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This article examines the accuracy and bias inherent in the wisdom of crowd effect. The purpose is to clarify what kind of bias crowds have when they make predictions. In the theoretical inquiry, the effect of the accumulated absolute deviation was simulated. In the empirical study, the observed biases were examined using data from forecasting foreign exchange rates.

In the theoretical inquiry, the effect of the accumulated absolute deviation was simulated based on mathematical propositions. In the empirical study, the data from 2004 to 2011 were provided by Nikkei, which holds the “Nikkei Yen Derby” competition. In total, 3,657 groups forecasted the foreign exchange rate, and the first prediction was done in early May to forecast the rate at the end of May. The second round took place in June in a similar manner.

The average absolute deviation in May was smaller than that in June. The first round of prediction was more accurate than the second round one. Predictors were affected by the observable real exchange rate, such that they modified their forecasts by referring to the actual data in early June. An actuality bias existed when the participants lost their diverse prospects. Since the standard deviations of the June forecasts were smaller than those of May, the fact-convergence effect was supported.

This article reports novel findings that affect the wisdom of crowd effect—referred to as actuality bias and fact-convergence effect. The former refers to a forecasting bias toward the observable rate near the forecasting date. The latter implies that predictors, as a whole, indicate smaller forecast deviations by observing the realized foreign exchange rate.

Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.

In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM.

The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τ_c method and P_d method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations.

At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time.

The current paper is a brief review of the emerging field of quantum-like modelling in game theory. This paper aims to explore several quantum games, which are superior compared to their classical counterparts, which means either they give rise to superior Nash equilibria or they make the game fairer. For example, quantum Prisoners Dilemma generates Pareto superior outcomes as compared to defection outcome in the famous classical case. Again, a quantum-like version of cards game can make the game fairer, increasing the chance of winning of players who are disadvantaged in the classical case. This paper explores all the virtues of simple quantum games, also highlighting some findings of the authors as regards Prisoners Dilemma game.

As this is a general review paper, the authors have not demonstrated any specific mathematical method, rather explored the well-known quantum probability framework, used for designing quantum games. They have a short appendix which explores basic structure of Hilbert space representation of human decision-making.

Along with the review of the extant literature, the authors have also highlighted some new findings for quantum Prisoners Dilemma game. Specifically, they have shown in the earlier studies (which are referred to here) that a pure quantum entanglement set up is not needed for designing better games, even a weaker condition, which is classical entanglement is sufficient for producing Pareto improved outcomes.

Theoretical research, with findings and implications for future game designs, it has been argued that it is not always needed to have true quantum entanglement for superior Nash Equilibria.

The main purpose here is to raise awareness mainly in the social science community about the possible applications of quantum-like game theory paradigm. The findings related to Prisoners Dilemma game are, however, original.

This method will become a new development trend in subgrade structure design for high speed railways.

This paper summarizes the structural types and design methods of subgrade bed for high speed railways in China, Japan, France, Germany, the United States and other countries based on the study and analysis of existing literature and combined with the research results and practices of high speed railway subgrade engineering at home and abroad.

It is found that in foreign countries, the layered reinforced structure is generally adopted for the subgrade bed of high speed railways, and the unified double-layer or multi-layer structure is adopted for the surface layer of subgrade bed, while the simple structure is adopted in China; in foreign countries, different inspection parameters are adopted to evaluate the compaction state of fillers according to their respective understanding and practice, while in China, compaction coefficient, subsoil coefficient and dynamic deformation modulus are adopted for such evaluation; in foreign countries, the subgrade top deformation control method, the subgrade bottom deformation control method, the subsurface fill strength control method are mainly adopted in subgrade bed structure design of high speed railways, while in China, dynamic deformation control of subgrade surface and dynamic strain control of subgrade bed bottom layer is adopted in the design. However, the cumulative deformation of subgrade caused by train cyclic vibration load is not considered in the existing design methods.

This paper introduces a new subgrade structure design method based on whole-process dynamics analysis that meets subgrade functional requirements and is established on the basis of the existing research at home and abroad on prediction methods for cumulative deformation of subgrade soil.

Pragmatism is very relevant to workplace management and performance measurement, yet in the accounting literature, it is a term used loosely and in a colloquial manner. By drawing on a framework based on classical pragmatism, this study aims to examine how a pragmatic perspective is discernible in the form and use of management control (MC) practices.

This study collects data using a case study of a firm in the green energy construction sector.

Building on the analytical framework, this study provides evidence that a pragmatic perspective is discernible in both form and use of MC practices, through a clear focus on targets rather than variance analysis, the presence of mutable local MC practices characterised by interaction and problem-solving and the absence of other common MC practices with no clear links to ends-in-view. This study also provides evidence of the potential limitations of a pragmatic perspective including myopia and an exacerbation of the inherent bias in organisations towards exploitation.

This research brings analytical clarity to the study of pragmatism in the accounting literature and insights into how a pragmatic perspective is discernible in the form and use of MC practices. Further, the study shows the potential limitations of a pragmatic perspective for management.

This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels.

This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review.

The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies.

This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.