技术创新网络内节点间知识流动适应性规则研究

随着研发网络化环境的形成,技术创新网络中节点间知识流动对网络结构演变的影响成为学者们关注的问题。同时,技术创新网络的本质体现为其自身就是一种知识流动的复杂适应系统。因此,从复杂适应系统（CAS）视角分析知识在技术创新网络中知识节点间的不同流动方式,以及在技术扩散过程中知识流动特征与核心节点形成过程,从节点的初始状态、节点间知识流动和节点状态变化等3个方面构建了技术创新网络中节点间知识流动规则,分析了网络中企业间知识流动过程对节点企业核心地位形成的影响,为下一步的仿真研究奠定了基础。     

Money demand, the Cagan model, testing rational expectations vs adaptive expectations: The case of Turkey

This paper estimates the Cagan type demand for money function for Turkish economy during the period 1986:1–1995:3 and tests whether Cagan's specification fits the Turkish data using an econometric technique assuming that forecasting errors are stationary. This paper also tests the hypothesis that monetary policy was implemented in aiming to maximize the inflation tax revenue. Finally, the Cagan model is estimated with the additional assumption of rational expectations for Turkey for the considered period. First version received: March 1998/final version received: October 1998     

无就业增长与非均衡劳工市场动态学

本文直接模拟了劳工市场的非均衡过程、工资动态轨迹、经济人的最优行为和适应性优化行为。劳动供给源于家庭的效用最大化 ,劳动需求源于企业的利润最大化。企业的适应性优化行为和若干制度因素构成了工资刚性的基本要素 ,劳工市场的供求力量也是影响工资变动的重要因素。根据劳动生产率的变化对工资进行适应性的调节是现实世界企业工资决策的普遍实践。工资刚性与高劳动生产率并存是造成无就业增长的真正原因     

Adaptive learning,endogenous uncertainty,and asymmetric dynamics

I present a simple model where forecasting confidence affects aggregate demand. It is shown that this model has similar stability properties, under statistical and evolutionary learning, as a model without a confidence affect. From this setup, I introduce “Expectational Business Cycles” where output fluctuates due to learning, heterogeneous forecasting models and random changes in the efficient forecasting model. Agents use one of two forecasting models to forecast future variables while heterogeneity is dictated via an evolutionary process. Increased uncertainty, due to a shock to the structure of the economy, may result in a sudden decrease in output. As agents learn the equilibrium, output slowly increases to its equilibrium value. Expectational business cycles tend to arrive faster, last longer and are more severe as agents possess less information.     

Platforms,systems competition,and innovation: Reassessing the foundations of communications policy

Focusing on the effects of policy on investment and innovation this paper examines whether the conceptual foundations of sector regulation are aligned with the current technological and economic conditions of advanced communications. One conclusion is that the prevailing theories and practices are only adequate if the policy challenge can be reasonably approximated as a static or steady-state problem but they may have serious shortcomings if this is not possible. The article proceeds with a review and critical examination of two approaches that could augment or possibly replace the traditional approach under conditions of dynamic competition—the theory of platform markets and systems approaches. Both frameworks model aspects of competition in interconnected systems in more detail and offer novel insights to inform communications policy in an era of continuous change. Nonetheless, important theoretical and implementation gaps remain that will require additional efforts by researchers and practitioners.     

Optimal fiscal policy under learning

This paper characterizes optimal fiscal policy when agents learn about future taxation. A benevolent and fully rational government chooses taxes on labor income and state-contingent bonds to finance public spending, considering that private agents form their expectations through a learning algorithm. Facing a trade-off between distortionary taxes and distorted expectations, the Ramsey planner chooses the policy that minimizes the total cost of distortions. The analysis produces two main results. First, the government will use fiscal variables to manipulate expectations, reducing taxes and issuing debt at times of pessimism and doing the opposite at times of optimism. This speeds up learning. Second, the expectation-dependent fiscal plan is also history-dependent, and it prescribes taxes that are not as smooth and more persistent than under rational expectations. These findings are robust to alternative learning algorithms.     

团队有效性研究新进展:团队适应研究综述

团队适应是团队应对变化时采取一系列措施以维持团队有效性并实现发展的过程。现有研究主要从两大视角定义和测量团队适应:组合式理论将团队适应作为个体适应的综合表现,合成式理论将团队适应视为个体成员通过互动在团队层面体现的对环境线索的有效回应。这两大视角均对适应性团队开发、团队适应影响机制以及团队适应测量3类主题进行了相关研究和讨论,但由于团队适应研究仍然处于起步阶段,对于概念内涵和结构、影响机制和结果以及测量等问题均未达成共识。未来研究需继续丰富和完善这三类主题的讨论,并进一步拓展团队适应研究在管理领域的应用。     

Integrated inventory-transportation model by synchronizing delivery and production cycles

This paper develops an integrated single-vendor multi-buyer inventory-transportation synchronized supply chain model. In this paper, the decisions of truck assignment and routing are also considered and as a result, a series of vehicle routing problems (VRP) are required to be solved. Due to the highly complicated objective function and the NP-hard VRP problems, the problem cannot be solved analytically. Hence, meta-heuristics are proposed. By means of the numerical examples and a case study, the meta-heuristics developed are shown to be very effective in solving such comprehensive supply chain models, and the results so obtained are promising.     

基于数字波束形成的自动跟踪系统

针对多目标测控问题，提出了一种实用的利用基于圆阵的数字波束形成技术来实现目标跟踪的系统。在系统实际工作于噪声和干扰环境中时，为避免计算梯度时可能出现的数值突变，提出了一种修正的梯度跟踪自适应算法，可靠地实现了权值更新，并对各阵元输出进行加权合并，以获得最大信号输出，同时实现对目标方位角和俯仰角的自动捕获和跟踪，且跟踪迅速、正确、稳定。仿真结果表明了所提出的方案的正确性。     

宽带通信中的干扰抑制处理方法研究

综述了DS/FH宽带通信系统中的干扰抑制预滤波处理方法，并侧重时频域的干扰消除技术，基于有关处理算法的适用条件和性能，探讨多系统共存环境下AIC（自适应干扰消除）处理的使用。