The article examines the specifics of the legal regulation of the grounds and procedure for bringing to responsibility for non-fulfillment of a particular definition. The existing problems of competition of types of liability (administrative and procedural), as well as judicial practice of their application, are analyzed, and ways to solve them are proposed.

Purpose This paper aims to study the extent and determinants of competition, entry and the effect of competition on prices in public procurement using rich data from Finland. Design/methodology/approach This paper applies quantitative methods to analyze public procurement bidding data linked to firm register data in an auction theoretical framework. More than 17,000 invitations to tender in Finland in 2010–2017 are examined using both descriptive regression analysis and an instrumental variable (IV) design for the price effects. Findings Public procurement in all regions, types of contracting authority and most industries faces the problem of low competition. This is a concern, because prices are shown to decrease with competition, and the estimated price effect is larger in a low competition environment. Contract design is predictive of competition and firm characteristics such as experience and location can predict entry patterns among the potential bidders indicating entry costs. Social implications The lack of competition in public procurement is costly for taxpayers. Reducing entry costs should be the focus of policy design and policy efforts should be targeted to tenders where competition is expected to be low. Originality/value This paper uses large and rich data sets to study competition in public procurement of all sizes in all industries and for a wide range of contracting authorities. Novel entry analysis is facilitated by observing the potential bidders using registrations as proxy and linking these potential bidders to firm data. Registration data also facilitates the use of IVs strategy to study the effects of competition.

Designing optimal trajectories for multi-flyby asteroid missions is scientifically critical but technically challenging due to nonlinear dynamics, intermediate constraints, and numerous local optima. This paper establishes a method that approaches global optimality for multi-flyby trajectory optimization under a given sequence. The original optimal control problem with interior-point equality constraints is transformed into a multistage decision formulation. This reformulation enables the direct application of dynamic programming in lower dimensions and follows Bellman’s principle of optimality. Moreover, the method provides a quantifiable bound on global optimum errors introduced by discretization and approximation assumptions, thus ensuring a measure of confidence in the obtained solution. The method accommodates both impulsive and low-thrust maneuver schemes in rendezvous and flyby scenarios. Several computational techniques are introduced to enhance efficiency, including a specialized solution for bi-impulse cases and an adaptive step-refinement strategy. The proposed method is validated on three Global Trajectory Optimization Competition problems, showing improved fuel efficiency over the best-known solutions and demonstrating its generality and effectiveness in global trajectory optimization.

ABSTRACT This article analyses the competitive problems potentially arising in the foundation models and generative AI value chain by exploring its structural complexities and distinguishing between upstream and downstream dynamics. While upstream markets, such as cloud services, proprietary data and AI chips, have drawn much of the competition policy attention, tipping dynamics, consumer lock-in and foreclosures are most likely to arise in the downstream part of the value chain. This is particularly relevant for mobile ecosystems, where the integration of foundation models with operating systems, on one side, and the development of agentic systems, on the other side, significantly enhances those competitive risks. Consequently, as a complement to competition law scrutiny, ex-ante regulatory intervention seems necessary to ensure market fairness and contestability, according to principles already embedded in the Digital Markets Act.

Breaking the barriers of the ubiquitous “one type of satellite, one system” resource management is an important exploration to enhance the effectiveness of collaborative work of various satellites in satellite networks. This means that flexible cross-domain mission scheduling (CMS) between different service domains becomes possible. However, the coexistence of various missions with differentiated requirements is challenging for efficient collaboration of resources in different service domains to ensure CMS performance. To this end, this paper first establishes a general mission requirement representation model to systematically characterize the differences of various missions to provide an intuitive decision-making reference for the CMS. Hereafter, based on the model, the CMS problem of coupling inter-domain mission transfer and intra-domain resource competition is solved by developing a multi-agent reinforcement learning-based hierarchical intelligent CMS algorithm. The proposed algorithm can dynamically adjust and match the CMS policy according to different mission requirements to obtain performance gain of cross-domain transmission of missions. Besides, the design of an invalid action filter and a collaboration mechanism provides efficient guidance for the CMS under resource and mission requirement constraints. Simulation results demonstrate the significant performance improvement of the proposed algorithm compared with independent domains and the existing CMS algorithms, and it can still guarantee high service performance under different network scales.

The nonnegative representation-based classification only imposes an overall nonnegative constraint on the representation coefficients but fails to apply differentiated penalties on the coefficients of different classes, which inevitably limits its effectiveness. In response, this article proposes a dual flexible competitive nonnegative representation method, which introduces two competitive mechanisms: mean competition and inter-class competition. Mean competition relies on the class-wise mean of training samples as the competitive target, encouraging the representation coefficients to accurately capture the unique features of each class and enhancing the discriminability between the representation coefficients. Inter-class competition fully considers the intrinsic relationship between the overall representation and the class representations, strengthening the competitive representation between the true class and all remaining classes, thereby improving classification performance. Meanwhile, flexible factors are ingeniously incorporated into both competitive terms to effectively reduce interference from incorrect classes in the classification decision. The alternating direction method of multipliers is employed to solve the dual flexible competitive nonnegative representation problem, with a comprehensive explanation of the iterative procedure provided. Experimental findings indicate that the dual flexible competitive nonnegative representation method demonstrates significant advantages in face recognition tasks. The source code will be made available upon the acceptance of this article at https://github.com/li-zi-qi/DFCNR .

A Grammar of the Distinctive Competence Development at the Firm for the Solution of Systemic Problems (JIM, 2020)

The study examines the importance of leadership in developing a strategy to ensure the economic security of enterprises in crisis conditions. The study emphasizes the significance of strategic leadership in fostering resilient and adaptable organizations that can thrive in a dynamic environment. The primary objective of this article is to establish a conceptual framework and methodological tools for identifying and prioritizing threats to economic security, utilizing a combination of classical statistical and ensemble machine learning methods. The study focuses on Ukrainian industrial enterprises using information from PrJSC Khmilnyk Sewing Factory “Lileia”, which faced the full-scale consequences of the turmoil associated with military actions. The empirical base comprises four rounds of expert surveys, yielding 67 reliable observations. These observations were analyzed using the “Random Forest” algorithm to rank twenty identified hazards according to their impact on economic security. The results confirmed the hypothesis that the leadership imperative, manifested through proactive management, is a determining factor in the effectiveness of strategic management and ensuring economic security. Seven key threats were prioritized, among which military actions, market competition, and logistical problems are the most critical. The novelty of the study lies in the integration of leadership-based strategic imperatives with quantitative risk modeling to transform a set of hazards into a ranked subset of threats expressed as normalized risk values. The results can be applied by senior management to improve decision-making processes, align organizational strategies with real threats, and develop adaptive mechanisms for enterprise resilience in crisis and post-crisis conditions.

Joshua Greene's research on moral cognitive processes, the main findings of which were presented to the general public in his book Moral Tribes (2013), forms an indispensable part of a new understanding of ethical reflection. Greene argues that morality has arisen from a set of psychological adaptations that promote in-group cooperation, but at the same time raise a problem of inter-tribal competition. His theory of the dual process of moral judgement and his advocacy of a refined version of utilitarianism as a meta-ethical guide to conflict resolution are essential elements of his ambitious theoretical proposal. Here, we vindicate the value of these ideas and discuss some aspects of his theory.

The rapid development of artificial intelligence (AI) has greatly increased the global demand for high-performance computing. This makes the AI server industry an important part of data centre and intelligent computing infrastructure. This study examines the valuation characteristics of the AI server industry, and pays attention to the Inspur Electronic Information Industry (IEIT), a representative enterprise from China. The study used the price-to-earnings ratio (PE) method to evaluate the company's financial performance, including its historical earnings, industry conditions and market sentiment. Analyze and use financial statement data, similar company indicators and expected returns to derive a reasonable valuation range. In addition, a qualitative assessment was also carried out to examine the company's technical capabilities, ecosystem advantages, supply chain resilience, market position, strategic initiatives and policy suitability. Research results show that the IEIT system benefits from strong technical barriers, economies of scale and favourable national policies, but it also faces challenges such as competition, technological progress and supply chain problems. The study found that the company's fair value is in a good price-to-earnings ratio range, which not only reflects its strategic advantages, but also shows that the AI server industry has cyclical characteristics.

This study was conducted to investigate the challenges farmers face in accessing local markets for agricultural products in the Hissa-e-Duwum Behsud district. The research employed a descriptive-exploratory methodology. Data were collected via a questionnaire administered to 384 farmers. The validity of the questionnaire was confirmed by the advising professor and subject matter experts, and its reliability was measured with a Cronbach's alpha coefficient of 0.715, indicating appropriate instrument reliability. The results revealed that the most significant barriers include high transportation costs, long distances to markets, insufficient information about prices and market demand, a lack of capital, and product storage difficulties. Exploratory factor analysis identified five principal factors: 1) Transportation Challenges to Market, 2) Marketing and Government Support Challenges, 3) Legal and Security Challenges, 4) Climatic and Market Volatility Challenges, and 5) Competition and Raw Material Supply Problems. Together, these factors explain 61.197% of the total variance. Based on the findings, the most critical solutions to mitigate these problems are developing roads and transportation means, providing marketing training, enhancing government support, and constructing adequate storage facilities.

A duopoly competition problem of shared autonomous vehicles in a multimodal transportation system with government regulation

Purpose In this paper, the dynamic system of digital technology, technology supplier and technology consumer is constructed by using the complex system method, and the competitive result of digital technology is predicted by system evolution analysis. Design/methodology/approach Digital technology is prevalent today, and its development and competition have become an important issue. Digital technology is inherently complex, so this paper will provide a forecast from the market perspective of digital technology to help provide management advice to technology providers and governments. Findings The authors estimated the probability of winning the competition for digital technologies and found the difference in effectiveness between the two forms of government funding. Research limitations/implications The authors only use two digital technology competitions as an example and have not yet generalized to three or more cases. Originality/value Analyze and forecast from the Conduct the analysis and prediction from a market perspective rather than a technical perspective. Analyze digital technology using a dynamical configuration model. The competition problem is transformed into a Markov process.

A long-standing goal of artificial intelligence (AI) is to build systems capable of complex reasoning in vast domains, a task epitomized by mathematics with its boundless concepts and demand for rigorous proof. Recent AI systems, often reliant on human data, typically lack the formal verification necessary to guarantee correctness. By contrast, formal languages such as Lean1 offer an interactive environment that grounds reasoning, and reinforcement learning(RL) provides a mechanism for learning in such environments. Here we present AlphaProof, an AlphaZero-inspired2 agent that learns to find formal proofs through RL by training on millions of auto-formalized problems. For the most difficult problems, it uses test-time RL, a method of generating and learning from millions of related problem variants at inference time to enable deep, problem-specific adaptation. AlphaProof substantially improves state-of-the-art results on historical mathematics competition problems. At the 2024 International Mathematical Olympiad competition, our AI system, with AlphaProof as its core reasoning engine, solved three out of the five non-geometry problems, including the competition's most difficult problem. Combined with AlphaGeometry 23, this performance, achieved with multi-day computation, resulted in reaching a score equivalent to that of a silver medallist, marking the first time an AI system achieved any medal-level performance, to our knowledge. Our work demonstrates that learning at scale from grounded experience produces agents with complex mathematical reasoning strategies, paving the way for a reliable AI tool in complex mathematical problem solving.

The problem of source-code analysis using machine-learning techniques has gained much attention recently, as several powerful code-embedding methods have been created. Having different embedding methods available for source code has opened the way to tackling many practical problems in source-code analysis. This paper addresses the problem of determining the number of distinct algorithmic strategies that may be found in a set of correct solutions to a competitive programming problem. To achieve this, we employ a novel unsupervised algorithm that uses a multiview interpretation of data based on different embedding and clustering methods, a multidimensional assignment problem (MAP) to determine a subset of a higher probability of correctness, and a self-training method based on voting to determine the correct clusters of the remaining set. We investigate the following two aspects: (1) whether the proposed unsupervised approach outperforms existing methods when the number K of distinct algorithmic strategies is known and (2) Whether the approach can also be applied to determine the optimal value of K. We have addressed these using seven embedding methods with three clustering strategies in a data-analysis pipeline that tackles the previously described issues on a newly created dataset consisting of 15 algorithmic problems. According to the results, for the first aspect, the proposed unsupervised voting algorithm significantly improves the baseline clustering approach for a known K. This improvement was observed across all problems in the dataset, except one. In the case of the second one, we prove that the proposed method has a negative impact on determining the optimal number K. Scale-up of the data-analysis pipeline to datasets of thousands of problems may yield the ability to profoundly understand and learn about the innovative process of correctly designing and writing code in the context of competitive programming or even industry code.

Indonesia is still classified as a country with a relatively high population growth rate. Economic growth embodied in increased industrial, service, and urban activities requires support from various sectors, including raw water supply. Water resources for food security are related to irrigation's quantity, quality, and accessibility. Irrigation development can take the form of new infrastructure development, maintenance, and protection of the irrigation water source area. The strategic issue of irrigation as a follow-up from this, cannot be separated from the problem of competition in water allocation, the burden of irrigation service costs, and local political issues regarding water control at the site level. On a policy scale, water resource management for food security requires strengthening regulations, political will, land-use change management, coordination, and adequate funding from the central to regional levels according to their respective authorities. This paper attempts to analyze irrigation development policies in the context of water resources development which aims to provide input on food security policies in Indonesia. This study uses qualitative methods through literature studies on water resources management for food security in Indonesia. The literature review method is based on five principal regulations, ten papers, and government reports. The analysis result recommends food security in the irrigated agriculture sector with strategies to increase production capacity and expand services of irrigation networks. Problems with irrigation water availability can be anticipated by conserving upstream watersheds, optimizing and normalizing reservoirs, and building dam infrastructure as water reservoirs for supplies throughout the planting period. Policies related to water resources affect food security through increasing agricultural production, diversifying production (sufficient water availability supports non-rice farming), reducing the risk of dry seasons, increasing resilience to climate change, and reducing water conflicts. Ensuring sustainable food security demands immediate resolution of irrigation challenges through comprehensive water resource management strategies including upstream conservation, reservoir normalization, infrastructure development, improved governance, and community participation.

The work is devoted to the study of organizational difficulties faced by agricultural enterprises when entering modern markets. It examines the current conditions of agricultural enterprises' functioning, the features of the market environment, and the factors that hinder the effective entry of products into domestic and foreign markets. Special attention is paid to the problems of competition, information asymmetry, insufficient financial support, and weak communication links. Practical recommendations are proposed for improving marketing strategies, optimizing product quality management processes, and increasing investment attractiveness.

ABSTRACTThis paper focuses on assortment competition problems where customers' purchase decisions are modeled using a multinomial logit choice model incorporating endogenous network effects. For non‐negative network effects, we introduce pseudo‐revenue‐ordered assortments to solve the best response problem. For negative network effects, where the pseudo‐revenue‐ordered policy is inapplicable, we propose an efficient implicit enumeration algorithm. We show that, under mild conditions, pure‐strategy Nash equilibria exist in assortment competition games, even with both common and exclusive products. Moreover, such equilibria can be computed in polynomial time under non‐negative network effects. Furthermore, we identify a Pareto‐dominant pure‐strategy Nash equilibrium and provide necessary and sufficient conditions for equilibrium uniqueness. Under cardinality constraints, we prove that games with only exclusive products always have a pure‐strategy Nash equilibrium, though such existence may fail to hold in more general cases. We also study cooperation among retailers and investigate the situations under which stable coalitions emerge in various settings. Our findings suggest that greater positive network effects lead to narrower assortments and increased equilibrium payoffs for all retailers, potentially fostering the blockbuster phenomenon. In contrast, stronger negative network effects drive broader assortments and decreased payoffs, potentially leading to a long tail. Thus, considering network effects in assortment decisions is crucial to avoid suboptimal payoffs, especially in competitive environments.

Combining state-of-the-art row generation methods for the competitive facility location problem with multinomial logit choice rule

This study aims to analyze the implementation of the Public Security and Order (Kamtibmas) policy in resolving traders' disputes in Bangsri Village, Sidoarjo Regency. The background of this research departs from the dynamic socio-economic conditions of the Bangsri community, especially in the trade sector, which often causes friction between traders such as stall disputes, unfair competition, and market cleanliness problems. These disputes have the potential to disrupt the stability of Kamtibmas if not handled quickly and wisely. This research uses a qualitative approach with the aim of understanding the process of policy implementation in a real social context. Data was collected through observation, in-depth interviews, and documentation studies, with the main informants consisting of Bhabinkamtibmas, Kasi Trantib, RT Chair, and market traders. The results of the study show that the implementation of the Kamtibmas policy in Bangsri Village has gone quite well, especially from the communication aspect which is the most dominant factor in determining the success of the policy implementation. Fast and transparent two-way communication has been proven to strengthen coordination between Bhabinkamtibmas, village governments, and the community. However, there are still limitations in terms of resources, executive disposition, and bureaucratic structure that are still hierarchical. The implications of this study emphasize the importance of strengthening communication across actors, increasing human resource capacity, and simplifying bureaucracy so that the handling of social conflicts at the village level can take place more effectively, responsively, and sustainably