Fourth International Seville Conference on Future-Oriented Technology Analysis (FTA)
FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations
Seville, 12-13May 2011
NBIC, GRAIN, BANG, and TechFARM™ – Advanced Sciences Convergence for Surveillance of Emerging S&TI Trendsto Address Global Grand Challenges
A. Vaseashta, E. W. Braman, and P. Susmann
Institute for Advanced Sciences Convergence
NUARI, 13873 Park Centre Rd. Suite 500
Herndon, VA20171USA
Abstract
The geopolitical landscape of the 21st century is more complex, dynamic, and unpredictable than that faced in the previous century. Making strategic decisions are complex and involve socio-economic, geo-political, techno-economic, forward-thinking and paradigm-shifting considerations to support policy and investment decisions to address global challenges. We posit advanced sciences convergence (ASC) as a decision support methodology supporting the early identification of emerging issues that will have an impact on international policies. The convergence of multiple disciplines creates a synergy capable of overcoming persistent barriers by mapping knowledge gaps. Prudent convergence methodologies inculcate, conceive and advance transformational, revolutionary, and embryonic opportunities allowing for next generation solutions to current and future societal and technical challenges. A “framework by design” of emerging scientific and technological advances and trends is developed through a systematic and strategic planning process to deepen the understanding of current, future, and varying challenges and opportunities and create fully integrated solution paths to address current and future global issues. Through a systematic introduction of the ASC, the methodology exploits futures-oriented analytical methodologies, including heuristics, data-mining, scientometrics, modelling and simulation, and scenario development to provide solutions and their potential for integrated, novel and/or unconventional manifestations. One of the futures-oriented approaches- TechFARMTM comprises of three overlapping practices of future studies, including: foresight; assessment; and forecasting. The desired outcome of TechFARMTM is to provide a technology roadmap (TRM), assessment of technology readiness level (TRL), and provide continuously evolving decision implementation guidance in a sustained and unified way, expanding awareness of technological options and outcomes. In addition to TechFARMTM, tools and research methodologies such as ADAMSTM and NESTTSTM are introduced to expand the identification of emerging disruptive and futuristic trends in science and technology intelligence (S&TI). The tools and research methodologies include in-depth analysis, using convergence of multi/trans-disciplinary S&T fields, and are focused on nanotechnology, biotechnology, information technology, cognitive sciences, artificial intelligence, robotics, and genetics. This construct presents an exceptional platform for transforming the manner in which state-of-the-art information is gathered, analyzed, and leveraged to enable RM development. The TRM is an evolving concept and is composed of numerous subsystems, with an expectation to minimize the impact of and manage technology uncertainty and surprise through exploiting futures-oriented analytical methods, modelling and simulation, and scenario development. Visionary and unbiased assessment of technology platforms is necessary to facilitate detection of weak signals in consideration of technological innovations, and reveal insight into “system of systems” strengths and weaknesses.
Keywords:TechFARM, GRAIN, BANG, ADAMS, NESTS, nanotechnology
1.Introduction
The challengesassociated with the geopolitical landscape of the 21st century have become relatively complex, dynamic, and unpredictable. Notwithstanding significant level of technological progresses worldwide, the world faces uncertainty through wide-spread financial crisis, terrorism and war, lack of clean and abundant water, energy shortage, poverty, and so on. To address these challenges, we propose several forward looking methodologies on intelligent ways to support policy makers by the use of comprehensive analysis that projects plausible “designed” future based on sound decisions involving the latest technology, social dynamics, and foresight that supportinvestment recommendations and implementation of policies that will have significant benefit to the public at large. The focus of such activities is primarily on policy-relevant foresight methodology development that are specific to a desired application, particularly by developing approaches to the early identification of emerging issues that will have a profound impact on decision making and policy developments. Strategic decision-making requires a knowledge base, analytical capability, foresight, risk-assessment, and optimization.Three platforms viz. technology foresight and roadmapping (TechFARMTM), new and emerging science and technology trends surveillance (NESTTSTM), and automated data analysis, mining and surveillance (ADAMSTM) described in this report,engage ASC to support decision making by providing knowledge base, analytical capability, foresight, and guidance.
1.1 Advanced Sciences Convergence
The process of ASC is to understand how advances in different disciplines, and focusing on diverse and discrete problems and applications, can coalesce into an integrated system to solve a seemingly intractable problem. It requires persistent monitoring of new and emerging domestic and international S&T developments in multiple disciplines and understanding of far-reaching, visionary, yet achievable, albeit undefined, outcomes that can be described in terms of desired actions or qualities of the eventual system. Multiple approaches, some of which may be high-yield and high-risk research, can be pursued simultaneously to create integral components of the system. Recent advances in both S&Thave provided the means to study, understand, control, and even manipulate transitional characteristics between isolated atoms and molecules, and bulk materials. Various “designer” materials capable of producing devices and systems with remarkable and desired properties have recently been fabricated (Vaseashta, 2008). Such innovations have occurred in multi-disciplinary environments. Such advances in conjunction with advances in biotechnology, information technology (IT), cognitive sciences, genetics, and artificial intelligence (AI) are employed to develop futures-oriented analytical methodologies by further including heuristics, data-mining, scientometrics, modelling and simulation, and scenario development to provide systematic recognition and evaluation of new or existing S&Tsolutions and their potential for integrated, novel and/or unconventional approaches to 21st century challenges. Based on expectation of outcome, several convergence models such as nanotechnology (NT), biotechnology, IT , and cognitive sciences (NBIC); bits, atoms, neurons, and genes (BANG) – also termed as biotechnology, AI, NT, and genetics; and genetics, robotics, AI and NT (GRAIN) are considered by scientific community. A common component in all these is NT- enabling innovations due to reduced dimension (Vaseashta, 2005, 2009) having applications of these developments in environment, health and safety (EHS), defense and security, consequence management, and enhancing the global quality of life (QoL) (Vaseashta 2009, 2011). Figure 1(a) maps intersecting trajectories of different disciplines and figure 1(b) shows a projection metaphor to show emergence of new and unique applications.
Figure 1: Intersecting trajectories of different disciplines, (b): projection metaphor showing emergence of new and unique applications
1.2Technological Lacunae and Innovations Foresight
Roadmapping efforts serve as both short and strategic alignment tools for monitoring and managing S&T innovations for many organizations. Yet an alternate potentially promising approach for new and emerging S&T is “configuration of mapping tools,” based on ”underlying patterns and indicators” of the “dynamics of emergence.” The dynamics of emerging mapping paths can be used to articulate a future structured in terms of prospective innovation chains and potential paradigms. Articulation of such futures approach informs management of realistic expectations by which emerging S&T are assessed and implemented. These tools are already in use across many federal agencies globallyfor strategic management of innovations in S&T policy planning process. These tools can effectively bridge technological lacunae if the technological gaps in the innovation chain are articulated and implemented by understanding S&T innovations and foresight.For new and emerging fields of S&T where architectural (radical) innovations might occur, conditions of non-linearity and high technology and market uncertainty are the norm. New S&T are not defined by eventual application, but are characterized by ‘generic richness’ or their potential for combining with a number of different fields, thus enabling a number of new innovations. New and emerging S&T are often assessed in terms of their potential to “break through” recognized frontiers, or “disrupt” existing technology product linkages — but the assessments may be affected by overestimated expectations. Literature in the management of innovation, expectations management, and sociology-of-technology fields has stressed repeatedly that for assessments during early stages of technological emergence, more ‘open-ended’, flexible yet effective strategies are often useful. A map of paths can be embedded as a central element in a support system to articulate the most robust strategy. A multi-path mapping (MPM) toolset provides SI and reflexive alignment and follows the ‘deployment cycle’ that mirrors dynamics underlying well-known technology S-curves.
2.Methodology- Foresight Innovation Pathways
The synergy arising from the NBIC convergence offers great potential for transformational and revolutionary opportunities with many technological applications. To explore this realm, TechFARM – a multi-dimensional futures-oriented modality that identifies and manages emerging and/or disruptive science and technology trends has been articulated.In recent years, burgeoning technological innovations have changed the landscape of traditional scientific research both in expectations and practice. Advances in NT, biotechnology, materials and information processing are propelling innovations at a phenomenal and ever accelerating rate, resulting in greater uncertainty about the future of not only research and development, but other fields of policy as well. The concept of operations (CONOPS)includes a decentralized “knowledge-centric” approach to develop an inquiry methodology. The operational success of the method depends on the ability to harness the knowledge embedded in individuals and transform it into qualitative and quantitative inquiry methods providing multidisciplinary information for S&T foresight analyses. The desired outcome is to identify system strengths and weaknesses and minimize the impact of and manage technology uncertainty through exploiting futures-oriented analytical methods, modelling and simulation, and scenario development.
2.1Futures-Oriented Approach to S&T Assessment
Futures research approaches can be broken down into nine families: Creativity, Descriptive & Matrices, Statistical, Expert Opinion, Monitoring & Intelligence, Modelling & Simulations, Scenarios, Trends Analyses, and Valuing/Decision/Economic, as shown in figure2. The diversity of futures studies practices reflects fundamental differences in process management and design between studies. The methods serve in different capacities, in the sense that some methods are definitive, while others address uncertainty through probabilistic analysis. The methods, which often overlap two or more of the nine major families, can be sub-categorized based on explorative or normative approaches, as well as soft (qualitative; judgment-based) or hard (quantitative; numerical-based) output. Explorative methods project futures analyses forward from the past or present situation, identifying the past development and current status of a given science or technology to postulate its future. In contrast, a normative approach traces backward from a hypothetical future situation to assess likelihood, timing and consequences of a science or technology, thereby identifying a future desired state and determining developments and innovations necessary to reach that future. The normative approach generally represents needs- or goal-driven planning and results in directive action. It facilitates investment decision by identifying critical technologies, gaps and ways to leverage R&D investments; however, it is more difficult to identify “weak signals” and new/emerging science and technology.The full potential of S&T futures analysis is realized only where exploratory and normative components are combined in an iterative cycle. Qualitative methods are best applied when there is no historical data and/or when unanticipated/uncontrollable external factors or moral considerations may alter future development. However, a major disadvantage to a qualitative, judgment-based approach is that it can be biased by pre-conceptions, what individuals believe to be feasible, or simply a lack of imagination. It should be noted that quantitative approaches also have their limits. For example, empirical models often assume a linear relationship among variables and ignore multivariate interactions and non-linearity. Therefore, in order to offset the weaknesses of any one method, it is prudent to implement a broad complementation of methods to ensure a deeper and more reliable perspective on S&T futures.It is the extensive suite of futures studies methods and techniques available which provide the tools for a strategic and systematic approach to understanding and anticipating potential directions and effects of ASC and hence reduce the risk of recreating the status quo. A comprehensive list of methods is listed in table 1.
Figure 2: Families of S&T futures analysis methods
2.2Technology Foresight, Assessment, and Roadmapping (TechFARM)
The field of futures research has long possessed complex algorithmic tool sets at its disposal, yet it was unable to effectively execute these approaches due primarily to the limited availability of and costly access to information. However, the IT era has provided powerful new capabilities for easy, immediate access to a massive and ever increasing body of scientific data and information. Although the question of how the scientific material should be mined continues to be an issue, one of the most pressing challenges is the translation of the information to ensure relevant and strategic research prioritization and implementation of science policy. The TechFARM methodology consists of team brainstorming, expert elicitations, and creating concept maps of potential solution paths. The methodology represents a strategic and significant step in the systematic creation of a framework to support policy recommendations and the identification of alternative and innovative investment strategies. It facilitates the development of visions and projections of future possible scientific and technological advances, environments, and productive collaborations that can promote and support transformational scientific research. The ASC convergence process and data mining operations are focused in areas identified through the strategic planning process as having a high potential for meeting some desired characteristic of the solution path. It is the constant cross-pollination among multiple disciplines that is critical to the identification of emerging patterns within the larger scientific community.
The dynamic relationship between contextual influence (socio-economical factors) and technological development is also of critical consideration for S&T futures analyses. The Tech-FARM approach comprises three over-lapping practices of futures studies, including: foresight, the identification of future S&T development and their interaction with society and environment with the purpose of guiding actions to produce a favourable future; assessment, which is concerned with the impacts of S&T and forecasting, which describes the emergence of performance, features, or impacts of a S&T at a future time [Porter, A. et al.,2004]. However, it is important to recognize that the ultimate outcome of futures research is not to predict the future or to influence social decisions in a predictable way. Rather, its goal is to illuminate future opportunities, threats, choices, and expand decision-makers’ awareness of alternative futures for decisions, policy actions, and other outcomes. The TechFARM methodology, in conjunction with ADAMS and NESTS, are likely to provide a unique framework of the future capabilities that are scientifically feasible, recognized and cross-validated by experts.
The Tech-FARM system employs the aforementioned methods and techniques of strategic foresight (including information on social and economic processes to the extent they affect local science policy infrastructure) to evaluate the potential benefits, disadvantages and effects of key S&T; devise a series of possible futures rather than set a single course of development; and identify and prioritize new, emerging, and/or disruptive technologies. There are two possible scenarios to which the Tech-FARM approach may be applied. In the first case, a more “normative” approach is adopted which starts with a clearly defined, future-desired technological need. Necessary developments and innovations required are determined working backwards to the present. The second case represents an ongoing, “persistent forecasting” endeavour for which a more “exploratory” approach is taken. This is a more capabilities-based perspective that addresses the growing uncertainty. It highlights the current status of, and past developments in, S&T (emerging and established, respectively) to develop a wide range of possible scenarios and postulates potential impacts and alternative futures. The recommended system evolves through three stages, as shown in Figure 3.
Figure 3: Tech-FARM framework
Table 1: Comprehensive list of TechFARM methods.
Stage I – Define topic and review current situation. The preliminary activity begins with extensive environmental scanning coupled with bibliometrics of scientific publication, news and media, books, interviews, conferences. This helps to define scope and boundaries of the envisioned ideal system. Furthermore, an assessment of the strengths and weaknesses of the internal capabilities, as well as the threats and opportunities of the external environment is critical in order that we may explore the uncertainties rather than repress them.Stage II – Identify change drivers and select critical drivers. Once the vision has been articulated, in order for the ideal system to become feasible we must identify operational and technical issues significant to the frontline research scientists. Again these include strengths and weakness of internal capabilities as well as threat and opportunities of the external environment that cannot be ignored. A comprehensive and systematic assessment, including identification of strategic trends, critical drivers and uncertainties, possible shocks (wild cards, high impact, and low probability factors) and scenario building, provides legitimacy to the envisioned ideal system. Stage III – synthesis and recommendation - providing a technologically workable ideal system to include identification of technology gaps that must be filled to meet target goals(s) as well as alternative futures and/or information needed to make trade-off decisions.