Rough Set-Game Theory Information Mining Model Considering Opponents’ Information

In multi-strategy games, the increase in the number of strategies makes it difficult to make a solution. To maintain the competition advantage and obtain maximal profits, one side of the game hopes to predict the opponent’s behavior. Building a model to predict an opponent’s behavior is helpful. In this paper, we propose a rough set-game theory model (RS-GT) considering uncertain information and the opponent’s decision rules. The uncertainty of strategies is obtained based on the rough set method, and an accurate solution is obtained based on game theory from the rough set-game theory model. The players obtain their competitors’ decision rules to predict the opponents’ behavior by mining the information from repeated games in the past. The players determine their strategy to obtain maximum profits by predicting the opponent’s actions, i.e., adopting a first-mover or second-mover strategy to build a favorable situation. The result suggests that the rough set-game theory model helps enterprises avoid unnecessary losses and allows them to obtain greater profits.

An Extension of Testlet-Based Equating to the Polytomous Testlet Response Theory Model

Educational assessments tests are often constructed using testlets because of the flexibility to test various aspects of the cognitive activities and broad content sampling. However, the violation of the local item independence assumption is inevitable when tests are built using testlet items. In this study, simulations are conducted to evaluate the performance of item response theory models and testlet response theory models for both the dichotomous and polytomous items in the context of equating tests composed of testlets. We also examine the impact of testlet effect, length of testlet items, and sample size on estimating item and person parameters. The results show that more accurate performance of testlet response theory models over item response theory models was consistently observed across the studies, which supports the benefits of using the testlet response theory models in equating for tests composed of testlets. Further, results of the study indicate that when sample size is large, item response theory models performed similarly to testlet response theory models across all studies.

Conceptualising Four Categories of Behaviours: Implications for Implementation Strategies to Achieve Behaviour Change

Background: Effectiveness of implementation strategies is influenced by the extent to which they are based on appropriate theories concerning the behaviours that the strategies intend to impact. Effectiveness may be limited simply because the strategies are based on theories that are limited in scope or are derived from partially inaccurate assumptions about the behaviours in question. It may therefore be important to combine insights from various theories to cover the range of influences on the behaviours that will be changed.Aim: This article aims to explore concepts, theories and empirical findings from different disciplines to categorise four types of behaviours and discuss the implications for implementation strategies attempting to change these behaviours.Influences on behaviours: Multilevel influences on behaviours are dichotomized into individual-level and collective-level influences, and behaviours that are guided by conscious cognitive processes are distinguished from those that rely on non-conscious processing. Combining the two dimensions (levels and cognitive modes) creates a 2 x 2 conceptual map consisting of four categories of behaviours. Explicitly conceptualising the levels and cognitive modes is crucial because different implementation strategies are required depending on the characteristics of the behaviours involved in the practise that needs to be changed.Conclusion: The 2 x 2 conceptual map can be used to consider and reflect on the nature of the behaviours that need to be changed, thus providing guidance on the type of theory, model or framework that might be most relevant for understanding and facilitating behaviour change.

Are smart contracts too smart for Supply Chain 4.0? A blockchain framework to mitigate challenges

PurposeSmart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.Design/methodology/approachA game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.FindingsThe numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.Research limitations/implicationsThis work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.Practical implicationsBy using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.Originality/valueThis work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.

Solutions to the routing problem: towards trustworthy autonomous vehicles

The general expectation is that the traffic in the cities will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. Each member of the collective of autonomous vehicles tries to adapt to the changing environment, therefore together they execute decentralised autonomous adaptation by exploiting real-time information about their environment. The routing of these vehicles needs proper computer science models to be able to develop the best information technology for their control. We review different traffic flow models in computer science, and we evaluate their usefulness and applicability to autonomous vehicles. The classical game theory model implies flow level decision making in route selection. Non-cooperative autonomous vehicles may produce unwanted traffic patterns. Improved decentralised autonomous adaptation techniques try to establish some kind of coordination among autonomous vehicles, mainly through intention awareness. The aggregation of the intentions of autonomous vehicles may help to predict future traffic situations. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The review helps to make the first steps towards research on global level control of autonomous vehicles by highlighting the strengths and weaknesses of the different formal models. The review also highlights the importance of research on intention-awareness and intention-aware traffic flow prediction methods.

Does goal ambiguity influence the program structure of nonprofit human service organizations?

This study presents an alternative to outcome management based on accountability by analyzing the ambiguity of nonprofit human service organizations’ program goals and program structures. A total of 761 programs were chosen from the 2010–2020 annual plans of P Rehabilitation Center for analysis. The program goals were analyzed according to quantitative accuracy and outcome attainability standards and the program structures were analyzed using the program theory model. Human service organizations need to have a logical program structure according to the program goal, and it is necessary to minimize logical errors in the program structure.

Study on Overlying Strata Movement and Surface Subsidence of Coal Workfaces with Karst Aquifer Water

The overlying strata layers of coal workfaces with karst aquifer water normally causes serious safety problems due to the precipitation, drainage and water inrush, such as a wide range and long term of surface subsidence. In this study, by taking 10,301 working faces of the Daojiao coal mine in Guizhou Province as the engineering background, the numerical model of water-bearing strata with fluid-solid coupling was established by using UDEC to illustrate the laws of overlying strata movement and surface subsidence. A theory model was proposed to calculate the surface settlement caused by the drainage of aquifer based on the principle of effective stress modified by the Biot coefficient αb. The results showed that the corresponding maximum value (0.72 m) and the range of the surface subsidence with the occurrence of karst aquifer water were larger than that of the overlying strata without karst aquifer water (e.g., the maximum value of surface subsidence with 0.1 m). Moreover, the surface subsidence caused by the drainage of aquifer accounted for 17.8% of the total surface subsidence caused by coal mining. According to the field monitoring of surface subsidence in 10,301 working faces, the maximum value was 0.74 m, which was highly consistent with the results of numerical simulation and theoretical analysis. It verified the accuracy and reliability of the numerical model and the theory model in this study.

Understanding biosphere-precipitation relationships: Theory, model simulations and logical inferences

The four major biophysical controls of vegetation which govern land-atmosphere interaction emanate from the ability or vegetation to. (a) evapotranspire (b) trap solar radiation within leaf organizations. (c) regulate evapotranspiration by stomatal control and (d) modify (generally increase) the surface roughness on the scale of turbulent eddies, Simulation studies with General Circulation Models together with a few observational analyses have provided a rational understanding of vegetation-precipitation interaction. In studies with artificially enhanced vegetation-related processes a strong dependence of rainfall on vegetation has been inferred. For Sahelian and other tropical desert-border regions, where evapotranspiration is small, increasing the surface-albedo (desertification) decreases rainfall. When evaportranspiration and or land-surface roughness are increased in some selected regions - a potential effect of vegetation an increase in local rainfall is produced. The above effects both individually and jointly have simulated increased monsoon rainfall over the Indian subcontinent. Modelling studies directed at understanding the relationship between tropical forests and rainfall with realistic models of the biosphere have simulated a warmer and drier climate in response to Amazonian deforestation. Since forests absorb more solar energy and produce much larger evaportranspiration, as well as moisture convergence through the surface-roughness effect, positive feedback effect of forests on precipitation can be expected naturally. Our new simulation experiments not only reaffirmed the above results but also suggested potential global consequences due to the ongoing deforestation. From a synthesis of modeling results of the last decade, it if further inferred that variations in the biosphere-atmospheric interactions play an important role in redistributing continental precipitation to fulfill the survival and growth requirements of different biomes: forests, pasture, agricultural lands, and deserts.