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"decision time"
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Entry-Exit Decisions with Implementation Delay Under Uncertainty
We employ a natural method from the perspective of the optimal stopping theory to analyze entry-exit decisions with implementation delay of a project, and provide closed expressions for optimal entry decision times, optimal exit decision times, and the maximal expected present value of the project. The results in conventional research were obtained under the restriction that the sum of the entry cost and exit cost is nonnegative. In practice, we may meet cases when this sum is negative, so it is necessary to remove the restriction. If the sum is negative, there may exist two trigger prices of entry decision, which does not happen when the sum is nonnegative, and it is not optimal to enter and then immediately exit the project even though it is an arbitrage opportunity.
Journal Article
HBR guide to getting the right work done
\"In the HBR Guide to Getting the Right Work Done, you'll discover how to focus your time and energy where they will yield the greatest reward. Not only will you end each day knowing you made progress-your improved productivity will also set you apart from the pack. Whether you're a new professional or an experienced one, this guide will help you: Prioritize and stay focused; Work less but accomplish more; Stop bad habits and develop good ones; Break overwhelming projects into manageable pieces; Conquer e-mail overload; Write to-do lists that really work.
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Analysis of a two grade system when Interdecision times have exponential geometric distribution
Consider any single graded marketing organization where depletion of manpower occurssince decisions, exit of personnel etc.. There is an assumption that the depletion due to voluntary exit is correlated. By assuming that the inter-involuntary exit times, inter-breaking decision times forms different modified renewal processes, estimate dmean and estimated variance of time to recruitment are determined. The stochastic model assuming that intercontact times between successive contacts as correlated random variables are proposedShock models with intercontact time have been obtained by assuming the threshold distribution as exponential. In this paper, it is assumed that threshold follows exponentialgeometric distribution.
Journal Article
Internet of Things, Real-Time Decision Making, and Artificial Intelligence
In several earlier papers, the author defined and detailed the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use. Adding another layer of physical sensors could then enhance its smartness and intelligence, especially if it were to be connected with other servgoods—thus, constituting an Internet of Things (IoT) or servgoods. More importantly, real-time decision making is central to the Internet of Things; it is about decision informatics and embraces the advanced technologies of sensing (i.e., Big Data), processing (i.e., real-time analytics), reacting (i.e., real-time decision-making), and learning (i.e., deep learning). Indeed, real-time decision making (RTDM) is becoming an integral aspect of IoT and artificial intelligence (AI), including its improving abilities at voice and video recognition, speech and predictive synthesis, and language and social-media understanding. These three key and mutually supportive technologies—IoT, RTDM, and AI—are considered herein, including their progress to date.
Journal Article
Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions
Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.
Journal Article
Response time in economic games reflects different types of decision conflict for prosocial and proself individuals
Behavioral and neuroscientific studies explore two pathways through which internalized social norms promote prosocial behavior. One pathway involves internal control of impulsive selfishness, and the other involves emotion-based prosocial preferences that are translated into behavior when they evade cognitive control for pursuing self-interest.Wemeasured 443 participants’ overall prosocial behavior in four economic games. Participants’ predispositions [social value orientation (SVO)] were more strongly reflected in their overall game behavior when they made decisions quickly than when they spent a longer time. Prosocially (or selfishly) predisposed participants behaved less prosocially (or less selfishly) when they spent more time in decision making, such that their SVO prosociality yielded limited effects in actual behavior in their slow decisions. The increase (or decrease) in slower decision makers was prominent among consistent prosocials (or proselfs) whose strong preference for prosocial (or proself) goals would make it less likely to experience conflict between prosocial and proself goals. The strong effect of RT on behavior in consistent prosocials (or proselfs) suggests that conflict between prosocial and selfish goals alone is not responsible for slow decisions. Specifically, we found that contemplation of the risk of being exploited by others (social risk aversion) was partly responsible for making consistent prosocials (but not consistent proselfs) spend longer time in decision making and behave less prosocially. Conflict between means rather than between goals (immediate versus strategic pursuit of self-interest) was suggested to be responsible for the time-related increase in consistent proselfs’ prosocial behavior. The findings of this study are generally in favor of the intuitive cooperation model of prosocial behavior.
Journal Article
Hierarchical under frequency load shedding scheme for inter-connected power systems
Severe disturbances in a power network can cause the system frequency to exceed the safe operating range. As the last defensive line for system emergency control, under frequency load shedding (UFLS) is an important method for preventing a wide range of frequency excursions. This paper proposes a hierarchical UFLS scheme of “centralized real-time decision-making and decentralized real-time control” for inter-connected systems. The centralized decision-layer of the scheme takes into account the importance of the load based on the equivalent transformation of kinetic energy (KE) and potential energy (PE) in the transient energy function (TEF), while the load PE is used to determine the load shedding amount (LSA) allocation in different loads after faults in real-time. At the same time, the influence of inertia loss is considered in the calculation of unbalanced power, and the decentralized control center is used to implement the one-stage UFLS process to compensate for the unbalanced power. Simulations are carried out on the modified New England 10-generator 39-bus system and 197-bus system in China to verify the performance of the proposed scheme. The results show that, compared with other LSA allocation indicators, the proposed allocation indicators can achieve better
f
nadir
and
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d
. At the same time, compared with other multi-stage UFLS schemes, the proposed scheme can obtain the maximum
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with a smaller LSA in scenarios with high renewable energy sources (RES) penetration.
Journal Article
Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data
The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT’s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience.
Journal Article
Optimization Strategy of Customer Relationship Management based on Big Data Analysis
This paper analyzes the basic customer management process and chooses the Apriori algorithm to build a CRM model based on data mining. In addition, this paper designs a customer relationship management system based on big data. The system is divided into three layers: data source, batch processing, and real-time processing. In the part of constructing the system architecture, this paper adopts the Hadoop platform. The batch processing layer, it has consisted of four parts, which include No SQL database, Oracle database, ETL architecture, and Hadoop platform. This paper gives the logical architecture design diagram. The real-time processing layer mainly includes a real-time decision engine and service bus. The key part of this layer is the real-time decision engine, in the design of which the Bayesian algorithm and product recommendation prediction model are used. Finally, this paper takes K company as an example to demonstrate the model and management system. After applying the analytical model and management system, the sales of K company keep increasing.
Journal Article