The Economics of Science Fiction

Academic discussion of the non-immediate future seems almost non-existent. Yet the topic is of widespread popular interest. Why? The explanation seems obvious: it seems very hard to say anything rigorous about the topic, and individual academics avoid the topic in order to distinguish themselves from the sloppy wishful thinkers who seem to dominate the topic.

Most discussion of the non-immediate future seems to take place in science fiction, and the small subset of science fiction where authors try hard to remain realistic is called hard science fiction. Loosely associated with hard science fiction is an intellectual community of people who try to make projections which are true to our best understanding of the world. They work out the broad science and engineering of plausible future space colonies, starships, virtual reality, computer networks, survellience, software assistants, genetically engineered people, tiny machines made to atomic accuracy, and much more.

Unfortunately, few if any people of these people know much social science. So their projections often combine a reasonable physics or computers with laughable economic assumptions. This often seriously compromises their ability to make useful projections.

Professional economists, on the other hand, do understand social science, and often do talk about the future. Quite a few of them are even paid a lot to think about what the next hot technology will be. Economists, however, seem to almost completely ignore the longer-term implications of specific envisionable technologies. When looking more than a few years out, they almost always abstract away from specific technologies and think in terms of aggregate economic processes, which are assumed to keep on functioning much as they do today.

This leaves an unfilled niche, which I call the "economics of science fiction", or the "economics of future technology." This is economic analysis of the sorts of assumptions typically explored in science fiction. It is distinguished from the typical hard science fiction analysis by using the tools of professional economics, rather than the intuitive social scientist of the typical engineer. And it is distinguished from most economics by taking seriously the idea that we can now envision the outlines of new technologies which may have dramatic impacts on our society.

The best way to explain what I mean in more detail is to give some examples of it. Here is a collection of articles by others I think of as examples.

• Paul Krugman, "The Theory of Interstellar Trade" July 1978.
• Jeffrey A. Frankel, "Is There Trade With Other Planets?" Univ. of Michigan, Oct. 1978.
• There are maybe a half dozen articles on the economic theory of telecommuting.

Most economists have viewed technological progress as an incremental process. A few have focused on the role of drastic innovations - those that introduce a discontinuity. The contributors to this volume are concerned with the type of drastic innovation called general purpose technologies (GPTs). A GPT has the potential to affect the entire economic system and can lead to far-reaching changes in such social factors as working hours and constraints on family life. Examples of GPTs are the steam engine, electricity, and the computer. The study of GPTs is relatively new. A universal theoretical framework for dealing with GPTs does not yet exist. The essays in this book both further our understanding of GPT-driven economic growth and lay the foundation for further developments of the available frameworks.

Economic Growth Given Machine Intelligence, Oct. 1998.

A simple exogenous growth model gives conservative estimates of the economic implications of machine intelligence. Machines complement human labor when they become more productive at the jobs they perform, but machines also substitute for human labor by taking over human jobs. At first, expensive hardware and software does only the few jobs where computers have the strongest advantage over humans. Eventually, computers do most jobs. At first, complementary effects dominate, and human wages rise with computer productivity. But eventually substitution can dominate, making wages fall as fast as computer prices now do. An intelligence population explosion makes per-intelligence consumption fall this fast, while economic growth rates rise by an order of magnitude or more. These results are robust to automating incrementally, and to distinguishing hardware, software, and human capital from other forms of capital.

Burning the Cosmic Commons: Evolutionary Strategies for Interstellar Colonization, Mar. '98

Attempts to model interstellar colonization may seem hopelessly compromised by uncertainties regarding the technologies and preferences of advanced civilizations. If light speed limits travel speeds, however, then a selection effect may eventually determine frontier behavior. Making weak assumptions about colonization technology, we use this selection effect to predict colonists' behavior, including which oases they colonize, how long they stay there, how many seeds they then launch, how fast and far those seeds fly, and how behavior changes with increasing congestion. This colonization model explains several astrophysical puzzles, predicting lone oases like ours, amid large quiet regions with vast unused resources.

What if we someday learn how to model small brain units, and so can "upload" ourselves into new computer brains? What if this happens before we learn how to make human-level artificial intelligences? The result could be a sharp transition to an upload-dominated world, with many dramatic consequences. In particular, fast and cheap replication may once again make Darwinian evolution of human values a powerful force in human history. With evolved values, most uploads would value life even when life is hard or short, uploads would reproduce quickly, and wages would fall. But total wealth should rise, so we could all do better by accepting uploads, or at worse taxing them, rather than trying to delay or segregate them.

Economic growth is determined by the supply and demand of investment capital; technology determines the demand for capital, while human nature determines the supply. The supply curve has two distinct parts, giving the world economy two distinct modes. In the familiar slow growth mode, rates of return are limited by human discount rates. In the fast growth mode, investment is limited by the world's wealth. Historical trends suggest that we may transition to the fast mode in roughly another century and a half.

Can some new technology switch us to the fast mode more quickly than this? Perhaps, but such a technology must greatly raise the rate of return for theworld's expected worst investment project. It must thus be very broadly applicable, improving almost all forms of capital and investment.  Furthermore, investment externalities must remain within certain limits.

The long-term history of world economic growth seems to be describable as sequence of exponential growth modes. In the current mode, which has lasted about a century, the economy doubles every 15 years. If history is a guide, the economy may transition within the next 50 years or so to a faster mode, with a doubling time between one month and four years.

Could Gambling Save Science? Encouraging an Honest Consensus. with Reply to Comments. Social Epistemology 9(1):3-33,45-48, 1995. First appeared in Gambling and Commercial Gaming: Essays in Business, Economics, Philosophy, and Science, ed. W. Eadington & J. Cornelius, 399-440. Institute for Study of Gambling and Commercial Gaming, 1992.

The pace of scientific progress may be hindered by the tendency of our academic institutions to reward being popular, rather than being right. A market-based alternative, where scientists more formally "stake their reputation", is presented here. It offers clear incentives to be careful and honest while contributing to a visible, self-consistent consensus on controversial (or routine) scientific questions. In addition, it allows funders to choose questions to be researched without choosing people or methods.

Buy Health, Not Health Care. CATO Journal 14(1):135-141, 1994.

To cure health care, give your care-givers a clear incentive to keep you well. Make sure that when you lose, they lose, and just as much. Buy lots of life and disability insurance from your care-givers, and have a third party, unable to act against your life or health, pay you to be the beneficiary. (A simple game-theoretic model illustrates this proposal.)

Humanity seems to have a bright future, i.e., a non-trivial chance of expanding to fill the universe with lasting life. But the fact that space near us seems dead now tells us that any given piece of dead matter faces an astronomically low chance of begating such a future. There thus exists a great filter between death and expanding lasting life, and humanity faces the ominous question: how far along this filter are we?

Combining standard stories of biologists, astronomers, physicists, and social scientists would lead us to expect a much smaller filter than we observe. Thus one of these stories must be wrong. To find out who is wrong, and to inform our choices, we should study and reconsider all these areas. In particular we should seek evidence of extraterrestrials, such as via radio signals, Mars fossils, or dark matter astronomy. But contrary to common expectations, evidence of extraterrestrials is likely bad (though valuable) news. The easier it was for life to evolve to our stage, the bleaker our future chances probably are.

Must Early Life Be Easy? The Rythm of Major Evolutionary Transitions. first version Sept. 1996.

If we are not to conclude that most planets like Earth have evolved life as intelligent as we are, we must presume Earth is not random. This selection effect, however, also implies that the origin of life need not be as easy as the early appearance of life on Earth suggests. If a series of major evolutionary transitions were required to produce intelligent life, selection implies that a subset of these were ``critical steps," with durations that are similarly distributed. The time remaining from now until simple life is no longer possible on Earth must also be similarly distributed. These results are used to constrain models of major evolutionary transitions.

Ordinary privacy is leaving; can online privacy replace it?

A clever and thought-provoking argument suggests we should expect the extinction of intelligent life on Earth soon. In the end, however, the argument is unpersuasive.