Stable Strategy Research Articles

Cooperation in the management of charitable resource flows

Charitable resources, as a special type of public good, need effective management to harness public charitable energy, ensure the smooth flow of resources, and promote efficient use of charitable assets. This paper uses systematic management theory and evolutionary game theory to analyze how to manage the flow of charitable resources. The study focuses on the behaviors and cost-benefit dynamics of different groups involved in the charitable resource flow, aiming to find the best management methods to support public welfare charities and maximize benefits. The research assumes three key groups in the charitable resource system: managers, users, and suppliers. It then examines the basic elements of their interactions. By exploring how these groups optimize their strategies and affect each other’s decisions and benefits, the study establishes replicator dynamic equations. Through theoretical analysis using Lyapunov stability theory, it identifies the system’s evolutionary stable strategies, assessing if a stable equilibrium can be achieved among charitable organizations. The numerical simulations verify the theoretical findings and demonstrate the impact of different management methods on the strategies of the three stakeholders. The results provide valuable insights for managers to optimize the flow and management of charitable resources.

Game-theoretic analysis of governance and corruption in China's pharmaceutical industry.

With the rapid development of China's pharmaceutical industry, issues of corruption and regulatory effectiveness have become increasingly prominent, posing critical challenges to public health safety and the industry's sustainable development. This paper adopts a bounded rationality perspective and employs a game-theoretic evolutionary approach to establish a tripartite evolutionary game model involving pharmaceutical companies, third-party auditing organizations, and health insurance regulatory agencies. It analyzes the stable strategies of the parties involved and the sensitivity of key parameters within this tripartite game system. The study reveals that adherence to health insurance regulations by pharmaceutical companies, refusal of bribes by third-party auditing organizations, and the implementation of lenient regulations by health insurance agencies can form an effective governance equilibrium. This equilibrium state contributes to reducing corruption in the pharmaceutical industry, balancing the interests of all parties, and promoting healthy industry development. Pharmaceutical companies must balance compliance costs against the risks of non-compliance benefits while maximizing profits; third-party auditing organizations need to choose between fulfilling their duties and accepting bribes, considering their economic benefits and professional reputation; health insurance regulatory agencies adjust their strategies between strict and lenient regulation to maximize social welfare. The paper suggests enhancing policy support, strengthening compliance supervision, improving audit independence, and adjusting regulatory strategies to optimize governance in the pharmaceutical industry. Additionally, the research highlights the role of collaborative efforts among the three parties in achieving sustainable governance. Furthermore, the study conducts a numerical simulation analysis to demonstrate the impact of various parameters on the evolutionary stability of the system, providing practical insights into the implementation of regulatory policies. This research offers new insights for policy formulation and governance in China's pharmaceutical sector, providing significant reference value for guiding the industry's sustainable development.

Analysis of low-carbon technology transfer strategies based on a quadrilateral evolutionary game

Promoting low-carbon technology transfer is crucial for reducing global greenhouse gas emissions, combating climate change, and fostering sustainable development. Based on the “efficient market” and “active government” concepts, a “government-intermediary-receiver-sender” game model was constructed, identifying evolutionary stable strategies and key factors affecting strategy selection. It was found that (1) receiver and sender needs and government policy incentives increase intermediaries' willingness to participate, reducing input costs and shifting government support from incentives to compensatory regulations; (2) optimal strategies emerge when incentives for low-carbon technology senders, receivers, and intermediaries, and penalties for serious defaults by senders and receivers exceed a threshold, fostering high participation willingness and leading to government implementation of compensatory regulations; (3) differentiated penalties are necessary to avoid a sender-receiver prisoner's dilemma but only ensure one side's willingness to participate, while government incentive-based regulation can overcome negative constraints and promote active participation from both sender and receiver. Compensatory regulations encourage sustained technology transfer costs and reduce incentive costs, with optimization focusing on lowering government publicity costs and increasing economic benefits for intermediaries.

A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods

A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods

Crowdsourcing solutions for innovation: An evolutionary examination of participant behavior strategy

AbstractDespite numerous enterprises embracing crowdsourcing to access several innovative solutions, the prevalence of information asymmetry among different participants has led to an increase in the submission of low‐quality solutions and payment disputes. To improve the efficiency of crowdsourcing solutions for innovation, this study aims to employ an evolutionary game model to capture the dynamic interaction and decision‐making process of the requesters, platforms, and solvers. Initially, we dissect the relevant factors influencing the behavioral decisions of participants to construct a tripartite evolutionary game model. Subsequently, we analyze five potential evolutionarily stable strategies and conditions. Ultimately, we simulate the dynamic evolution of participant decision‐making behavior and the sensitivity of related parameters. The simulation results depict that the initial selection probabilities of populations bear no correlation to the system stability, which only influences the time required to reach equilibrium. The participant's behaviors are affected by price, loss, penalty, compensation, cost, and reputation recognition. Reward and punishment mechanisms help effectively mitigate the emergence of free‐riding and collusion. These findings provide important implications for the sustainable development of crowdsourcing solutions for innovation.

Lunar rhythms and their carry-over effects may shape environmental sex determination in a coral reef fish.

Lunar rhythms shape spawning phenology and subsequent risks and rewards for early life-history stages in the sea. Here, we consider a perplexing spawning phenology of the sixbar wrasse (Thalassoma hardwicke), in which parents spawn disproportionately around the new moon, despite the low survival of these larvae. Because primary sex determination in this system is highly plastic and sensitive to social environments experienced early in development, we ask whether this puzzling pattern of spawning is explained by fitness trade-offs associated with primary sexual maturation. We used otoliths from 871 fish to explore how spawning on different phases of the moon shapes the environments and phenotypes of settling larvae. Offspring that were born at the new moon were more likely to settle (i) before other larvae, (ii) at a larger body size, (iii) at an older age, (iv) to the best quality sites, and (v) as part of a social group-all increasing the likelihood of primary maturation to male. Selection of birthdates across life stage transitions suggests that the perplexing spawning phenology of adults may reflect an evolutionarily stable strategy that includes new moon spawning for compensatory benefits later in life, including preferential production of primary males at certain times.

Inter-individual variability (but intra-individual stability) of overt versus covert rehearsal strategies in a digital Corsi task.

The Corsi (block-tapping) paradigm is a classic and well-established visuospatial working memory task in humans involving internal computations (memorizing of item sequences, organizing and updating the memorandum, and recall processes), as well as both overt and covert shifts of attention to facilitate rehearsal, serving to maintain the Corsi sequences during the retention phase. Here, we introduce a novel digital version of a Corsi task in which i) the difficulty of the memorandum (using sequence lengths ranging from 3 to 8) was controlled, ii) the execution of overt and/or covert attention as well as the visuospatial working memory load during the retention phase was manipulated, and iii) shifts of attention were quantified in all experimental phases. With this, we present behavioral data that demonstrate, characterize, and classify the individual effects of overt and covert strategies used as a means of encoding and rehearsal. In a full within-subject design, we tested 28 participants who had to solve three different Corsi conditions. While in condition A neither of the two strategies were restricted, in condition B the overt and in condition C the overt as well as the covert strategies were suppressed. Analyzing Corsi span, (eye) exploration index, and pupil size (change), data clearly show a continuum between overt and covert strategies over all participants (indicating inter-individual variability). Further, all participants showed stable strategy choice (indicating intra-individual stability), meaning that the preferred strategy was maintained in all three conditions, phases, and sequence lengths of the experiment.

An evolutionary game model for indirect data sharing in manufacturing big data consortium

In the context of the explosive growth of manufacturing data, a manufacturing big data consortium is a collaborative organization aimed at promoting efficient decision-making and technological innovation. It is of vital significance to study how to effectively promote the data sharing among the members of manufacturing big data consortium. In this paper, we propose an evolutionary game model to investigate how to facilitate data sharing in the manufacturing big data consortium. Then, we analyze the synergistic revenue allocation method based on the indirect data sharing mode, and calculate the probability of the ideal event. We explore the influencing factors of data sharing in the manufacturing big data consortium by simulating Evolutionary Stable Strategies. Our findings reveal that the initial willingness of participants has a profound impact on their sharing behavior. Furthermore, the optimal interval revenue allocation coefficient can improve the probability of the manufacturing big data consortium to share data. Factors such as incentive revenue and social penalties positively influence the evolution towards active sharing strategies. In contrast, factors such as speculative revenue and data sharing costs inhibit the evolution towards active sharing strategies.

Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things

By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.

How can rural China achieve sustainable development through inclusive innovation? A tripartite evolutionary game analysis

Promoting inclusive innovation is crucial for achieving inclusive growth and sustainable development. This study investigated the decision-making behaviors and interaction mechanisms among the government, enterprises, and base of the pyramid (BoP) customers in the context of inclusive innovation in rural China by applying evolutionary game theory. A tripartite evolutionary game model was developed, and evolutionarily stable strategies along with their corresponding conditions were derived. Numerical simulations were employed to analyze the dynamic evolution process of stakeholders’ strategy choices under various scenarios and their sensitivity to key parameters. The findings suggest that the government plays different roles throughout the lifecycle of inclusive innovation in rural China, acting as an advocate in the initial stage, a promoter in the growth stage, and gradually diminishing its involvement as the market matures. Furthermore, the study reveals that low subsidies are not conducive to the evolution of optimal solutions for the three stakeholders, while high supervision costs reduce the government’s regulatory willingness, thereby hindering enterprises and the BoP customers from implementing and engaging in inclusive innovation in rural areas. Based on these insights, this research proposed some promotional mechanisms tailored to the different developmental stages of inclusive innovation, drawing from the industry life cycle theory. The insights and model developed in this study extend beyond rural China and offer a valuable framework for enhancing sustainable development in rural areas across other developing countries and regions.

Exponential Stability of Impulsive Switched Nonlinear Systems With Partial Unmeasurable States on Time Scales

Abstract This paper investigates the stability of a class of partially unmeasurable nonlinear impulsive switched dynamical systems on time scales. It provides sufficient conditions for ensuring exponential stability of impulsive switched systems in hybrid time domains using Gronwall's inequality, dimensionality reduction technique, and time scale theory. These conditions apply to systems where nonlinearities affect both continuous and discrete subsystems. Stable strategies involving partial unmeasurable states are obtained. Furthermore, the proposed results allow the switched system to have incomplete measurable states. The effectiveness of these results is demonstrated by several examples, which are accompanied by simulations for comparison.

Multi-Objective Optimization and Stable Operation Control Strategy for Fractional Frequency Transmission System Based on Large Signal Stability Analysis

Multi-Objective Optimization and Stable Operation Control Strategy for Fractional Frequency Transmission System Based on Large Signal Stability Analysis

Amidoxime-based radio-microspheres for Internal Irradiation combined with a checkpoint-blocking nanobody boost antitumor immunity

Trans-arterial radioembolization (TARE) therapy confronts significant technical challenges in the preparation of metal nuclide-labeled microsphere carriers, the development of stable and efficient radionuclide labeling strategies, and the ability to treat multiple small lesions in solid tumors. In this study, we synthesized 177Lu-labeled amidoxime-based polyvinyl alcohol microspheres (177Lu-PVA-g-PAO-Ms) using in situ synthesis of trunk compounds with chem-induced grafting polymerization. The resulting 177Lu-PVA-g-PAO-Ms showed excellent stability in radiolabeling and demonstrated high accumulation and prolonged retention in various preclinical rodent models, as observed through SPECT/CT imaging. The cumulative radioactivity uptake in the tumor reached as high as 12.27 %ID/g. In a mouse subcutaneous metastatic tumor model, we observed a significant abscopal effect of radioimmunotherapy after administering a combination of 177Lu-PVA-g-PAO-Ms and an anti-PD-L1 nanobody. These findings highlight the ability of PVA-g-PAO-Ms to chelate radionuclides efficiently and securely. Furthermore, when combined with nanobodies with enhanced tissue penetration capabilities, these microspheres hold great potential as innovative carrier platforms, offering new therapeutic strategies for integrating TARE with systemic immunotherapy.

Tagging large CNV blocks in wheat boosts digitalization of germplasm resources by ultra-low-coverage sequencing

BackgroundThe massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources.ResultsWe develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb (http://wheat.cau.edu.cn/WheatCNVb/), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties.ConclusionsThe CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.

Brain asymmetry as minimization of free energy: a theoretical model.

The asymmetry between the left and right sides seems to be a general principle of organization of the nervous systems in Bilateria, providing the foundations for a plethora of leftward and rightward biases in behaviour as documented in species ranging from Caenorhabditis elegans nematodes to humans. Several theories have been put forward to account for the existence and maintenance in the evolution of the asymmetric organization of the brain at both individual and population levels. However, what is missing in theorizing about the evolution of brain asymmetry is an overarching general hypothesis that may subsume all different aspects of current models. Here, we tried to provide an overarching general framework based on the energy and free-energy minimization principle, which proved so valuable in other areas of neuroscience. We found that at the individual level the antisymmetric singlet configuration realizes the lowest energy state of the system, whereas at the group level, the spontaneous emergence of directional asymmetry arises as a consequence of the minimization of the free energy of the system, which guarantees its stability and equilibrium. We thus argue that the various phenomenological aspects of brain asymmetry that have been captured in biology-e.g. sparing of neural tissue, control of unitary motor responses and, at the population level, evolutionarily stable strategies described by mathematical games theory-may be thought of as the manifestation of a more general principle of energy minimization generating, among others, asymmetry of the brains.

A study of the evolutionary game of carbon offset involving tourism stakeholders under incentive and constraint mechanisms

Tourism carbon offsetting is a crucial pathway to achieving peak carbon and carbon neutrality in the tourism industry. Accurately grasping the collaborative evolutionary mechanisms among local governments, tourism enterprises, and tourists is key to promoting the implementation of tourism carbon offsetting. By constructing an evolutionary game model involving local governments, tourism enterprises, and tourists in carbon offsetting, this study uses MATLAB to simulate the evolutionary stable strategies under various conditions. Additionally, it dynamically simulates the collaborative strategies of the three parties under the influence of local government incentive and constraint mechanisms. The results indicate that under strong governmental constraint mechanisms, there is a promotion of active participation in carbon offsetting by local governments, tourism enterprises, and tourists. Incentive policies at certain levels also play a positive guiding role. As incentives increase, local subsidies and intervention costs also rise, leading to an evolution towards less enthusiastic participation among the three parties. Appropriately balanced government incentives and penalties are beneficial in achieving an equilibrium of benefits among multiple stakeholders involved in carbon offsetting, thus helping to attain carbon neutrality goals.

An evolutionary game for analysis of implementation strategies in inter-regional diffusion of clean technology

Green transition driven by clean technology diffusion attaches importance to sustainable development. Therefore, choosing the optimal strategy in the inter-regional diffusion of clean technology has attracted the attention of local governments. In this regard, developed regions leading the R&D activities of clean technology can choose to fund or not fund diffusion, and developing regions can adopt absorption strategy or substitution strategy as well. An evolutionary game model was developed in this paper to analyze the stable strategies of local governments in the inter-regional diffusion of clean technology. In general, there are four possible stable equilibrium points. The result indicates that a collaboration relationship aiming at technology transfer among regions is likely to be established when developed regions and developing regions gain more from funding diffusion and subsidizing absorption, respectively. Especially, an increase of incentives from the central government, as well as an increase of cost performing independent clean R&D activities, improves the inter-regional diffusion network. This paper, therefore, provides a mathematical approach to examine the interaction of clean technology diffusion, and sheds light on actor behavior in games of inter-regional coordination.

Warming climate apathy to mitigate the disparity in climate policy support across distinct income strata

Worldwide, nations strive to curtail carbon emissions, but income disparities create challenges in climate policy support. While the Chinese government has employed economic incentives to mitigate the financial burden of green energy transition, these measures may not fully address the underlying issue of climate apathy, wherein individuals prioritize immediate interests over long-term climate concerns. Using a three-year online tracking sample of Chinese adults (n = 4,699), this study investigates the enduring predictive role of income poverty on climate policy support (e.g., green energy transition), alongside the explanatory power of climate apathy and economic burden for this effect. The research reveals that income poverty results in lower climate policy support, with climate apathy explaining 38.29% of this effect, while economic burden accounts for only 8%. Moreover, compared to economic burden, climate apathy has a stronger impact on policy support in regions with different levels of economic development. These findings underscore the potential of addressing climate apathy as a non-economic approach to enhance support for climate policies among low-income groups, offering a cost-effective and stable strategy to bridge the policy support gap caused by income disparity and regional economic imbalances.

Carboxymethyl chitosan modification of cobalt-zinc bimetallic MOF for tetracycline hydrochloride removal: Exploration of the enhancement mechanism of the process

This study synthesized a carboxymethyl chitosan-modified bimetallic Co/Zn-ZIF (CZ@CMC) with strong hydrophilicity and adsorption performance via the one-pot method. Tetracycline hydrochloride (TCH) was used as the model contaminant to evaluate the adsorption and peroxymonosulfate (PMS) activation properties of CZ@CMC. Mechanism showed that the adsorption behavior occurred through pore filling, electrostatic attraction, surface complexation, hydrogen bonding, and π-π stacking. In addition, a CZ@CMC/PMS system was constructed, which had excellent catalytic performance. The hydrophilicity and selective adsorption properties of CMC conferred a greatly accelerated CZ@CMC in catalyzing the PMS process with kobs of 0.095 min−1, in which OH, 1O2, SO4–, O2–, and Co(III) were the main ROS which quenching tests, EPR, and chemical probe experiments verified. In addition, the degradation pathways of TCH were obtained utilizing DFT and HPLC-MS and analyzed to show that the system possessed a good detoxification capacity. This work is expected to provide a green, efficient, and stable strategy to enhance the adsorption properties of catalytic materials and subsequently their co-catalytic properties.

Research on collaborative innovation behavior of enterprise innovation ecosystem under evolutionary game

Establishing an innovation ecosystem revitalizes enterprises by fostering collaboration with other entities within the system. This collaboration results in increased resources for innovation and development, further strengthening participants' innovation and survival capacities. As the existing literature on collaborative innovation behavior within innovation ecosystems is relatively limited, the primary objective of this study is to investigate the evolutionary process and impact factors of players' collaborative innovation behavior in the enterprise innovation ecosystem (EIE). This study establishes an evolutionary game (EG) model that includes three players in an EIE to analyze their collaborative innovation behaviors in scenarios where their initial strategic probabilities are low, medium, and high. The simulation results indicate the existence of six potential evolutionary stable strategies (ESSs) within the system. The ideal ESS identified in this three-party evolutionary game model is characterized by “active collaboration, active collaboration, participation”. Furthermore, the player's behaviors in different states are affected by initial probability, synergy sensitivity, transformation efficiency, output elasticity, subsidy, punishment, and public impact. The results of this study reveal the mechanism of collaborative innovation behavior among different players in the EIE, providing beneficial references for its sustainable development.