Presentation on theme: "New leading sciences and the changing boundaries between public and private Andrea Bonaccorsi University of Pisa Member of the High Level Expert Group."— Presentation transcript:
New leading sciences and the changing boundaries between public and private Andrea Bonaccorsi University of Pisa Member of the High Level Expert Group on Maximizing the wider benefit of basic research and the European Research Council European Commission, DG Research Six countries programme Rotterdam, April 21, 2005
Outline The new scientific landscape: the emergence of new leading sciences The performance of European science in new leading sciences Moving boundaries between public and private
The new scientific landscape A new scientific landscape has taken shape in the last 20 years or so. It results from the combination of several revolutionary advances: - the molecular biology revolution, particularly after the recombinant DNA discovery and the development of PCR; - the pervasive information technology revolution, resulting from advances in algorithms, computer science, microelectronics, and more recently from the convergence with telecommunication; - new advances in materials science; - new opportunities in nanotechnology, particularly after the invention of resonance microscopy.
The new scientific landscape These new fields and disciplines share some intrinsic characteristics: - are based on reductionist explanation strategies, but deal with complex systems at various levels of resolution; - evolve through a complex interaction between scientific understanding and engineering manipulation, i.e. between sciences of nature and sciences of artificial; - include many general purpose technologies; - cut across disciplinary boundaries and actively promote overlapping.
Search regimes Rate of growth - fast growing vs slow growing Degree of diversity - convergent dynamics vs divergent dynamics Level of complementarity - physical infrastructure vs. human capital and institutional complementarity New leading sciences (materials science, life sciences, computer science, incl. biotech and nanotech): fast growing, divergent dynamics, human capital and institutional complementarity HLEG: definition of frontier research.
Rate of growth How do new fields of research are generated within disciplines? Which is the post-entry dynamics of growth? Which is the steady state rate of growth? We study the entry of new words in scientific publications: - post-entry dynamics - arrival process within the scientific discipline and turnover ratio (new words/existing words)
Evidence from Nanopublications Source: Bonaccorsi and Thoma (2005)
Number of occurrences of the word Genetic algorithm in the publications of the top 1000 scientists in computer science
Number of occurrences of the word Neural network in the publications of the top 1000 scientists in computer science
Number of occurrences of the word Wireless in the publications of the top 1000 scientists in computer science
Number of occurrences of the word Atomic force microscope in the publications of the top 1000 scientists in high energy physics
Number of occurrences of the word Hadron collider in the publications of the top 1000 scientists in high energy physics
Total numberNew born Total number of keywords and number of newly- appearing keywords in publications of top 1000 high energy physicists
Total numberNew born Total number of keywords and number of newly- appearing keywords in publications of top 1000 computer scientists
0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 100, Computer scienceHigh energy physics Ratio between newly appearing keywords and total number of keywords in high energy physics and computer science
Stylized evidence on rates of growth First, scientific fields grow at very different rates after entry. As a first broad distinction, there are fields that grow extremely rapidly and fields characterized by slow growth after entry. Post-entry growth rates sharply differ. Second, disciplines largely differ in the composition of fields characterized by different rates of growth. In some disciplines it seems that new fields are generated continuously, so that the turnover ratio is extremely high, while in other disciplines the turnover is much lower.
Degree of diversity: How many directions does search take? Units of analysis: research programme (Lakatos) combined with the network of socio-technical constructs (Callon-Latour-Laredo-Pickering). Levels of diversity: 1. Theory 2. Research question, goal or problem 3. Experimental technique and equipment 4. Object or locus of observation Diversity may take place at any level.
Degree of diversity Because diversity is defined across all levels, our definition does not overlap with diversity in paradigms. Paradigmatic change takes place mainly at the level of theories and research questions. Within the same paradigm we may observe large diversity due to - the use of different experimental infrastructure, with the associated procedures, practices and localized learning processes - the selection of different objects or loci of observation, corresponding to different sub-hypotheses within the same general paradigm.
Degree of diversity How many different directions does search take? Even within the same paradigm and theory, research programmes may differ by: - specific hypotheses or research question - experimental technique - object or locus of observation A dynamics of increasing diversity may be defined divergent. Divergence may be: - strong (competing, non compatible hypotheses) - weak or complementary (mutually compatible hypotheses but diverging search strategies)
Convergent dynamics diversity is stable or tends to decrease most research programmes follow the same set of specific hypotheses and use the same tools e.g. high energy physics, nuclear physics, astronomy, traditional chemistry, nuclear technology, aerospace, TLC, conventional engineering Divergent dynamics diversity explodes the same theory or paradigm (e.g. molecular biology) gives origin to many competing or just diverse programmes e.g. HIV, Alzheimer, molecular oncology, nanotechnology, computer languages, computational chemistry
We study the concentration of keywords used in scientific publications - highly concentrated disciplines: a few keywords absorb a large share of publications- research programmes tend to converge along the same directions - highly fragmented disciplines: there are many directions of research, no dominant pattern. Future research: mapping/ clustering of keywords over time
Concentration of keywords in publications of top 1000 scientists in Computer science and High energy physics Computer High science energy physics Number of publications of top 1,000 scientists 9,062 41,770 Number of publications with keywords6,40134,379 Publications with keywords (%)71%82% Number of different keywords18,03150,952 Average number of keywords per author Concentration ratio (C 250 )*26.5%29.3% * Cumulative market share of top 250 keywords (Number of occurrences of the top 250 keywords/ total number of occurrences in all publications)
Relative frequency of top keywords in High energy physics and Computer science
Plot of rank correlation of top 250 keywords in High energy physics (r=.79)
Plot of rank correlation of top 250 keywords in Computer science (r=.49)
Level of complementarity Traditional type of complementarity in science: - physical facilities (e.g. big science) New forms of complementarity: - human capital complementarity (different disciplinary background, need for flexibility in education, career, affiliation, organizational setting) - institutional complementarity (different institutions involved, e.g. university/laboratory/ hospital in molecular medicine)
The performance of Europe in new leading sciences (a) European science is strong in fields characterized by convergent search regimes and weak in fields characterized by divergent search regimes. (b European science is strong in fields characterized by high levels of infrastructural complementarities while it is much less prepared in fields characterized by human capital and institutional complementarities. (c) Consequently, European science is strong in fields characterized by slow growth and weak in fields characterized by turbulent growth. (d) European science is only quantitatively comparable to US science but is weaker in the overall quality and is severely under-represented in the upper tail of scientific quality.
Specialisation patterns (Revealed Comparative Advantages, ) no European country is specialised in Computer science no European country is specialised in Engineering; in biology and biochemistry small European countries (Netherlands, Sweden, Denmark, Norway, Finland) exhibit strong specialisation while large countries have an index lower than unity; in molecular biology several large countries (United Kingdom, Germany and France) and small countries (Netherlands, Finland, in addition to Switzerland) are specialised; Europe as a whole is specialised in a few biomedical areas (pharmacology, immunology, microbiology) and in the large traditional disciplines of chemistry, physics and astronomy.
In materials science EU-15 produce 40,108 papers and receive 83,748 citations, while NAFTA produce 31,620 papers but receive 106,841 citations In the life sciences EU-15 produce 616,212 papers and US 529,608 in the period , but the citation impact ( ) is 1.35 in USA and only 0.90 in EU-15 In computer science the citation impact ( ) is 1.33 for Israel, 1.17 for US, but only in the range between 0.81 (Germany) and 0.95 (Italy) for the four largest countries Source: Third European Report on S&T Indicators (2003)
What do these disciplines have in common? A divergent search regime a dynamics of proliferating research programmes, often generated within the same paradigm, that increase the diversity of the field in terms of hypotheses, experimental techniques, objects of investigation. European science is strong in fields characterized by convergent search regimes and weak in fields characterized by divergent search regimes
What do these disciplines have in common? A search regime characterized by new forms of complementarities Not much physical infrastructure complementarity (big science) But: - human capital complementarity - institutional complementarity European science is strong in fields characterized by high levels of infrastructural complementarities while it is much less prepared in fields characterized by human capital and institutional complementarities.
European science has developed separate institutions at national, intergovernmental and European level, for dealing with search regimes with strong physical infrastructure complementarities (e.g. high energy physics, astronomy, space research, oceanography, nuclear technology). It is much more difficult to provide emerging fields the required complementarities in terms of human capital within the common institutional framework. There are few rapid growth mechanisms.
Rate of growth of broad disciplines over the period the fastest growing area has been computer science with a growth rate of almost 10% earth sciences, engineering and mathematics also show high growth rates, varying between 4.2 and 4.6% biology and agriculture have the lowest growth rates with 1.4 and 1.6% respectively the broad field of life sciences as a whole experienced a growth rate of 2.33% the growth rate for the broad field of engineering was 4.5%, of which 35% was materials science, that grew at 1.9% per year. European science is strong in fields characterized by slow growth and weak in fields characterized by turbulent growth
Upper tail in quality of research. Piece of evidence # 1 Data on the most cited scientists worldwide have been recently made available by ISI on the basis of the analysis of 19 million papers in the period , authored by 5 million scientists. They refer to around 5,000 scientists worldwide in all fields, selected as those 250 that receive the largest number of total citations in any subject area (0.1% of the total). In all 21 fields US scientists largely dominate, with a proportion of highly cited scientists ranging from 40% in pharmacology and agricultural sciences to over 90% in economics/business and social sciences and an average around 60-70% of the total. Among the 21 areas, only in other three areas non-US countries represent more than 40% of the total: physics, chemistry and plant and animal science (see Basu, 2004).
US scientists dominate in each of the 21 subject areas of science USA (Source: Basu, 2004)
Piece of evidence # 2 We examined (with non-ISI sources) the publications of top 1,000 scientists by citations received along all their scientific career in - Computer science - High energy physics and all publications in nanotechnology for the period (ISI source). We identified the most productive institutions in terms of total number of publications in the period and ranked the first 100.
MIT University of CaliforniaUniversity of California University of California MITStanford University Indian Institute of Technology Stanford UniversityMIT National Taiwan University Harvard UniversityHarvard University Harvard University University of MassachusettsUniversity of Illinois Cambridge University Cornell UniversityCarnegie-Mellon University Yale University Carnegie-Mellon UniversityCornell University University of Michigan University of IllinoisUniversity of Michigan Seoul National University Purdue UniversityUniversity of Wisconsin California Institute of University of MichiganUniversity of Texas Technology Bachelor Master PhD Piece of evidence # 3 Institutions awarding degrees of the top 1,000 scientists in Computer science. Top 10 list
The performance of European science Europe (possibly with the exception of UK and Scandinavian countries) has problems in matching a rapid quantitative growth with adequate quality and the ability to dominate the upper tail of scientific reputation These problems largely come from the mismatch between new leading sciences and the prevailing institutional setting in most European countries The European institutional setting: exhibits weaker selection properties has less flexibility has few mechanisms for rapid massive growth encourages lower mobility Under conditions of rapid and divergent growth, opportunity costs for scientists strongly increase.
Plot of rate of growth (average number of personnel per each year of life, T_PERS/INSTAG) against size (number of personnel, T_PERS). CNR Source: Bonaccorsi and Daraio (2003)
Plot of rate of growth (average number of researchers per each year of life, T_RES/INSTAG) against size (number of researchers, T_RES). CNR Source: Bonaccorsi and Daraio (2003)
The terms of the public-private debate Traditional rationale for public intervention- market failures in the provision of quasi-public goods (Nelson, 1959; Arrow, 1962). Criticism to the linear model of science-technology interaction (Rosenberg, 1976; 1982; Kline and Rosenberg s chain-linked model) The problem of increasing returns, path-dependence and the public role as variety-preserving (Callon). The notion of innovation as unfolding process and the public role against system failure (Metcalfe, 2005).
The new terms of the public-private debate Under divergent search regimes, the trade-off between exploration and exploitation is not sustainable for private firms in the long run. Private investment is not interested in reducing variety (Callon). Rather, it is aimed to maximize the potential for exploration and the ability to absorb external knowledge via the acquisition of access rights. Divergence forces a redefinition of the division of innovative labour between private and public.
The redefinition of the division of labour between public and private Traditional boundaries cut across the nature of knowledge as a product: -basic knowledge (general, abstract, indeterminate use value,, imperfectly appropriable): public -applied knowledge (specific, concrete, quantifiable use value, easily appropriated): private In the new landscape these boundaries are not appropriate.
The redefinition of the division of labour between public and private (2) New boundaries follow the nature of the search process: -public sector has the interest to advance the whole tree of search and to keep options open: constitution of human capital + new types of infrastructures (eg databases) -private sector has the interest to access the search tree under conditions of non exclusivity
The new policy agenda -Joint laboratories -Matching funds -Mobility of researchers -Redesign of research careers -Time management -Redefinition of intellectual property rights (to grant access while keeping incentive to innovation high)