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This paper provides a range of alternatives for analysts when dealing with geo-spatial data addressing activities in science, technology and innovation. There are two sets of theory which frame the debate concerning science, technology and innovation, and which drive further methodological advancements. The power of a good visualization in providing insight to decision-makers is well known. Visualizations complete a full cycle of decision-making involving analysis, design, action and then further monitoring. We advance the paper by assessing available geographic information in science and technology databases. The paper then systematically outlines current best practices and alternatives for visualizing geographic data. Different geographic map options provide different possibilities for the display of data. We show some of these options in the paper. Future research is needed into both the available tools and techniques, as well as a more in depth specification of the kinds of decision support needs that exist and have a geo-spatial component.

Scenarios are commonly used to communicate and characterize uncertainty in many policy fields. One of the main challenges of scenario approaches is that analysts have to try and capture the full breadth of uncertainty about the future in a small set of scenarios. In the presence of deep uncertainty, this is even more challenging. Scenario discovery is a model-based technique inspired by the scenario logic school that addresses this challenge. In scenario discovery, an ensemble of model runs is created that encompasses the various uncertainties perceived by the actors involved in particular decision making situations. The ensemble is subsequently screened to identify runs of interest, and their conditions for occurring are identified through machine learning. Here, we extend scenario discovery to cope with dynamics over time. To this end, a time series clustering approach is applied to the ensemble of model runs in order to identify different types of dynamics. The types of dynamics are subsequently analyzed to identify dynamics that are of interest, and their causes for occurrence are revealed. This dynamic scenario discovery approach is illustrated with a case about copper scarcity.

Developing strategies, or policies, that automatically adapt to changing conditions is called adaptive decision-making, respectively adaptive policy-making. In this paper, we propose an iterative computational model-based approach to support adaptive decision-making under deep uncertainty. This approach combines an adaptive policy-making framework with a computational approach to generate and explore thousands of plausible scenarios using simulation models, data mining techniques, and robust optimization. The proposed approach, which is very useful for Future-Oriented Technology Analysis (FTA) studies, is illustrated on a policy-making case related to energy transitions. This case demonstrates how the performance of a policy can be improved iteratively by exploring its performance across thousands of plausible scenarios, identifying problematic subsets that require improvement, identifying adaptive high leverage actions with which the adaptive policy needs to be extended until a satisfying dynamic adaptive policy is found for the entire ensemble of plausible scenarios. The approach is not only appropriate for energy transitions; it is also appropriate for any long-term structural and systematic transformation characterized by dynamic complexity and deep uncertainty.

Exploratory Modeling and Analysis (EMA) is an approach that uses computational experiments to analyze complex and uncertain issues. It has been developed mainly for model-based decision support. This paper investigates the extent to which EMA is a promising approach for future oriented technology analysis (FTA). We report on three applications of EMA, using different modeling approaches, in three different technical domains. In the first case, EMA is combined with System Dynamics (SD) to study plausible dynamics for mineral and metal scarcity. The main purpose of this combination of EMA and SD is to gain insight into what kinds of surprising dynamics can occur given a variety of uncertainties and a basic understanding of the system. In the second case, EMA is combined with a hybrid model for airport performance calculations to develop an adaptive strategic plan. This case shows how one can iteratively improve a strategic plan through the identification of plausible external conditions that would cause the plan to perform poorly. In the final case, EMA is combined with an agent-based model to study transition dynamics in the electricity sector and identify crucial factors that positively and negatively affect a transition towards more sustainable functioning of the electricity sector. This paper concludes that EMA is useful for generating foresights and studying systemic and structural transformations despite the presence of a plethora of uncertainties, and for designing robust policies and plans, which are key activities of FTA.

Adaptive Policymaking (APM) is a policymaking approach for dealing involving policy problems with deep uncertainty. Two barriers to getting APM used are that (1) there are few actual examples of adaptive policies developed by policymakers or domain experts, and (2) APM has rarely been used in a real-world policy setting. More specifically, there are questions that remain about the costs and benefits of APM, and the efficacy and performance of adaptive policies compared to traditional static policies. This paper addresses these questions and barriers by reporting on a workshop with experts that was aimed at developing an adaptive policy for the implementation of ISA in the Netherlands. We report on their evaluation of both the process of developing the policy and their assessment of the efficacy of the developed policy compared to that of the current policy under consideration by the Dutch national government. The experts indicated that APM increases the chances of reaching policy goals and makes better use of decision support information that is gathered during the policy analysis process. However, they also indicated that, despite these potential benefits of APM, it may be hard to implement adaptive policies due to institutional issues.

The following paper contributes to the methodology of innovation forecasting. The paper analyzes the literature of engineering and technology management. A brief history and justification for interest in engineering and technology management is presented. The field has a sixty year history of interdisciplinary, and is therefore a ripe source for closer investigation into time trends of knowledge. The paper reviews the literature of innovation forecasting, examining a range of theoretical and methodological literatures interested in the evolution of knowledge. A new application of a model, suitable for sparse and count-like publication data, is presented. A mathematical presentation of the model is offered. A discussion is offered on how the model may be implemented in an approachable way within spreadsheet software. A time history of engineering management literature is extracted from a database and analyzed using the model. A projection of keyword growth is offered, and key features of the emerging knowledge base within engineering management are discussed. Recommendations for future research, as well as for those monitoring the status of the discipline of engineering management, are made.