Soft Computing最新文献

Complementors who provide content on platforms are increasingly threatened by the entry of platform owners. Platform owners may enter the content market through offering vertically differentiated content either by self producing or hiring the complementor to produce. We build a game-theoretic model to analyze the platform owner’s entry decisions and the complementor’s response strategy considering the effects of demand complementarity, vertical content differentiation and consumer heterogeneity to both players’ strategies. We find that vertical content differentiation relaxes boundary conditions of entry, and it is more obvious when the platform owner has advantage in content value. However, we show that though the complementor may hold advantages on content value, price, or sales volume, it faces dependent dilemma once entry happens. Further, we demonstrate that second-party cooperation may mitigate the dependent dilemma and create a “win–win” situation through leveraging the platform owner’s efficiency in marketing and the complementor’s efficiency in content producing.

In the social media environment, ad (advertise) information diffusion can effectively increase the ad promotion effect and promote product marketing. Therefore, it is essential to study the mechanism of ad information diffusion in social media. This study aims to help cope with the complexity and uncertainty in social media ad information diffusion systems by identifying the causal relationships behind ad information diffusion behavior and clarifying the feedback mechanisms of system factors, and then testing the validity of the system model through simulation. Specifically, this study innovatively combines system dynamics and uncertainty theory to construct a dynamic model of ad information diffusion system. Particularly, the uncertainty effect of environmental noise on the ad information diffusion system is considered and portrayed as a Liu process. This study can explore the diffusion mechanism of ad information more precisely, so as to better serve the ad promotion industry.

In traditional regression analysis, the observed data are all accurate, but the observed data that we can obtain in real life is often not accurate. For that reason, there is the uncertain regression analysis based on the uncertain variable, in the framework of the uncertainty theory. Under the premise of imprecise observations, the data obtained often contains outliers due to human input errors or incorrect measurements. Outliers can affect parameter estimation, resulting in misleading results and making model fitting inaccurate. In parameter estimation, the most commonly used method is least squares estimation, but this method is extremely sensitive to outliers and makes parameter estimation inaccurate. To solve this problem, this paper proposes an uncertain regression model based on ridge estimation, which adds a square penalty term when performing least squares estimation of unknown parameters. The advantage of ridge estimation is that the tolerance of pathological data is much better than other parameter estimation methods, which can reduce the influence of outliers. In this paper, the optimal shrinkage parameter is determined by K-fold cross-validation to estimate the parameters of the regression model, and then we conduct the residual analysis and hypothesis test on the fitted model to obtain the predicted value and the predicted confidence interval. Finally, the validity of the model is demonstrated by two numerical examples.

Humanities and Social Sciences (HSS) play a key role for deepening human understanding and transforming the world, providing essential value orientation for the advancement of natural sciences. Evaluating the efficiency of HSS in higher education institutions is crucial for optimizing the distribution of research resources and improving scientific research efficiency. By utilizing statistical data from HSS in colleges and universities across 31 provinces (municipalities, regions) of China (excluding Hong Kong, Macao, and Taiwan) from 2017 to 2020, this study employs the Data Envelopment Analysis (DEA) and Malmquist models to assess both the static and dynamic scientific research efficiencies. Additionally, the Theil index is used to examine regional differences. The findings reveal that: (1) HSS in colleges and universities exhibit relatively high static efficiency, with the Malmquist index indicating a general trend of improvement primarily driven by efficiency change (EC); (2) colleges and universities in China’s central region display higher static efficiency than those in the western region, yet they lack the internal driving forces for fostering research efficiency; (3) an analysis of static and dynamic efficiencies via the Theil index shows widening regional differences, with within-group differences being the main source of overall differences. The above results imply that each region should adopt different countermeasures to improve scientific research efficiency.

In this paper, we study optimal pricing strategy decisions of the monopolist in a social network under a fuzzy environment, in which consumers experience a nonnegative network effect that is influenced by their neighbors’ consumption level, the extent to which they are affected is considered as a fuzzy variable. To derive the equilibrium solution, we establish a two-stage game model for decision processes in consumer social networks. Utilizing the backward induction, we first get the expected consumption equilibrium, then figure out the matrix expression of unique pricing equilibrium by maximizing the monopolist’s profit. In addition, we introduce Fuzzy Bonacich Centrality, and find out components of the price each consumer charged by the monopolist in a fuzzy network, this points out the importance of the monopolist knowing consumer network structure. By conducting numerical studies, we find that the network effect plays an essential role in deciding pricing strategies in fuzzy social networks, but fuzziness would weaken this impact. For social networks with fuzziness existing, the monopolist should choose discriminatory pricing strategy to benefit most. The results of our model can provide valuable managerial insights when helping the monopolist make pricing decisions.

In 2012, the European Court of Justice introduced the ban on differentiating car insurance premiums for gender to avoid gender inequality. This paper deals with a gender analysis of driving ability by investigating the relationship between gender and the relative total claim amount in Motor Third Party Liability insurance, also considering the effect of age. Leveraging a two-part model based on parametric quantile regression, we want to investigate the average behaviour of drivers and their tail behaviour in order to highlight the importance of dispersion and the impact of largest claims. As a consequence, the purpose of our contribution is to study how gender and age can influence the entire probability distribution of the insurance claim with a particular focus on the quantiles with high probability levels, which are very important indicators to determine the effective riskiness of a driver. We apply our model to an Australian insurance dataset; our results suggest that men are in general riskier in terms of both average and tail behaviour.

In this study, we introduce the Sheffer stroke L-algebra and prove some fundamental theorems, propositions and lemmas of Sheffer Stroke L-algebras. The notions of filter and ultrafilter for Sheffer stroke L-algebra are studied. We give subalgebra and normal subset definitions of a Sheffer stroke L-algebras. Moreover, a homomorphism between Sheffer stroke L-algebras is introduced and isomorphism theorems are presented. Finally, we give three new algorithms for Sheffer stroke L-algebras. Thus, it is contributed to researchers on different application areas by presenting an algorithmic approach on this subject, for the first time in the literature.

The rapid growth of private car ownership has led to significant issues such as traffic congestion and environmental pollution. Ridesharing has emerged as a promising solution to alleviate the negative impacts associated with private car usage. This paper focuses on the stability of ridesharing systems and establishes a single-driver multiple-rider ridesharing matching model. To solve this model, a filtering algorithm for the pre-matching set and a fast-solving algorithm for stable matching scheme are proposed. Furthermore, we introduce the concept of subsidy distance upper limit into the ridesharing system. Remarkably, our findings indicate that with a limit of 0.1km, the distance saved generated by the subsidy amounts to 560.5% of the total subsidy. To validate our approach, we simulate ridesharing demand data using real taxi data, and design computational experiments to prove the computational efficiency of the filtering algorithm and fast-solving algorithm. The impact of various parameters on ridesharing systems is also explored.

The aim of this paper is to discuss algebraic rough set and its relationships with convex space, rough set and generalized neighborhood space. Specifically, the notion of algebraic relations is introduced and a pair of lower approximation operator and upper approximation operator are presented. Then, several conditions of algebraic relations such as seriality, reflexivity, (resp., weak, primitive) symmetry and (resp., strong) transitivity are characterized by algebraic approximation operators. Based on this, relationships among algebraic rough sets, convex structures and generalized neighborhood systems are investigated. It is proved that the category of reflexive and transitive algebraic rough spaces is isomorphic to the category of convex spaces. In particular, the category of reflexive, weakly symmetric and transitive algebraic rough spaces is isomorphic to the category of convex matroids and the category of reflexive, weakly symmetric and transitive algebraic generalized neighborhood spaces.

Grapheme-to-phoneme (G2P) conversion technology is currently dominated by two methodologies: knowledge-based and data-based approaches. Knowledge-driven methods struggle to adapt to extensive datasets, while data-driven methods rely heavily on high-quality data and require precise feature selection for model construction. To address these challenges, this research aims to propose an integrated approach that combines prior knowledge with data-driven techniques for automatic G2P conversion in the Korean language. In this work, we extract attributes based on pronunciation rules and phonetic transformations between Korean words to construct a decision tree. Subsequently, the model is trained using a data-driven approach for automated phonetic transcription. The proposed integrated model achieves more accurate alignment between input and output variables, effectively capturing phonological variations in continuous Korean speech, and determining corresponding phonemes for graphemes. Rigorous cross-validation confirms its superiority, with an average accuracy of 94.63% in grapheme-to-phoneme conversion, outperforming existing methodologies. In conclusion, this research demonstrates the effectiveness of an integrated approach combining prior knowledge and data-driven techniques for G2P conversion in Korean. The high accuracy and performance of this method are significant for Korean G2P. Our approach can also be applied to low-resource or endangered languages that already have some linguistic research foundation to improve the accuracy of the pronunciation lexicon of the language.