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Scientific Convergence: Dealing with the Elephant in the Room

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Igor Linkov
Igor Linkov
Igor Linkov
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Matthew Wood
Matthew Wood
Matthew E Bates at US Army Corps of Engineers
Matthew E Bates
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Scientific Convergence: Dealing with the Elephant in the Room
Igor Linkov,
†,
*Matthew Wood,
†
and Matthew Bates
†
†
U.S. Army Engineer Research and Development Center, Environmental Laboratory, 696 Virginia Road, Concord, Massachusetts
01742, United States
Breakthrough innovations in science often require more
than just interdisciplinary collaboration. Rather, they rely
on the convergence of different tools, skill sets, knowledge, and
problem solving approaches from complementary disciples to
explore new areas of science.
1
Like the parable of the blind men
and the elephant, scientists independently working in individual
domains are each unable to see the full underlying nature and
implications of a problem (isolated view, Figure 1a). Those
who get input from or provide output to colleagues in other
domains have a better but still limited understanding
(coordinated view, Figure 1b), while those who wholly
collaborate with colleagues and take collective action toward
discovery have the best understanding of the problem’s nature
and complexities (convergent view, Figure 1c). We argue that
decision-analytic techniques like multicriteria decision analysis
which provide a mathematical approach to problem decom-
position and preference ranking
2,3
can enable funding and
academic institutions to more effectively promote convergence
using the action alternatives available to them and fuel
technology innovation.
Convergence has been used to describe a growing need for
collaboration between different fields of inquiry to foster
innovation on inherently interdisciplinary problems of increas-
ing complexity. The National Science Foundation (NSF
1,4
) has
acknowledged the importance of these efforts and has made
recommendations to promote convergence, particularly among
areas of research where nanotechnology can play a meaningful
role. The National Academy of Science (NAS) has made
sustained efforts to promote convergence as well, for example,
within the natural sciences, and recently issued a report
evaluating key challenge areas for convergence and provide
practical recommendations to institutions.
5
Two widely used institutional approaches to promote
convergence, which are recommended in the NAS report
include organizing scientists into committees and working
groups, and colocating scientists from different disciplines to
achieve innovations. The National Nanotechnology Initiative
(NNI, www.nano.gov), a pioneering application of convergence
within the government, is comprised of representatives from a
variety of federal organizations (NIH, DOD, DOE, FDA, etc.)
responsible for nanotechnology research and development, and
regulation. The NNI provides a forum for coordinating funding
priorities across agencies and organizing working groups to
develop recommended actions to address a host of
interdisciplinary issues in the area of nanotechnology. While
the structure of NNI is well-defined, the way in which
individual member organizations provide recommendations
and decide on how best to coordinate their individual actions
could benefit from prescriptive guidance in service of achieving
mutually beneficial and convergent outcomes.
In another example, the MIT-Harvard Center of Cancer
Nanotechnology Excellence, housed at the Koch Institute for
Integrative Cancer Research (ki.mit.edu), promotes conver-
gence by colocating scientists from different fields in the hopes
of developing interdisciplinary solutions (e.g., cancer nano-
therapies) through chance exposures to other researchers from
other fields during the normal course of business. Researchers
from complementary disciplines are sited strategically so they
walk by each other to access shared resources (e.g., printers, lab
space). Similar collaborative research facilities construct
versatile working spaces to facilitate interaction between
scientists and engineers with complementary research foci.
This encourages scientists to be creative in connecting with
others and move beyond the comfort zone of their limited
disciplinary expertise. Both the composition of interagency
committees and selection of scientists for centers were designed
based on ad-hoc hypotheses on which disciplines would
interface best with which others in the service of common
goals.
Although both NNI and the MIT-Harvard Center are
examples of successful institutional actions that promote
aspects of convergence and are consistent with NAS
recommendations, we believe initiatives like these could benefit
from a deliberate decision-analytic process to evaluate options
for fostering convergence. These processes can help identify
and encourage the right scientists from the right disciplines to
take collective action toward solving complex interdisciplinary
Received: July 23, 2014
Published: August 22, 2014
Viewpoint
pubs.acs.org/est
© 2014 American Chemical Society 10539 dx.doi.org/10.1021/es503585u |Environ. Sci. Technol. 2014, 48, 10539−10540
scientific problems by giving institutions the tools needed to
create and strategically evaluate actions related to coordination,
colocation, grant funding, etc. Decision-analytic approaches
guide organizations in identifying and prioritizing their
common objectives and identifying specific criteria and metrics
that can support those goals. Through this framework of
objectives and supporting factors, the decision-analytical
process enables transparent evaluation of alternative ways to
indirectly influencing scientists from the desired disciplines to
work together in a manner that provides the best chance of
developing new knowledge about the problem and solving it. In
the analogy outlined in Figure 1, this would be similar to taking
actions that would engage the best combination of nodes in the
“Convergent View”(Figure 1c) to reveal the largest and most
appropriate area of the problem between network edges.
Multicriteria decision analysis (MCDA),
2
one of several
formal decision-analytical techniques,
3
is ideally suited for
promoting convergence and provides a structure that
institutions can use to evaluate different actions based on the
following:
•The objective(s) they would like to achieve, in this case
solving one or more scientific problems requiring
convergence of experts from different disciplines;
•The criteria which contribute to achieving objectives,
here the collection of disciplines which the institution
hypothesizes are required to achieve the objectives;
•and metrics that can be used to quantify the relative
effectiveness of any one alternative in addressing the
criteria and therefore objectives. Alternatives in this
context are different institutional actions which promote
convergence, e.g., an interdisciplinary institute with
colocated scientists engaging in strategically funded
collaborative investigations.
The MCDA process should be incorporated into the process
of designing and evaluating institutional convergence efforts
actions either formally when selecting among actions, or
informally to facilitate the design and development of
convergence actions in a way that directly address convergence
objectives and relevant constraints. This process will help
institutions like funding agencies, universities, and other
research organizations to evolve beyond historic research
approaches that focus on identifying which discipline should
be responsible for solving a specific problem. It promotes an
approach where different unique disciplinary synergies and
perspectives can be leveraged to solve breakthrough problems.
The result of implementing these processes over time will be a
research environment that through careful reflection and
prioritization and positioned itself to better promote
convergence and to be more productive with respect to the
hard problems facing the scientific and technological
community now and in the future.
■AUTHOR INFORMATION
Corresponding Author
*E-mail: Igor.Linkov@usace.army.mil.
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
Special thanks to Mihail Roco (NSF) and Susan Hochfield
(MIT) for their review of earlier versions of this manuscript.
Permission was granted by the USACE Chief of Engineers to
publish this material. The views and opinions expressed in this
paper are those of the individual authors and not those of the
U.S. Army, or other sponsor organizations.
■REFERENCES
(1) Roco, M. C.; Bainbridge, W. S.; Tonn, B.; Whitesides, G.
Convergence of Knowledge, Technology, and Society: Beyond Convergence
of Nano-Bio-Info-Cognitive Technologies; Springer: New York, 2013.
(2) Linkov, I.; Moberg, E. Multi-Criteria Decision Analysis: Environ-
mental Applications and Case Studies; CRC Press: Boca Raton, FL,
2011.
(3) Clemen, R. T.; Reilly, T. Making Hard Decisions with
DecisionTools; Duxbury/Thomson Learning: New York, 2000.
(4) Roco, M. C.; Bainbridge, W. S. Converging Technologies for
Improving Human Performance: Nanotechnology, Biotechnology, In-
formation Technology and Cognitive Science; Kluwer Academic Publish-
ers: Dordrecht, The Netherlands, 2003.
(5) National Academies. Convergence: Facilitating Transdisciplinary
Integration of Life Sciences, Physical Sciences, Engineering, and Beyond;
National Academies Press: Washington, DC, 2014.
Figure 1. Isolated, coordinated, and convergent views of science collaboration. Circles represent independent views of individual disciplines.
Scientists operating under the convergent view wholly collaborate with colleagues from other disciplines, fully integrating and coordinate their
research activities. The distinct scientific disciplines converge here to provide the greatest understanding of the underlying problem.
Environmental Science & Technology Viewpoint
dx.doi.org/10.1021/es503585u |Environ. Sci. Technol. 2014, 48, 10539−1054010540

Supplementary resource (1)

Linkov.Wood.Bates.2014
Data
October 2014
Igor Linkov · Matthew Wood · Matthew Bates
... These methods utilize the distinct branch structure of the MeSH ontology to de¯ne a measure of cross-domain knowledge integration, operationalized as variation and disparity of MeSH at the article level, thereby developing a high-resolution measure of recombinant innovation [1,2] tailored for analyzing PubMed/MeSH. To address RQ2 we analyze the combination of distinct knowledge domains within individual research articles, which we quantify by measuring the interdisciplinary integration of knowledge across originally distinct domains according to the de¯nition of convergence science proposed by the US National Research Council [18,21,23,24,[29][30][31][32][33]. And¯nally, to address RQ3 we apply statistical analysis to sets of PubMed article grouped by year, team size and journal to identify robust trends and systematic variation. ...
... Another notable relation regarding convergence is its formulation as a facilitator of inventive forms not intended within the original logics of interdisciplinarity [11]. As such, convergence represents an intrepid form of interdisciplinarity in terms of the number, distance and novelty of the disciplinary con¯gurations entailed [24,63], that together foster holistic pathways towards understanding intractable phenomena, complex systems, and wicked problems [18,32,35,65]. ...
... Structural analysis of MeSH co-occurrence networks visualized in Fig. 2 identify three robust macro-knowledge clusters: (a) the vast universe of microscopic biological entities and structures; (b) systems, disease and diagnostics; and (c) biological and social phenomena capturing emergent properties, processes and functions. To identify periods of disruption within this conceptual model of science, we also developed a method for quantifying cross-temporal transitions in the macro-knowledge clusters by tracking individual MeSH as they°uctuate between clusters, as illustrated in Fig. 3. Research at the health, behavioral and brain science frontiers typically integrate multiple distinct knowledge domains, signaling the emergence and future potential of convergence science [18,[21][22][23][24][29][30][31][32][33]. The convergence nexus of Health Care (N), Behavior and Behavior Mechanisms (F01) and Information Science (L01) is a prime example, making way for transdisciplinary brain science [24] to map and model brain circuits [90] that are fundamental to addressing the grand challenge underlying the \global burden of mental disorders" [91,92]. ...
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We leverage the knowledge network representation of the Medical Subject Heading (MeSH) ontology to infer conceptual distances between roughly 30,000 distinct MeSH keywords — each being prescribed to particular knowledge domains — in order to quantify the origins of cross-domain biomedical convergence. Analysis of MeSH co-occurrence networks based upon 21.6 million research articles indexed by PubMed identifies three robust knowledge clusters: micro-level biological entities and structures; meso-level representations of systems, and diseases and diagnostics; and emergent macro-level biological and social phenomena. Analysis of cross-cluster dynamics shows how these domains integrated from the 1990s onward via technological and informatic capabilities — captured by MeSH belonging to the “Technology, Industry, and Agriculture” (J) and “Information Science” (L) branches — representing highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental yet transformative structure–function–behavior questions. Our results indicate that 8.2% of biomedical research from 2000 to 2018 include MeSH terms from both the J and L MeSH branches, representing a 291% increase from 1980s levels. Article-level MeSH analysis further identifies the increasing prominence of cross-domain integration, and confirms a positive relationship between team size and topical diversity. Journal-level analysis reveals variable trends in topical diversity, suggesting that demand and appreciation for convergence science vary by scholarly community. Altogether, we develop a knowledge network framework that identifies the critical role of techno-informatic inputs as convergence bridges — or catalyzers of integration across distinct knowledge domains — as highlighted by the 1990s genomics revolution, and onward in contemporary brain, behavior and health science initiatives.
View
... In response, the convergence science paradigm-defined by its originators as 'the coming together of insights and approaches from originally distinct fields' (National Research Council 2014)-has emerged as an organizational model constructed around a mission-oriented agenda that promotes social-engineering to fortify existing interdisciplinary approaches to addressing boundary-spanning grand challenges (NSF, accessed February 2021). With team science becoming the predominant mode of knowledge production (Wuchty, Jones and Uzzi 2007;Bö rner et al. 2010;Pavlidis, Petersen and Semendeferi 2014;Petersen, Pavlidis and Semendeferi 2014), convergence represents a holistic strategy for harnessing social and conceptual diversity, and for accelerating action on multi-dimensional problems (Page 2008;Linkov, Wood and Bates 2014;Pavlidis, Akleman and Petersen 2022). Specific examples include deforestation and illicit wildlife trade (Di Minin et al. 2018;Arroyave et al. 2020Arroyave et al. , 2021, two wicked problems that span sociocultural, technological, political, and environmental dimensions (Orsatti, Quatraro and Pezzoni 2020). ...
... Even in the best-case scenario, where traditional monodomain approaches exist that address certain facets of the target problem, convergence is needed to address the multidimensionality of such problems, as partial solutions are likely to be fragmented and all together incomplete (Linkov, Wood and Bates 2014). As such, designing and assembling a complete and feasible composite solution is a principal barrier to addressing grand challenges. ...
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Convergence science is an intrepid form of interdisciplinarity defined by the US National Research Council as 'the coming together of insights and approaches from originally distinct fields' to strategically address grand challenges. Despite its increasing relevance to science policy and institutional design, there is still no practical framework for measuring convergence. We address this gap by developing a measure of disciplinary distance based upon disciplinary boundaries delineated by hierarchical ontologies. We apply this approach using two widely used ontologies-the Classification of Instructional Programs and the Medical Subject Headings-each comprised of thousands of entities that facilitate classifying two distinct research dimensions, respectively. The social dimension codifies the disciplinary pedigree of individual scholars, connoting core expertise associated with traditional modes of mono-disciplinary graduate education. The conceptual dimension codifies the knowledge, methods, and equipment fundamental to a given target problem, which together may exceed the researchers' core expertise. Considered in tandem, this decomposition facilitates measuring social-conceptual alignment and optimizing team assembly around domain-spanning problems-a key aspect that eludes other approaches. We demonstrate the utility of this framework in a case study of the human brain science (HBS) ecosystem, a relevant convergence nexus that highlights several practical considerations for designing, evaluating, institutionalizing, and accelerating convergence. Econometric analysis of 655,386 publications derived from 9,121 distinct HBS scholars reveals a 11.4% article-level citation premium attributable to research featuring full topical convergence, and an additional 2.7% citation premium if the social (disciplinary) configuration of scholars is maximally aligned with the conceptual (topical) configuration of the research.
View
... Indeed, problems derived from anthropogenic drivers are socially situated [10,11]. Hence, addressing wicked problems facing society and planet requires convergent research spanning traditional disciplinary boundaries that leverages crosssectoral integration of expertise [8,20,33,[36][37][38]. Consequently, the variety of stakeholders, interests, and objectives engaged in the social context may involve a large collection of opinions and ideas about the problem itself and its causes that can hinder consensus formation around a shared vision [3, 9-11, 14, 34]. ...
... As such, environmental wicked problems appear to necessitate integrated diversification [36,38,50] in which multiple voices and approaches can be included while consolidation of existing research agendas and communities of expertise takes place [6]. Balancing the tension associated with this paradox of cross-disciplinary integration will help distribute efforts and capabilities toward specific solutions that iterate towards addressing the underling complexity [3,10,33]. ...
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View
... Indeed, problems derived from anthropogenic drivers are socially situated [10,11]. Hence, addressing wicked problems facing society and planet requires convergent research spanning traditional disciplinary boundaries that leverages crosssectoral integration of expertise [8,20,33,[36][37][38]. Consequently, the variety of stakeholders, interests, and objectives engaged in the social context may involve a large collection of opinions and ideas about the problem itself and its causes that can hinder consensus formation around a shared vision [3, 9-11, 14, 34]. ...
... As such, environmental wicked problems appear to necessitate integrated diversification [36,38,50] in which multiple voices and approaches can be included while consolidation of existing research agendas and communities of expertise takes place [6]. Balancing the tension associated with this paradox of cross-disciplinary integration will help distribute efforts and capabilities toward specific solutions that iterate towards addressing the underling complexity [3,10,33]. ...
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... Therefore, an enrichment of the bibliometric data with social context data and rich author information is useful for measuring and understanding science dynamics [4], [48], [62], [63]. Such data enrichment is particularly important due to the growing relevance of cross-disciplinary research teams [48], especially since national governmental initiatives and academia promote scientific convergence by colocating scientists from different disciplines or by providing funding for interdisciplinary research projects [64]. ...
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View
... A complementary argument for classic convergence derives from the advantage of diversity to harness collective intelligence for identifying successful hybrid strategies (Page, 2008), while also avoiding misinterpretations and incomplete ontologies (Barry et al., 2008, Linkov et al., 2014. Recent work also provides support for the competitive advantage of diversity stemming from cross-border mobility . ...
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To address complex problems, scholars are increasingly faced with challenges of integrating diverse domains. We analyzed the evolution of this convergence paradigm in the ecosystem of brain science, a research frontier that provides a contemporary testbed for evaluating two modes of cross-domain integration: (a) cross-disciplinary collaboration among experts from academic departments associated with disparate disciplines; and (b) cross-topic knowledge recombination across distinct subject areas. We show that research involving both modes features a 16% citation premium relative to a mono-domain baseline. We further show that the cross-disciplinary mode is essential for integrating across large epistemic distances. Yet we find research utilizing cross-topic exploration alone—a convergence shortcut—to be growing in prevalence at roughly 3% per year, significantly outpacing the more essential cross-disciplinary convergence mode. By measuring shifts in the prevalence and impact of different convergence modes in the 5-year intervals up to and after 2013, we find that shortcut patterns may relate to competitive pressures associated with Human Brain funding initiatives launched that year. Without policy adjustments, flagship funding programs may unintentionally incentivize suboptimal integration patterns, thereby undercutting convergence science’s potential in tackling grand challenges.
View
... Macro-level analysis of MeSH co-occurrence networks identifies three robust knowledge clusters: the vast universe of microscopic biological entities and structures; systems, disease and diagnostics; and biological and social phenomena capturing emergent properties, processes and functions. Research at the health, behavioral and brain science frontiers typically integrate these knowledge domains, signaling the emergence and future potential of convergence science [20,21,25,29,56,66,67]. The convergence nexus of Health Care (N), Behavior and Behavior Mechanisms (F01) and Information Science (L01) is a prime example, making way for transdisciplinary brain science [29] to map and model brain circuits [68] that are fundamental to addressing the grand challenge regarding the "global burden of mental disorders" [69,70]. ...
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Preprint
Full-text available
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We analyzed Medical Subject Headings (MeSH) from 21.6 million research articles indexed by PubMed to map this vast space of entities and their relations, providing insights into the origins and future of biomedical convergence. Detailed analysis of MeSH co-occurrence networks identifies three robust knowledge clusters: the vast universe of microscopic biological entities and structures; systems, disease and diagnostics; and emergent biological and social phenomena underlying the complex problems driving the health, behavioral and brain science frontiers. These domains integrated from the 1990s onward by way of technological and informatic capabilities that introduced highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental structure-function-behavior questions. Article-level analysis confirms a positive relationship between team size and topical diversity, and shows convergence to be increasing in prominence but with recent saturation. Together, our results invite additional policy support for cross-disciplinary team assembly to harness transdisciplinary convergence.
View
... We found that in multiple cases, before the workshop different experts identified multiple, varied activities as prominent risks because of their contribution to a particular stressor, whereas during the workshop with facilitated discussion experts were able to agree on consistent terminology pointing to the dominant stressor and converge on a shared model of how activities pose risk. The similarity of language used between experts is thought to be an important factor in leading to convergent mental models of a given problem (such as understanding the prominent risks to a coastal ecosystem), which can help move towards a comprehensive understanding of that problem [30,42]. For example, individual experts pointed to agriculture, forestry, general land clearing (and other land uses), as well as dredging activities as important risks because of their role in sedimentation and estimated impact from these. ...
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The elicitation of expert judgment is an important tool for assessment of risks and impacts in environmental management contexts, and especially important as decision-makers face novel challenges where prior empirical research is lacking or insufficient. Evidence-driven elicitation approaches typically involve techniques to derive more accurate probability distributions under fairly specific contexts. Experts are, however, prone to overconfidence in their judgements. Group elicitations with diverse experts can reduce expert overconfidence by allowing cross-examination and reassessment of prior judgements, but groups are also prone to uncritical “groupthink” errors. When the problem context is underspecified the probability that experts commit groupthink errors may increase. This study addresses how structured workshops affect expert variability among and certainty within responses in a New Zealand case study. We find that experts’ risk estimates before and after a workshop differ, and that group elicitations provided greater consistency of estimates, yet also greater uncertainty among experts, when addressing prominent impacts to four different ecosystem services in coastal New Zealand. After group workshops, experts provided more consistent ranking of risks and more consistent best estimates of impact through increased clarity in terminology and dampening of extreme positions, yet probability distributions for impacts widened. The results from this case study suggest that group elicitations have favorable consequences for the quality and uncertainty of risk judgments within and across experts, making group elicitation techniques invaluable tools in contexts of limited data.
View
... Significant accelerations of learning and scientific advances often take place when science of one field is combined with another ( Linkov et al. 2014). In a recent study, we attempted to expand the knowledge of climate change, human impacts and land-use legacy by merging the fields of tree physiology and forest ecology . ...
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Convergence of Knowledge, Technology and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologies
Book
  • Jan 2013
Convergence of knowledge and technology for the benefit of society (CKTS) is the core opportunity for progress in the 21st century, based on five principles: (1) the interdependence of all components of nature and society, (2) enhancement of creativity and innovation through evolutionary processes of convergence that combine existing principles, and divergence that generates new ones, (3) decision analysis for research and development based on system-logic deduction, (4) higher-level cross-domain languages to generate new solutions and support transfer of new knowledge, and (5) vision-inspired basic research embodied in grand challenges. Solutions are outlined for key societal challenges, including creating new industries and jobs, improving lifelong wellness and human potential, achieving personalized and integrated healthcare and education, and securing a sustainable quality of life for all. This report provides a ten-year “NBIC2” vision within a longer-term framework for converging technology and human progress that began with a previous study on “NBIC” fields: nanotechnology, biotechnology, information technology, and cognitive science (Roco and Bainbridge, 2003). This is truly an impressive body of work, which advances a transformative collection of concepts that could impact many areas of society and science. The ideas of this study are exciting. Tinsley Oden, University of Texas, Austin (April 2013) The CKTS study presents inspirational ideas behind the concept of convergence and identifies ground-breaking opportunities for human progress through such convergence. Christos Tokamanis, Nanotechnology and Converging Technologies, EU, Brussels (May 2013) The study provides a systematic and unified, internationally benchmarked framework for convergence that is relevant to policymakers, entrepreneurs, researchers, and the general public. Jo-Won Lee, Hanyang University, Korea (June 2013) I consider .. the first NBIC study in 2001.. as an historical landmark that has caused a new dynamic in the reflection on these new technologies within the broad scientific and governmental community. Frank Theys, Co-producer for public broadcasters ZDF/ARTE, Germany & France (June 2013)
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Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Science
Book
  • Jan 2002
1. Background We stand at the threshold of a New Renaissance in science and technology, based on a comprehensive understanding of the structure and behavior of matter from the nanoscale up to the most complex system yet discovered, the human brain. Unification of science based on unity in nature and its holistic investigation will lead to technological convergence and a more efficient societal structure. In the early decades of the twenty-first century, concentrated effort can bring together nanotechnology, biotechnology, information technology, and new humane technologies based in cognitive science. With proper attention to ethical issues and societal needs, the result can be a tremendous improvement in human abilities, societal outcomes and quality of life. Rapid advances in convergent technologies have the potential to enhance both human performance and the nation's productivity. Examples of payoffs will include improving work efficiency and learning, enhancing individual sensory and cognitive capabilities, revolutionary changes in healthcare, improving both individual and group efficiency, highly effective communication techniques including brain to brain interaction, perfecting human-machine interfaces including neuromorphic engineering for industrial and personal use, enhancing human capabilities for defense purposes, reaching sustainable development using NBIC tools, and ameliorating the physical and cognitive decline that is common to the aging mind. This report addresses several main issues: What are the implications of unifying sciences and converging technologies. What should be done to achieve the best results over the next 10 to 20 years? What visionary ideas can guide research to accomplish broad benefits for humanity? What are the most pressing research and education issues? How can we develop a transforming national strategy to enhance individual capabilities and overall societal outcomes ? These issues were discussed on December 3-4, 2001, at the workshop on Convergent Technologies to Improve Human Performance, and in contributions submitted after that meeting for this report. The phrase "convergent technologies" refers to the synergistic combination of four major "NBIC" (Nano-Bio-Info-Cogno) provinces of science and technology, each of which is currently progressing at a rapid rate: (a) nanoscience and nanotechnology; (b) biotechnology and biomedicine, including genetic engineering; (c) information technology, including advanced computing and communications; and, (d) cognitive science, including cognitive neuroscience. Accelerated scientific and social progress can be achieved by combining research methods and results across these provinces in duos, trios, and the full quartet. Figure 1 shows the NBIC tetrahedron, in which each field is represented by a vertex, each pair of fields by a line, each set of three fields by a surface, and the entire union of all four fields by the volume of the tetrahedron. This progress is expected to change the main societal paths, towards a more functional and coarser mesh instead of the less organized and finer one we have now.
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Convergence of Knowledge, Technology, and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologies
  • Jan 2011
  • M C Roco
  • W S Bainbridge
  • B Tonn
  • G Whitesides
Roco, M. C.; Bainbridge, W. S.; Tonn, B.; Whitesides, G. Convergence of Knowledge, Technology, and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologies; Springer: New York, 2013. (2) Linkov, I.; Moberg, E. Multi-Criteria Decision Analysis: Environmental Applications and Case Studies; CRC Press: Boca Raton, FL, 2011. (3) Clemen, R. T.; Reilly, T. Making Hard Decisions with DecisionTools;