By Robert Harken
Harken Media Non-Fiction: Economics & Technology
Imagine a world where technology understands the human condition—a world that values relationships more than diamonds—a place where people earn a living, not from selling, but from compassion. We stand on the threshold of such a world. The door is open.
We must walk through. In the early nineteenth century, at the dawn of the Industrial Revolution, English textile workers destroyed machines that increased productivity and replaced skilled labor. Then they swung from the gallows. The argument that technology advances create permanent unemployment became known as the Luddite Fallacy. After two hundred years of innovation, technology has established the foundation for productivity increases that will make the Luddite fallacy true.
The largest impacts arise from ubiquitous deployment of sensors, massive networks, and advanced predictive modeling. Sensors, in both software and hardware, and networks, i.e. intranets, wireless networks, and the Internet, create data needed by algorithms to make sophisticated predictions. Predictive capabilities enable computer systems to replace human labor in both legacy jobs and new roles created by innovation. Thus, our future differs from those faced by nineteenth century workers.
Employment and wages will decline. Bureau of Labor Statistics data for forty-nine non-manufacturing industries and sectors reveal early signs. Labor factor share, labor costs divided by the value of production, show declines in thirty-two industries, flat share in one, and increases in sixteen between 1987 and 2011. Eighteen of twenty-one manufacturing industries and sectors show labor share declines over the same period. Labor factor share accounts for both employment and wages. The industries with declining labor shares represent the vanguard of innovation adoption. We’ve already passed the halfway mark. This data provides an indicator of technology-driven economic impacts yet fails to offer definitive proof because people may shift industries or remain in their jobs but produce more. Unequivocal results require a combination of labor factor share and employment data; however, employment data contains too much noise from economic cycles and human behavior, i.e. unemployed people giving up job searches and dropping from the labor pool. Over time, the magnitude of technology impacts will make the trend obvious in employment data. We cannot wait for a clear signal because the deleterious effects at that point will prevent adequate time for preparation.
Destabilizing economic effects will arise before innovation produces the technological singularity, i.e. when artificial intelligence exceeds human intelligence, because productivity gains will create temporary technological unemployment that becomes permanent when the rate of change outstrips people’s ability to retrain. Switching careers every four years seems feasible to most people. How about every year? Every month? Daily? Historical software development has released new products on three year cycles. Data-intensive, networked applications, such as web services, release new versions on a monthly cycle with major changes accumulating every six months. Web services with predictive algorithms modify parameters in real time. Rapid change will permeate the economy as companies adopt these innovations.
The source of negative economic effects originates not in innovation but rather the proper functioning of capitalism based upon production and consumption of goods and services. Competition drives prices to production costs while innovation shrinks those costs. Extrapolating these trends results in a situation where goods are nearly free but few people earn incomes sufficient to pay the low prices, creating an economic collapse. Antitrust laws and social biases for bargains support this outcome by fostering price competition.
Despite negative side effects, capitalism offers the economic structure most suited to human nature. Socialism and communism have failed to produce stable, productive economies long term. Corruption and incentive problems hinder both systems. A few Nordic countries have created transient stability under quasi-socialist economies, but these examples exist in conditions of natural resource abundance or cultural norms unlikely to persist over centuries. Technology-based solutions may exist for short-comings in socialism and communism; however, tweaking the capitalist system used by the majority of countries today offers a viable solution with less disruption.
Societies have made structural adjustments to capitalism in the past. In 1933, President Roosevelt took the US off the gold standard. Other countries have adopted hybrid gold/fiat monetary standards. We changed how we measure value. A dollar no longer equals a quantum of gold; the dollar's value is set by the government. A growing population with expectations of stable or increasing standards of living requires a growing economy not limited by the medium of exchange. If the U.S. Federal Reserve wants to change the value of the dollar, they can print money, change interest rates, and buy or sell bonds. The government changes the dollar's value directly or indirectly during recessions to stimulate the economy. In so doing, we set a precedent for changing the measurement of value in our economy to meet society's needs. We evolve capitalism.
A combination of technical innovations and our behavioral responses have created a new capitalism adjustment opportunity that can prevent the future economic collapse. The opportunity arises in people’s use of technology for relating to other people. A 2010 survey from Pew Internet1 showed Internet users trust people at a rate twice that of non-Internet users. Facebook users trust at a rate three times higher than non-Internet users. Non-Internet users experience social isolation at more than twice the rate of Internet users and have 23% fewer confidants. On the MOS Social Support Scale, Internet users score 14% higher in total support, 15% higher in emotional support, 12% higher in tangible support, and 17% higher in companionship than non-Internet users. Social network users score higher than general Internet users and produce a greater support gap compared to non-Internet users.
Does this data indicate technology improves human relationships? The answer remains unknown. The average person’s online social network contains 636 people of which only 2.5 qualify as core confidants. The half person listens half the time. This compares to 1.7 core confidants for non-Internet users. Yet Internet and social network users feel most people can be trusted at two to three times the rate of non-Internet users. These feelings of trust persist despite research that shows people lie more online about age, appearance, jobs, and education.2 Our behavior online differs from the physical world in many instances because of anonymity, approximation of the real world, moral ambiguity in digital venues compared to mores in the physical world, and the ability to approximate our ideal selves online.3 Yet the freedom to express an unfettered self fails to produce higher quality friendships online versus offline.4
Human behavior offers opportunities for technological assistance. For example, psychologists have known, since at least 1976, that gift giving creates an expectation of reciprocity in the giver and a sense of obligation in the receiver. Every gift giver, even those giving without expectation of return, seeks reward in the form of a smile, praise, or self-satisfaction in performing a good deed. These types of innate responses evolved to enable a society beneficial to survival of both the group and the individual. Social media enables new, quantifiable channels for reciprocity.
Robert Cialdini has written about the use of stereotypes to simplify decision making in an increasingly complex world.5 As mental shortcuts, stereotypes enable easier decisions, but open us to mistakes and exploitation when our stereotypes differ from reality. Existing technology has simultaneously increased the volume and reduced the diversity of information we must process. Our stereotypes fail us without diverse signals in body language, appearance, and tone of voice tuning our interactions. Text-based interaction has limited our ability to relate through technology.
We can fix this problem. Imagine a world where relationships on the Internet bolster self-esteem and foster personal growth—a world where the human experience online rivals our experience offline. Technical solutions exist that can achieve these results. We simply need the will to act.
Early attempts to incorporate intangible aspects of human relationships focus on influence and reputation, calculated for use in marketing. These efforts fail to enrich our online lives because influence implies, but doesn't necessitate, merit and limits measurement to a single dimension. Relationships require more: give and take, acts of kindness, altruism, understanding. We need to enable self-correcting mechanisms in digital forms of human interaction.
Please purchase the article to continue reading. "Social Capitalism" is available at Amazon.com.
1. Hampton, H., Goulet, L., Rainie, L., & Purcell, K. (2011). Social Networking Sites and Our Lives. Pew Internet & American Life Project.
2. Konecny, S. (2009). Virtual Environment and Lying: Perspective of Czech Adolescents and Young Adults. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 3(2), article 4.
3. Hertlein, K., & Stevenson, A. (2010). The Seven “As” Contributing to Internet-Related Intimacy Problems: A Literature Review. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 4(1), article 3.
4. Anthenunis, M. L., Valkenburg, P. M., & Peter, J. (2012). The quality of online, offline, and mixed-mode friendships among users of a social networking site. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 6(3), article 6.
5. Cialdini, R. (1993). Influence: The Psychology of Persuasion. William Morrow & Company, New York, 320pp.
6. Attrill, A. (2012). Sharing Only Parts of Me: Selective Categorical Self-Disclosure Across Internet Arenas. International Journal of Internet Science 2012, 7 (1), 55–77.