From hierarchies to levels: new solutions for games with hierarchical structure

Recently, applications of cooperative game theory to economic allocation problems have gained popularity. In many of these problems, players are organized according to either a hierarchical structure or a levels structure that restrict the players’ possibilities to cooperate. In this paper, we propose three new solutions for games with hierarchical structure and characterize them by properties that relate a player’s payoff to the payoffs of other players located in specific positions in the hierarchical structure relative to that player. To define each solution, we consider a certain mapping that transforms the hierarchical structure into a levels structure, and then we apply the standard generalization of the Shapley value to the class of games with levels structure. Such transformation mappings are studied by means of properties that relate a player’s position in both types of structure.     

Can chunking reduce syntactic complexity of natural languages?

Natural language is a complex adaptive system with multiple levels. The hierarchical structure may have much to do with the complexity of language. Dependency Distance has been invoked to explain various linguistic patterns regarding syntactic complexity. However, little attention has been paid to how the structural properties of language to minimize dependency distance. This article computationally simulates several chunked artificial languages, and shows, through comparison with Mandarin Chinese, that chunking may significantly reduce mean dependency distance of linear sequences. These results suggest that language may have evolved the mechanism of dynamic chunking to reduce the complexity for the sake of efficient communication. © 2016 Wiley Periodicals, Inc. Complexity 21: 33–41, 2016     

Astrophysical Dynamics and Cosmology

First, the essence of a physical theory for a multilevel system is through coupling different physical laws in different levels by a symmetry-breaking principle, rather than through a unification using larger symmetry. In astrophysical dynamics, the symmetry-breaking mechanism and the coupling are achieved by prescribing the coordinate system so that the laws of fluid dynamics and heat conductivity are coupled with gravitational field equations. Another important ingredient in modeling fluid motion in astrophysics is to use the momentum density field to replace the velocity field as the state function of cosmic objects. Second, by applying the new symmetry-breaking mechanism and the new coupled astrophysical dynamics model, we rigorously prove a basic theorem on black holes: Assume the validity of the Einstein theory of general relativity, then black holes are closed, innate and incompressible. Third, we prove a theorem on structure of universes. Assume the Einstein theory of general relativity, and the principle of cosmological principle that the universe is homogeneous and isotropic. Then we show that 1) all universes are bounded, are not originated from a Big-Bang, and are static; and 2) The topological structure of our Universe can only be the 3D sphere. Also, thanks to the basic properties of black holes, we show that our results on our Universe resolve such fundamental problems as dark matter and dark energy, redshifts and CMB. Fourth, we discovered that both supernovae explosion and AGN jets, as well as many astronomical phenomena, are due to combined relativistic, magnetic and thermal effects. The radial temperature gradient causes vertical Bénard convection cells, and the relativistic viscous force (via electromagnetic, the weak and the strong interactions) gives rise to an huge explosive radial force near the Schwarzschild radius, leading e.g. to supernovae explosion and AGN jets.     

Computation of Screening Phenonema in a Vertical Tumbling Cylinder

Granular materials are an integral part of our environment. Due to their wide variety of applications in industrial and technological processes, they have captured a great interest in the recent research, see [1] and [2]. The related studies are often based on numerical simulations and it is considered as challenging to investigate computational phenomena of dense granular systems. Particle screening is an essential technology in many industrial fields and important in granular studies. The particular problem of interest is the separation of round shape particles of different geometrical sizes using a rotating tumbling vertical cylinder. The concept of discrete element method (DEM) that considers the motion of each single particle individually is applied in this study. Particle-to-particle and particle-to-wall collisions will appear under the tumbling motion of the rotating structure. The normal and frictional forces between particles themselves and particles and surrounding walls of the structure are calculated according to the rules of a penalty method, which employs spring-damper models for this purpose. As a result of collisions, the particles will dissipate kinetic energy due to the normal and frictional contact losses. Particle distribution and sifting rate of the separated particles have been studied taking into consideration different rotational speeds of the machine, various damping and frictional coefficients and different sizes of holes in the sifting plates at different levels of the structure. In an attempt to better understand the mechanism of the particle transport between the different layers of the sifting system, different computational studies have been performed. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On the Multilevel Structure of Global Optimization Problems

In this paper we will discuss the multilevel structure of global optimization problems. Such problems can often be seen at different levels, the number of which varies from problem to problem. At each level different objects are observed, but all levels display a similar structure. The number of levels which can be recognized for a given optimization problem represents a more complete measure of the difficulty of the problem with respect to the standard measure given by the total number of local minima. Moreover, the subdivision in levels will also suggest the introduction of appropriate tools, which will be different for each level but, in accordance with the fact that all levels display a similar structure, will all be based on a common concept namely that of local move. Some computational experiments will reveal the effectiveness of such tools.     

泥石流对速流结构的磨损作用研究

泥石流对砼防治结构的磨损是泥石流研究极其薄弱的环节,泥石流磨损大大缩短了防治结构的有效使用年限．作为大型特大型公路泥石流有效防治结构的速流结构,因泥石流的磨损使其有效使用寿命缩短了近三分之一．基于泥石流两相流流速计算理论,研究了泥石流对速流结构的磨损机理,包括均质浆体对速流槽的磨蚀机理和固相颗粒对速流槽的切削机理;基于牛顿运动原理建立了泥石流液相浆体及固相颗粒的磨损动力学方程;运用量纲分析法构建了速流槽磨损量计算式,计算结果与实际情况吻合．研究成果为速流结构的有效设计提供了理论依据．     

具有限制结盟结构的供应链合作创新及其收益分配

针对具有限制结盟结构特征的三级供应链合作创新联盟问题,分析了由单个供应商、制造商和销售商组成的不同联盟系统下的收益情况,并运用图合作博弈的average tree solution(简称“A-T解”)法对各成员的收益进行了分配。结果表明,所有成员参与供应链联盟时总收益最大,且A-T解分配法与Shapley值法相比更具合理性和有效性,能充分突出成员在合作联盟中的特殊地位(位置)及其重要性,该结论进一步通过比较不同级别结构下的供应链收益分配问题进行说明。     

具有限制结盟结构的供应链合作创新及其收益分配

针对具有限制结盟结构特征的三级供应链合作创新联盟问题,分析了由单个供应商、制造商和销售商组成的不同联盟系统下的收益情况,并运用图合作博弈的average tree solution(简称“A-T解”)法对各成员的收益进行了分配。结果表明,所有成员参与供应链联盟时总收益最大,且A-T解分配法与Shapley值法相比更具合理性和有效性,能充分突出成员在合作联盟中的特殊地位(位置)及其重要性,该结论进一步通过比较不同级别结构下的供应链收益分配问题进行说明。     

Organizational growth and hierarchical change in formal structure

Three hypotheses about aggregate change in organizational size (measured as number of employees) and formal structure over time are tested: (1.) the frequency of hierarchical change is higher at lower levels of hierarchy; (2.) periodic pressures for organizational change are translated into periodic changes in organizational size; and (3.) changes in size are more readily translated into increases in structure at lower levels of hierarchy. These hypotheses are tested by using spectral analysis to decompose the variance in the time series of size and structure. The implications of the confirmation of these hypotheses for organizational change over time are discussed.     

Construction of long MDS self-dual codes from short codes

In this paper, we propose a mechanism on how to construct long MDS self-dual codes from short ones. These codes are special types of generalized Reed-Solomon (GRS) codes or extended generalized Reed-Solomon codes. The main tool is utilizing additive structure or multiplicative structure on finite fields. By applying this method, more MDS self-dual codes can be constructed.     

Optimal capital allocation in a hierarchical corporate structure

We consider capital allocation in a hierarchical corporate structure where stakeholders at two organizational levels (e.g., board members vs line managers) may have conflicting objectives, preferences, and beliefs about risk. Capital allocation is considered as the solution to an optimization problem whereby a quadratic deviation measure between individual losses (at both levels) and allocated capital amounts is minimized. Thus, this paper generalizes the framework of Dhaene et al. (2012), by allowing potentially diverging risk preferences in a hierarchical structure. An explicit unique solution to this optimization problem is given. In several examples, it is shown how the optimal capital allocation achieves a compromise between conflicting views of risk within the organization.     

Parameter estimation in a structural acoustic system with fully nonlinear coupling conditions

A methodology for estimating physical parameters in a class of structural acoustic systems is presented. The general model under consideration consists of an interior cavity which is separated from an exterior disturbance by an enclosing elastic structure. Piezoceramic patches are bonded to or embedded in the structure; these can be used both as actuators and sensors in applications ranging from the control of interior noise levels to the determination of structural flaws through nondestructive evaluation techniques. The presence and excitation of the patches, however, changes the geometry and material properties of the structure as well as involves unknown patch parameters, thus necessitating the development of parameter estimation techniques which are applicable in this coupled setting. In developing a framework for approximation, parameter estimation and implementation, strong consideration is given to the fact that the input operator is unbonded due to the discrete nature of the patches. Moreover, the model is weakly nonlinear as a result of the coupling mechanism between the structural vibrations and the interior acoustic dynamics. Within this context, an illustrating model is given, well-posedness and approximation results are discussed and an applicable parameter estimation methodology is presented. The scheme is then illustrated through several numerical examples with simulations modeling a variety of commonly used structural acoustic techniques for system excitation and data collection.     

EB-GLS: an improved guided local search based on the big valley structure

Local search is a basic building block in memetic algorithms. Guided local search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called elite biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS.     

Lamellar structure and plasticity micromechanisms in bulk crystalline polymers. Communication 1

It is shown by electron microscopy that the lamellar structure typical of bulk crystalline polymers is identical in its structural-morphological characteristics with the typical products of martensite transformations in metal systems. It is also established that the polymer crystallization process has the typical characteristics of transformations governed by the shear (martensite) mechanism. These conclusions are used as a basis for an examination of the principal factors controlling the formation of the real structure of bulk polymers. The thermodynamic conditions under which bulk polymers crystallize require that the lamellar-spherulitic structure be formed in accordance with a self-consistent shear mechanism. In accordance with the new model, the spherulites represent an organization of the martensite lamellae in which the structural stress fields are mutually compensated. The proposed model underlines the fact that polycrystalline polymers and metal systems in the martensitic state are structurally similar materials.E. O. Paton Electrowelding Institute, Academy of Sciences of the Ukrainian SSR, Kiev. Translated from Mekhanika Polimerov, No. 3, pp. 387–394, May–June, 1976.     

Multiple levels of preference in interactive strategic decisions

A multiple-level preference ranking structure is developed within the paradigm of the Graph Model for Conflict Resolution to study multi-objective decision making in conflict situations more realistically. In this structure, a decision maker may have multiple levels of preference for one state or scenario over another; for example, if state A is preferred to state B, it may be mildly preferred at level 1, more strongly preferred at level 2, …, or maximally preferred at level r, where r>0 is a fixed parameter. The number of levels, r, is unrestricted in this system, thereby extending earlier two-level (r=1) and three-level (r=2) structures. Multilevel versions of four stability definitions, Nash stability, general metarationality, symmetric metarationality, and sequential stability, are defined for the graph model with this extended preference structure, and the relationships among them are investigated. A specific case study, including multiple decision makers and multiple levels of preference, is carried out to illustrate how the new solution concepts can be applied in practice.     

On the Description of Growth in Saturated Living Tissues

A comprehensive model for biological tissues must include the anisotropic tissue structure, the interstitial liquid wich saturated the tissue and the growth mechanism of the tissue. In the present contribution this is done by use of a three phasic model with a solid, liquid and nutrient phase in the framework of the porous media theory (TPM). In order to characterize the transversal isotropic skeleton behavior, an invariant formulation of the Helmholtz free energy function and the permeability tensor is suggested. The growth mechanism is characterizes by a mass transfer between the nutrient and solid phase. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Finding an efficient solution to linear bilevel programming problem: An effective approach

Multilevel programming is developed to solve the decentralized problem in which decision makers (DMs) are often arranged within a hierarchical administrative structure. The linear bilevel programming (BLP) problem, i.e., a special case of multilevel programming problems with a two level structure, is a set of nested linear optimization problems over polyhedral set of constraints. Two DMs are located at the different hierarchical levels, both controlling one set of decision variables independently, with different and perhaps conflicting objective functions. One of the interesting features of the linear BLP problem is that its solution may not be Paretooptimal. There may exist a feasible solution where one or both levels may increase their objective values without decreasing the objective value of any level. The result from such a system may be economically inadmissible. If the decision makers of the two levels are willing to find an efficient compromise solution, we propose a solution procedure which can generate effcient solutions, without finding the optimal solution in advance. When the near-optimal solution of the BLP problem is used as the reference point for finding the efficient solution, the result can be easily found during the decision process.     

Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

This work analyzes the problem of community structure in real-world networks based on the synchronization of nonidentical coupled chaotic Rössler oscillators each one characterized by a defined natural frequency, and coupled according to a predefined network topology. The interaction scheme contemplates an uniformly increasing coupling force to simulate a society in which the association between the agents grows in time. To enhance the stability of the correlated states that could emerge from the synchronization process, we propose a parameterless mechanism that adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. We show that the characteristic frequency vector that results from the adaptation mechanism reveals the underlying community structure present in the network.     

企业人力资本价值测度研究

如何运用数学模型测度企业中不同层次的人力资本价值，为人力资本参与企业利润分配提供科学的依据是当前学术界与实业界研究的热点与难点课题。本在分析研究不同层次的人力资本价值传递机制及其测度方法，并用一个案例来检验。     

Adaptive neural network model for time-series forecasting

In this study, a novel adaptive neural network (ADNN) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time-series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust forecasting results by the relative error and make them more accurate. The proposed ADNN method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the auto-regression (AR), artificial neural network (ANN), and adaptive k-nearest neighbors (AKN) models. The ADNN model is proved to benefit from the merits of the ANN and the AKN through its’ novel structure with high robustness particularly for both chaotic and real time-series predictions.