silently. Most parents, doubtless, are too busy to spare time to educate their
sons and daughters by reading to them, and as the children grow older they
find their hours too crowded to devote any of them simply to listening. “What
is the use” they would say, if asked. “Tastes differ, and we can read what we
want in a fraction of the time that would be consumed if we had to sit still and
hear it.3
However, as the salon and family circle faded, magazine clubs and book clubs
took off into the twentieth century.
Another cultural factor altering the spread of narratives has been an
international movement toward providing mentors for young people, with roots
back to the Big Brothers (now Big Brothers and Big Sisters) movement starting
in 1904, and later diversifying into an epidemic of sorts since around 1980.
Having regular communications with successful or socially committed people
helps a young person gain a sense of identity in the mentor’s life stories, or in
stories that the mentor tells of others in the same circle.4 Mentoring groups are
especially effective for women and minorities who may have felt little ownership
of such stories.5
Two new phrases, influencer marketing (since 2015) and social media
marketing (since 2009), have been gaining popularity. Marketing firms, notably
shareablee. com 
and 
hawkemedia. com, 
offer 
influencer 
marketing,
systematically finding influential people who allow marketing to them or with
them via social media. Such sites should increase contagion rates for promoted
stories and ideas.
Even as information technology is affecting the transmission of economic
narratives that affect the human mind, it could conceivably go further and
replace some of the ultimate decision-making process that individuals use. For
example, we already have robo-advisers that offer advice on how much to
consume and save and how much to put into the stock market versus other
investments. The first robo-adviser was launched in 1996 with William Sharpe’s
Financial Engines. Since then, automated advisers such as Schwab Intelligent
Portfolios, Betterment, and Wealthfront have proliferated. There are other efforts
to automate economic decisions too, such as target date funds, first attracting
interest around 2007, that automatically rebalance a long-term investor’s
portfolio based on a target retirement date. There are many other applications of
algorithmic trading. Nonetheless, today, people write the programs and make the

ultimate foundational decisions. Someday people may defer massively to
machines for life decisions, in which case economic processes may be
fundamentally altered. But that day appears likely still to be far-off.
Modeling technology’s effects on communications will be easier to trace
when there is better science behind the spread of economic narratives. Already,
our models show that it is not easy to predict these narratives and their effects.
For example, the epidemic’s ultimate size may not change when an increase in
the contagion parameter is matched by a corresponding change in the recovery
parameter. Rather, the epidemic will just happen faster. We must integrate formal
models of contagion into economic models to begin to understand the impact of
such technology.

The Future of Research in Narrative Economics
It is very important, if we are ever to have a substantial understanding of the
kinds of big economic events that have surprised us so often in the past, that we
have some scientific methods of studying the narrative element of these, even if
the science is not complete and still involves some human judgment. Otherwise
the field will be left to prognosticators or prophets who give the whole enterprise
a bad name.
Economic research has not emphasized the stories that people tell to one
another and to themselves about their economic lives. The research misses any
discernible meaning that appears in the form of narratives. By missing the
popular narratives, it also misses possibly valid explanations of major economic
changes.
If one searches newspapers of the twentieth century for contemporary
explanations of recessions as they begin, one finds that most talk concerns
leading indicators rather than ultimate causes. For example, economists tend to
bring up central bank policy, or confidence indexes, or the level of unsold
inventories. But if asked what caused the changes in these leading indicators,
they are typically silent. It is usually changing narratives that account for these
changes, but there is no professional consensus regarding the most impactful
narratives through time. Economists are reluctant to bring up popular narratives
that they have heard that seem important and relevant to forecasts, since their
only source about the narratives is hearsay, friends’ or neighbors’ talk. They
usually have no way of knowing whether similar narratives were extant in past
economic events. So, in their analyses, they do not mention changing narratives
at all, as if they did not exist.
We can already today learn something about popular economic narratives by
counting words and phrases in the digitized texts that are available, but there has
not been enough organized research to measure the strength of the competing
narratives that combine and recombine over time to cause major economic
events. Artificial intelligence can help with this—especially with unstructured
data. The perennial narratives described in part III of this book are works in
progress, not final and exhaustive quantifications of all truly important
narratives.
Research on narrative economics has already begun and surely will continue,