The Competitiveness of Nations in a Global Knowledge-Based Economy
Partha Dasgupta [a] and Paul A. David [b]
Toward a new economics of science
Content
Abstract
1. Introduction and motivation: Science, economics and politics
2. The old economics of basic research, and the emergence of a new
economics of science
3. Knowledge: Codified or tacit? Public or private?
3.1. Knowledge, information, and the endogeneity
of tacitness
3.2. Science and Technology: Public and private knowledge
4. Priority and adherence to the norm of disclosure in the reward
system of science
4.1. Priority and the science reward system
4.2. Priority and secrecy in Science: Public virtue and private vices
4.3. Culture: The enforcement of cooperative rivalry and collective
regulation in Science
5. Resource allocation within scientific fields and programs
6. The timing of research programs within Science
7. Policy challenges: Maintaining Science and Technology in dynamic
balance
7.1. Capturing the training and screening externalities generated by
open science
7.2. Managing competition for scientists between complementary research
activities
7.3. Promoting greater ‘industrial transferrability’
of university research findings
8. Conclusion
9. Acknowledgements
10. References
4. Priority and adherence to the norm
of disclosure in the reward system of science
It has long been recognized
in the sociology of science that priority of discovery or development is the
basis for legitimate reputation-building claims, and that an individual’s
reputation for ‘contributions’ acknowledged within his or her collegiate
reference groups is the fundamental ‘currency’ in the reward structure that
governs the community of academic scientists. [31] That scientists take intense interest in
disputes over ‘priority’, and spend much effort collectively in determining for
what and to whom this ‘coin’ shall be distributed, suggested to a functionalist
like Merton (1973) the central role that competition for priority was playing
in the organization of
28. To the extent that information-commodities are exchanged against
money, or other goods, the system governing the production, dissemination and
use of knowledge within the realms of Technology, resembles the market
mechanism, most especially markets in which commodities are auctioned. The analytical similarity between competition
among producers of (private) technological information and competition among
bidders at auctions has been explored by Dasgupta (1986).
29. This retains the emphasis placed upon differences in the norms
regulating information disclosure, and the functional rationalization provides
for them in our earlier papers in this vein (David, 1984; Dasgupta
and David. 1987, 1988). An expression of
some reservations concerning the explanatory value of functionalist
interpretations for economic institutions that have evolved historically, and a
proposed historical explanation for the differentiation that arose between the
two communities in the West from the late sixteenth century onwards are
provided by David (1991).
30. We shall examine the arrangements within Science more closely in the
following sections (5, 6 and 7) in order to support this assertion. The efficiency of resource allocation for
R&D in a regime based on intellectual property rights is discussed in many
places in the modern economics literature, including David (1993a, 1993b, pp.
225-229).
31. See, for example, Merton (1973). See also Blume
(1974), and Whitley (1984). Gaston (1970), Cole and Cole (1973), and Cole
(1978) provide quantitative evidence supporting the view that science is an
unusual institution in the sense that it comes close to achieving a reward
system that is ‘universalistic’, in Merton’s sense, rather than particularistic,
i.e. achievement-oriented rather than ascription-oriented. In a study of university physicists, Cole and
Cole (1967) found that quality of published research was the most important
determinant of recognition that came in the forms of honorific awards,
appointments to professorships at prestigious departments, and wide citation. While the relative weighing of quality and
quantity of ‘contributions’ have not been established across different fields
of science, there is a presumption that where a dominant paradigm, or research
program reigns, there will be greater consensus concerning ‘quality’. As fields of inquiry go through paradigm
shifts, the quantity-quality weighing is likely to be disrupted, and it should
not be supposed that stability in the relative weights will be maintained
across fields.
498
the Science community. [32] In the context of the reward system in science, the rule of priority
serves two purposes at once: hastening discoveries, and hastening their
disclosure. How it does that is seen
readily enough. [33]
4.1. Priority and the science reward system
First, tieing
rewards to priority sets up a contest, a race, for scientific discoveries. Since a scientist’s effort cannot in general
be observed by outside monitors, payment cannot be based upon it. If funds were to be allocated for ‘effort’,
scientists like anyone else would be given an incentive to slack off while
declaring that they were working hard. Nor
can intention be the basis of payment, for intention cannot be observed
publicly either. By contrast,
performance, if disclosed, can be observed and vetted publicly. So rewards can be based upon it; the greater
the achievement, the larger the rewards - which may come, eventually if not immediately,
in the form of salary increases, subsequent research grants, scientific prizes,
eponymy and, most generally, peer-group esteem.
A method of payment
alternative to one based on priority would be a fixed fee for entering science,
but this would dull the individual’s incentive to work hard, since scientists
could collect the fee irrespective of whether they produced anything of
interest. So the reward has to be based
in some way on achievement. However, it
is often difficult to determine how far behind the winner the losers of a
scientific race are when the winner announces his discovery. (Those who were left behind can merely copy
the winner’s results and claim that they were very nearly there). For this reason, it is not possible in general
to award prizes on rank. Thus, unlike
tennis tournaments, science does not pay big rewards to the runners-up. This suggests a system of payment which is
compatible with individual incentives. It is one where, roughly speaking, the winner
is awarded all that is to be dispersed by the community for the discovery. The rule of priority mimics this.
We have offered a rationale
for the rule of priority among scientists involved in parallel research on the
basis of what is publicly verifiable. Fortunately
for society, there is congruence between this requirement and the relative
social values of the outputs of parallel research teams. For note that among the discoveries (or
inventions) made by rivals involved in parallel research only the first is worthwhile
to society; there is no social value-added when the same discovery is made a
second, third or fourth time. [34]
There is, however, one
immediately apparent difficulty about the rule of priority. If the losers of a scientific race were to
receive absolutely nothing, the rule would place all the risks involved in the
production of knowledge firmly on the shoulders of scientists. This cannot be an efficient system if
scientists, like other mortals, are averse to taking risks which involve their
survival and comforts. (One would expect
individuals without private means to be particularly averse to absorbing all
the risks. [35]) We conclude, then, that those who regularly engage in
basic science research need to be paid something regardless of the extent of
their success in the scientific races they choose to enter. Qtherwise many, if
not most, scientists today would enter other professions.
All this suggests the
desirability of a payment schedule which consists of something like a flat
salary for entering science, supplemented by rewards to winners of scientific
competitions, with the proviso that the better is the performance,
the higher will be the reward. The
flat-salary
32. See Lamb and Easton (1984, Chap. 10) for a historical survey of
priority disputes and races for priority. Scientists may be motivated to establish
claims of ‘priority’ because they seek fame through the attachment of their
name to a discovery or hypothesis; because, as creative individuals, they need
to secure the validation of their creation - in this case from an expert
audience the feeling of having produced something new to the world and not just
to the self (see Storer 1966), or because material
rewards like salary and access to research facilities are linked to their reputational standing among their scientific colleagues. For the purposes of the immediate argument,
the precise nature of the underlying motivation does not really matter.
33. Material in this section draws upon our earlier papers (Dasgupta and David, 1987, 1988).
34. By this we do not, of course, mean independent confirmations of a
scientific discovery, which is a different matter altogether.
35. The analysis here is thus most appropriately interpreted to apply to
arrangements for the patronage (or employment) of professional scientists, from
either private or public sources, and does not bear on the pursuit of
scientific knowledge by ‘amateurs’.
499
component of the public payment schedule acts as a drag on
incentives to do research (for this reason it must not be all that high), but
as we have seen, it is a socially necessary drag if there is to be Science. [36] Fortunately for the evolution of ‘academic science’, it has been
found possible to tailor the flat salary to a complementary, productive
activity - teaching - and thereby reduce the wastage occasioned by the drag. Roughly speaking, a modern scientist is paid
in the form of a fixed salary (e.g. for teaching, should he be in academia) and
bonuses (e.g. promotions, scientific awards, and general recognition) for
priority in discoveries and inventions. [37]
The second purpose the rule
of priority serves is in eliciting public disclosure of new findings. Priority creates a privately-owned asset from
the very act of relinquishing exclusive possession of the new knowledge. To put it dramatically, priority in science is
the prize. Now, the public disclosure of
new findings provides two additional-social benefits. First, it widens the span of application in
the search for new knowledge. It raises
the social value of knowledge by lowering the chance that it will reside with
persons and groups who lack the resources and ability to exploit it. Second, disclosure enables peer groups to
screen and evaluate the new finding. The
result is a new finding containing a smaller margin of error. The social value of ‘reliability’ established
by disclosure to the community of scientists is that users of new discoveries
can thereby tolerate a higher degree of risk arising from other sources of
incomplete knowledge and information.
There is a third beneficial
consequence, stemming from the fact that for priority to matter the race must
be run towards a goal that is widely recognized, either at the outset, or
subsequently, as one worth achieving. The autonomous governance system that has
characterized academic (and, indeed, much non-academic) science in the West
means that communities of scientific peers define what contributions to
knowledge it is worth bothering to have arrived at before others. What effect does this have? It creates a cumulative, chain-linked impetus
to the advance of knowledge, because what turns out generally to be appreciated
is the disclosure of knowledge that aids (or is expected to aid) colleagues in
the field in generating findings on the basis of which they can establish
priority claims of their own. [38]
4.2. Priority and secrecy in Science: Public virtue
and private vices
Of course, the reward system
sets up an immediate tension between cooperative compliance with the norm of
full disclosure (to assist oneself and colleagues in the communal search for
knowledge), and the individualistic competitive urge to win priority races. This can engender neurotic anxieties on the
part of researchers and ‘deviant’ patterns of secretive behavior. [39] Conceptually one
may want to distinguish between departures from the norm of disclosure that
take the form of remaining taciturn until ‘a result’ has been obtained and can
be publicly announced, and incompleteness in disclosure - i.e. not revealing
all that has been learned. The cleanest
basis for such a distinction presents itself when results are put into codified
form, as in the draft of a
36. In addition, most academic institutions limit the potential
inefficiency of paying flat-fees, unrelated to research productivity, by
awarding tenure only after some extended period of trial during which research
capabilities and motivation may be assessed (albeit with some error). Advancement to tenure, and the security
represented by entitlement to future flat-fee payments, is thus a part of the
performance-based-bonus feature contained in the young researcher’s ‘contract’.
37. Employment contracts in industry and government research
establishments also exhibit the generic two-part structure of compensation
described as characteristic in academic research institutions, although in the
former cases the fixed portion has to be viewed as a payment corresponding to the
option value of the proprietary rights to the knowledge about future inventions
and discoveries that are relinquished as a condition of employment. It may be noted that these arrangements are
not uniquely modern; the post-Renaissance system of noble patronage of
mathematician-scientists also generated a two-part payment structure, as David
(1991) points out.
38. The channelling of individual research
efforts by means of the emergence of collegiate consensus as to which priority
races are worth entering does certainly impart momentum to particular research
programs, but, as will be seen in Section 5 below, it also has some undesirable
effects on resource allocation within scientific disciplines.
39. The sociologist W.O. Hagstrom, in a 1967
study, found that 30-40% of American (university) scientists in some fields
expressed concern over being ‘scooped’ in their current work, to the extent
that it could inhibit their willingness to discuss it freely with colleagues. See unpublished paper cited by Blume (1974, p. 38).
500
publishable paper, but this is not circulated for some time. Such delays are risky for priority-seekers, so
typically, even when the hazard is borne for some advantageous purpose such as
to permit a patent application to be filed, the delay periods are kept quite
short. [40]
Secretiveness that takes
the form of partial disclosure is rather more difficult to identify, but as it
is a more pervasive and persistent pattern of behavior among researchers, it
seems potentially the more serious source of wastage of social resources. Incomplete disclosure at the publication stage
expresses itself in two forms, one of which is more readily detectable, i.e.
.omission of information required for replication of experimental results. Since non-replicability
will be reported, this reduces to a stratagem for ‘buying time’ and raising
rivals’ costs while attempting to establish a claim to priority. [41] Self-evident incompleteness, such as the practice of not
divulging at the time of journal publication the coordinates of the large
protein molecule whose structure has been determined by use of X-ray crystallography
and synchrotron radiation, appears to have increased in frequency and emerged
as a point of controversy among investigators in the field in recent years. [42] Whatever else may be said of such practices, it should be
observed that they represent a less socially wasteful mode of ‘post-publication
delay’, inasmuch as resources will not be expended in the process of detecting
the omissions.
More problematic is
secretiveness about the special technical apparatus that has been created along
with the published ‘results’. If, for
example, researchers develop a more efficient computer algorithm for rapid
computation or computerized database searches (as, for example, in mass
spectrometry), without access to which replication of the findings reported by
others will be infeasible, do the norms of Science call for them to share it
with colleagues as soon as it becomes available? Whether or not they might do so in some
centrally administered organization, the community of decentralized
university-based researchers, each with control over access to their
laboratories, cannot readily detect the suppression of ‘intermediate research
products’ of this kind; it would be infeasible to enforce so strict a standard
of disclosure. So, the conflict point
typically arises among individual scientists or rival research groups when ‘a
result’ has been codified and made public.
The value of intertemporal spillovers of tacit knowledge (between one
project and the next), and the costs to the original possessor of transferring
(‘sharing’) tacit knowledge, lie at the heart of these conflicts. The techniques that are created as a
by-product of research leading to the first set of results often become the
basis for the creators’ hopes of winning the race to the next set of results,
and their claims for continuing research support. Preserving these as ‘craft mysteries’ is
valuable, and taking the time and resources to calibrate instruments, or to
adequately document computer code and datasets for use by other investigators,
detracts from the private pursuit of new results. However, these private incentives may result
in the new, more powerful techniques
40. Even so, the clash between the interest of researchers who fear
being ‘scooped’ by rivals and those of (corporate) research sponsors who wish
to be able to defer disclosure for periods of 30-90 days is well known to
occasion difficulties in setting up university-industry cooperative research
programs. See Peters
and Fusfeld (1983 pp. 39-40). Of 23 universities for which
there is information about prepublication review policy (from an N.Y.U. field
survey in the early 1980s), only six insisted on no delays whatsoever or none
exceeding 30 days. Pre-publication
review requests were found to be considerably less of a sticking point for
university officials than is industry interest in proprietary control. Other conflicts in establishing university-industry
cooperative agreements to facilitate ‘transfer’ of academic research for
commercial exploitation are noted below in Section 7.3.
41. When the practice is regularized, readers of publications from that
source should eventually learn not to bother attempting replication
experiments, and the publication ceases to communicate anything save the
message that the researcher has obtained a new result. We would say that such a practice puts the
research organization into the Technology camp, even though it may make a
pretext of belonging to the Republic of Science. It has been alleged, although to date it
remains undocumented, that chemical abstracts published by some research
institutes in Germany during the 1920s were widely known to systematically
misreport some important specification (such as the temperature applied) for
the new synthesis that was being announced.
42. See discussion of the decline of the ethic of data-sharing in
Science, 1990
501
remaining, at least for, a time, exclusively in the hands of their
developers rather than being placed at the disposal of others who might have
the complementary talents, techniques and resources to put them to more
productive use. This wastage must be
viewed as a regrettable necessity only if the reward system at each stage
cannot sufficiently compensate researchers to induce them to develop research
tools that would be useable (by anyone) in subsequent inquiries.
These same economic forces
work to determine the location, in each field, of the customary boundary line
between what gets codified and what gets left in tacit form. More generally, it can be seen that the
boundary line between tacit and codified knowledge is not simply a question of
epistemology; it is a matter, also, of economics, for it is determined endogenously
by the costs and benefits of secrecy in relation to those of codification. One can see that accelerations in the progress
of instrumentation and research techniques, made possible in part by synergisms
and feedbacks between developments in the realms of Science and Technology, can
raise the private marginal benefits of (a greater degree of) ‘tacitness’ for researchers in Science. If the marginal costs of transferring tacit
information to others, being largely the time of the researchers and their
support staff, is constant or rising, there would be an understandable tendency
for the boundary between private tacit knowledge and shared tacit knowledge to
shift towards the former, which in turn would weaken the motivation to bear the
marginal costs of codification for the purposes of public disclosure. At the same time, however, falling costs of
information transmittal, deriving in large part from computer and
telecommunications advances, have lately been encouraging a general social
presumption favoring more circulation of timely information and a reduced
degree of tacitness. One of the resultants of these conflicting
forces would seem to be the emergence of more active sharing of intermediate
results, via computer networks, among quasi-private alliances of researchers. The advantages of pooling knowledge and
swapping complementary techniques, being no less than formerly, and the costs
of communication required for selective cooperation having fallen, this
phenomenon is explicable by reference to the workings of self-interest. In other words, it is possible that
cooperative behavior within a limited sphere can emerge and be sustained
without requiring the prior socialization researchers to conform,
altruistically, to the norm of communalism.
We have here a rather straightforward
instance in which insights from the theory of repeated games are applicable to
explaining cooperative behavior among potentially rivalrous
researchers. To give precision to the
essential ideas, we may start with the simplified case of two researchers (or
compact research teams) working towards the same scientific goal, which entails
solving two subproblems. Suppose that each ‘team’ has solved one of the
problems. Once each gets the other
solution, it will be a matter of writing up the result and sending it off for
publication, the first to do so being awarded priority. We may further assume that the write-up time
is, determined by a random process, and that if both get both halves of the
problem at the same moment, each will have the same (one half) probability of
being the first of the pair to submit for publication. Whether the winner will be awarded priority
will depend, however, on whether or not some other researchers also obtain the
full solution and succeed in preempting its publication. The question is: should the first research
team to learn any part of the solution follow the strategy (S) of sharing that
information with the other one, or should they adopt the strategy (W) of
withholding? If, without prior communication,
they play the strategy pair (S, S) they can proceed immediately to the write-up
stage; if they play (S, W) the second member of the pair will be able to
proceed to the write-up, and the opposite will be true if they play (W, S). Should they both withhold (W, W), they must
both spend further time working on the other problem. It is evident that if they are only going to
be in this situation once, the rule of priority alone will induce each of them
to withhold, and they will end up (collectively, if not individually) at a
relative disadvantage vis-a-vis other researchers who
are hurrying to publish. If nobody else
has the full solution yet, society also will have been forced to wait
needlessly, because each member of the pair has a dominant (private) strategy
of withholding what it has discovered.
The game just described
will be recognized to have the structure of a classic two-person ‘Prisoners’
Dilemma’, from which bad consequences
502
can be anticipated. [43] It is well known, however, that an escape from the pessimal
outcome (W, W) is possible in certain circumstances - namely, were this a game
that was part of an open-ended sequence of such encounters (i.e. an infinitely
repeated game), were the future not discounted too heavily, and were the
players to expect the other team members to remember, and punish on future
occasions their present refusal to cooperate. [44] However, the value in the future of developing and
maintaining a good reputation for sharing has to be large to discipline the
self-interested researcher into adhering to the sharing mode of behavior in the
current period. If repetitive play comes
to an end, or if the future is valued only slightly, cooperation will unravel
from the distant terminal point in the game right back to its inception.
4.3. Culture: The enforcement of cooperative rivalry
and collective regulation in Science
Yet, that is not the whole
of the story. As there are other
researchers in the picture, we should really be considering an n-agent
game (again, where the agents may be individuals or small teams), involving the
solution of an m-part problem, given n > m. Now the question of sharing information becomes
one of sharing not only what you have learned yourself, but also what you have
been told by others. It is obviously
advantageous to belong to a coalition among whom information will be pooled,
because that will give the coalition members a better chance of quickly
acquiring all m parts of the puzzle and being the first to send it in
for publication. On the other hand, if
there are individuals who behave opportunistically by exchanging what they have
learned from one group for information from people outside that group, but do
not share everything they know within their group, they can expect to do still
better in their current race for priority of publication. However, because others would see that such
‘double-dealing’ will be a tempting strategy, cooperation will be unlikely to
emerge unless ‘double-dealers’ (who disclose what you tell them to third
parties, but don’t share their full knowledge with you) can be detected and
punished. What is the form that
retribution can take? Most straightforward
will be punishment by exclusion from the circle of cooperators in the future;
and even more severely, not only from the circle that had been ‘betrayed’ but
also from any other such circle. This
may be accomplished readily enough by publicizing ‘deviance’ from the sharing
norms of the group, thereby spoiling the deviators’ reputation and destroying
their acceptability among other groups. [45]
43. It is not only in the fantasies of game theorists that bad things
happen. Consider the following recent
British newspaper account, appearing in The Independent on Sunday (31 October
1993):
Several teams of scientists closing in on the discovery of a gene that
causes breast cancer have abandoned collaboration in their intense rivalry to
win the race. Secrecy - spiced with
misinformation - has replaced the co-operation that once aided the efforts of
geneticists in Britain, the United States, Canada and France; such are the
rewards of coming first... Three years ago, when Mary-Claire King at the
University of California at Berkeley placed the breast-cancer gene somewhere on
chromosome 17, scientific teams around the world formed a consortium to pool
their resources in an effort to isolate it. They exchanged information regularly to
identify regions of the chromosome that could be eliminated. But as the groups edged closer to identifying
the gene they began to split apart, said Simon Smith, head of a Cambridge
University research team funded by the Cancer Research Campaign. ‘Things have now gone quiet because none of us
wants to give information to the others’, he said. ‘In an ideal world we’d be talking to each
other and not holding back information. But our work is judged on what is published. If we are always second, it’s no good.
It is interesting to observe that the original consortium, or
coalitional agreement, did not involve pre-commitments to joint authorship,
presumably, because the number of participants was so large that internal
monitoring of effort would be difficult, and because to do so would vitiate the
point of a race for priority. Our two-person PD game, above, abstracts from the possibility of
forming sub-coalitions against the rest of the field, a consideration that will
be developed in the text below.
44. Indeed, there is a so-called ‘folk theorem’ to that effect. For a non-technical introduction to the
literature on the repeated Prisoners’ Dilemma and its broader implications, see
Axelrod (1984). The ‘folk-theorem’ of game theory holds that
(if future payoffs are discounted by each player at a low rate) in the ‘super
game’ obtained by repeating a finite, two-person game indefinitely, any outcome
that is individually rational can be implemented by a suitable choice among the
multiplicity of Nash equilibria that exist. See Rubinstein (1979, 1980) and Fudenberg and Maskin (1984).
45. See Greif (1989) and Milgrom
et al. (1990) for analysis of repeated games of incomplete information that
have this structure.
503
What, then, is the
likelihood that this form of effective deterrence will be perceived and
therefore induce cooperative behavior among self-interested individuals? If a coalition, i.e. ‘a research network’
numbering g players (g
The foregoing suggests that
small cooperative ‘networks’ of information-sharing can be supported among
researchers because cooperative behavior furthers their self-interest in the
race for priority, and denial of access to pools of shared information would
place them at a severe disadvantage vis-a-vis
competitors. [47] Does this imply that the normative content of Merton’s
communalistic norm of disclosure is really redundant, and plays no essential
role in fostering conditions of cooperation among citizens of the Republic of
Science? Not at all! For it can be shown that networks of
cooperative information sharing will be more likely to form spontaneously if
the potential participants start by expecting others to cooperate than if they
expect ‘trust’ to be betrayed, and cooperative patterns of behavior will be sustained
longer if participants have reason to expect refusals to cooperate will be
encountered only in retaliation for transgressions on their part. Furthermore, detection of deviant behavior
warranting punishment and implementation of the retribution of ostracism from a
particular network will have more broadly damaging reputational
consequences when the norms of behavior involved (i.e. the ‘custom’ within the
network in question) are common knowledge, and part of the shared socialization
among all the potential members of networks. It is evident from this that even if the
process of socialization among academic scientists were weak and imperfect, the
common ‘culture of Science’ makes it much more possible for the rule of
priority to engage the self-interest of researchers in reinforcing adherence to
the norm of disclosure, at least among a restricted circle of colleagues. [48]
The more general burden of
the analysis presented in this section is that the workings of the rule of
priority and its interaction with the norm(s) of disclosure are not just a
matter of parochial
46. However, when there are inhomogeneities in
communications that would tend to divide the coalition into tighter
‘sub-cliques’, a grand coalition will be vulnerable to defections by some among
its members. This seems to have been the
situation of the breast cancer gene research consortium, which was formed from
a number of pre-existing national research teams (as described above, in note
43).
47. These ‘circles’ or ‘networks’, which informally facilitate the pooling
of knowledge among distinct research entities on a restricted basis, can exist
as exceptions to both the dominant mode of ‘public knowledge’ characterizing
Science, or the dominant mode of ‘private knowledge’ characterizing Technology.
Thus, von Hippel
(1990) and others have described how firms in fact tacitly sanction covert
exchanges of information (otherwise treated as proprietary and protected under
the law of trade secrets) among their engineer-employees. Participants in these ‘information networks’
who accepted money or remuneration other than in kind would most probably be
dismissed and prosecuted for theft of trade secrets.
48. The formal structure of the argument made here parallels a point
about the role of ‘culture’ in defining and transmitting mutually held
expectations about the consequences of ‘off-diagonal play’ in coordination
games, which has been elaborated in quite another context by Greif (1992).
504
concern to egotistical scientists; even though the public
typically remains unaware of its centrality in the reward system that controls
academic science, priority matters greatly to society at large. The collegiate role of the scientific
community extends into other forms of social service. We have so far concentrated on the inherent
uncertainties involved in scientific research, but these uncertainties are
conditioned by the quality of the researchers themselves. For the public at large are incapable of
screening scientists by their innate abilities, and they are equally incapable
of evaluating the relative importance of scientific discoveries; not only does
one scientist look much like another, one publication looks pretty much like
another as well! So scientists are
themselves commodities of uncertain quality to the public, as are their past
publications. Here, too, the community
of scientists plays a crucial role, functioning collectively as an “agent’ for
the society at large. It produces new
scientists, and provides a check on their quality. It fails some, bestows a stamp of superior
approval on others, and so forth. It
constantly vets their research outputs, ranks their quality, and so on. There is, of course, an underlying danger of
professional bodies abusing the public trust upon which rests the autonomy
permitted them in their performance of the functions of an agent in these
specialized matters. Professional bodies
are often tempted to use their control of screening and evaluation mechanisms
to make entry qualifications unduly stiff, and the costs of certification needlessly
high; it is not unknown for some to succumb to the temptation, especially where
society has delegated regulatory jurisdiction, by allowing the profession to
set the terms on which its members will be ‘licensed’ to practice. There is less of a danger where the
professional body is only a loose-knit one, so that there is some competition
among sub-groups and sub-disciplines. This would appear to be the case with Science.
[49]
In fact, the cohesiveness
of the scientific community plays another role. It reinforces the political claims of
scientists to ‘autonomy’ during periods when the public, or their putative
political representatives, or the bureaucracy of the funding agencies try to
impose closer direction and control. [50] Now, in addition to the benefits that
individual scientists may enjoy in being left freer from the vexations of
strict supervision, especially from attempts at strict control by inexpert
authorities, the exercise of autonomy in the sense of the scientific
community’s self-governance and control over the research agenda carries some
obvious benefits for a society that values the growth of knowledge. Self-governance enables those who know to
decide where research priorities lie (by combining societal evaluations of the
importance of various research problems with expert assessments of the
prospects of their being solvable within some relevant time-frame). It also leads to a better matching of
scientific talent with the problems such talents are encouraged to attack. Furthermore, it encourages a better matching
between scientific talent and the methods they pursue for solving these
problems.
We thus see that uncertainty
in the outcome of research and the inevitable privacy of much relevant
information, taken together, provide the basis for
offering a rationale, or functionalist explanation of much that is observed in
the social organization and salient institutions of modern science. Sticking to this very gross level of
observation, our discussion suggests that the distinctive institutional
features and the reward system of Science does rather well in satisfying the
requirement of social efficiency in the allocation of resources, but when one
looks more closely at the detailed workings of this system, its many inherent
inefficiencies begin to come into view. Taking
these ‘fine-grain’ inefficiencies together, the resulting mal-allocation of
valuable resources may be far from negligible. The following two sections, therefore, will be
devoted to examining some of their main manifestations and underlying causes.
5. Resource allocation within
scientific fields and programs
Because the outcomes of
research projects are uncertain, it is generally in society’s interest to
49. The practice of medicine, as distinguished from medical research,
clearly lies within the realm of Technology as we have defined it.
50. On distinctions between individual and group autonomy in regard to
science, and the relationship between autonomy and power, see Cozzens (1990) and Turner (1990, pp. 198-204).
505
hold a portfolio of active projects which are run ‘in
parallel’ within a particular field, or under the auspices of any specific
scientific program that is determined currently to be worth pursuing. Therefore, in and of itself, parallelism or a
multiplicity of projects aiming at essentially the same result - isolation of a
virus, or development of a vaccine, or development of superconducting ceramic
filaments - does not imply waste. [51] Society should thus be prepared to tolerate multiple
discoveries in the sense of Merton (1973). [52] Nevertheless, a legitimate question arises as to whether the rule
of priority and the reward structure in academic science encourage a more than desireable degree of duplication of research efforts,
leading both to too many projects being discontinued by those who perceive that
they have lost a race for priority, and to an excessive probability that
researchers will unknowingly ‘multiply’ the findings of others. There are, in fact, a number of reasons why
the incentive structure built around the rule of priority in Science is prone
to cause wastage of resources in the form of excessive numbers of projects being
launched in the same area, and an excessive correlation of research strategies
among them. To identify these may
suggest at least the broad lines along which remedial institutional adjustments
and public policy interventions might usefully proceed.
We can begin here by
calling attention to one generic cause of inefficiencies arising from the
reward system in science, a cause that resembles the root of the conventional
market mechanism’s allocative ‘fai1ures’ in many
situations: the non-congruence of the way in which society at large benefits
from the activities of scientists and the benefits being held out as
inducements to individual researchers in this institutional setting - which is
to say, under the operation of the rule of priority. The fundamental point is that society does not
care who is successful in solving a given scientific problem, it cares
that the problem is solved; and, in all save for the most exceptional of
circumstances, society does not care whether the solution is obtained an hour,
a day, or a month sooner or later. Yet
for the individual scientists (or the scientific team), the identity of the
problem-solver and the precise time at which his/her solution can be announced
are matters of great concern; the priority-based reward system imparts great
significance to differences in timing that are inconsequential from a societal
standpoint. This sort of non-congruence
between private and social rankings of final outcomes creates fundamental
grounds for suspecting that the research portfolio that would be, in effect,
selected, for society by the self-governing community of scientists will be an
inefficient one. Now, a misallocation of
research resources can manifest itself here in at least three ways. First, competition among researchers may
encourage rival teams to undertake what in the aggregate turns out to be an
unduly risky set of research projects (strategies) within a given program. Second, competition may encourage them to
choose overly similar (i.e. positively correlated) projects within the program.
In the third form of inefficiency, the
system of rewards attracts too many research teams to a given race, to the
possible neglect of other areas in which the entry of even a few competitors
might be socially beneficial.
[53]
It turns out that, provided
private gain from a research success is made commensurate with the
51. The exception to this rule is, of course, the set of circumstances
where experimental facilities are indivisible and the fixed costs entailed are
so large as to rule out the benefits of diversification. Under such conditions, which more or less fit
the case of the superconducting supercollider project (see Office of Technology
Assessment, 1989), it is desirable to pursue only one
project within the program, if it is desirable to embark on the program at all.
However, within such large and complex
projects, typically, there will be many sub-projects that present opportunities
to pursue several solutions in parallel.
52. The term ‘multiples’ is. ambiguous but,
following Merton (1973, p. 364 ff), its use among sociologists of science is
not. Multiplicity connotes the
occurrence of more than one research entity expressing essentially the same
theory, or making what is essentially the same discovery or invention
(including inventions of apparatus), and not that of a given research unit
making more than one discovery. See Lamb
and Easton (1984) for a recent treatment of the subject, which argues that the
phenomenon of multiple discovery is inherent in the
collective, evolutionary process through which scientific knowledge grows. In the present discussion, however, we are
less concerned to account for what might be thought of as a ‘normal’, or
‘background’, level of multiplicity, and more with ‘excess multiplicity’
created by certain features of the science-resource allocation mechanism.
53. We need hardly add that there would be a resource misallocation if
the reverse of what we have described were to occur under each of the three
categories we have just listed.
506
benefits accruing to the collectivity of researchers from the
success of any individual investigator or team, there is a tendency for the
rule of priority to give rise to the first two of these three forms of
misallocation. [54] To see why the first two kinds of distortion result from
the rule of priority, it is sufficient to expose the tendency favoring
excessive positive correlation between projects within a field or program. Consider a portfolio of possible research
projects within a given field or program, each pursuing a particular strategy
or experimental design, all of which offer the same expected social payoff. [55] The risks associated with some strategy pairs are highly
correlated, those with other pairs less so. Assume that one of the research teams has
chosen one of these research designs (projects). It -remains for the other to choose its
project design. As between any two
available projects, were the second team to choose the one which is less
correlated with the one chosen by the first team, it would bestow a positive
benefit to its rival. Specifically, the
likelihood that team I is successful when team II is
not would be higher. As we have already
seen, this kind of portfolio diversification, or ‘insurance’, is socially
desirable, but it is not necessarily considered in the second team’s private
calculations concerning the course it should commit itself to follow. If the team were altruistic and more
other-regarding, they would recognize that by picking a project identical to
that pursued by another group, they would be lowering their (other) colleagues’
chances of achieving priority, and they would choose to be less duplicative in
their research design. However, the
‘all-or-nothing’ aspect of priority-based reward structures encourages
self-regarding egoistic choices among the community of scientists, rather than
altruism, and so reinforces the tendency for researchers to be drawn into
duplicative ‘races’. This works against
the emergence of a diversified societal research portfolio. [56]
The fact that the reward
structure it faces is hitched to the rule of priority pulls each research
entity toward entering some well-defined ‘race’ in which the contestants are
lined up along essentially the same track. Each may believe that some particular feature
of their research design, say some special instrumentation or data analysis
technique that has not been mastered by others, will give it a competitive
edge, and all observe that winning a bigger race, in which there are a larger
number of entrants, will do more for one’s collegiate status. The positive correlation among projects will
not be perfect, of course. Having some
feature of one’s research design differentiated from that adopted by competitors,
even when the entire design is made common knowledge (say, by the process of
peer review of proposals), may remain an attractive strategy for a risk-averse
researcher who finds him or herself in head-to-head competition with a small
number of identifiable rivals; creating some possibly inessential dimension of
non-comparability in the outcomes may make it more difficult to pronounce the
competition to have had a unique winner, and so allow those who arrive at a
successful result later to share in the award of prizes. [57]
54. For the third phenomenon to occur we must
assume in addition that the program involves Little Science, or in other words,
projects that do not involve large fixed costs. In the next section, we will assume this to be
the case.
55. How to conceptualize and measure the societal ‘payoffs’ from basic
research is an immensely complicated question. Even the narrower question of what determines
the magnitudes of the distribution of purely economic ‘payoffs’, and how these
can be assessed, cannot be entered into here. For a critique of the methodology attempting
to apply the techniques of cost-benefit analysis by tracing the commercial
sequels of basic scientific discoveries and inventions, and the proposal of an
alternative framework of analysis, see David et al. (1992).
56. The argument in the text does not, of course, constitute a proof. It offers a hint about how the proof goes. Since we wish to avoid technicalities here, we
will not go into the reasons why under a wide range of circumstances the rule
of priority encourages what from the point of society is excessive risk-taking
on the part of rival teams (for this, see Dasgupta
and Maskin, 1987).
57. An instance of inessential differentiation, undertaken largely for
strategic rather than scientific reasons, is documented by Nicholas Wade’s (1978, esp. p. 279) account of the famous 21-year
race between Andrew Schally and Roger Guillemin, who eventually shared the 1977 Nobel Prize for
their work on the endocrinology of the brain. It seems that after 1962, when Schally ended 5 years of collaborative work with Guillemin aimed at discovering the hypothalamic hormone in
the brains of sheep and formed a competing research group, he changed his
research material and sought to obtain the hypothalami
of cattle. This switch was justified by Schally on the ground that if Guillemin
discovered the hypothalamic hormone first, his own work might be considered
worthless were he also using the hypothalami of
sheep. Other aspects of this complex case
are discussed also by Lamb and Easton (1984, pp. 152-155).
507
There are, then, important
respects in which academic scientists, while wanting to differentiate their
work, come under strong systemic inducements not to be ‘lone wolves’ ranging
far from the rest of the pack in their selection of research problems and
approaches. To be sure, in any community
there will be some people who seek to avoid the conflicts engendered when
community members have goals strongly imposed upon them (seek priority) without
being allowed recourse to effective means (maintain non-cooperative secrecy)
whereby the goals can be obtained. [58] To the extent
that they can do so by retreating to areas where there is little competition
for priority, this imperfection in the collective response to the private
reward structure will occasion some countervailing measure of diversification
of the social research portfolio, but this functions simply as a random
dispersal mechanism. There is nothing
systematically operating, as far as we can see, that would match the talents of
those scientists whose personalities and individual values dispose them to
avoid competitive situations to the research requirements of those fields where
few competitors are to be found. Moreover, this is the behavior of ‘deviants’;
it is the more pervasive tendency towards the positive correlation of strategy
choices in the race for priority among the more typical members of the research
community (as much as the open communication of scientific theories and techniques)
that, at certain moments in time, makes particular discoveries and inventions
imminent and, so to speak, ‘in the air’, thereby promoting the occurrence of
too many of Merton’s multiples.
6. The timing of research programs
within Science
In recent years the choice
of the current mix of investment projects has been much discussed in the
literature on social cost-benefit analysis; less so the timing of investments. [59] In almost all spheres of economic activity, most projects that are
offered for appraisal are rejected. This
is inevitable, but the fact that a project is not worth undertaking now does
not mean that it will never be worth undertaking. Often enough, the right thing to do is to
accept as a package a set of projects that are better when sequenced than when
run simultaneously. Success in the first
project might, for example, mean a reduction in the cost of running the second
project, and so forth.
What is alluded
to here is that there may be important positive spillovers across projects in
the form of ‘learning effects’. Quite
aside from the conceptual contributions that the codified findings of a
research program in one field may make to accelerate research progress in
another field, there is the question of spillovers affecting scientific
equipment and skills, which often remain in the region of tacit knowledge but
are nonetheless transferrable. Productivity gains in the performance of
specified experimental tasks are likely to emerge as a by-product of the
conduct of research through the development of superior instrumentation
techniques (see, e.g. Moulton et al., 1990) including the development of
generic computer software for performing data processing, storage, retrieval
and network transmission. The training
of specialized technical staff and post-doctoral researchers, whose skills
eventually become available to other projects, represents another important
source of spillovers.
The underlying idea here is
that of optimal ‘waiting’. If society
were indifferent about the order in which scientific advances occurred, but
cared about the costs of the program as a whole, it would obviously want to
sequence research projects so as to take into consideration the expected
magnitude of the productivity spillovers from one project to the next. However, allocative
decisions about science as a rule are not so centralized, and individuals and
groups within the
58. Merton (1957) makes the point that norm inconsistencies of this sort
can stimulate a range of ‘deviant’ responses, which include not only
‘innovations’ - such as new and ingenious forms of partial disclosure
(secretiveness) among scientists, which we have examined in Section 4, but also
‘retreatism’. Gaston
(1971) found that among those high energy physicists in his sample whose
research findings had not been anticipated by other scientists, 44% said this
was because they preferred working in the ‘less fashionable’ areas, where
competition presumably was less intense.
59. On social portfolio choice and cost-benefit
analysis, see e.g. Dasgupta et al. (1972), Little and
Mirrlees (1974), Squire and Van der
Taak (1975) and Lind (1982). David et al. (1992) seriously question the
usefulness of applying the cost-benefit approach to public project evaluation
in the case of basic science research projects.
508
scientific community, as well as in society at large, certainly
take an interest in the timing of results. To make some further headway in the analysis
of this, we can introduce a convenient simplification: imagine that the
productivity spillovers take the form of reductions in the time (and resource
inputs) required to reach a specified research goal, so that delaying the
initiation of a project (until better techniques have been developed elsewhere)
will not delay the expected date at which its findings can be announced. In this way we can put to one side the general
motivation that all project leaders would have to start racing for priority as
soon as possible. Consider, then, the
implication that when rival funding agencies are involved the resulting
competition could assume the form of a waiting game rather than a race (see Dasgupta, 1988).
Thus, it may be that it
would be individually (privately) optimal for researcher A to delay initiating
his project should researcher B initiate his project now. For example, if a major effort were being
undertaken in the field of superconductivity, which promised reductions in the
costs of building powerful electromagnets, it would be advantageous (under the
conditions assumed) for high energy particle researchers to wait until those
advances were available for incorporation into the designs for their
‘superconducting supercollider’. Moreover,
it may be that it is privately optimal for B to initiate his project now should
A choose to delay. In this case the
sequence ‘A following B’ is consistent with individual incentives, but there
are also may be situations in which it is privately optimal for A to initiate
his project now should B choose to delay, and where it is privately optimal for
B to delay should A choose to initiate his project now. In this case the sequence ‘B following A’ is
also consistent with incentives. However,
it could be that the sequence ‘A following B’ is, from the scientific
community’s point of view, the superior alternative. Only a coordinated plan from the funding
agency can ensure that this will come about.
In all this there is a key
underlying assumption that the funding agency (or agencies) can make credible
commitments about future funding. A
would be willing (possibly even eager) to wait until B’s project is completed
only if (s)he is assured now that (s)he will obtain the funds at the right time
as part of an optimally packaged sequence of research endeavors. (S)he will, however,
not be so willing to delay if there is substantial uncertainty surrounding the
funding agency’s promises concerning projects to be supported at future dates. In this case, what should ideally be a
measured plan of sequenced investment activity becomes merely a scramble for
early support. If the investigators are
equally well-credentialed, they would each expect to receive some funding, and
this could be the worst outcome from a societal standpoint, with both projects
now having to forward more slowly on their own.
The problems of attempting
to sequence basic science research projects are exacerbated by the
uncertainties surrounding the estimates of completion times. While some part of this difficult is inherent
in the very nature of the activity, that should not be exaggerated; ‘the nature
of the activity’ characteristic of Science reflects the influence of the reward
system under which the researchers are motivated and organized. The winner-takes-all (of ‘almost all’)
structure of the payoffs makes individuals and teams of scientists more inclined
to select programs of research that are characterized by a high variance in the
distribution of completion times for the constituent sub-projects. The difficulties of
predicting completion dates in basic research are remarked upon frequently, and
sometimes are viewed as an inherent characteristic of inquires of this
nature, as has been remarked above. Yet,
it is obvious that the researchers’ interests in early success is tantamount to
making them concerned not with the mean and variance of the distribution of
completion times, but rather with the extreme value distribution derived from
it; they care about the expected minimum completion times, and the dispersion
around that statistic. Now, it is known
that in a sample of a given size, drawn from a continuous unimodal
probability distribution, the expected minimum (maximum) value will be smaller
(bigger) where the variance of the underlying distribution is larger. [60] The search domain characterized by the bigger dispersion
of outcomes (here, project completion times) will be
60 See David et al. (1992) for citations to the statistical literature
on extreme value distributions and discussion of other respects in which
researchers may be hypothesized to be concerned with the shape of the extreme
value distributions of payoffs and costs, and their implications.
509
the more attractive on that score, other things being
equal. So, even if fundamental science
was not such a hard activity to predict, and distinctive steps in some lines of
inquiry were easier to plan in sequence, the prevailing reward system (while it
encourages scientists to work quickly towards the completion of any step that is
expected to bear a publishable result) draws them to work mostly on the less
accurately predictable, and hence less dynamically ‘sequenceable’,
among the available classes of problems!