general, but it does happen on the downside too. We really need to promote the innovation and more inclusive side of the market structure.
Rob Johnson:
I’m coming and listening to you, and I’m thinking about how the information got much more fast, and perfect, and transparent, and costs less. Now, I’m seeing that if I were setting up a hedge fund today, I would be looking for people who are very sophisticated in analyzing big data to find which companies to invest in. When you said that moments ago about the data is only good for about a year, you really got to become gifted in diagnosis and pattern recognition in big data whose half-life is very short.
I imagine there are some people that are just beating the market by leaps and bounds now because they have mastered the kind of skills that you’re describing inside of the knowledge systems that this new technology is offering.
Chen Long:
Yes or no. So, I think there’s huge benefits of using the three V of the information, not only the volume but really the variety and the instant thought of this. So in practice, I think this is really crucial. What we’re really observing is that it’s really changing the business models. Now, the business is much more customer-driven. The so-called C2B, customer to business because now you have so much more instant customers’ response.
So that’s why when you have the new on your products’ upgrade, revision of your products, it’s much more customer-driven. You have much more connectivity. So business models are really changing because of instant quality and risk assessment. What I observed in China, I looked at on financial. They are doing things because of the sporadity of the data.
So those are the things we are doing. I’m not exactly sure, let’s say in the capital market. There are very few hedge funds that can brag about the big data. Maybe they don’t know enough about it. I don’t know, but as observed, very few hedge funds or the funds, they claim big data funds, but actually they are making superb profits. So, I think that’s still very elusive and a challenge.
Rob Johnson:
Going a little bit further with what you’re saying, I remember I worked with a very brilliant man at Soros Fund Management named Stanley Druckenmiller. He said to me, “We have to study what the world thinks. Then we have to have our own idea that differs from that consensus. Now, we have to understand the process by which they will come to understand that we’re right and then prices will move.”
I think what you’re saying partly is this data is so fast now. The volume, the velocity parts are so fast that you got to be real quick to diagnose what’s going on because time, when people who also understand these systems will catch on and change their view. It’s much more rapid than the old days, which makes it harder to capture that arbitrage.
I do wonder if there are people who are mastering the use of these platforms in a way that they can’t be as confident. That everybody else will catch on because they know not everybody else knows how to use these information systems. So they may have to be patient, take longer time for us, or understand structural things that will not reverse or be just transient in order to make money. I would imagine, there are some young, very smart computer scientists who might become very good hedge fund managers in the years to come.
Chen Long:
I always suspect that, but it’s coming slower than I thought. What I am observing more generally, I think is that in a lot of the business, many of a lot of sectors nowadays, we have opportunity to get better use of the data we’re having. On platforms, we can see that those data services are coming up, make the use of that much more inclusive. So, if we do not think too fancy about those things, it’s really about the average company.
Nowadays, the cost of starting a business becomes so much lower nowadays because instantly they can connect it globally, if you think about. If they have good products and they have a lot of instant response, and these were not imaginable 10 years ago. So, I think those are the lot of things that are happening and make us very excited.
Rob Johnson:
When you work through data, the inspirations come from a stimulus provided by patterns, puzzles, anomalies revealed by the systematic gathering of data particularly “when the prime need is to break our existing habits of thought.” So, what you’re doing in this report is breaking down what I will call the echoes of past patterns of thought that came from a different structure. This structure has to be looked at with fresh eyes and with the data to illuminate what is happening. I thought that paragraph describe the essence of the gift that the Luohan Academy, and you and your team were giving us all.
Chen Long:
Well, thank you again for your kind words. So indeed, a lot of time we cannot distinguish what we believe is what really are seeing, what we are worried about, and what we really can understand. So we are really affected by what we worry about, and our experience, or inspired by that. So I think, if we really want to understand things, we really have to respect the evidence. For example privacy, of course, we care about privacy. Really, what’s the nature of privacy? If we observe everybody’s, what’s going on? Exactly, how do we share information? Then, we see that. We share information all the time. In the meantime, we care about privacy. So, those are something.
For example, ownership. Sometimes, we feel like … For example, let’s say Rob, we are talking to each other. This fact, does that belong to you or does that belong to me? The fact is that it belongs to both of us, and we actually have a different version of information. For example, I probably notice that you wear glasses. You have a mirror in background and you might not observe what’s going on in my background.
Actually, so that’s the nature of the data of information that it has this. It can be produced and reused unlimited times without consuming the subjects, us here. So that means, there’s a lot of different version of ownership. So that also means that it’s not about define who owns it because there’s unlimited version of it. It’s really more about how to properly use it, how to not abuse the information. So, we really need to understand the value of where does value come from? It comes from exchanging. The proper way is to protect it right away.
Anyways, so there’s a lot of things. It goes beyond our instant, immediate concerns. Then that requires us especially economic series because it’s very easy to come up with a beautiful model but with the wrong assumption. You are trying to use the mathematics to convince the world, “I’m right,” but really, sometimes it’s terrible. So, we need to have to attempt a rigor, but really that’s going back to. As you mentioned, we need to be inspired by the reality.
Rob Johnson:
Well as you said at the conclusion of your foreword, “When this new things are coming on to the radar, many people are afraid of change until they become affiliated and see its possibilities.” What you say at the end is you don’t want to dismember the goose that lay the golden egg. I think the prospects of these platforms and technology particularly for the emerging countries, which can constitute a profound transformation in Southern Asia, Africa, Latin America, we don’t want to dismember that goose.
I think that you and the Luohan Academy, I can say it at INET, we’re very proud to work with you, and we’re very excited to learn from you. We’re very interested in how they say, how your insights can particularly be applied to the people most in need and who are suffering most on this planet. So, thank you very much for the work that you do, for your partnership with INET. I look forward for many future episodes to continue to learn from you.
Chen Long:
Thanks so much, Rob.
Rob Johnson:
Check out more from the Institute for New Economic Thinking at ineteconomics.org. (singing)
