Abstract: Can artificial intelligence, in particular, machine learning algorithms, replace the idea of simple rules, such as first possession and voluntary exchange in free markets, as a foundation for public policy? This paper argues that the preponderance of the evidence sides with the interpretation that while artificial intelligence will help public policy along with several important aspects, simple rules will remain the fundamental guideline for the design of institutions and legal environments. “Digital socialism” might be a hipster thing to talk about in Williamsburg or Shoreditch, but is as much of a chimera as “analog socialism.”
Keywords: Artificial intelligence, machine learning, economics, law, rule of law.
JEL codes: D85, H10, H30.
5 ML and central planning
Over the last few years, a few observers have made the bold prediction that, thanks to AI,
central planning is about to return (Saros, 2014, Wang and Li, 2017, Phillips and Rozworski,
2019, and Morozov, 2019). Some of these observers are rather prominent. For example, Jack
Ma, founder of Alibaba, stated in November 2016:
Over the past 100 years, we have come to believe that the market economy is
the best system, but in my opinion, there will be a significant change in the next
three decades, and the planned economy will become increasingly big. Why?
Because with access to all kinds of data, we may be able to find the invisible
hand of the market.
The planned economy I am talking about is not the same as the one used by
the Soviet Union or at the beginning of the founding of the People’s Republic of
China. The biggest difference between the market economy and planned economy
is that the former has the invisible hand of market forces. In the era of big data,
the abilities of human beings in obtaining and processing data are greater than
you can imagine.
With the help of artificial intelligence or multiple intelligence, our perception of
the world will be elevated to a new level. As such, big data will make the market
smarter and make it possible to plan and predict market forces so as to allow us
to finally achieve a planned economy.19
These proposals forget the final lesson of the socialist calculation debate, which came from Hayek (1945). The objections to central planning are not that solving the associated
optimization problem is extremely complex, which it is and increasingly so in an economy
with a maddening explosion of products, or that we need to gather the data and process it
sufficiently fast. If that were the case, AI and ML could perhaps solve the problem, if not
now, then in a few more iterations of Moore’s Law. The objections to central planning are that the information one needs to undertake is dispersed and, in the absence of a market
system, agents will never have the incentives to reveal it or even to create new information
through the entrepreneurial and innovative activity. As Steve Jobs put it: “A lot of times,
people don’t know what they want until you show it to them.”20
A simple, real-life application of central planning illustrates the point. Every year, the
department of economics at the University of Pennsylvania faces the challenge of setting up
a teaching matrix for the next academic year.21 Each member of the faculty submits her
preferences in terms of courses to be taught, day of week, time of day, etc. Given the teaching
needs and submitted requests, the computational burden of finding the optimal allocation
is quite manageable. We have around 32 faculty members and, once you consider that the
average member of the theory group will never request to teach econometrics and vice versa,
the permutations to consider are limited. A few hours in front of Excel deliver the answer:
it seems that the central teaching planner at Penn Economics can do her job.
The real challenge is that, when I submit my teaching requests, I do not have an incentive
to reveal the truth about my preferences or to think too hard about developing a new course
that students might enjoy. I might not mind too much teaching a large undergraduate
session on a brand-new hot topic and, if I am a good instructor, the students will be better
off. However, I will not be compensated for the extra effort, even if it is not high, and I will
have an incentive to request a small section for advanced undergrads on an old-fashioned
topic. This request is not optimal: if the Dean could, for instance, pay me an extra stipend,
I would teach the large, innovative section, the students would be happier, and I would be
wealthier.22
An obvious solution would be, then, not to submit a teaching request, but a schedule of
teaching requests and a supply curve to do so, i.e., I will teach “the economics of big data”
at 9.00 am on Mondays and Wednesdays at price x or “advanced monetary theory” at 1.00
pm on Tuesdays and Wednesdays at price 0.4x. The central teaching planner will use the
supply curves to clear the teaching market and assign a faculty member to each course. This
new scheme would increase the computational challenge of setting up the teaching matrix
by one order of magnitude, but I can still write a short Julia program that will deliver an answer in a few minutes.
The drawback is that such a system of teaching requests and supply curves would open
the door to all sorts of strategic behavior: I will consider, when I submit my supply curve,
what I know about my colleagues’ tastes regarding teaching large, innovative courses. If I
believe they genuinely dislike doing so, I will communicate a higher supply curve to teach
such courses in order to clear the market at a higher price and increase my revenue. The
outcome of the teaching matrix will not be efficient because I am not telling the truth, but
playing strategically.
We can push the argument further. Knowing that the department will assign duties
using a teaching request and a supply curve, I can manipulate from the day I am hired how
I behave in front of my colleagues and the teaching requests and supply curves I submit. In
such a way, I can introduce noise in their signal about my teaching preferences and exploit
their incorrect inferences about my type when I submit my teaching requests and supply
curve in the future. My colleagues would know that and act accordingly, changing their
supply curve to reflect that they understand I tried to manipulate them. But I would also
know my colleagues know that and I will respond appropriately, and so on and so forth for
one iteration after another. Those who do not believe the faculty would behave in such a
way have not had experience managing academic departments.23
There is an additional problem. Once I am assigned a course, how does Penn ensure
I teach it at the “optimal” quality level? Note that “optimal” cannot mean the highest
possible quality. If I were to prepare every lecture that I give as a job market talk at
my dream department, the current students would love it, but I would not have time to
undertake research, and my future students would get worse lectures, since my knowledge of
the field would depreciate as I fall behind the frontier.
Even forgetting about that intertemporal aspect, how do we trade off one extra minute of
research (which increases Penn’s visibility and reputation) with one extra minute of teaching
preparation?24 And how do we address heterogeneity in the comparative ability between
research and teaching among faculty members when both efforts into each activity are, to a
large extent, unobservable?
Finally, we face the friction that I can carry my research with me to my next job (i.e., the
publications in my C.V.) much more easily than my teaching evaluations (i.e., I can always
“lose” the terrible teaching evaluation I got 15 years ago and nobody will be the wiser; after
all, most recruiting committees only ask for the most recent evaluations). Also, once I get
over some threshold of minimum quality in the teaching evaluations, nobody will pay much
attention to an extra half point. Thus, I have an incentive to teach a course that is below
the socially-optimal quality.
ML will never fix the problem of how to determine the teaching matrix at Penn Economics and to induce the “optimal” quality of the course. The problem was never about computing an optimal solution to teaching assignments given some data. The problem is, and will always be, determining the preferences, abilities, and effort of the faculty in a world where everyone has an incentive to misrepresent those preferences, abilities, and effort.
The only reliable method we have found to aggregate those preferences, abilities, and efforts is the market because it aligns, through the price system, incentives with information revelation. The method is not perfect, and the outcomes that come from it are often unsatisfactory. Nevertheless, like democracy, all the other alternatives, including “digital socialism,” are worse.25
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