Enhancing Strategic Planning With Massive Scenario Generation
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Enhancing Strategic Planning with Massive Scenario Generation
This report extends research on using scenarios for strategic planning, with experiments in what can be called massive scenario generation (MSG), a computationally intensive technique that seeks to combine virtues of human- and model-based exploration of "the possibility space." The authors measure particular approaches to MSG against four metrics: not needing a good initial model; the dimensionality of the possibility space considered; the degree of exploration of that space; and the quality of resulting knowledge. The authors then describe two MSG experiments for contrasting cases, one that began with a reasonable but untested analytical model, and one that began without an analytical model, but with a thoughtful list of the conditions that might characterize and distinguish among circumstances in the situation considered, a list derived from a combination of single-analyst thinking and group brainstorming. The authors experimented with a variety of methods and tools for interpreting and making sense of the "data" arising from MSG, using ordinary linear sensitivity analysis, a generalization using analyst-inspired aggregation fragments, some advanced filtering methods drawing on data-mining and machine-learning methods, and motivated metamodeling. On the basis of this preliminary work, we conclude that MSG has the potential to expand the scope of what are recognized as possible developments, provide an understanding of how those developments might come about, and help identify aspects of the world that should be studied more carefully, tested, or monitored. It should assist planners by enriching their mental library of the patterns used to guide reasoning and action at the time of crisis or decision and should help them identify anomalous situations requiring unusual actions. Finally, it should identify crucial issues worthy of testing or experimentation in games or other venues and, in some cases, suggest better ways to design mission rehearsals. If MSG can be built into training, education, research, and socialization exercises, it should leave participants with a wider and better sense of the possible, while developing skill at problem-solving in situations other than those of the "best estimate." Much development is needed, but prospects are encouraging.
Anticipating Future Innovation Pathways Through Large Data Analysis
This book aims to identify promising future developmental opportunities and applications for Tech Mining. Specifically, the enclosed contributions will pursue three converging themes: The increasing availability of electronic text data resources relating to Science, Technology and Innovation (ST&I). The multiple methods that are able to treat this data effectively and incorporate means to tap into human expertise and interests. Translating those analyses to provide useful intelligence on likely future developments of particular emerging S&T targets. Tech Mining can be defined as text analyses of ST&I information resources to generate Competitive Technical Intelligence (CTI). It combines bibliometrics and advanced text analytic, drawing on specialized knowledge pertaining to ST&I. Tech Mining may also be viewed as a special form of “Big Data” analytics because it searches on a target emerging technology (or key organization) of interest in global databases. One then downloads, typically, thousands of field-structured text records (usually abstracts), and analyses those for useful CTI. Forecasting Innovation Pathways (FIP) is a methodology drawing on Tech Mining plus additional steps to elicit stakeholder and expert knowledge to link recent ST&I activity to likely future development. A decade ago, we demeaned Management of Technology (MOT) as somewhat self-satisfied and ignorant. Most technology managers relied overwhelmingly on casual human judgment, largely oblivious of the potential of empirical analyses to inform R&D management and science policy. CTI, Tech Mining, and FIP are changing that. The accumulation of Tech Mining research over the past decade offers a rich resource of means to get at emerging technology developments and organizational networks to date. Efforts to bridge from those recent histories of development to project likely FIP, however, prove considerably harder. One focus of this volume is to extend the repertoire of information resources; that will enrich FIP. Featuring cases of novel approaches and applications of Tech Mining and FIP, this volume will present frontier advances in ST&I text analytics that will be of interest to students, researchers, practitioners, scholars and policy makers in the fields of R&D planning, technology management, science policy and innovation strategy.
Unifying Themes in Complex Systems IX
Unifying Themes in Complex Systems is a well-established series of carefully edited conference proceedings that serve to document and archive the progress made regarding cross-fertilization in this field. The International Conference on Complex Systems (ICCS) creates a unique atmosphere for scientists from all fields, engineers, physicians, executives, and a host of other professionals, allowing them to explore common themes and applications of complex systems science. With this new volume, Unifying Themes in Complex Systems continues to establish common ground between the wide-ranging domains of complex systems science.