Налоговое или монетарное стимулирование? Эволюционные аргументы в пользу налоговых реформ

Статья посвящена исследованию проблемы обоснования мер регулирования развития эмерджентной экономики - фискальных и (или) монетарных, с использованием методов эволюционного моделирования. Для этого была построена экономико-математическая модель, имитирующая процессы коэволюции развитой и развивающейся стран, связанных через глобальные цепочки создания стоимости. В этой модели каждая из стран характеризуется собственной исходной структурой экономических субъектов, определяемой соотношением предприятий-эгоистов (предрасположенных к консервативному поведению) и предприятий-альтруистов (предрасположенных к инновационному поведению), а также специфическим населением и демографическими процессами. Результаты вычислительных экспериментов показали, что успех того или иного способа экономического регулирования принципиально зависит от особенностей исходного состояния институциональной среды хозяйствования. В институциональной среде с «прозрачными» длинными правилами игры и, соответственно, длинным горизонтом хозяйственного планирования наилучший результат в виде высоких темпов роста производства в эмерджентной экономике дает политика дешевых денег в сочетании с высокими «европейскими» налогами. Иная ситуация наблюдается в более реалистичной ситуации с короткими правилами игры и, соответственно, коротким (не более 5 лет) горизонтом хозяйственного планирования. В этом случае любая налоговая политика (низкие или высокие налоги) в сочетании любыми деньгами (дешевыми или дорогими), в определенном смысле теряет значение, поскольку изначально отсталая инновационная система не позволяет быстро получать высокие результаты, а преимущества экономического роста в отделенном будущем не принимаются во внимание. Вместе с тем, для постепенного формирования лучшей инновационной системы низкие налоги и дешевые деньги имеют важное значение, поскольку создают лучшие условия для выживания предприятийальтруистов, облегчая им инвестиционную деятельность, способную принести многократный прирост технической производительности и экономической эффективности. В любом случае, в контексте эволюционной экономической теории, исходя из проведенных вычислительных экспериментов, налоговая политика в условиях эмерджентных рынков сохраняет свой регуляторный потенциал, и, таким образом, требует дальнейшего реформирования в контексте «новой реальности», основанной на глобальных цепочках создания стоимости.The article deals with the problem of substantiation of the emergent economies development regulatory measures (fiscal and / or monetary), using the evolutionary modelling methods. For this purpose, the mathematical model was constructed that simulates the co-evolution process of the advanced and developing countries, linked by global value chains. In this model, each country is characterized by its original structure of economic entities, defined by the ratio of the egoistic enterprises (predisposed to conservative behaviour) to the altruistic enterprises (predisposed to innovation), as well as by specific population and demographic processes. The results of the computational experiments have shown that the success of economic regulation fundamentally depends on the peculiarities of the initial state of the institutional environment. In the institutional environment with the «transparent» long behaviour and, accordingly, a long economic planning horizon, the best result in the form of average annual production growth rate of the emergent economies is provided by the cheap money policy combined with the high European taxes. A different situation is observed in more realistic short behaviour and, accordingly, short (under 5 years) economic planning horizon. In this case, any tax policy (neither low nor high taxes) together with any money (neither cheap nor expensive), to a certain extent loses its significance, as the initially backward innovative system does not allow to quickly get good results, and the long-term benefits of the potential economic growth are not taken into consideration. However, low taxes and cheap money are important as they create better conditions for survival of the altruistic enterprises, facilitating their investment activities, which can multiply increase their technical performance and economic efficiency. Still, in the context of the evolutionary economics and following the conducted computational experiments, the fiscal policy in terms of emerging markets retains its regulatory capacity, and therefore requires further reforms in the context of the «new reality» based on the global value chains

Optimal distribution of incentives for public cooperation in heterogeneous interaction environments

In the framework of evolutionary games with institutional reciprocity, limited incentives are at disposal for rewarding cooperators and punishing defectors. In the simplest case, it can be assumed that, depending on their strategies, all players receive equal incentives from the common pool. The question arises, however, what is the optimal distribution of institutional incentives? How should we best reward and punish individuals for cooperation to thrive? We study this problem for the public goods game on a scale-free network. We show that if the synergetic effects of group interactions are weak, the level of cooperation in the population can be maximized simply by adopting the simplest "equal distribution" scheme. If synergetic effects are strong, however, it is best to reward high-degree nodes more than low-degree nodes. These distribution schemes for institutional rewards are independent of payoff normalization. For institutional punishment, however, the same optimization problem is more complex, and its solution depends on whether absolute or degree-normalized payoffs are used. We find that degree-normalized payoffs require high-degree nodes be punished more lenient than low-degree nodes. Conversely, if absolute payoffs count, then high-degree nodes should be punished stronger than low-degree nodes.Comment: 19 pages, 8 figures; accepted for publication in Frontiers in Behavioral Neuroscienc

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

Learning to Coordinate with Anyone

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that need to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

A review of population-based metaheuristics for large-scale black-box global optimization: Part B

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research

Monte Carlo Tree Descent for Black-Box Optimization

The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima. Monte Carlo Tree Search (MCTS) methods have recently been introduced to improve Bayesian optimization by computing better partitioning of the search space that balances exploration and exploitation. Extending this promising framework, we study how to further integrate sample-based descent for faster optimization. We design novel ways of expanding Monte Carlo search trees, with new descent methods at vertices that incorporate stochastic search and Gaussian Processes. We propose the corresponding rules for balancing progress and uncertainty, branch selection, tree expansion, and backpropagation. The designed search process puts more emphasis on sampling for faster descent and uses localized Gaussian Processes as auxiliary metrics for both exploitation and exploration. We show empirically that the proposed algorithms can outperform state-of-the-art methods on many challenging benchmark problems.Comment: 17 pages, published in NeurIPS 202