Decision Mechanism Research Articles

The Executioner Paradox: understanding self-referential dilemma in computational systems

AbstractAs computational systems burgeon with advancing artificial intelligence (AI), the deterministic frameworks underlying them face novel challenges, especially when interfacing with self-modifying code. The Executioner Paradox, introduced herein, exemplifies such a challenge where a deterministic Executioner Machine (EM) grapples with self-aware and self-modifying code. This unveils a self-referential dilemma, highlighting a gap in current deterministic computational frameworks when faced with self-evolving code. In this article, the Executioner Paradox is proposed, highlighting the nuanced interactions between deterministic decision-making and self-aware code, and the ensuing challenges. This article advocates for a re-evaluation of existing deterministic frameworks, emphasizing the need for adaptive decision-making mechanisms in computational systems. By dissecting the Executioner Paradox, the aim is to foster a robust discussion on evolving deterministic frameworks to accommodate the dynamic nature of self-modifying code, thereby contributing a forward-looking lens to the discourse on computational systems amidst advancing AI.

Analysis Of Decision-Making Mechanism for Pricing and Replenishment of Vegetable Commodities Based on Least Squares And ARIMA

This study provides an in-depth study of automatic pricing and replenishment decision-making mechanisms for vegetable commodities based on the relevant data provided. In the research process, firstly, the data were carried out for pre-processing. Secondly, a one-way linear regression model was developed using the least squares method to investigate the relationship between sales volume and cost-plus pricing for each category. The resulting one-dimensional linear regression equations were well fitted, with goodness of fit R2 greater than 0.6. Then, based on the data characteristics of the different categories, appropriate values of q, d and p were selected and a time series (ARIMA) model was built to predict the wholesale price of each category in the coming week. With the final superstore profit as the objective function and the total daily replenishment as the decision variable, an optimization model was constructed for determining the daily replenishment and pricing strategy of each category, and a sensitivity analysis of the model was conducted.

Credit Assignment: Challenges and Opportunities in Developing Human-like Learning Agents

Temporal credit assignment is the process of distributing delayed outcomes to each action in a sequence, which is essential for learning to adapt and make decisions in dynamic environments. While computational methods in reinforcement learning, such as temporal difference (TD), have shown success in tackling this issue, it remains unclear whether these mechanisms accurately reflect how humans handle feedback delays. Furthermore, cognitive science research has not fully explored the credit assignment problem in humans and cognitive models. Our study uses a cognitive model based on Instance-Based Learning Theory (IBLT) to investigate various credit assignment mechanisms, including equal credit, exponential credit, and TD credit, using the IBL decision mechanism in a goal-seeking navigation task with feedback delays and varying levels of decision complexity. We compare the performance and process measures of the different models with human decision-making in two experiments. Our findings indicate that the human learning process cannot be fully explained by any of the mechanisms. We also observe that decision complexity affects human behavior but not model behavior. By examining the similarities and differences between human and model behavior, we summarize the challenges and opportunities for developing learning agents that emulate human decisions in dynamic environments.

Tensile and creep resistance improvement of GH4975 alloy for turbine discs via electron beam smelting layer solidification technology

A difficult-to-deform superalloy for turbine disks, which can be used at 850 °C for a long time, was prepared by electron beam melting layer solidification (EBSL) technology with high purity and fine structure. Subsequently, the ingot was processed by hot extrusion technology instead of conventional hot forging, and then the tensile and creep tests were performed after standard heat treatment. The results show that the EBSL alloy exhibits excellent high-temperature strength and creep rupture life compared to the conventional preparation method. Among them, the yield strengths at 20 °C and 850 °C are 8.1 % and 5.2 % higher than those in the literature, and the creep rupture life at 850 °C and 412 MPa is 1.53 times higher than that in the literature. The dislocation-precipitate interaction reveals that dislocation shearing γ′ phase is the major mechanism at 20 °C and 850 °C tensile, and Orowan looping and stacking fault shearing are the decisive mechanism during creep at 850 °C and 412 MPa. MC carbides remaining during solidification are the main source of cracks during deformation by fracture analysis. The reasons for refining the microstructure and improving the purity by electron beam melting are analyzed and explained. We believe this work can provide new ideas and methods for preparing superalloys.

A generative benchmark for evaluating the performance of fluorescent cell image segmentation

Fluorescent cell imaging technology is fundamental in life science research, offering a rich source of image data crucial for understanding cell spatial positioning, differentiation, and decision-making mechanisms. As the volume of this data expands, precise image analysis becomes increasingly critical. Cell segmentation, a key analysis step, significantly influences quantitative analysis outcomes. However, selecting the most effective segmentation method is challenging, hindered by existing evaluation methods' inaccuracies, lack of graded evaluation, and narrow assessment scope. Addressing this, we developed a novel framework with two modules: StyleGAN2-based contour generation and Pix2PixHD-based image rendering, producing diverse, graded-density cell images. Using this dataset, we evaluated three leading cell segmentation methods: DeepCell, CellProfiler, and CellPose. Our comprehensive comparison revealed CellProfiler's superior accuracy in segmenting cytoplasm and nuclei. Our framework diversifies cell image data generation and systematically addresses evaluation challenges in cell segmentation technologies, establishing a solid foundation for advancing research and applications in cell image analysis.

Data-Driven Mechanisms for a Newsvendor Problem: A Case Study

Reducing food waste is paramount for a sustainable future as its implications are important to achieve sustainable development goals set by the United Nations. In many industry groups, the public awareness of reducing food waste that may potentially emerge along firms’ operations has grown. In the era of Big Data, one of the most pursued exercises of this escalating attention on reducing food waste is to utilize artificial intelligence techniques to incorporate sustainability concerns into the decision framework. Many firms embrace machine learning methods to build effective decision mechanisms that help make efficient and sustainable decisions. In this study, we analyze the impact of blending machine learning approaches with demand forecasting and order quantity decisions for a firm operating in a setting where the market demand is random, and the demand structure is not observable to the firm. The performance of the methodology is evaluated on sunflower seed demand data taken from Tadım company. Our results suggest that the joint consideration of forecasting and ordering decisions using the quantile regression approach can lead the firm to decrease its operational cost by 6% on average.

Exploring the dual routes in influencing sales and adoption in augmented reality retailing: a mixed approach of SEM and FsQCA

Exploring the dual routes in influencing sales and adoption in augmented reality retailing: a mixed approach of SEM and FsQCA

Multi-objective robust optimization of staff scheduling for emergency under stochastic demand

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption.

Quantum physics: A better model to understand consciousness-related brain functions

With the development of quantum mechanics, it is applied to different fields, including biology. As intricate as human brains, quantum physics is replacing classical physics in explaining consciousness-related brain functions. The bilayer phospholipid membrane enables neurons in the brain to store and protect quantum information, and the abundance of 1/2-spin phosphorous creates potential for quantum entanglement that allows information to transfer along long distances and process consciousness. Scientists have used Schrdinger's cat thought experiment to explain how the uncertain and superimposed states in quantum physics can be applied to our decision-making behavior with conditions of "Yes" or "No." Scientists also conducted experiments to witness the quantum entanglement of particles in the brain. The observation of the phenomenon broke the pre-assumption that quantum entanglement is too fragile to occur in the chaotic environment in human brains, and it allows the possibility of ongoing conscious processing there. To further understand the decision-making mechanism, physicists should also integrate the knowledge in neuroscience, psychology, sociology, and other interdisciplinary subjects.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

Decision-Making Mechanism of Farmers in Land Transfer Processes Based on Sustainable Livelihood Analysis Framework: A Study in Rural China

The act of land transfer in rural areas is an important decision-making mechanism for farmers, to enhance resource allocation efficiency and promote capital mobility, and this act is of strategic importance in promoting the level of agricultural scale and mechanization, land system change, and, thus, the sustainable development of livelihoods and production in China. This study aims to explore farmers’ decision-making mechanisms in the process of land transfer in rural areas, by constructing a theoretical framework. Structural equation modeling was used, based on data from a survey of rural families in the Liaoning Province area of Northeastern China. The main findings are as follows: (1) The seven types of exogenous latent variables, including environmental vulnerability, policy, and five main livelihood assets (natural capital, physical capital, financial capital, human capital, and social capital), are intermediary in farmers’ land transfer behavior, which then positively affect farmers’ livelihood outcomes. (2) Among the exogenous latent variables affecting farmers’ land transfer, human assets have the most significant positive effect, followed by social assets and physical assets, family labor force share, frequency of work information exchange, and number of production tools, greatly affect the corresponding variable. (3) Natural assets play the most important role and have a negative effect on farmers’ land transfer decision; contracted area of land per family is the greatest impacted measurable variable of this. The results of the study suggest that the government should strengthen skills training for farmers, improve the land transfer policy system, and provide appropriate subsidies in a regionally targeted manner. Thus, it can promote the transformation of Chinese-style agricultural modernization and achieve rural revitalization.

Overtaking maneuvers on two-lane highways under the microscope: Exploration of a multidimensional framework for the analysis of safety, comfort and efficiency using simulator data

An unreasonable overtaking attempt on two-lane highways could cause drivers to suffer in terms of driving safety, comfort, and efficiency. Several external factors related to the traffic environment (e.g., speed and car type of surrounding vehicles), were found to be the significant factors in drivers’ overtaking performance in the previous studies. However, the microscopic decision-making (e.g., the moments of the occupation of the opposite lane) mechanisms during overtaking, by means of which drivers react to changes in the external traffic environment and adjust their overtaking trajectories, are still need to be explored. Hence, this study had three goals: (i) To explore the spatial characteristics of micro-decisions (MDs) (such as the start and end point) in overtaking trajectories; (ii) To measure three types of performance indicators (i.e., safety, comfort, and efficiency) for the execution of overtaking maneuvers; (iii) To quantitatively explain the microscopic decision-making mechanism in overtaking. Data for overtaking trajectories were collected from driving a simulation experiment where 52 Chinese student drivers completed a series of overtaking maneuvers on a typical two-lane highway under different traffic conditions. Two analyses were conducted: firstly, the distributions of the relative distance between the ego and surrounding vehicles at four key points (i.e., the start, entry, back, and end) in the overtaking trajectory were investigated and clustered to uncover the spatial characteristics of the MDs. Secondly, the safety, comfort, and efficiency of the overtaking were measured by the aggregations of multi-targets collision risks, triaxial acceleration variances, and spatial consumptions respectively based on the Data Envelopment Analysis (DEA), which were further applied in a two-stage SEM model to reveal the quantitative interrelationships among the external factors, microscope decisions and performances in overtaking. We confirmed that the MDs could be considered as the mediating variables between the external factors and overtaking performances. In the presence of the more hazardous traffic environment (e.g., faster traffic flow and impeded by a truck), the safety, comfort and efficiency of overtaking would be deteriorated inevitably. But drivers would execute the overtaking under the longer passing sight distance, migrate their trajectories forward, and shorten the spatial duration to significantly improve the overtaking performances. Based on this mechanism, a overtaking trajectory optimization strategy for the advanced or automatic driving system, was confirmed and concluded that 1) the passing gap should be firstly planned according to the sight distance acceptance of different drivers, which directly determine the upper limit of the safety performance in the overtaking; 2) the trajectory forward migration and shortening the whole duration in overtaking could be effective to enhance the overtaking performances of the overtaking on the two-lane highway; 3) the guidance of the stable control of the steering wheel and gas/brake pedals is essential in the overtaking.

Approaching lifelong learning: An integrated framework for explaining decision-making processes in personal and professional development

Background: Individual differences in commitment to lifelong learning, a process aimed at seizing opportunities for self-development, have not been extensively studied. Objective: Our aim is to provide a comprehensive understanding of the decision-making mechanisms involved in pursuing learning for self-development. Method: We conducted a literature review on the taxing nature of cognitive exertion and its impact on the inclination to engage in cognitively demanding tasks for learning, as well as individual differences in sensitivity to aversive or rewarding outcomes inherent in the learning process. Results: Our findings indicate that the Expected Value of Control (EVC) theory can elucidate the former, while research on approach-avoidance motivation can shed light on the latter. Conclusion: We propose and develop an integrated framework that incorporates both lines of research. This framework holds relevance for neuropsychology, experimental psychology, and education psychology, offering theoretical guidance for tailoring learning experiences to enhance engagement and commitment to self-development.

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Abstract Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

Using Drift Diffusion and RL Models to Disentangle Effects of Depression On Decision-Making vs. Learning in the Probabilistic Reward Task.

The Probabilistic Reward Task (PRT) is widely used to investigate the impact of Major Depressive Disorder (MDD) on reinforcement learning (RL), and recent studies have used it to provide insight into decision-making mechanisms affected by MDD. The current project used PRT data from unmedicated, treatment-seeking adults with MDD to extend these efforts by: (1) providing a more detailed analysis of standard PRT metrics-response bias and discriminability-to better understand how the task is performed; (2) analyzing the data with two computational models and providing psychometric analyses of both; and (3) determining whether response bias, discriminability, or model parameters predicted responses to treatment with placebo or the atypical antidepressant bupropion. Analysis of standard metrics replicated recent work by demonstrating a dependency between response bias and response time (RT), and by showing that reward totals in the PRT are governed by discriminability. Behavior was well-captured by the Hierarchical Drift Diffusion Model (HDDM), which models decision-making processes; the HDDM showed excellent internal consistency and acceptable retest reliability. A separate "belief" model reproduced the evolution of response bias over time better than the HDDM, but its psychometric properties were weaker. Finally, the predictive utility of the PRT was limited by small samples; nevertheless, depressed adults who responded to bupropion showed larger pre-treatment starting point biases in the HDDM than non-responders, indicating greater sensitivity to the PRT's asymmetric reinforcement contingencies. Together, these findings enhance our understanding of reward and decision-making mechanisms that are implicated in MDD and probed by the PRT.

Rural community response to accidental toxic gas release: An individual emergency response model during self-organized evacuations

The emergency response ability in rural areas is weaker than in urban areas. Once a major accident occurs in a rural area, it is more likely to cause serious consequences. Thus, we propose a individual emergency response model during self-organized evacuations after accidental toxic gas releases in rural areas. The proposed model can reproduce emergency information dissemination and individual emergency decision-making in self-organized evacuations. We propose an improved susceptible infected recovered model for emergency information dissemination process. At the same time, we propose a emergency decision-making mechanism to simulate individual emergency actions. In this model, individual personality characteristics will affect the accessing and disseminating individual emergency information. Then, we conduct a simulation experiment based on methanol leakage in a rural community. Through comparative experiments, we found that the traditional model may be too pessimistic in estimating emergency information dissemination results, underestimating shadow evacuation. In noncompliance evacuation areas, loudspeakers can inhibit individuals from unnecessarily evacuating who are close to it, and may cause individuals far away from it to evacuate unnecessarily. When formulating emergency response strategies, managers should focus on groups with weak access to information and consider the impact of loudspeaker locations on individual emergency response actions.

Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

The Divergence of Psychiatric and Medical Decision-Making Mechanisms for Patients Who Lack Capacity and Its Lasting Impact on the Delivery of Psychiatric Care

The Divergence of Psychiatric and Medical Decision-Making Mechanisms for Patients Who Lack Capacity and Its Lasting Impact on the Delivery of Psychiatric Care

Comprehensive Exploration and Implementation of Multi-Armed Bandit Algorithms Across Various Domains

This paper presents a comprehensive analysis of Multi-Armed Bandit (MAB) algorithms, elucidating their decision-making mechanisms in uncertain environments and their widespread applications in fields such as recommendation systems, advertisement delivery, network traffic control, and medical experiment design. Despite the notable successes of MAB algorithms, they encounter significant challenges in practical deployment, including the balance between exploration and exploitation, addressing unfairness, and managing large-scale implementations. An in-depth theoretical and practical examination of MAB algorithms is thus both theoretically and practically vital. This study conducts a meticulous literature review, theoretical analysis, and empirical research to offer insights into the current state and future prospects of MAB algorithms. It begins with a literature review, mapping out the research landscape and developmental trajectory of MAB algorithms. This is followed by a theoretical dissection, delving into the fundamental theories and diverse applications of MAB algorithms, with a focus on their utilization in recommendation systems, vehicle edge computing, and taxi route recommendation systems. Empirical research is then employed to validate proposed solutions and enhancement strategies. The study reinterprets the internal mechanics of MAB algorithms, affirming their effectiveness and superiority across various domains. The final section addresses the practical challenges and issues faced by MAB algorithms, such as fairness concerns and scalability. Corresponding solutions and improvement strategies are suggested, aiming to enhance the efficiency and applicability of MAB algorithms in real-world scenarios.

Microsoft's Business Model and Strategy Adjustment under ESG Standard

Environmental, Social and Governance (ESG) is an increasingly important consideration in investment and business operations. It covers corporate performance in environmental protection, social responsibility, and good governance. ESG factors can influence the long-term sustainability of a business, positively impacting shareholders, employees, customers, and society. Microsoft, as the world's leading technology company, is also actively paying attention to and practicing ESG principles. The company has undertaken initiatives aimed at reducing carbon emissions, promoting the use of renewable energy, and reducing waste of resources. At the same time, Microsoft is also working to promote diversity and inclusion to ensure equal rights and opportunities for employees. In terms of governance, the company has implemented a sound decision-making and oversight mechanism to ensure transparency and compliance in its business.