Friday, February 21, 2020

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?

Simple Rules for a Complex World with Artificial Intelligence. Jesus Fernandez-Villaverde. , February 21, 2020.

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
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

The Neurology of Acquired Pedophilia: Twenty-two cases fit our inclusion criteria, all but one were men, and in only one case the injury was localized to the left hemisphere; 18 were hypersexualized

The Neurology of Acquired Pedophilia. Pedro Maranhão Gomes Lopes et al. The Neural Basis of Cognition, Feb 20 2020.

ABSTRACT: The clinicoanatomic cases of acquired pedophilia that have been published in the medical and forensic literature up to 2019 are reviewed. Twenty-two cases fit our inclusion criteria. All but one were men, and in only one case the injury was localized to the left hemisphere. Hypersexuality was present in 18 cases. The damaged areas fell within the frontotemporoinsular cortices and related subcortical nuclei; however, the anterior hypothalamus was spared. Damage to parts of the right frontotemporoinsular lobes with sparing of the anterior hypothalamus seems to be critical for the emergence of acquired pedophilia.

KEYWORDS: Acquired pedophilia, acquired sociopathy, frontotemporoinsular cortices, medial forebrain bundle, hypersexuality, perifornical region

Online sexually explicit material appeared to have a negligible role in individuals’ current sexual functioning and mental well-being

A lack of association between online pornography exposure, sexual functioning, and mental well-being. Ruth Chari et al. Sexual and Relationship Therapy, Feb 20 2020.

Abstract: To inform debate around potential influences of online pornography, we applied a contemporary media-effects model to examine the relationship between online sexually explicit material (oSEM) exposure and several psychosocial outcomes – including sexual satisfaction, body satisfaction, sexist attitudes, and mental well-being. Perceived realism of oSEM (the extent to which it is believed to be a realistic portrayal of sexual experience) was assessed as a potential mediator of exposure-outcome relationships. Furthermore, family communication about sex and gender were investigated as potential moderators of any indirect relationships (via perceived realism). Using a convenience sample of cisgender, heterosexual adults (N = 252) and a cross-sectional questionnaire design, we found no significant direct or indirect relationships between oSEM-use and the psychosocial outcomes in question; equivalence testing demonstrated that (for all outcomes other than body satisfaction) we could reject effect sizes (rs) > ±.20. Overall, findings do not favour a negative or positive relationship between oSEM and the psychosocial outcomes under examination – oSEM appeared to have a negligible role in individuals’ current sexual functioning and mental well-being.

Keywords: Pornography, sexuality, media effects, perceived realism, family communication, online

Randomly assigned confederates or participants to act extraverted or introverted; interaction partners showed more positive social behaviors with extraverted actors

Does acting extraverted evoke positive social feedback? Mariya Davydenko et al. Personality and Individual Differences, Volume 159, 1 June 2020, 109883.

•    Randomly assigned confederates or participants to act extraverted or introverted.
•    Interaction partners showed more positive social behaviors with extraverted actors.
•    Behaviors were rated by the participant, confederate, and an observer.
•    Extraverted behavior has the potential to evoke positive social feedback in others.

Abstract: Personality traits describe average tendencies, yet momentary behaviors in trait domains vary widely. Notably, both dispositional introverts and extraverts experience greater positive affect when behaving in extraverted ways. We test a potential explanation: extraverted behavior may evoke more positive social feedback from others. In Study 1, participants who were randomly assigned to interact with confederates who acted extraverted (vs. introverted) displayed more positive verbal and nonverbal social behaviors during interactions. Behaviors were rated by the participant, confederate, and an observer (via video). Study 2 reversed roles; neutral confederates who interacted with participants who were randomly assigned to act extraverted (vs. introverted) displayed more positive social behaviors. This research extends previous findings by examining how enacted extraversion influences interaction dynamics.

Dark personality features were positively associated with competitive worldviews; sometimes had indirect associations with social dominance orientation through competitive worldviews, but not with right-wing authoritarianism

The darker angels of our nature: Do social worldviews mediate the associations that dark personality features have with ideological attitudes? Virgil Zeigler-Hill et al. Personality and Individual Differences, Volume 160, 1 July 2020, 109920.

•    Dark personality features were positively associated with competitive worldviews.
•    Dark personality features sometimes had indirect associations with SDO through competitive worldviews.
•    Dark personality features had, at best, weak associations with dangerous worldviews.

Abstract: The present studies examined the associations that narcissism, Machiavellianism, psychopathy, sadism, and spitefulness had with the competitive and dangerous social worldviews as well as the possibility that these worldviews may explain, at least in part, the associations that these dark personality features had with the ideological attitudes of social dominance orientation (SDO) and right-wing authoritarianism (RWA). Across three studies (N = 2,103), we found the dark personality features to be positively associated with the competitive social worldview in Studies 1 and 3 but these associations were much weaker in Study 2. Narcissism, psychopathy, and spitefulness had indirect associations with the dominance and anti-egalitarianism aspects of SDO through the competitive social worldview in Study 3 but not in Study 2. In contrast, the dark personality features had, at best, weak associations with the dangerous social worldview as well as divergent associations with aspects of RWA. More specifically, narcissism and spitefulness were positively associated with aspects of RWA but psychopathy was negatively associated with RWA. Discussion focuses on the role that social worldviews – especially perceptions of the world as being a highly competitive environment – may play in the connections that dark personality features have with various outcomes including ideological attitudes.