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Social value of basic science research

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This page discusses the social value of basic science research | View other pages on the social value of particular activities

This page discusses the value of doing basic science research that is not geared to immediate application. Almost all mathematics research is of this sort, and a significant fraction of physics, chemistry, and computer science research also fits this model.

Until the nineteenth century, basic science research was generally done in spare time by people who had other day jobs (examples are mathematician Pierre Fermat, chemist John Dalton, and biologist Gregor Mendel). In the present era, this sort of "science-on-the-side" model has fallen out of favor, and basic scientific research is generally done by people in academia. Therefore, to some extent, evaluating basic science research is similar to evaluating academia (see more on academia as a career option and social value of academia).

There are some areas of research that fall within the realm of science research but are sufficiently close to application that it is inaccurate to characterize them as basic science research. Examples including machine learning and artificial intelligence research (see social value of working in artificial intelligence and machine learning) and biomedical research (see biomedical research as a career option and social value of biomedical research).

See also:

Should there be more basic science research?

Societies that engage in basic science research

There seems to be a positive correlation between the wealth of a society and the fraction of money that is spent on basic science research. However, causality could arguably run in any of these ways:

  • Wealthier societies have more money to spend on luxuries such as basic science research, in the same way as they have more money to spare for entertainment or art.
  • Basic science research makes societies wealthier by producing ideas that feed into technology that improved productivity.
  • Both wealth and the fraction of money spent on basic science research have an independent common cause. This cause could be the education level or intelligence of the members, the value they place on intellectual pursuit, the value they place on making long-term investments, or something else.

The knowledge goods argument

As a general rule, income is a good proxy for social value generated. Higher income attracts more people. So, naively, we would expect the "right" number of people in every profession.

However, basic science research produces knowledge goods. Knowledge goods have the property that they have high fixed production costs but, once produced, are free to consume at the margin. The production and dissemination of knowledge goods is non-rival -- one person's knowing something doesn't reduce the amount there is for another person to know. In fact, some forms of knowledge benefit from network effects: the more others know, the greater the value there is to knowing it.

The high production costs and low replication or dissemination costs suggest two possible models for the production and dissemination of knowledge goods:

  1. People invest in producing knowledge goods and then charge others for access to the knowledge goods. To prevent the knowledge "leaking" they may use measures for secrecy and intellectual property protection.
  2. The production of knowledge goods is funded philanthropically, and the goods are then disseminated in the widest and lowest-cost manner possible.

It has been argued that for basic science research, model (2) promotes more long-run scientific and technological progress, because:

  • It enables the production of ideas that may not have an immediate market to sell to, but may be part of a long chain of ideas that eventually lead to something useful.
  • The wider dissemination helps make sure that more people have access to the idea and can build on it.

Note that philanthropically funded basic science research is not the only instance of knowledge goods being disseminated for free. In industry, there are many instances of freemium and ad-supported models used to distribute goods with high fixed production costs but low distribution costs. In the free and open source software (FOSS) world, software code is available for free to use and modify, and people make money either by having foundations that can be donated to, or by partnering with for-profit companies that benefit from the growth of that software ecosystem.

Bacon's chain

The standard argument for the social value of basic science research has been dubbed "Bacon's chain" by William Niskanen, a critic of the argument. Niskanen formulates the argument as follows in his article Reflections of a Political Economist, as quoted by David Henderson in the blog post Basic Research Does Not Equal Technology:

Government financing is necessary to provide the adequate level of basic research, which is necessary to provide the scientific foundation for advanced technology, which accounts for a large part of economic growth.

Schematically:

Government funding Basic research Advanced technology Economic growth

However, Niskanen and some others have argued that every link in the chain is flawed. In particular, the link from basic science to advanced technology has been argued to be weak. Henderson quotes David P. Billington's The Tower and the Bridge: The New Art of Structural Engineering to offer an explanation for the link from basic research to advanced technology being weak:

There is a fundamental difference between science and technology. Engineering or technology is the making of things that did not previously exist, whereas science is the discovering of things that have long existed. Technological results are forms that exist only because people want to make them, whereas scientific results are formulations of what exists independently of human intentions. Technology deals with the artificial, science with the natural.

Henderson also quotes Billington quoting Michael Mulkay writing:

[S]cience seems to accumulate mainly on the basis of past science, and technology primarily on the basis of past technology.

The Cato Unbound discussion Who Pays for Science? (August 2013) is also relevant.

Basic science and high-impact science

We look at explorations of the question: How strong is the overlap between basic science and high-impact science? Is all high-impact science the sort that is likely to be immediately applied?

Nowadays most high-impact science is not basic science

Consider the following most impressive instances of progress in human health and wealth arising from scientific breakthroughs in the late 19th and early 20th century:

  • The development of flight, leading eventually to commercial flight
  • The development of vaccines
  • The Green Revolution
  • Breakthroughs in computing technology (including the ideas of programming languages and compilers)
  • Improvements in computer hardware
  • The development of nuclear power (the impact here remains to be seen)

Note that:

  1. In all of the cases, the requisite technological development was directly motivated by the need to solve a real-world problem, rather than a basic scientific urge to understand a phenomenon. This is consistent with the idea that science does not feed into technology too tightly.
  2. In most cases, the technological development did not depend on cutting-edge science of the time. The basic scientific breakthroughs needed for the technology were generally at least a few decades old.

Point (2) could be interpreted in either of two ways. It could be viewed as saying that basic science has value for the far future, and therefore is likely to be underproduced by our short-sighted society, so it makes sense at the margin to produce basic science. On the other hand, it could be argued that the significant uncertainty and huge time lag before basic science gets used suggest that it's not that high value to produce more basic science at the margin. Moreover, a lot of basic science doesn't get used in any technology at all. Unlike with applied science, it's hard to know in advance what basic science will get used. Unless you believe you have special insight on what basic science will be valuable, this is an argument against basic science research.

The speedup argument

In the blog post High Impact Science, Carl Shulman argues that for some forms of high impact science, even speeding up scientific progress by a few seconds can generate huge humanitarian impacts. The examples cited by Shulman are the Green Revolution and the fight against diseases such as malaria: speeding up the process of development of hybrid varieties or the process of malaria eradication by one year can save thousands, or even hundreds of thousands, of lives.

Does Shulman's argument extend to basic science research? If there were a fixed lag between the development of the basic science research and the applied science or technology that used it, then the argument would extend. If making a scientific breakthrough in 2015 instead of 2019 meant that the corresponding technological breakthrough would occur four years earlier, then all the gains from speeding up the technological breakthrough apply to speeding up the scientific breakthrough. But this rarely describes the real world: the constraining factor for the timing of technological breakthroughs is not the amount of time the required basic science has been in circulation. Rather, demand for that technology, availability of funding, availability of other requisite technologies, and stochastic elements are more important.

Basic science as a springboard for people to move to other areas

One argument in favor of supporting the enterprise of basic science research is that even though the research itself has little value, the network of people with basic science knowledge can be leveraged for other purposes. Because they have plenty of free time and can flexibly explore problems, they could switch to an upcoming applied scientific or technological discipline where they can use their basic science training. If, on the other hand, they took up jobs in finance or consulting, they would no longer be in the sort of intellectual environment where they would either be aware of or in the right mental framework to make such a switch.

Consider some examples:

  • Richard Stallman, computer scientist at MIT, started the free software movement in the 1980s. This has been highly influential in shaping the world of computers and hence the world at large. The academic environment that Stallman operated in may have been crucial in giving him the flexibility needed in the initial stages of the experiment.
  • Donald Knuth, a theoretical computer scientist, is known as the father of the analysis of algorithms. He worked in many applied areas, and also created TeX, one of the earliest typesetting languages that is still used by mathematicians around the world to format their documents.
  • A number of science popularizers have day jobs as academics. These include Carl Sagan, Keith Devlin, and others.
  • Machine learning researchers have, in recent years, left academia to join companies such as Google and Facebook. Some have taken part-time appointments at these companies while still maintaining some academic connections.
  • Many professors have part-time jobs as consultants at companies that are working on products closely related to their area of research.

The most impressive examples above, as you can see, come from people whose work is in areas that are already close enough to application, even if their main research is not itself applied.

Resources

  • Vannevar Group is a group of scientists and former scientists who are interested both in science and social value.