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PurposeSince the term's introduction two decades ago, “psychological capital” has been described as an intangible strategic organizational resource. However, there remains a dearth of research regarding its application, especially in the macro-management fields. This paper addresses this gap by elevating the individual-level psychological capital (ILPC) concept to an equivalent organizational-level psychological capital (OLPC) model with appropriate methodological considerations.Design/methodology/approachThe elevation of ILPC to OLPC is conducted using referent shift approach. Then, the proposed OLPC model is tested and validated using the partial least-squares structural equation modeling analysis technique.FindingsTesting the OLPC model adequately validated the basic requirements of reliability, validity, multicollinearity and redundancy analysis. To ensure the practical use of the proposed model, a simulation study was also conducted, and the results confirmed the strength of ILPC being elevated to a model of OLPC.Research limitations/implicationsThe findings imply a better understanding of psychological capital from a multilevel perspective and present cross-level opportunities to enrich the scholarly corpus. The current paper also provides an insight into psychological capital management efficiency at all organizational levels so that the greatest benefits can be obtained.Originality/valueThis paper shows, for the first time, the possibility of borrowing ILPC concepts to help enact OLPC in organizations by means of a survey study. This paves the way for further investigation into the use of psychological capital at strategic and other macromanagement levels. Finally, future studies are encouraged to triangulate and strengthen the main arguments presented here.

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot.

We have done some research on the macro-management modeling and strategy classification in the RTS(real-time strategy) game StarCraft. Similar to the real military battlefield, simulation in the virtual environment could do favor to modeling, realize and predict the opponent strategy which could lead us to the counter strategy. Firstly we collect several typical replays played by professional player, then we do some preprocess and extract the key features to readable data, finally, we use HMM-KNN to do the strategy classification.

The real-time strategy game StarCraft has proven to be a challenging environment for artificial intelligence techniques, and as a result, current state-of-the-art solutions consist of numerous hand-crafted modules. In this paper, we show how macromanagement decisions in StarCraft can be learned directly from game replays using deep learning. Neural networks are trained on 789,571 state-action pairs extracted from 2,005 replays of highly skilled players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting the next build action. By integrating the trained network into UAlbertaBot, an open source StarCraft bot, the system can significantly outperform the game's built-in Terran bot, and play competitively against UAlbertaBot with a fixed rush strategy. To our knowledge, this is the first time macromanagement tasks are learned directly from replays in StarCraft. While the best hand-crafted strategies are still the state-of-the-art, the deep network approach is able to express a wide range of different strategies and thus improving the network's performance further with deep reinforcement learning is an immediately promising avenue for future research. Ultimately this approach could lead to strong StarCraft bots that are less reliant on hard-coded strategies., Comment: 8 pages, to appear in the proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2017)

'The Positive Arc of Systemic Strengths' explores the important question: when is it that the best in human systems comes out most naturally and easily—especially in collective action, planning and design initiatives encompassing regions and cities, extended enterprises, industries and UN-level world summits? By analysing the per- formance and impacts of six case studies of the 'whole system in the room' Apprecia- tive Inquiry design summit, this article provides a bird's eye view of the opportunities, challenges and exciting new vistas opening up in this, the collaborative age—a time when systemic action and macromanagement skill are the primary leverage points for game-changing innovation, scalable solutions and industry leadership. While management innovation is rare, this article proposes that the tools are at hand for us to stand up, step up and scale up as we build a world where businesses can excel, people can thrive and nature can flourish.

The constitution of Brazil directs that the country’s health system, the Unified Health System (Sistema Unico de Saude), be politically and administratively decentralized. Nevertheless, handing over competencies, responsibilities, and resources to subnational levels, especially to municipal governments, has been a slow process, lasting almost two decades. Advances have been brought about by the Unified Health System, which, from a analytical perspective, is a public and universal system. Despite that, the decentralization process needs to overcome norms that keep all levels of management dependent on Brazil’s federal Government. The subnational levels have consistently faced difficulties in performing their macromanagement functions with autonomy, especially when it comes to financing and to the establishment or organization of health care networks. Boldness and responsibility will be needed to prevent Brazil’s health decentralization process from leading to fragmentation. New political agreements between different levels of government, with a reassignment of responsibilities and the enhancement of a culture of technical cooperation, are fundamental requisites to making the Unified Health System have a health policy that is truly public and universal.

Cash projection is the most important treasury macromanagement tool. It is a dynamic report that extends into the future covering the time frame within the limits that should make sense for a particular business. The report’s main purposes are 1. to provide you with a clear vision of whether the company will be able to meet its cash demands at any given point within the time scope of the forecast; and 2. to prompt you to take appropriate long-term (liquidity management) and short-term (borrowing and disbursements administration) actions ensuring that these demands are satisfied. Generally speaking, these objectives are achieved through the accurate assessment of two basic cash movements: inflows and outflows.

The essence of industrial structural optimization is now transforming from more input in the past to the dependence on informationization, the enhancement of independent innovation and being creative in macro-management to realize the industrial development. In order to solve the problems which was reflected during the course of the rapid development of Chinese pollution- free food industry , and the problems includes the product and scale structure of the enterprises which is the factor that affect the quality of the product , the key point is to disclose the general rules of industrial structure evolution and macro management innovation theoretically, to clarify the inherent basis of industrial structure optimization and how to adjust macro management to this requirement for developing China's pollution- free food industry. According to the theory of industry economy management, this paper systematically analyzes the industrial structure characteristics, problems and causes, and constructs the model of industrial structure optimization and upgrading. It also discloses the bases, conditions, the main contents and methods by way of the policy orientation for promoting industrial structure optimization of China's pollution- free food.

To study the sharing of tacit knowledge in SDN enterprise, a multi-dimensional tacit knowledge model is built at first. Then, as the way of tacit knowledge shared, blog system is researched to be interfaced into SDN enterprise system. The sharing study on tacit knowledge focuses on the realizing ideas and realizing codes of the enterprise management "blog" module which embodies the macro-management of sharing. For knowledge further shared, the macro-management is carried on through three guidance table for blog registering and classifying the signing up successfully blogs into three communities. This study presents the theory and method reference to the effective implementation of "blog" for the tacit knowledge sharing.

Despite the wide variety of health care systems in industrialized democracies, a universal paradigm for financing, organization, and macromanagement has been emerging through reforms of the past decade. The policies within this paradigm attempt to promote equity, social efficiency, and consumer satisfaction by combining the advantages of public finance principles--universal access and control of spending--with the advantages of competitive market principles--consumer satisfaction and internal efficiency. This paradigm is characterized by three systemic functions: (1) financing of care, based on public finance principles, not necessarily carried out by government; (2) organization and management of publicly funded care consumption by either competing nongovernmental entities or noncompeting public administrations; and (3) provision of care based on competitive market principles. The institutional arrangement of these functions lends itself to the creation of two internal markets for consumer choice and, of the three, the second function is a key component of the emerging paradigm.