Innovation policy in an open economy: A normative framework for strategic and tactical issues

In this paper we present a normative analysis of innovation policy grounded in traditional economic principles which responds to the Schumpeterian critique of previous efforts in the neoclassical vein. Focusing directly on specific policy issues we pursue our analysis in two main directions, investigating the role of government in developing an infrastructure for innovation, and the optimal design of support schemes for R&D projects.The analysis suggests that while the structural and developmental issues stressed by the Schumpeterian school are of fundamental importance, identification of disparities between social and private gains, the “market failure” approach, provides an invaluable tool for understanding and formulating innovation policy. Focusing our attention on such factors as economics of scale, the incidence of externalities, and the Supranormal profits offered by imperfectly competitive markets, it provides a rational and realistic basis for anticipating where and how government intervention on behalf of industrial innovation is likely to be most effective. We view our contribution as a step towards bridging the gap between the neoclassical mainstream of economic analysis and the practice of innovation policy.     

Less conservative stabilization conditions for Markovian jump systems with partly unknown transition probabilities

This paper is devoted to the robust stabilization problem for a class of MJLSs with mode transition probabilities taken to be partly known, unknown, and unknown but with known bounds. To meet the needs of less conservative stabilization conditions, this paper proposes a valuable approach that (1) can express the conditions in terms of matrix inequalities with homogeneous polynomial dependence on partly unknown transition probabilities and (2) can include all possible slack variables related to transition probability constraints in the relaxation process coupled with deriving a finite set of linear matrix inequalities (LMIs). Finally, two numerical examples are reported to illustrate the effectiveness of the derived stabilization conditions.     

A dynamic extremum seeking scheme for three-player attack-defense with unknown gradient

In this paper, a dynamic extremum seeking scheme is designed for a class of three-player attack-defense problems with unknown gradient, where an attacker intends to strike a non-maneuverable headquarters, while a defender attempts to prevent this attack. Firstly, a kinematic equation is utilized to model the defender’s patrolling algorithm based on the law of gravitation. Then, a gradient estimation algorithm is employed to obtain derivatives of performance functions. In virtue of the Lyapunov function method, sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop dynamic extremum seeking system. Finally, a group of simulations on wheeled mobile robot systems are exploited to illustrate the effectiveness of the proposed extremum seeking scheme.     

海洋经济核心示范区人才发展对策研究

海洋经济人才发展已成为宁波市＂十二五＂时期重要的战略任务,时代对海洋人才队伍建设从专业背景、层次结构与分布领域等方面提出了新的要求。目前宁波市的海洋人才发展在目标定位、研究机构层次、人才布局等方面依然存在若干问题,需从加强院、校、所建设和优化人才政策体系两方面加以改善。     

A welfare policy analysis in the Turkish economy: A simulation based macroeconomic application of the deficit financing policies

In this study, by including 15 production sectors and linking real and financial sub-models through various channels of fund flows, interest rates, commercial bank intermediation, monetary and fiscal policies, a financial computable general equilibrium (CGE) model, which represents salient features of the Turkish economy, is formulated and counterfactual simulations are performed to explore the question of how changing the government deficit financing options affects the stakeholders and macroeconomic variables in the Turkish economy under both fixed and flexible interest rate regimes. In the benchmark, treasury bonds and central bank credits finance a certain percentage of the government deficit. In the deficit financing simulation scenario, the proportion financed by treasury bonds is increased while the proportion financed by the central bank is decreased under both fixed and flexible interest rate regimes to observe the effects of policy changing. Our simulation results show that deficit financing policy change makes commercial banks better off in both interest rate regimes particularly in flexible interest rate regime while household and enterprise sectors slightly suffer. The macroeconomic impact of the policy change is in line with the theory as the inflation rate decreases, and real interest rate increases in both interest rage regimes. Besides, though the real GNP slightly decreases in the short run, it slightly increases in the long run under both interest rage regimes.     

Decentralized adaptive synchronization with bounded identification errors for discrete-time nonlinear multi-agent systems with unknown parameters and unknown high-frequency gains

This paper addresses the challenging problem of decentralized adaptive control for a class of coupled hidden leader-follower multi-agent systems, in which each agent is described by a nonlinearly parameterized uncertain model in discrete time and can interact with its neighbors via the history information from its neighbors. One of the agents is a leader, who knows the desired reference trajectory, while other agents cannot receive the desired reference signal or are unaware of existence of the leader. In order to tackle unknown internal parameters and unknown high-frequency gains, a projection-type parameter estimation algorithm is proposed. Based on the certainty equivalence principle and neighborhood history information, the decentralized adaptive control is designed, under which, the boundedness of identification error is guaranteed with the help of the Lyapunov theory. Under some conditions, it is shown that the multi-agent system eventually achieves synchronization in the presence of strong couplings. Finally, a simulation example is given to support the results of the proposed scheme.     

Towards an integrated framework for evaluating transformative innovation policy

In recent years, a new frame for innovation policy has emerged, namely “transformative innovation policy” (TIP), which aims at addressing transformative change or “Grand Challenges”. Such a shift in policy theory should, ideally, be reflected in policy evaluation, but the literature has so far provided little advice on how to address TIP-related evaluation challenges such as directionality and system-level behavioural additionality. This paper discusses how the evaluation of policy interventions targeting system innovation can be designed to address these challenges. Combining the literature on sustainability transitions with policy evaluation, we propose an integrated evaluation framework composed of three main components: (i) programme theory (programme goals, systems boundaries and desired (or accepted) development paths); (ii) system analysis (transformative outcomes); and (iii) synthesis and overall assessment (including revision of programme theory). By integrating the two sets of literature, we provide a bridge between academic research on transitions and current evaluation practices. We briefly illustrate the applicability of the integrated framework in the BioInnovation Strategic Innovation Programme in Sweden.     

循环经济:实现可持续发展的理想经济模式

循环经济(circular economy)是指借鉴自然生态系统进化原理,依据物质循环和能量守恒规律而重构的经济系统。该系统将和谐地纳入到“自然-经济-社会”复杂巨系统全面、协调、可持续的运行之中。这是以产品清洁生产、资源循环利用、废物无害再生为特征的高级生态经济形态。循环经济遵循“减量化”、“资源化”、“无害化”三原则,形成一种效率高、物料省、生态环境维系良好的区域发展模式。     

CDMA技术创新过程的特点及其政策启示

从民用扩频通信的兴起、CDMA技术标准的最初确立，到CDMA技术标准的不断演进，CDMA的技术创新过程表现出主体多元性、多进程性和阶段性的特点。主体多元性是指CDMA的技术创新涉及军方、大学、企业和中介机构等多元主体的相互作用；多进程性是指CDMA创新过程包含了从基础研究到商业应用、从军用技术到民用技术溢出的过程；阶段性则表明，CDMA的创新过程由军方、大学主导的根本性创新和企业主导的渐进性创新两个阶段组成。以演化框架分析CDMA的技术创新过程，其政策启示是：要以提高创新系统整体效能为政策基点。具体政策主要包括两个方面：构筑有利于多种主体间相互作用的制度安排和基础设施。     

RHONN identifier-control scheme for nonlinear discrete-time systems with unknown time-delays

This work presents a neural identifier-control scheme for uncertain nonlinear discrete-time systems with unknown time-delays. This scheme is based on a neural identifier to get a model of the system and a discrete-time block control technique based on sliding modes to generate the control law. The neural identifier is based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) based algorithm. Applicability is shown using real-time test results for linear induction motors. Also, a Lyapunov analysis is added in order to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed neural identifier-control scheme.     

Distributed MPC-based adaptive control for linear systems with unknown parameters

This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique.     

Optimal LQG controller for linear stochastic systems with unknown parameters

This paper presents the optimal LQG controller for linear systems with unknown parameters. The optimal controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. Performance of the obtained optimal controller is verified in the illustrative example against the conventional LQG controller that is optimal for linear systems with known parameters. Simulation graphs verifying overall performance and computational accuracy of the designed optimal controller are included.     

Stabilization for time-delay nonlinear systems with unknown time-varying control coefficients

A control scheme based on dynamic gains is proposed for the time-varying nonlinear time-delay systems with unknown control coefficients. A class of Nussbaum functions are introduced to deal with the problem of unknown control directions. Dynamic gains technique and Lyapunov–Krasovskii functional are developed to handle the time delays in nonlinear system. It is shown that the system state is regulated to origin asymptotically, and the boundedness of all closed-loop signals is guaranteed. Simulation results are provided to demonstrate the effectiveness of the proposed methodology.     

Finite-time stochastic stabilization for BAM neural networks with uncertainties

This paper is concerned with the finite-time stabilization for a class of stochastic BAM neural networks with parameter uncertainties. Compared with the previous references, a continuous stabilizator is designed for stabilizing the states of stochastic BAM neural networks in finite time. Based on the finite-time stability theorem of stochastic nonlinear systems, several sufficient conditions are proposed for guaranteeing the finite-time stability of the controlled neural networks in probability. Meanwhile, the gains of the finite-time controller could be designed by solving some linear matrix inequalities. Furthermore, for the stochastic BAM neural networks with uncertain parameters, the problem of robust finite-time stabilization could also be ensured as well. Finally, two numerical examples are given to illustrate the effectiveness of the obtained theoretical results.     

Design of an event-triggered joint adaptive high-Gain observer for a class of nonlinear system with unknown states and parameters

In this paper, an event-triggered joint adaptive high-gain observer design method is proposed for a class of nonlinear systems that are characterized by an unknown parameter entering the system state equations. The main difficulty in the observer design is that the event-triggered mechanism (ETM) is affected by variable delayed-sampled data and the system’s unknown parameter. To overcome this difficulty, a closed-loop output predictor is incorporated into the design of the event-triggered mechanism to compensate for the sampling and the delay affecting the system outputs. To prevent the Zeno phenomenon, and to guarantee the exponential convergence of the observer, an exponential decay factor is considered in the ETM. The effectiveness of our proposed observer is demonstrated through numerical simulations, experiments and performances comparison with previous works in the literature.     

A flow and routing control policy for communication networks with multiple competitive users

This paper is concerned with deriving an optimal flow and routing control policy for two-node parallel link communication networks with multiple competing users. The model assumes that each user has a flow demand which needs to be optimally selected and routed on the network links. The flow and routing control policy for each user seeks to maximize the user's total throughput and minimize its expected delay. To derive such a policy, we consider a utility function for each user that combines these two objectives in an additive fashion with preference constants that can be adjusted to reflect the user's own preferences between maximizing throughput and minimizing delay. Additionally, we provide each user with the ability to make these preferences link-dependent to reflect the user's preferences for certain links over others in the network. A condition that depends on the link capacities and preference constants is derived to guarantee the existence and uniqueness of a non-symmetric flow and routing control policy solution which satisfies the Nash equilibrium of non-cooperative game theory. The Nash equilibrium is a desirable solution for such networks because it insures that no user can improve its utility by unilaterally deviating from its Nash control policy. We discuss in detail several interesting properties of this equilibrium and in particular, its relationship to the users’ preference constants. Two illustrative examples are also presented.     

整合照料视角下的老年家庭照护床位政策研究

作为嵌入式养老的创新模式,家庭照护床位是解决当前养老服务困境,推动养老服务体系高质量发展、精细化治理的重要举措.然而,当前的试点仍存在需求不足、供给乏力的双重困境.究其原因,主要在于对家庭照护床位认识不清、理解错位;政策实施中尚未有效解决社区居家养老服务资源碎片化问题,资源整合定位不准、整合层次低.基于此,本文提出回归家庭照护床位政策本色,强调其准市场化服务属性,明确其补充、协调、整合的功能定位,探索以街镇-社区为基点、以"养老管家"为媒介、以智能科技为支撑、以老年人住所为场域、以老年人个体为中心的服务终端功能性整合路径,完善配套激励相容机制、支付保障机制,推动实现"医-养-康-护"一体化居家养老.     

A Bandwidth expanding scheme for communication channels with noiseless feedback in the presence of unknown jamming noise

This paper treats the problem of transmitting a Gaussian discrete-time Markov process over a time-discrete additive white Gaussian channel with noiseless feedback which is subjected to statistically unknown jamming noise (satisfying a given power constraint). The channel is used more than once during the interval between the production of successive source letters, and the jamming noise is either allowed to be correlated with the encoder output, or forced to be totally independent of it. The complete solution obtained in the paper under a minimax criterion indicates that the optimum encoder-decoder structures are linear,and the least favorable jamming noise is a Gaussian process.     

Distributed data driven control for multi-agent consensus with unknown system dynamics

This study investigates the consensus tracking problem for unknown multi-agent systems (MASs) with time-varying communication topology by using the methods of data-driven control and model predictive control. Under the proposed distributed iterative protocol, sufficient conditions for reducing tracking error are analyzed for both time invariable and time varying desired trajectories. The main feature of the proposed protocol is that the dynamics of the multi-agent systems are not required to be known and only local input-output data are utilized for each agent. Numerical simulations are presented to illustrate the effectiveness of the derived consensus conditions.