The university system is undergoing deep changes in all countries. There are two challenges science is facing. One is to build a balance between demand of economy and quality of education and basic research and the other is an adaptation of new information technology in full.
It is becoming more and more evident that science has great impact on economic growth and productivity. That has become a major reason to fund science. On the other hand the basic, traditional functions of universities, higher education and production of new knowledge, are in increasing problems with funding. It’s a great challenge how to combine improving competitiveness of economy and raising the quality of education and basic research.
The development of information technology has created new conditions for solving this kind of discrepancy. Global information networks are radically changing the ways knowledge is created and distributed in society and economy. There are at least two points to note: network is increasing the amount of information available (big data) and it is providing new ways for people to learn and contribute to science.
Is market logic dominating universities?
In a recent book Creating the Market University (Princeton 2012) Elizabeth Berman analyzes a shift of dominant logic in US University system in last decades of 20th century. Before 70’s the traditional science logic dominated and science was seen mainly “the pursuit of knowledge”. In 80-90’s the market logic started to strength and science used to be seen as “an engine of growth”.
Berman expressed the differences between these two institutional logics in a clear way: “science-as-resource” vs. “science-as-engine” (p. 3).The logic of science is based on idea of science as a resource for society. Universities produce new knowledge for society but their “responsibility ended with making that resource as accessible as possible” (p. 33).
On the other hand market logic is based on idea that science is an economic engine: “the knowledge that university produce is a source of innovation that can lead to new products, jobs, and even industries” (p. 30). University gets an entrepreneurial role. Universities realized this role through technology transfer, faculty entrepreneurship, spinoff firms and research partnership with industries. Berman studies carefully three developments: faculty entrepreneurship in the Biosciences, patenting university inventions and creating university-industry research centers.
What is interesting in Berman’s study is that this shift towards market logic is not based on universities’ own strategy or even pressure from industry. According to Berman government decisions were the most important driver of the change. The argument government used was based on observations that technological innovation drives economic growth. That belief emerged in 50’s and 60’s in the circle of economists like Arrow, Nelson, and Solow. It was estimated that technological change was the source as much as 87.5 % of U.S. economic growth in the first half of twentieth century (“residual growth”, p. 45-46).
Technology was a major link between science and innovation. Therefore to invest in science will lead to innovation and finally to higher productivity and economic growth. Government took an active role to encourage universities to treat academic science as an economic valuable product (p. 2). With the success of some biotechnology startups, like Genentech (IPO 1980, value of $532 million), passing the Bayh-Dole Act (1980) giving universities right to patent government-funded inventions and some other events the attitudes in universities became more favorable towards entrepreneurship and market logic.
Could we say that market logic has replaced the traditional logic of science? Berman does not say so. Instead she stresses that market logic has became “more visible and legitimate alternative to the logic of science” (p. 157). One can talk about “uneasy coexistence” of market logic with the logic of science. The coexistence is balancing between
- Creating knowledge vs. applying knowledge
- Long-term vs. short-term orientation
- Theory vs. practice
- Curiosity vs. utility
Berman’s conclusion from this discussion is interesting (p. 157). “Externally” universities might justify themselves as economic engine. “Internally” many scientists continue to be motivated by the joy of discovery and the desire for the recognition of their peers. In this “internal logic” peer-reviewed publications remain the gold standard of scientific research. So internally the old logic of science is still alive and even lives well. Berman says that “academics continued to value scientific interesting work, even when it had no obvious practical implications” (p. 157).
Where is knowledge?
David Weinberger makes a provocative statement in his new book Too Big to Know that knowledge is a property of networks:
“Knowledge new lives not just in libraries and museums and academic journals. It lives not just in the skulls of individuals. Our skulls and our institutions are simple not big enough to contain knowledge. Knowledge is now a property of the network, and the network embraces businesses, governments, media, museums, curated collections, and minds in communication.” (p. xiii).
To get an idea of the capacity of network just remember that there are about 200 million blogs, about a trillion pages and about 3-5 zettabytes information (1021 bytes). A rational question is “how the new overload affects our basic strategy of knowing-by-reducing” (p. 9). The thesis is of Weinberger is that “Internet enables group to develop ideas further than any individual could” (p. 45-46). This thesis is similar than Surowiecki’s The Wisdom of Crowds. So what is amazing with Internet is expressed by five theses by Weinberger (p. 51-63):
1. The Internet connects lots of people
2. The Internet has many different types of people in it
3. The Internet is like most oatmeal: sticky and lumpy
4. The Internet is cumulative
5. The Internet scales indefinitely
In my mind the most interesting topic considered by Weinberger is the nature of expertise. The new expertise is embedded in a digital network. Let’s call it networked expertise because it is (in) network (Weinberger doesn’t introduce any special concept for that). The traditional expertise is based on books and authorities (p. 65-67, note tempus):
- Expertise was topic-based.
- Expertise’s value was the certainty of its conclusions.
- Expertise was often opaque.
- Expertise was one-way.
- Experts were a special class.
- Expertise preferred to speak in a single voice.
My topic in this blog is science, however. Is it so that these features of old expertise are exactly what are prevailing in science? At least Weinberger put forward a hypothesis that scientific knowledge is transforming itself into new medium and becoming huge, less hierarchical, more continuously public, less centrally filtered, more open to differences, and hyperlinked (Chapter 7: Too Much Science). This hypothesis is still a hypothesis, but it’s reasonable vision of science as we will see.
The New Era of Networked Science
Michael Nielsen’s new book Reinventing Discovery (Princeton 2012) offers a deep and careful analysis of science in Net. Nielsen in fact gives more evidence to Weinberger’s hypothesis without knowing that.
According to Nielsen “We are, piece by piece, assembling all the world’s knowledge into a single giant edifice. That edifice is too vast to be comprehended by any individual working alone. But new computerized tools can help us find meaning hidden in all that knowledge.” (p. 4-5)
Nielsen analyzes the whole phenomena of networked science by developing a set of useful concepts. Net is forming a kind of collective intelligence, a superior power to solve hard problems. As an example Nielsen refers to InnoCentive and many other networks devoting to scientific problems (like Polymath Project). The idea is that participating organizations can post online scientific problems they want solved, so called Challenges. Anyone can download a description of a Challenge and try to solve it. More than 160.000 people from 175 counties has signed up to InnoCentive and more that 200 Challenges has awarded (one might get an award for acceptable solution).
Online tool allows amplifying collective intelligence and extending that to solving scientific problems. The core in amplifying is reaching a conversational critical mass, where many people with different background work together. In an ideal case each person in this mass owns a microexpertise, an area of knowledge where he/she is especially good. The process of collective problem solving is a kind a spiral, where new and new proposal from different perspectives are presented and discussed. Nielsen calls this model designed serendipity, because it produces innovative solutions or new directions.
Nielsen summaries his model for amplifying collective intelligence to three points (p. 33):
1. Modularizing the collaboration (independent tasks)
2. Encouraging small contributions (low barrier to entry)
3. Developing a rich and well structures information commons (availability of earlier work)
These are similar than points presented by Steven Weber in his book The Success of Open Source (Harvard 2004). See also my book Sustainable innovation (Sitra 2010)
There is another implication of net to science, namely formation of huge data. There are in fact two forms of data. One is the huge amount of information in net in these millions of sites and social media and any information devices. A good example is to use Google to predict the spreading of influenza (p. 93-95). To benefit from information in net very effective new algorithms are needed and under development. On the other hand collected data bases can make open. For example Sloan Digital Sky Survey (SDSS) contains about images of about million galaxies. Thousands of scientific discoveries was made using SDSS data base (p. 98-102). Human Genome Project is also offering openly all data about human genome.
Nielsen uses the concept data web to refer all open data, taken together in aggregate (p. 120). Data web provides amazing new possibilities to create new knowledge. Nielsen called a new kind of intelligence, data-driven intelligence. It is needed if we like to get knowledge out of big data, with millions and millions items, or perhaps from date base of size zettabytes.
Data Web will transform science in two ways. It will increase the number and variety of scientific questions that one can answer. It will change the nature of explanation itself, too. An impressive case is the translation machine based on analyzing documents en masse. The machine does not use rules of grammar or language, it just explore huge amount of texts and make statistical generalization what might be the most probable translation. Google uses that model in its translations. Nielsen makes a nice point that these kind of data-driven models are theories or explanation, although extremely complex and hard to understand for us as human being.
Open science – an utopia or the future of science?
Open science means many things, among others:
- Open data
- Open access
- Data-web and big data
- Exploring the information in net
- Open problem solving
It’s easy to see that open science is open also to citizens leading to citizen science. This is useful enlargement of community involved to solving hard problems. Remember the design of serendipity. But I believe that “open science” is the biggest challenge to traditional scientific community. Why so? Basically, the leading idea of science is to explore the universe and to be open to all challenging problems. Like Berman says, the logic of science sees the search for truth as having intrinsic value. Science is the pursuit of knowledge.
But is science an individualistic enterprise: a solitary genius solving hard problems in his secret lab? Sometimes, but a central part of scientific method is its cumulativeness: every scientist is standing on the shoulders of giants. This principle refers to importance of collaboration in science. Still there are some obstacles in accepting full body open science. These obstacles are not so much related to market logic than competition inside science community. Berman emphasizes that in the internally logic of science publications matter more than any other merits.
There is an extremely hard competition among scientists to get positions in universities or in grant applications. This leads to keep your data closed until you have published your results. In this competitive situation researchers have to use all available time for writing papers and grant applications. They have not time or motives to share their data and knowledge and participate to open discussion in social media or science wikis. This is a major obstacle to develop open science.
One step to open science is to create new incentives for open collaboration. Nielsen puts it clearly:
“Today’s scientists show a relentless drive to write papers because that’s what’s valued by scientific community. We need new incentives that create a similar drive to share data, code and other knowledge.” (p. 193)
What motivates people to participate to open projects like creating Linux operating system? One major reason is reputation among peers. In science we have created “an economy based on reputation”, and it has worked well so far. Nielsen proposes that we enlarge the scope of reputation so that also sharing data and knowledge and contributions in open discussion are considered valuable merit. We have to create also a citation system to give merit for originators of ideas, initiatives and public computer programs etc.
Although market logic is not in strong opposition to open science, there are some warring features in Market Logic. Market logic means evaluating all things according to their economic value. The problem here is that there are many important things with low economic value or no value at all. Especially if we take the present capitalism to be the framework for economic thinking, we are in danger to fall into corporate trap.
Marker economy is changing towards entrepreneurship economy, where small innovative enterprises create new value and jobs. Corporations will be in big troubles with their hierarchical and inflexible structure and inhuman management practices. Corporations are also keen to protect their immaterial property rights and demand universities to close results funded by them.
In entrepreneurship economy the scope of businesses is much large from green technology to bottom of pyramid markets to social enterprises. Also the knowledge they need if different than the knowledge corporations are using to maximize their profit. Open science is also open to cultural, social, and planetary issues like climate change, underdevelopment, insecurity, polarization etc.
I see the future of science in solving wicked problems with citizens and other stakeholders, sharing all data and knowledge. Science will be public good or commons getting its legitimacy from citizens and public. Scientists could build their carries on large reputational base including scientific publications as well as contributions in open science.