Hasil untuk "Competition"

J. Bikker, K. Haaf

T. E. Moore, M. Blum, Nancy A. Blum

J. Weiner

B. Nalebuff, J. Stiglitz

M. Spence

A. Sih, P. Crowley, M. McPeek et al.

John D. Wilson

Franklin Allen

M. Niederle, L. Vesterlund

Carmelo Cennamo, Juan Santaló

E. Weyl, M. Fabinger, Miguel Espinosa et al.

K. Schaeck, M. Čihák

G. Charness, David Masclet, M. Villeval

L. Nesta, F. Vona, F. Nicolli

Byung-Jin Park, M. Srivastava, Devi R. Gnyawali

Conghao Zhu, Tingting Wang

Against the backdrop of China's growing emphasis on corporate ESG performance and the prevalence of involutional competition within industries, this study examines the impact of new firm entry on the ESG performance of incumbent firms, using panel data from Chinese A-share listed companies spanning 2008 to 2022. The results reveal a significant inverted U-shaped relationship: moderate levels of new entry enhance ESG performance, whereas excessive entry suppresses it due to intensified resource constraints. Mechanism analysis identifies innovation capacity, profitability, and disclosure quality as key transmission channels. Further moderation analysis shows that supply chain resilience and ownership concentration amplify the effects of competitive pressure, while firm age mitigates them. Moreover, new firm entry primarily affects the social dimension of ESG, especially employee compensation. This study enriches the literature on ESG behavior from a competition-based perspective, uncovers the nonlinear dynamics between market competition and sustainable strategy, and offers both theoretical and empirical insights for corporate governance and policy formulation in emerging markets.

Jim Kioko Katu, Tamás Tóth, László Varga

Feed accounts for up to 80% of poultry production costs, with high-quality grains such as soybean meal and corn traditionally serving as primary ingredients. However, increasing costs and competition for these grains have driven interest in low-grade and unconventional feed ingredients, including by-products like rapeseed meal and cottonseed meal. These alternatives are often constrained by high fiber content, anti-nutritional factors, and reduced nutrient bioavailability. Fermentation has emerged as a promising strategy to address these limitations, enhancing digestibility, palatability, and antioxidant properties while degrading harmful compounds such as tannins, trypsin inhibitors, and free gossypol. Solid- and liquid-state fermentation techniques utilize microbial inoculants, including lactobacilli and <i>Bacillus</i> species, to enzymatically break down complex macromolecules, thereby releasing essential nutrients. When combined with pretreatments like enzymatic hydrolysis, fermentation significantly improves the nutritional quality of feed ingredients while reducing costs without compromising poultry health or performance. This review examines the mechanisms, benefits, and challenges of fermentation techniques in poultry feed production, underscoring the importance of further research to optimize fermentation parameters, identify novel microbial strains, and ensure scalability and safety in industrial applications.

W. Davies

The Disenchantment of Politics: Neoliberalism, Sovereignty and Economics The Promise and Paradox of Competition: Markets, Competitive Agency and Authority The Liberal Spirit of Economics: Competition, Anti-Trust and the Chicago Critique of Law The Violent Threat of Management: Competitiveness, Strategy and the Audit of Political Decision Contingent Neoliberalism: Financial Crisis and beyond Afterword: Critique in and of Neoliberalism

Scott Goodfriend

Scripted agents have predominantly won the five previous iterations of the IEEE microRTS ($μ$RTS) competitions hosted at CIG and CoG. Despite Deep Reinforcement Learning (DRL) algorithms making significant strides in real-time strategy (RTS) games, their adoption in this primarily academic competition has been limited due to the considerable training resources required and the complexity inherent in creating and debugging such agents. RAISocketAI is the first DRL agent to win the IEEE microRTS competition. In a benchmark without performance constraints, RAISocketAI regularly defeated the two prior competition winners. This first competition-winning DRL submission can be a benchmark for future microRTS competitions and a starting point for future DRL research. Iteratively fine-tuning the base policy and transfer learning to specific maps were critical to RAISocketAI's winning performance. These strategies can be used to economically train future DRL agents. Further work in Imitation Learning using Behavior Cloning and fine-tuning these models with DRL has proven promising as an efficient way to bootstrap models with demonstrated, competitive behaviors.

Zishuo Zhu

At present, most of the prediction studies related to antagonistic event-group competitions focus on the prediction of competition results, and less on the prediction of the competition process, which can not provide real-time feedback of the athletes' state information in the actual competition, and thus can not analyze the changes of the competition situation. In order to solve this problem, this paper proposes a prediction model based on Random Forest for the turning point of the antagonistic event-group. Firstly, the quantitative equation of competitive potential energy is proposed; Secondly, the quantitative value of competitive potential energy is obtained by using the dynamic combination of weights method, and the turning point of the competition situation of the antagonistic event-group is marked according to the quantitative time series graph; Finally, the random forest prediction model based on the optimisation of the KM-SMOTE algorithm and the grid search method is established. The experimental analysis shows that: The quantitative equation of competitive potential energy can effectively reflect the dynamic situation of the competition; The model can effectively predict the turning point of the competition situation of the antagonistic event-group, and the recall rate of the model in the test set is 86.13%; The model has certain significance for the future study of the competition situation of the antagonistic event-group.