Rethinking the Dutch Innovation Agenda: Management and Organization Matter Most

In this essay, we challenge the present dominant emphasis in the Dutch Innovation Debate on the creation of technological innovations, the focus on a few core technologies, and the allocation of more financial resources. We argue that managerial capabilities and organizing principles for innovation should have a higher priority on the Dutch Innovation Agenda. Managerial capabilities for innovation deal with cognitive elements such as the capacity to absorb knowledge, create entrepreneurial mindsets, and facilitate managerial experimentation and higher-order learning abilities. These capacities can only be developed by distinctive managerial roles that enhance hierarchy, teaming and shared norms. Utilizing these unique managerial capabilities requires novel organizing principles, such as managing internal rates of change, nurturing self-organization and balancing high levels of exploration and exploitation. These managerial capabilities and organizing principles of innovation create new sources of productivity growth and competitive advantage.The dramatic fall back of the Netherlands in the league of innovative and high productivity countries of the World Economic Forum-Report can be mainly attributed to the present lack in the Netherlands of these key managerial and organizational enablers of innovation and productivity growth. We provide various levers for building unique managerial capabilities and novel organizing principles of innovation. Moreover, we describe the necessary roles that different actors have to play in this innovation arena. In particular, we focus on the often neglected but important role of strategic regulations that speed up innovation and productivity growth. They are the least expensive way to boost innovation in organizations in both the Dutch private and public sector. Finally, we discuss the implications for the Dutch Innovation Agenda. It should start with setting a challenging ambition, namely the return of The Netherlands within the WEF- league of the top-ten most innovative and productive countries of the world. Considering the under-utilization of available knowledge stemming from technological innovations, managerial and organizational determinants of innovation should receive first priority. These determinants have a high strategic relevance and should receive more public recognition. We suggest to organize an annual innovation ranking of the most outstanding Dutch firms, to develop an innovation audit that measures firms’ non-technological innovation capacity, and to create an overall innovation policy for fast diffusion of new managerial capabilities and adequate organizing principles throughout the Dutch private and public sector.In conclusion, we add five new items to the Dutch Innovation Agenda:1. Prioritize administrative innovationsInvestments in management and organization determinants of absorption of knowledge and its successful application (administrative innovation) should have a higher priority than investments in technological innovations.2. Build new managerial capabilities and develop novel organizing principlesFor these administrative innovations to succeed, firms have to build managerial capabilities (broad knowledge-base, absorptive capacity, managerial experimentation, higher-order learning) and various management roles (hierarchy, teaming, shared norms) to increase the assimilation of external knowledge and the utilization for innovation. Moreover, they have to develop novel organizing principles that increase internal rates of change, nurture self-organization and synchronize high levels of exploration and exploitation.3. Set levers of innovation by creating selection environments that favor innovation and by redefining the roles of key actors Management has to create a proper organizational context to foster entrepreneurship and innovation (internal selection environment). Governmental agencies have to focus on innovation and productivity enabling strategic regulations (external selection environment). Moreover, research institutes, business schools, and consulting firms should not only focus on technological knowledge, but also on managerial and organizational knowledge for innovation. In the end, private small and large firms and public institutions have to recognize that they all must contribute to the national goal of increasing innovation and productivity growth.4. Create a new challenging national ambition: return of the Netherlands within the top-10The Netherlands has to return to the top-ten most innovative and productive countries in the world as reflected in international rankings such as the World Economic Forum’s Global Competitiveness Index.5. Proliferate an awareness and passion for innovation:Create public awareness and recognition of the societal relevance of outstanding managerial capabilities and organizing principles to innovation and productivity growth:o Initiate a Dutch innovation ranking in terms of management and organization;o Develop proper assessment tools for innovations in management and organization;o Enhance reporting on the progress on managerial and organizational innovation as part of modern corporate governance and as part of outstanding annual reports.These issues may contribute to rethinking the fundamental sources of innovation, productivity growth and sustainable competitive advantage of the Dutch economy.dynamic capabilities;knowledge transfer;exploitation;exploration;mANAGEMENT;mindsets;organizing pinciples;srategic rgulation;strategy innovation

Coevolutionary algorithms for the optimization of strategies for red teaming applications

Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as intelligent agents in a multi-agent system and the idea is to run many computer simulations, pitting the plan of the red team against the plan of blue team. This research project investigated techniques that can support automated red teaming by conducting a systematic study involving a genetic algorithm (GA), a basic coevolutionary algorithm and three variants of the coevolutionary algorithm. An initial pilot study involving the GA showed some limitations, as GAs only support the optimization of a single population at a time against a fixed strategy. However, in red teaming it is not sufficient to consider just one, or even a few, opponent‟s strategies as, in reality, each team needs to adjust their strategy to account for different strategies that competing teams may utilize at different points. Coevolutionary algorithms (CEAs) were identified as suitable algorithms which were capable of optimizing two teams simultaneously for red teaming. The subsequent investigation of CEAs examined their performance in addressing the characteristics of red teaming problems, such as intransitivity relationships and multimodality, before employing them to optimize two red teaming scenarios. A number of measures were used to evaluate the performance of CEAs and in terms of multimodality, this study introduced a novel n-peak problem and a new performance measure based on the Circular Earth Movers‟ Distance. Results from the investigations involving an intransitive number problem, multimodal problem and two red teaming scenarios showed that in terms of the performance measures used, there is not a single algorithm that consistently outperforms the others across the four test problems. Applications of CEAs on the red teaming scenarios showed that all four variants produced interesting evolved strategies at the end of the optimization process, as well as providing evidence of the potential of CEAs in their future application in red teaming. The developed techniques can potentially be used for red teaming in military operations or analysis for protection of critical infrastructure. The benefits include the modelling of more realistic interactions between the teams, the ability to anticipate and to counteract potentially new types of attacks as well as providing a cost effective solution

Knowledge Collaboration: Working with Data and Web Specialists

When resources are finite, people strive to manage resources jointly (if they do not rudely take possession of them). Organizing helps achieve—and even amplify—common purpose but often succumbs in time to organizational silos, teaming for the sake of teaming, and the obstacle course of organizational learning. The result is that organizations, be they in the form of hierarchies, markets, or networks (or, gradually more, hybrids of these), fail to create the right value for the right people at the right time. In the 21st century, most organizations are in any event lopsided and should be redesigned to serve a harmonious mix of economic, human, and social functions. In libraries as elsewhere, the three Ss of Strategy—Structure—Systems must give way to the three Ps of Purpose—Processes—People. Thence, with entrepreneurship and knowledge behaviors, data and web specialists can synergize in mutually supportive relationships of shared destiny

Rethinking the Dutch Innovation Agenda: Management and Organization Matter Most

In this essay, we challenge the present dominant emphasis in the Dutch Innovation Debate on the creation of technological innovations, the focus on a few core technologies, and the allocation of more financial resources. We argue that managerial capabilities and organizing principles for innovation should have a higher priority on the Dutch Innovation Agenda. Managerial capabilities for innovation deal with cognitive elements such as the capacity to absorb knowledge, create entrepreneurial mindsets, and facilitate managerial experimentation and higher-order learning abilities. These capacities can only be developed by distinctive managerial roles that enhance hierarchy, teaming and shared norms. Utilizing these unique managerial capabilities requires novel organizing principles, such as managing internal rates of change, nurturing self-organization and balancing high levels of exploration and exploitation. These managerial capabilities and organizing principles of innovation create new sources of productivity growth and competitive advantage. The dramatic fall back of the Netherlands in the league of innovative and high productivity countries of the World Economic Forum-Report can be mainly attributed to the present lack in the Netherlands of these key managerial and organizational enablers of innovation and productivity growth. We provide various levers for building unique managerial capabilities and novel organizing principles of innovation. Moreover, we describe the necessary roles that different actors have to play in this innovation arena. In particular, we focus on the often neglected but important role of strategic regulations that speed up innovation and productivity growth. They are the least expensive way to boost innovation in organizations in both the Dutch private and public sector. Finally, we discuss the implications for the Dutch Innovation Agenda. It should start with setting a challenging ambition, namely the return of The Netherlands within the WEF- league of the top-ten most innovative and productive countries of the world. Considering the under-utilization of available knowledge stemming from technological innovations, managerial and organizational determinants of innovation should receive first priority. These determinants have a high strategic relevance and should receive more public recognition. We suggest to organize an annual innovation ranking of the most outstanding Dutch firms, to develop an innovation audit that measures firms’ non-technological innovation capacity, and to create an overall innovation policy for fast diffusion of new managerial capabilities and adequate organizing principles throughout the Dutch private and public sector. In conclusion, we add five new items to the Dutch Innovation Agenda: 1. Prioritize administrative innovations Investments in management and organization determinants of absorption of knowledge and its successful application (administrative innovation) should have a higher priority than investments in technological innovations. 2. Build new managerial capabilities and develop novel organizing principles For these administrative innovations to succeed, firms have to build managerial capabilities (broad knowledge-base, absorptive capacity, managerial experimentation, higher-order learning) and various management roles (hierarchy, teaming, shared norms) to increase the assimilation of external knowledge and the utilization for innovation. Moreover, they have to develop novel organizing principles that increase internal rates of change, nurture self-organization and synchronize high levels of exploration and exploitation. 3. Set levers of innovation by creating selection environments that favor innovation and by redefining the roles of key actors Management has to create a proper organizational context to foster entrepreneurship and innovation (internal selection environment). Governmental agencies have to focus on innovation and productivity enabling strategic regulations (external selection environment). Moreover, research institutes, business schools, and consulting firms should not only focus on technological knowledge, but also on managerial and organizational knowledge for innovation. In the end, private small and large firms and public institutions have to recognize that they all must contribute to the national goal of increasing innovation and productivity growth. 4. Create a new challenging national ambition: return of the Netherlands within the top-10 The Netherlands has to return to the top-ten most innovative and productive countries in the world as reflected in international rankings such as the World Economic Forum’s Global Competitiveness Index. 5. Proliferate an awareness and passion for innovation: Create public awareness and recognition of the societal relevance of outstanding managerial capabilities and organizing principles to innovation and productivity growth: o Initiate a Dutch innovation ranking in terms of management and organization; o Develop proper assessment tools for innovations in management and organization; o Enhance reporting on the progress on managerial and organizational innovation as part of modern corporate governance and as part of outstanding annual reports. These issues may contribute to rethinking the fundamental sources of innovation, productivity growth and sustainable competitive advantage of the Dutch economy

RedTNet: A network model for strategy games

In this work, we develop a simple, graph-based framework, RedTNet, for computational modeling of strategy games and simulations. The framework applies the concept of red teaming as a means by which to explore alternative strategies. We show how the model supports computer-based red teaming in several applications: realtime strategy games and critical infrastructure protection, using an evolutionary algorithm to automatically detect good and often surprising strategies

Robustness and Adaptiveness Analysis of Future Fleets

Making decisions about the structure of a future military fleet is a challenging task. Several issues need to be considered such as the existence of multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future might hold. It is uncertain what future events might be encountered; how fleet design decisions will influence and shape the future; and how present and future decision makers will act based on available information, their personal biases regarding the importance of different objectives, and their economic preferences. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different classes of uncertainty are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present clear risks to the fleet. We call this the analysis of a fleet's strategic positioning. This paper introduces how strategic positioning can be evaluated using computer simulations. Our main aim is to introduce a framework for capturing information that can be useful to a decision maker and for defining the concepts of robustness and adaptiveness in the context of future fleet design. We demonstrate our conceptual framework using simulation studies of an air transportation fleet. We capture uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions, different model assumptions, and different economic preferences. Proposed changes to a fleet are then analysed based on their influence on the fleet's robustness, adaptiveness, and risk to different scenarios

Robustness and Adaptability Analysis of Future Military Air Transportation Fleets

Making decisions about the structure of a future military fleet is challenging. Several issues need to be considered, including multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future holds. It is uncertain what future events might be encountered and how fleet design decisions will influence these events. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different uncertainties are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present risks to the fleet. We call this the analysis of a fleet’s strategic positioning. Our main aim is to introduce a framework that captures information useful to a decision maker and defines the concepts of robustness and adaptability in the context of future fleet design. We demonstrate our conceptual framework by simulating an air transportation fleet problem. We account for uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions and different model assumptions. Proposed changes to a fleet are then analysed based on their influence on the fleet’s robustness, adaptability, and risk to different scenarios

Evolving attackers against wireless sensor networks using genetic programming

Recent hardware developments have made it possible for the Internet of Things (IoT) to be built. A wide variety of industry sectors, including manufacturing, utilities, agriculture, transportation, and healthcare are actively seeking to incorporate IoT technologies in their operations. The increased connectivity and data sharing that give IoT systems their advantages also increase their vulnerability to attack. In this study, the authors explore the automated generation of attacks using genetic programming (GP), so that defences can be tested objectively in advance of deployment. In the authors' system, the GP-generated attackers targeted publish-subscribe communications within a wireless sensor networks that was protected by an artificial immune intrusion detection system (IDS) taken from the literature. The GP attackers successfully suppressed more legitimate messages than the hand-coded attack used originally to test the IDS, whilst reducing the likelihood of detection. Based on the results, it was possible to reconfigure the IDS to improve its performance. Whilst the experiments were focussed on establishing a proof-of-principle rather than a turnkey solution, they indicate that GP-generated attackers have the potential to improve the protection of systems with large attack surfaces, in a way that is complementary to traditional testing and certification

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.