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Would IOET Make Economics More Behavioral? 179

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enhance human well-being dramatically. Thanks to IoE, when the whole
life span of humans can fundamentally be copied (mapped) into a cyber
space, which is “topologically equivalent” to our real world, its digital land-
scape will be very “machine-friendly” for the artificial agents who are
designed and dispatched to this space to search for and explore all possible
opportunities.
As an illustration, consider the following example concerning the labor
market. A college student upon his/her graduation has his/her resume auto-
matically generated, which is then automatically sent to a matching pool
where vacancies are matched with talents, followed by the provision of
the most favorable deals for both (employer/employee) sides. A sequence
of information processing, preference discovering, and skill endorsing, skill
matching wage bargaining problems is automatically handled by artificial
agents (robots) in cyberspace. When this happens, a collection of artificial
agents takes over the labor market, which was for a long time run by
humans, and the labor market as we understand it in the conventional sphere
is gone and becomes unmanned.
One side effect associated with the advancement in matching technology
carried out by the artificial agents in the IoE space is the development of the
customization-oriented economy. The advancement in matching technology
also facilitates the emergence of new production models, such as peer pro-
duction, sharing and a pro-social economy. We can add more to extend this
list, but what will happen if something we say is not correct?

10.3.3 Trend Sustaining

The above argument is built upon the key assumption that we have artificial
agents smart enough to learn from humans, to “nudge” them, 10 and even

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Matching is a subject in cooperative game theory. Lloyd Shapley (1923–2016), the 2012 Nobel Laureate in
economics, is acknowledged for his prominent work in cooperative game theory. One of his
masterpieces is the solution of the stable matching problem, which he proposed jointly with David
Gale (1921–2008), known as the Gale–Shapley algorithm (Gale & Shapley, 1962). This algorithm
provides a foundation for the study of the two-sided matching mechanism, and has many far-reaching
implications, including its application to the New York public school systems in assigning students to
schools (Abdulkadiroglu & Sonmez, 2003; Roth, 2002).
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Here we borrow the term from Richard Thaler and Cass Sunstein (Thaler & Sunstein, 2008). Nudging
can be considered as a kind of soft paternalism. Nudges are there because decision makers are simply
not fully rational. Hence nudges are a kind of decision support. While Thaler and Sunstein authored
the book before the advent of the era of IoE, they refer to many kinds of nudges, such as the design of
choice architectures, which can benefit from using ubiquitous networking technology. Hence, it is not
surprising to see that nudges will be incorporated into the IoE economy. Doing so may also evoke
some ethical concerns, which are beyond the scope of this chapter. The interested reader is, however,
referred to Standing (2011).