Pareto Optimality

جداگر آونگی اصطکاکی سه‌گانه نوعی از جداگرهای انطباق‌پذیر است که می‌تواند در برابر زلزله‌های متفاوت سختی و میرایی متغیری از خود نشان دهد. بنابراین نیاز به بهینه سازی پارامترهای موثر این جداگر برای دستیابی به عملکرد مطلوب قابل توجه است. در این تحقیق، از الگوریتم استراتژی تکاملی انطباق ماتریس کوواریانس برای بهینه سازی پارامترهای موثر جداگر آونگی اصطکاکی سه‌گانه به منظور کمینه کردن حداکثر جابجایی نسبی بین طبقات و شتاب سقف در دو فرآیند بهینه سازی مجزا استفاده شده است. از آنجایی که بهینه سازی مدل های پیچیده از نظر محاسباتی زمان بر است، جایگزینی پاسخ های واقعی مدل اجزای محدود با مدل های جایگزین مقرون به صرفه منجر به صرفه جویی در زمان می شود. در این مقاله، تحلیل سازه با استفاده از یک مدل غیرخطی پیاده‌سازی شده در نرم‌افزار OpenSees انجام می‌شود که فرآیند بهینه‌سازی را زمان‌بر و از نظر محاسباتی گران می‌کند. برای پرداختن به این چالش، مدل کریجینگ به عنوان جایگزینی برای محاسبات تحلیل المان محدود لازم برای محاسبه پاسخ‌ها، یعنی حداکثر جابجایی نسبی بین طبقات و شتاب سقف، استفاده شده است. نتایج بهینه‌سازی با استفاده از ترکیب الگوریتم CMA-ES و مدل جایگزین با بهینه‌سازی با استفاده از الگوریتم ژنتیک مقایسه گردیده است. یافته ها نشان می دهد که با استفاده از مدل کریجینگ به عنوان مدل جایگزین برای بهینه سازی، جابجایی نسبی بین طبقات را تا 66.39 درصد و شتاب سقف را 12.28 درصد کاهش یافنه است. علاوه بر این، هزینه های محاسباتی نیز بیش از 84٪ کاهش می یابد.

جداگر آونگی اصطکاکی سه‌گانه نوعی از جداگرهای انطباق‌پذیر است که می‌تواند در برابر زلزله‌های متفاوت سختی و میرایی متغیری از خود نشان دهد. بنابراین نیاز به بهینه سازی پارامترهای موثر این جداگر برای دستیابی به عملکرد مطلوب قابل توجه است. در این تحقیق، از الگوریتم استراتژی تکاملی انطباق ماتریس کوواریانس برای بهینه سازی پارامترهای موثر جداگر آونگی اصطکاکی سه‌گانه به منظور کمینه کردن حداکثر جابجایی نسبی بین طبقات و شتاب سقف در دو فرآیند بهینه سازی مجزا استفاده شده است. از آنجایی که بهینه سازی مدل های پیچیده از نظر محاسباتی زمان بر است، جایگزینی پاسخ های واقعی مدل اجزای محدود با مدل های جایگزین مقرون به صرفه منجر به صرفه جویی در زمان می شود. در این مقاله، تحلیل سازه با استفاده از یک مدل غیرخطی پیاده‌سازی شده در نرم‌افزار OpenSees انجام می‌شود که فرآیند بهینه‌سازی را زمان‌بر و از نظر محاسباتی گران می‌کند. برای پرداختن به این چالش، مدل کریجینگ به عنوان جایگزینی برای محاسبات تحلیل المان محدود لازم برای محاسبه پاسخ‌ها، یعنی حداکثر جابجایی نسبی بین طبقات و شتاب سقف، استفاده شده است. نتایج بهینه‌سازی با استفاده از ترکیب الگوریتم CMA-ES و مدل جایگزین با بهینه‌سازی با استفاده از الگوریتم ژنتیک مقایسه گردیده است. یافته ها نشان می دهد که با استفاده از مدل کریجینگ به عنوان مدل جایگزین برای بهینه سازی، جابجایی نسبی بین طبقات را تا 66.39 درصد و شتاب سقف را 12.28 درصد کاهش یافنه است. علاوه بر این، هزینه های محاسباتی نیز بیش از 84٪ کاهش می یابد.

Traffic grooming in optical networks is the process of multiplexing and demultiplexing low-speed traffic streams onto high-speed wavelengths. The research in the domain of traffic grooming mainly focuses on minimizing number of SONET add/drop multiplexers (SADMs) in SONET/WDM rings and it has been shown that they can potentially be reduced by careful assignment of low-speed traffic streams onto high-speed wavelengths. However, the cost of the network not only depends on the number of SAD-Ms, but also the number of wavelengths and the grooming ratio. It is often the case that all of them cannot be minimized simultaneously. In this article, the problem of minimization of cost of a SONET/WDM unidirectional ring has been modeled as a multiobjective optimization problem which simultaneously minimizes the number of SADMs, the number of wavelengths, and the grooming ratio. A popular multiobjective genetic algorithm (NSGA-II) has been used as the underlying optimization tool. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements.

For the past decade or so, evolutionary multiobjective optimization (EMO) methodologies have earned wide popularity for solving complex practical optimization problems, simply due to their ability to find a representative set of Pareto-optimal solutions for mostly two, three, and some extent to four and five-objective optimization problems. Recently, emphasis has been made in addressing the decision-making activities in arriving at a single preferred solution. The multiple criteria decision making (MCDM) literature offers a number of possibilities for such a task involving user preferences which can be supplied in different forms. This paper presents an interactive methodology for finding a preferred set of solutions, instead of the complete Pareto-optimal frontier, by incorporating preference information of the decision maker. Particularly, we borrow the concept of light beam search and combine it with the NSGA-II procedure. The working of this procedure has been demonstrated on a set of test problems and on engineering design problems having two to ten objectives, where the obtained solutions are found to match with the true Pareto-optimal solutions. The results highlight the utility of this approach towards eventually facilitating a better and more reliable optimization-cum-decision-making task.

The usage of UML in specific contexts (like real-time systems or process modelling) is specially appealing since it provides a standard modelling notation widely used by the software engineering community. However, such usage usually requires to tailor (extend) the UML metamodel. The standard extension mechanisms suffer from several expressiveness limitations. In this report we identify these limitations and we define a two-tiered methodology to construct standard metamodels as extensions of the UML metamodel. Specifically, we present a methodology to obtain a conservative extension of the UML 1.4 metamodel (as a particular case of any well-defined MOF-model instance) and another to transform an extended UML 1.4 metamodel into a UML profile. As a result, we are able to combine the expressiveness provided by the explicit extension with the standardization coming from the use of profiles, which allows also the usage of existing tools.

The standard power utility function is widely used to explain asset prices. It assumes that the coefficient of relative risk aversion is the inverse of the elasticity of substitution. Here I use the Kihlstrom and Mirman (1974) expected utility approach to relax this assumption. I use time consistent preferences that lead to time consistent plans. In our examples, the past does not matter much for current portfolio decisions. The risk aversion parameter can be inferred from experiments and introspections about bets in terms of permanent consumption (wealth). Evidence about the change in the attitude towards bets over the life cycle may also restrict the value of the risk aversion parameter. Monotonic transformations of the standard power utility function do not change the predictions about asset prices by much. Both the elasticity of substitution and risk aversion play a role in determining the equity premium.

Parallelism arises as an attractive option when solving Multi-Objective optimization problems (MOPs). Moreover, it seems interesting when metaheuristics demand an intensive use of CPU or memory. In this paper, we propose a parallel implementation of a hybrid ant colony optimization metaheuristic for the multiobjective knapsack problem using the OpenMP framework called MHAC_OMP. The proposed approach combined a MultiObjective Ant Colony Optimization (MOACO) algorithm with Tchebycheff based Local Search (TLS) procedure. The idea behind MHAC_OMP is to evolve several independent MOACO in parallel. Each MOACO hold a local archive to maintain diversity. The parallelization is defined as assuming a shared-memory based on threads in which the initialization phase begins with a single thread called the master thread and executed sequentially. Afterward, a parallel region is defined where many threads are created, each one of them executing its own copy of the proposed ant colony algorithm in...

This paper reports an experimental study of the public goods game in which contributions are not submitted all at once but incrementally as coordinated in real time by a clock. Individuals press a button as soon as the clock equals their willingness to contribute. By providing information about the point at which subjects stop further contributions we find evidence for conditionally cooperative behaviour: A subject's decision to stop contributing induces an immediate reaction of the other group members.

The Nash Equilibrium has long been a cornerstone of game theory, providing a robust framework for understanding strategic interactions in diverse fields such as economics, biology, and artificial intelligence. Despite its theoretical elegance, the classical Nash Equilibrium framework is often critiqued for its static nature and limited applicability to dynamic, real-world systems characterized by evolving strategies and uncertainty. This paper revisits the foundational principles of Nash Equilibrium, proposing a novel framework that extends its applicability to dynamic and adaptive systems. By integrating temporal feedback, hierarchical decision layers, and stochastic elements, the proposed model-Dynamic and Adaptive Nash Equilibrium (DANE)-addresses the limitations of the traditional equilibrium concept. Through rigorous mathematical formulations and comparative analysis, we demonstrate the superiority of DANE in predicting and explaining strategic behavior in complex systems. Applications of this enhanced framework are explored in contexts such as multiagent reinforcement learning, financial markets, and cooperative social systems, showcasing its potential to revolutionize decision-making and optimization. This article not only redefines the Nash Equilibrium for the modern era but also sets a trajectory for future innovations in game theory.

We discuss the role of uncertainty in the resource/service provisioning, investment, operational cost, programming models, etc. that have not yet been adequately addressed in the scientific literature. Clouds differ from previous computing environments in the way that they introduce a continuous uncertainty into the computational process. The uncertainty becomes the main hassle of cloud computing bringing additional challenges to both end-users and resource providers. It requires to waive habitual computing paradigms, adapt current computing models, and design novel resource management strategies to handle uncertainty in an effective way [1]. In spite of extensive research of uncertainty issues in computational biology, decision making in economics, etc. a study of uncertainty for cloud computing is limited. Most of works examine uncertainty phenomena in users’ perceptions of the qualities, intentions and actions of providers, privacy, security and availability [2]. We discuss sever...

We consider rate allocation for video users in the case where the available bandwidth fluctuates. Simply minimizing the objective distortion or optimizing the stability of video qualities does not optimize subjective quality. We formulate a utility-based solution, considering that a user's preference of video quality often varies over a range with upper and lower thresholds of quality. Our iterative pricing-based resource allocation procedure reallocates the bandwidth not only between different users within a time slot but also between different time slots, such that no user suffers quality degradation on average by participating in the multiplexing process. Experimental results show that, compared with equal resource allocation and existing rate allocation solutions, the subjective result becomes increasingly better with the increase of bandwidth fluctuation rate or bandwidth fluctuation range. Moreover, as the number of users increases, the results improve.

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm-parallel genetic simulated annealing (PGSA)was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.

Particle Swarm Optimization (PSO) algorithm have been used to solve Multi-Objective Optimization Problems (MOP) and have achieved good results. But finding a good convergence and distribution of solutions near the Pareto optimal front in little computational time is still a hard work especially for some complex functions. This paper introduces an improved multi-objective PSO algorithm. It is called Strength Pareto Particle Swarm Optimization algorithm(SPPSO) which uses the ranking and sharing strategies of Strength Pareto Evolutionary Algorithm II (SPEA2). The hyper-volume metric (Zitzler 1999) is introduced to evaluate overall performance of the obtained solutions. Simulation results on five difficult test problems show that the proposed algorithm is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to CMOPSO.

Memory devices often consume more energy than microprocessors in current portable embedded systems, but their energy consumption changes significantly with the type of transaction, data values, and access timing, as well as depending on the total number of transactions. These variabilities mean that an innovative tool and framework are required to characterize modern memory devices running in embedded system architectures. We introduce an energy measurement and characterization platform for memory devices, and demonstrate an application to multilevel-cell (MLC) flash memories, in which we discover significant value-dependent programming energy variations. We introduce an energy-aware data compression method that minimizes the flash programming energy, rather than the size of the compressed data, which is formulated as an entropy coding with unequal bit-pattern costs. Deploying a probabilistic approach, we derive energy-optimal bit-pattern probabilities and expected values of the bit...

Evolutionary algorithms have shown some success in solving multiobjective optimization problems. The methods of fitness assignment are mainly based on the information about the dominance relation between individuals. We propose a Pareto fitness genetic algorithm (PFGA) in which we introduce a modified ranking procedure and a promising way of sharing; a new fitness function based on the rank of the individual and its density value is designed. This is considered as our main contribution. The performance of our algorithm is evaluated on six multiobjective benchmarks with different Pareto front features. Computational results (quality of the approximation of the Pareto optimal set and the number of fitness function evaluations) proving its efficiency are reported.

The practical problem we are trying to model is: we have to cover a room with a material (carpet or linolleum). This material is in a roll of fixed width. We want to cover the room with a minimum number of pieces and to waste a minimum amount of material. We ...

The development of a sustainable chemical processes aims at optimizing its economic, environmental and societal sustainability simultaneously. This can be solved as a multi-objective optimization (MOO) problem. However, in real systems, there are many uncertainties that affect the ...

Grandmont (1985) found that the parameter space of the most classical dynamic models are stratified into an infinite number of subsets supporting an infinite number of different kinds of dynamics, from monotonic stability at one extreme to chaos at the other extreme, and with all forms of multiperiodic dynamics between. The econometric implications of Grandmont's findings are particularly important, if bifurcation boundaries cross the confidence regions surrounding parameter estimates in policy-relevant models. Stratification of a confidence region into bifurcated subsets seriously damages robustness of dynamical inferences. But Grandmont provided his result with a model in which all policies are Ricardian equivalent, no frictions exist, employment is always full, competition is perfect, and all solutions are Pareto optimal. Hence he was not able to reach conclusions about the policy relevance of his dramatic discovery. As a result, Barnett and He (1999, 2001, 2002) investigated a Keynesian structural model, and found results supporting Grandmont's conclusions within the parameter space of the Bergstrom-Wymer continuous-time dynamic macroeconometric model of the UK economy. That highly regarded, prototypical Keynesian model was produced from a system of second order differential equations. The model contains frictions through adjustment lags, displays reasonable dynamics fitting the UK economy's data, and is clearly policy relevant. Criticism of Keynesian structural models by the Lucas critique have motivated development of Euler equations models having policy-invariant deep parameters, which are invariant to policy rule changes. Hence, Barnett and He (2006) chose to continue the investigation of policy-relevant bifurcation by searching the parameter space of the best known of the Euler equations macroeconometric models: the Leeper and Sims (1994) model. The results further confirm Grandmont's views. Even more recently, interest in policy in some circles has moved to New Keynesian models. As a result, in this paper we explore bifurcation within the class of New Keynesian models. We develop the econometric theory needed to locate bifurcation boundaries in log-linearized New-Keynesian models with Taylor policy rules or inflation-targeting policy rules. Empirical implementation will be the subject of a future paper, in which we shall solve numerically for the location and properties of the bifurcation boundaries and their dependency upon policy-rule parameter settings. Central results needed in this research are our theorems on the existence and location of Hopf bifurcation boundaries in each of the cases that we consider. We provide the proofs of those propositions in this paper. One surprising result from these proofs is the finding that a common setting of a parameter in the future-looking New-Keynesian model can put the model directly onto a Hopf bifurcation boundary. Beginning with Grandmont's findings with a classical model, we continue to follow the path from the Bergstrom-Wymer policy-relevant Keynesian model, then to the Euler equation macroeconomic models, and now to the New Keynesian models. So far, all of our results suggest that Barnett and He's initial findings with the path-breaking policy-relevant Bergstrom-Wymer model appear to be generic.

We consider the problem of learning a probabilistic model from the viewpoint of an expected utility maximizing decision maker/investor who would use the model to make decisions (bets), which result in well defined payoffs. In our new approach, we seek good out-of-sample model performance by considering a one-parameter family of Pareto optimal models, which we define in terms of consistency

A set of n players is to bargain over which one of m possible outcomes will be chosen. The preference of outcome i to player j is assumed to be a random variable with all preference rankings equally likely and without ties. This would arise in case the payoffs are chosen as independent samplings from a continuous distribution Fj. The mean and distribution of the number of outcomes in the Pareto-optimal set are calculated for finite m. As m → ∞ the mean is asymptotic to (log m + 0.577)n−1/(n − 1)! and for n = 2, m → ∞, the distribution approaches the normal distribution. The results are also applied to a problem in multiattribute utility theory. Suppose we wish to select an individual with high values on two personal attributes that are independent and continuously measurable. Of a world population of four billion, the efficient set would have an expectation of 22.7 individuals.

This paper considers a basic MIMO information-energy broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Assuming that physical layer security techniques are adopted for secure transmission, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific fullstream case. Then, we propose inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We proves that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the effectiveness of the proposed beamforming algorithms.

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This paper provides an analysis of the asymptotic properties of Pareto optimal consumption allocations in a stochastic general equilibrium model with heterogeneous consumers. In particular we investigate the market selection hypothesis, that markets favor traders with more accurate beliefs. We show that in any Pareto optimal allocation whether each consumer vanishes or survives is determined entirely by discount factors and beliefs. Since equilibrium allocations in economies with complete markets are Pareto optimal, our results characterize the limit behavior of these economies. We show that, all else equal, the market selects for consumers who use Bayesian learning with the truth in the support of their prior and selects among Bayesians according to the size of their parameter space. Finally, we show that in economies with incomplete markets these conclusions may not hold. With incomplete markets payoff functions can matter for long run survival, and the market selection hypothesis fails.

We study GHZ-type and W-type three-mode entangled coherent states. Both the types of entangled coherent states violate Mermin's version of the Bell inequality with threshold photon detection (i.e., without photon counting). Such an experiment can be performed using linear optics elements and threshold detectors with significant Bell violations for GHZ-type entangled coherent states. However, to demonstrate Bell-type inequality violations for W-type entangled coherent states, additional nonlinear interactions are needed. We also propose an optical scheme to generate W-type entangled coherent states in free-traveling optical fields. The required resources for the generation are a single-photon source, a coherent state source, beam splitters, phase shifters, photodetectors, and Kerr nonlinearities. Our scheme does not necessarily require strong Kerr nonlinear interactions, i.e., weak nonlinearities can be used for the generation of the W-type entangled coherent states. Furthermore, it is also robust against inefficiencies of the single-photon source and the photon detectors.

This paper shows how problems in 'non-life'-(re)insurance can be modeled as cooperative games with stochastic payoffs. Pareto optimal allocations of the risks faced by the insurers and the insureds are determined. It is shown that the core of the corresponding insurance games is nonempty. Moreover, it is shown that specific core allocations are obtained when the zero-utility principle is used for calculating premiums. Finally, game theory is used to give a justification for subadditive premiums.

As Very Large Scale Integration (VLSI) technology advances, the need to efficiently balance cost and performance in VLSI systems becomes paramount. To address this challenge, we propose a novel approach that leverages the RMSPROP algorithm for assisted design space exploration. The RMSPROP algorithm, which has proven effective in the field of deep learning optimization, is adapted to navigate the complex design space of VLSI systems. By integrating RMSPROP into the design space exploration process, we can intelligently search for optimal trade-offs between cost and performance, leading to highly efficient VLSI designs. Our experimental results demonstrate the effectiveness of the RMSPROP algorithm-assisted design space exploration, showcasing significant improvements in cost-performance trade-offs compared to traditional design methodologies. This research opens new avenues for designing VLSI systems with improved efficiency, enabling the realization of high-performance yet costeffective integrated circuits.

His research interests center around parallel algorithms and architectures. Facilitating a transition into ubiquitous parallel computing has been a strategic objective for computer science and engineering since its inception in the 1940s. A theory enthusiast, the overriding theme guiding his work was using theory to guide the rest of the field in addressing this strategic objective. Key components in his comprehensive plan include: (i) the very rich PRAM parallel algorithmic theory, and (ii) a PRAM-On-Chip vision comprising the explicit multi-threaded (XMT) computer system framework he invented. The latter provides a powerful approach to multi-core architectures, and, in particular, the exponential increase in the number of cores in the roadmap of most vendors into the late 2010s. Recently, he has focused on the feedback loop between algorithms, their programming and implementation, and architecture, as well as their performance modeling with an eye towards softer aspects, such as their ease-of-programming, teachability and learnability.

The Receiver Operating Characteristic (ROC) has become a standard tool for the analysis and comparision of classifiers when the costs of misclassification are unknown. There has been relatively little work, however, examining ROC for more than two classes. Here we define the ROC surface for the Qclass problem in terms of a multi-objective optimisation problem in which the goal is to simultaneously minimise the Q(Q − 1) misclassification rates, when the misclassification costs and parameters governing the classifier's behaviour are unknown. We present an evolutionary algorithm to locate the optimal trade-off surface between misclassifications of different types. The performance of the evolutionary algorithm is illustrated on a synthetic three class problem. In addition the use of the Pareto optimal surface to compare classifiers is discussed, and we present a straightforward multi-class analogue of the Gini coefficient. This is illustrated on synthetic and standard machine learning data.

For aircraft propulsion, land-based power generation, and other industrial applications such as trains, marines, automobiles, etc., gas turbines are commonly used. The operating temperature must be raised to increase the thermal performance and output work of the gas turbine engine to accommodate the accelerated production of modern gas turbines. The heat transmitted to the turbine blade, however is greatly increased as the temperature of the turbine inlet is constantly increased. For a long service life and healthy running, it is also very important to cool the turbine blades. Currently available methods for cooling turbine blades include film cooling with impingement cooling for the front edge, turbulent rib cooling with serpentine passages for the middle section of the blade, and pin-fin cooling for the trailing edge of the turbine blade. The cooling system for turbine blades must provide cooling for all potential regions that are subjected to hot gas flow. This paper provides a ...

We study the evolution of Walrasian expectations equilibrium in a sequence of asymmetric information economies. Equilibrium allocations generate additional information that changes the information structures over time. Agents are bounded rational in the sense that a small error within maximization is allowed. We address the following: (i) given a sequence of equilibrium in each period, there is a subsequence that converges to an equilibrium for the limit full information economy, (ii) any equilibrium in the limit full information economy can be reached by a sequence of approximate equilibria.

In the original model of pure price competition, Bertrand (1883), firms have linear cost functions. For any number n ≥ 2 of identical such price-setting firms, the unique equilibrium price equal the firms' (constant) marginal cost. This paper provides a generalization of Bertrand's model from linear to convex cost functions. I analyze pure price competition both in a static setting-where the firms interact once and for all-and in dynamic settingwhere they interact repeatedly over an indefinite future. Sufficient conditions are given for the existence of Nash equilibrium in the static setting and for subgame perfect equilibrium in the dynamic setting, and the equilibrium sets are characterized. It is shown that there typically exists a whole interval of equilibrium prices both in the static and dynamic setting. Firms may earn sizable profits and equilibrium profits may be increasing in their production costs.

In the original model of pure price competition, due to Joseph Bertrand (1883), firms have linear cost functions. For any number of identical such price-setting firms, this results in the perfectly competitive outcome; the equilibrium price equal the firms’ (constant) marginal cost. This paper provides a generalization of Bertrand’s model from linear to convex cost functions. I analyze pure price

Hayek's critical attitude towards Walrasian modelling was based on informational considerations. In his view, a meaningful notion of equilibrium has to deal with the consistency of agents' plans when information is dispersed throughout the economy. He emphasized that only in this context could the role of market prices as aggregators of information be correctly analyzed. Recent developments in general equilibrium theory with rational expectations have taken up the issue formally. In this paper, Hayek's main ideas about the competitive mechanism are compared with these recent results. It is argued that Hayek's notion of private information is different from that used in modern equilibrium theory, not only because of its dynamic content, as many critics claim, but also because it refers to a kind of knowledge that cannot be publicly observed ex post, as is usually required in a general equilibrium framework. The issue of whether modern notions of private information, namely that used in contract theory, are good substitutes for Hayek's notion of personal knowledge is also examined in detail. From this analysis, it is possible to conclude that Hayek's notion of equilibrium neither entails Pareto optimality properties nor full informational efficiency.

In a multiuser cognitive radio setting, multiple secondary systems coexist with multiple primary systems. We assume the secondary systems consist of transmitter-receiver pairs, and each transmitter is equipped with multiple antennas while all receivers use a single antenna. In this setting, the secondary transmitters are to operate under the constraints of producing no interference at the primary users. Such constraints on the secondary systems are referred to as null-shaping constraints. Three results are derived in this work. First, the Pareto optimal operation points for the secondary systems under null-shaping constraints are characterized by real-valued parametrization of the transmission strategies. Second, we show that all points on the Pareto boundary of the secondary systems achievable rate region without interference constraints can be achieved as the outcome of a noncooperative game by imposing certain virtual null-shaping constraints. Third, motivated by the surprising result that all Pareto efficient points can be achieved as a Nash equilibrium, we consider the problem of selecting a subset of the noncooperative secondary users for operation under the objective of maximizing their achievable sum rate. A low complexity suboptimal greedy secondary user selection algorithm is proposed and its performance is illustrated by simulations.

A major difficulty in bioinformatics is due to the size of the datasets, which contain frequently large numbers of variables. In this study, we present a two-step procedure for feature selection. In a first "filtering" stage, a relatively small subset of features is identified on the basis of several criteria. In the second stage, the importance of the selected variables is evaluated based on the frequency of their participation in relevant patterns and low impact variables are eliminated. This step is applied iteratively, until arriving to a Pareto-optimal "support set", which balances the conflicting criteria of simplicity and accuracy.

*Phần giới thiệu tổng hợp từ ý kiến phản biện của PGS.TS Phạm Quỳnh Phương (VNU)  và PGS.TS Đỗ Thị Thanh Thủy (VICAS)

Trong bối cảnh di sản hóa phổ biến ở Việt Nam, nghiên cứu là một đề tài có tính thời sự, khám phá mối quan hệ phức tạp, đa chiều giữa di sản với địa điểm, giữa tiến trình di sản hóa (heritagization), tạo tác di sản (heritage-making) với kiến tạo địa điểm (placemaking).
Dựa vào trường hợp thực tiễn ở Tràng An, sử dụng cách tiếp cận kiến tạo và các phương pháp liên ngành, nghiên cứu đã chạm đến một vấn đề mang tính cốt lõi trong nhận thức về di sản. Đó là: “Bản chất của di sản là gì?” và “Di sản được tạo ra như thế nào?”.
Do vậy, đây là một đề tài hay, có ý nghĩa trong việc nhận thức sự hình thành các địa điểm di sản ở Việt Nam. Mặt khác, đề tài cũng được nhận định có độ khó, độ phức tạp bởi tính mới và tính trừu tượng cao, nhưng vẫn mang đến sức hấp dẫn khi tác giả đã mạnh dạn khám phá và áp dụng những quan điểm học thuật tiệm cận với thế giới, nỗ lực thu thập tài liệu, phân tích để đi đến những phát hiện có giá trị.
Về nội dung cụ thể, nghiên cứu được nhận định là công trình dày dặn, có chất lượng tốt với nhiều thành công đáng ghi nhận như:
1, Thay vì sử dụng cách tiếp cận Quy chất luận (Essentialism) phổ biến với giới học giả trong nước, thường mang tính giá trị hóa di sản, tác giả đã sử dụng cách tiếp cận Kiến tạo luận (Constructivism) và lấy địa điểm di sản làm đối tượng nghiên cứu để khám phá những chiều kích phức tạp trong sự hình thành di sản.
2, Công trình đã làm khá tốt, tổng quan được những khía cạnh lý thuyết liên quan đến kiến tạo địa điểm (placemaking) nói chung và kiến tạo địa điểm trong di sản nói riêng. Tác giả đã khá thấu đáo trong việc chỉ ra những lớp lang khái niệm khác nhau, ý nghĩa cũng như sự khác biệt của các khái niệm này. Chỉ ra được sự khác biệt giữa địa điểm (place), không gian (space), nơi chốn và kiến tạo nơi chốn… Lý giải tại sao lựa chọn chuyển ngữ của “placemaking” thành “kiến tạo địa điểm”?
3, Với năng lực tổng quan các tài liệu tiếng nước ngoài, tác giả đã chỉ ra rất nhiều chiều kích gắn với kiến tạo địa điểm trong công trình của các học giả đi trước. Nổi bật trong đó, tác giả nhấn mạnh đến 2 khía cạnh: Kiến tạo địa điểm là cách in dấu bản sắc, giá trị, nhận thức, ký ức của chủ thể lên cảnh quan, đồng thời cũng làm biến đổi cảnh quan, biến đổi ý nghĩa ban đầu và phủ lên đó những lớp nghĩa mới. Từ đây có thể thấy cách tiếp cận này giúp hiển lộ mối quan hệ đa chiều giữa con người/cộng đồng và không gian, giữa thiên nhiên và văn hóa.
4, Công trình cũng cung cấp tài liệu tốt, chi tiết và hữu ích trong trường hợp kiến tạo địa điểm đối với QTDT Tràng An. Đây được coi là phần thành công nhất của nghiên cứu khi tác giả đã làm thao tác lịch sử hóa sự tồn tại của di sản này qua việc mô tả tiến trình biến đổi của Tràng An từ một vùng đất “buồn, bực, bụi, bẩn” vào những năm 90 của thế kỷ XX thành một thương hiệu di sản ở thập kỷ đầu của thế kỷ XXI một cách sinh động, thú vị với vai trò của những tác nhân ngẫu nhiên (nhà làm phim người Pháp, bộ phim Indochine…) cho đến tính chủ thể đầy chiến lược của các doanh nhân, các nhà đầu tư, chuyên gia nước ngoài, chính quyền. Các luận điểm được tác giả nêu ra khá thuyết phục, giúp người đọc hiểu sự hình thành của di sản hỗn hợp thế giới Tràng An là một tiến trình lâu dài với nỗ lực “đọc địa điểm”, phát hiện, tạo ra giá trị, tái cấu trúc, tinh chỉnh cảnh quan, thương thỏa, thuyết phục UNECO. Ở đó, di sản hóa đi cùng công cuộc khai thác tài nguyên và tầm nhìn phát triển kinh tế du lịch đã có thể thay thế cho hướng phát triển công nghiệp nặng. Người đọc cũng tìm thấy bên trong tiến trình kiến tạo địa điểm và di sản hóa cũng tạo ra/đi kèm với những xung đột, bất đồng, mâu thuẫn của các bên liên quan.

Với 4 thành công nêu trên, có thể nói nghiên cứu “Di sản như một quá trình kiến tạo địa điểm: Trường hợp QTDT Tràng An” là một đề tài mới, có tính cấp thiết.
Về mặt lý luận, nghiên cứu góp phần hệ thống hóa các quan điểm, lần đầu tiên đưa cách tiếp cận kiến tạo địa điểm (placemaking) đặt trong mối quan hệ với quan điểm về di sản hóa (heritagization) trong bối cảnh học thuật tại Việt Nam. Đây là những cách tiếp cận rất mới mẻ đối với chuyên ngành nghiên cứu về di sản văn hóa trong cả nước.
Về mặt thực tiễn qua nghiên cứu tại QTDT Tràng An với quá trình kiến tạo địa điểm và di sản hóa, đề tài đã cung cấp những bài học thú vị, hữu ích về quá trình xây dựng hồ sơ di sản thế giới, quá trình UNESCO ghi danh một di sản… Nghiên cứu có thể là một tham khảo cho các nhà quản lý cũng như cho công tác đào tạo, nghiên cứu di sản, xây dựng và biên soạn hồ sơ di sản thế giới.
Với những khiếm khuyết trong bối cảnh nghiên cứu chung được chỉ ra, công trình thực sự là một nỗ lực đáng khích lệ nhằm góp phần thu hẹp, khỏa lấp khoảng trống học thuật và cũng là nỗ lực có ý nghĩa đáng trân trọng đối với một chuyên ngành khoa học mới như chuyên ngành Di sản học. Sự phân tích kỹ càng, đi sâu vào nghiên cứu trường hợp giúp công trình trở thành tài liệu tốt cho những ai quan tâm đến tiến trình di sản ở Việt Nam.
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Giới thiệu cách tiếp cận kiến tạo địa điểm và quá trình cách tiếp cận này chuyển dịch từ lĩnh vực địa lý nhân văn, thiết kế đô thị sang di sản học; làm rõ mục đích, ý nghĩa; hệ thống hóa các nghiên cứu nổi bật trong nước và quốc tế về di sản thông qua chủ đề này. Cung cấp những hiểu biết sâu hơn về sự tạo thành hệ thống hình ảnh nhận diện, các công trình kiến trúc, bản sắc đương đại của cộng đồng sinh sống trong khu vực Tràng An. Cùng với đó, thông qua dữ liệu nghiên cứu, có thể khắc họa phần nào công cuộc lựa chọn, tinh chỉnh, đàm phán những giá trị/ ý nghĩa đang góp mặt trong Hồ sơ ghi danh Di sản thế giới của Tràng An; qua đó lý giải cách các nhóm đã tham gia như thế nào vào quá trình tạo tác di sản (making heritage) ở khu vực này. Nghiên cứu chỉ ra tác động của quá trình kiến tạo địa điểm theo chiến lược di sản tại QLDT Tràng An lên các nhóm tiếp nhận, cụ thể là người dân địa phương và khách du lịch. Từ đó, nhận diện tiềm năng và thách thức trong công tác bảo tồn, phát huy giá trị tại một khu di sản thế giới ở Việt Nam.
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This text introduces the place-making approach and its evolution from human geography and urban design to heritage studies. The purpose is to systematize national and international studies on heritage, providing a deeper understanding of the formation of identity images, architectural works, and contemporary identities in the Trang An area. Additionally, it aims to depict the process of selecting, refining, and negotiating values in the World Heritage List of Trang An and explain community participation in the heritage creation process in this area. The study also examines the impact of the place-making process and heritage strategy on local people and tourists managed by the Trang An Urban Management Board. It aims to identify the potential and challenges in preserving and promoting values at the Trang An Landscape Complex in Vietnam.

Stage shop scheduling is an emergent area in the field of scheduling. This paper has proposed a biobjective stage shop scheduling problem with total completion time and total tardiness of jobs, as objectives. In order to solve the proposed formulated problem, modified NSGA-II (Nondominated Sorting Genetic Algorithm-II) and modified MOPSO (Multi-Objective Particle Swarm Optimization) have been proposed. A mutation algorithm for NSGA-II and a velocity updation algorithm based on circular motion of alleles (for NSGA-II) and circular motion of particles (for MOPSO) have also been introduced and have been embedded in the proposed algorithms. The experimental results show that NSGA-II performs better than MOPSO in some aspects, whereas MOPSO performs better than NSGA-II in some other aspects.