Dynamic Decision Research Articles

Dynamical analysis and decision support system of production management

Nonlinear system dynamics and feedback control theory are presented for management optimization of supply chain systems. Linearization and simplification methods are widely used in analyzing the system dynamics of supply chains because actual production models are highly complex and nonlinear systems. With advanced system dynamics, it is possible to deal directly with nonlinear dynamical problems without linear approximate methods so that the decision-makers can obtain more accurate results for systematic management strategies. This paper proposes a nonlinear system theory to explore dynamical behavior and control synthesis of production-distribution systems using Forrester’s model. A novel super-twisting sliding mode control (SWT-SMC) algorithm has been presented based on adaptation law, ensuring management optimization against disruptions. The closed-loop system stability has been guaranteed by using the Lyapunov theory. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the adaptive super-twisting sliding mode control (ASWT-SMC) algorithm. Four types of decision criteria have been employed to compare system performance between control strategies. With a superb decision scheme powered by a control algorithm, novel supply chain software can learn an ever-fluctuating production flow and anticipate the need for changes in a real market.

Research on the decision-making model of power purchase in electricity spot market oriented to market force suppression

Abstract In this study, for the electricity spot market under the market power suppression scenario, the market power suppression effect of contractual decomposed electricity is quantitatively evaluated by first reconstructing different electricity consumption loads using the combined MLR method and then combining the market power assessment model with the Lerner index. In addition, a contract for difference based on surveillance price is designed to inhibit market members from utilizing market power by adjusting the space of competitive electricity and the space of contracted electricity to avoid the market risk of rising electricity prices. Finally, a dynamic power purchase optimization decision model is constructed with CVaR as the risk metric and the profit of the power selling company and customer satisfaction as the objectives, and the MOSADE algorithm is used to solve the model and explore the optimal power purchase scheme. The analysis results show that the risk of the spot market can be avoided by bilateral contracts, option contracts, and unit power generation, and the price of electricity sales can be reduced. The expected returns of the four combinations are 21.37M$, 20.43M$, 19.24M$, 18.88M$, and 17.49M$ under the five risk coefficients, respectively. The CVaR values are −23.14, 2.84, 8.83, 10.75, 12.58. Higher than all the other cases, and their selling price for electricity is lower than all the different cases.

Comparative Analysis of Ethical Decision-Making and Trust Dynamics: Human Reasoning vs. ChatGPT-3 Narratives

Comparative Analysis of Ethical Decision-Making and Trust Dynamics: Human Reasoning vs. ChatGPT-3 Narratives

Linear-quadratic two-person differential game: Nash game versus stackelberg game, local information versus global information

In this paper, we present a unified framework to study a variety of two-person dynamic decision problems, including stochastic (zero-sum, non-zero-sum) Nash game, Stackelberg game with global information. For these games, the solvability of these problems is discussed via progressive formulations respectively: the abstract quadratic functional, Hamiltonian system for open-loop, and Riccati equation for closed-loop (feedback) representation. Based on the unified framework, time consistency/inconsistency property of related equilibrium is studied. Then we introduce a new type of game, Stackelberg game with local information. For this, the classical best-response machinery adopted for global information is no longer workable. As resolution, a repeated game approach is employed to construct the equilibrium strategies via a backward- and forward-procedure. Moreover, connection of local information pattern to time-inconsistency is also revealed. Finally, relations among zero-sum Nash game, zero-sum Stackelberg game with global information and local information are also identified.

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Digital Twin Based Resource Optimization and Dynamic Decision Making in Air Terminal Yards

Abstract With the growth of globalized trade, the efficient operation of airports is crucial. In this paper, we study the use of digital twin technology to optimize the yard resources of airports, which can improve their operational efficiency and decision-making quality. The study first constructs a digital twin framework for intelligent yard scheduling, which contains physical and virtual space and the connection between them. Data is collected through the physical perception module, while the virtual space is used for real-time simulation and optimized decision-making. The research methodology includes mixed integer programming and adaptive immune cloning algorithms for optimizing yard resource allocation and box space allocation. The experimental results show that the method can effectively reduce the total transportation distance in the air terminal yard operation and improve operation efficiency. The experiments demonstrate an average improvement in operational efficiency of around 20% and a cost reduction of around 15%. It is concluded that digital twin technology has apparent advantages in allocating resources in air terminal yards, which can achieve efficient utilization of resources and dynamic optimization of decision-making.

Two-Dimensional Following Lane-Changing (2DF-LC): A Framework for Dynamic Decision-Making and Rapid Behavior Planning

Two-Dimensional Following Lane-Changing (2DF-LC): A Framework for Dynamic Decision-Making and Rapid Behavior Planning

Optimal DoS attack on multi-channel cyber-physical systems: A Stackelberg game analysis

In this commentary, optimal denial-of-service (DoS) attack strategies on multi-channel cyber-physical systems (CPSs) are considered, which focus on energy allocation on communication channels. For simplicity, a Stackelberg game between one defender and one attacker is constructed. Compared with the existing literature, which mainly pay attention to static equilibrium, the dynamic process of the game is also exhibited in this paper, which fills the gap in the demonstration of dynamic decision-making between both players of the game. In the solution of Stackelberg equilibrium, a self-adaptive particle swarm optimization (PSO) algorithm with Sigmoid-like update function is applied to cope with the nonlinearity of the reward function with faster convergence and wider adaptability. Besides, to acquire better performance of both sides to allocate energy, an online computation algorithm is proposed for dynamic Stackelberg game. Finally, numerical examples are provided to illustrate similarities between theoretic static equilibrium and optimal strategies obtained by Monte Carlo simulations.

An Introduction to Radical Biocracy: A relational, autonomous approach to decision-making towards emergent and symbiotic design

Abstract Any system health work must look at decision-making because decisions propagate throughout a system, shaping system dynamics. Usually, human decision-making is conducted from an individualist, objectivist perspective. What happens when we use an approach based on the radical relationality of Radical Participatory Design and Relational Design? This is the fourth paper in a series of papers which introduced Radical Participatory Design in the first two papers and Relational Design in the third paper. In this fourth paper, we explore the decision-making dynamics in Radical Participatory Design and Relational Design projects. We use the term political ecology to speak about the power dynamics within any ecological system – a geographical population, a community, an ecosystem, and so forth. We analyze the political ecologies of individualist decision-making models. Then we explore how to embody a relational ontology within decision-filled human ecosystems and how a relational way of being changes decision-making. Referring to biology, we discuss ingredients for relational decision-making – relationality, emergent design principles, and autonomy. Those ingredients can lead to emergent and symbiotic design. Emergent design refers to design that emerges from consistently following a few basic principles. Symbiotic design occurs over time when deeply, relationally embedded entities retain autonomy and indirectly evolve to create a design that would not have occurred through an intentional design process. We then introduce Radical Biocracy as a type of decision-making model where decisions are not deliberated by groups or team members but emerge from the relationally autonomous choices and actions of individuals.

Deciphering Decision-Making Dynamics: The Art of Taking Decisions Theory

Deciphering Decision-Making Dynamics: The Art of Taking Decisions Theory

Identification and Prioritization of Decision-Making Aspects on High-Rise Fast-track Buildings – A Pilot Study

For decades, the construction industry has been facing time overrun challenges on almost all mega projects. Rarely there are no schedule slippages. Schedule compression is an approach, applied to meet the tight project deadlines and overcome time overrun issues. Fast-track and crashing are the two techniques used for schedule compression. Worldwide, fast-track has been used on projects for more than 50 years, but Pakistan is yet to adapt it as a construction norm in a scientific way. This reluctance on behalf of the client, the contractor and the consultant is primarily due to the inherently uncertain and risky nature of fast-track projects which requires dynamic decision-making. Moreover, these dynamic decision-making aspects have never been identified and parameterized to develop a decision-making model which can lead to project success in relation to time, cost, and quality. The aim of this research is to identify and parameterize the decision-making aspects into a research questionnaire which can further be used to develop a comprehensive decision-making model for high-rise construction in Pakistan. After an extensive literature review, initially 41 fast-track decision-making aspects were identified which were reduced to 37 after consultation with research community. The refined questionnaire containing 37 decision-making aspects was used for pilot survey in Pakistan’s construction industry to check its validity. Based on the input from the respondents, 19 most important decision-making aspects were discussed. The questionnaire consisted of a 5-point Likert scale, used to study the impact of each identified decision-making aspect on time variance, cost variance, scope variance and quality variance. This questionnaire will act as a stepping-stone for further development of a 3C’s (client, contractor, and consultant) decision-making model for fast-track high-rise projects in Pakistan using structural equation modelling (SEM).

Financial Satisfaction: The Role of Shared Financial Responsibilities and Shared Financial Values Among Couples

Managing shared finances is an important aspect of a romantic relationship, and satisfaction with one’s financial situation depends on a complex host of issues, including decision-making dynamics and resource sharing. This paper provides insight into this relationship by reporting the results from a study designed to provide evidence of an association between couple’s shared financial decision-making behavior and their financial satisfaction. Using a sample of 602 individuals in a committed romantic relationship, this project evaluated how couples’ division of financial responsibilities and agreement on spending and saving behavior affected their perceived financial satisfaction. Results of the analysis indicated that the way household finances were shared was associated with perceptions of financial satisfaction. Specifically, those who reported combining their finances with their partner were more financially satisfied. Couples who reported higher levels of agreement on spending were more likely to be satisfied with their current financial situation. Finally, couples with higher levels of agreement on saving were more satisfied financially.

Dynamic decision-making for knowledge-enabled distributed resource configuration in cloud manufacturing considering stochastic order arrival

The emergence of COVID-19 caused the stagnation of production activities and promoted the changing market demand. These uncertainties not only brought great challenges to the manufacturing approaches led by a single enterprise, but also threatened the stability of inherent supply chain. To maintain market competitiveness, an efficient distributed manufacturing resource allocation method is urgently needed by manufacturers. Cloud manufacturing (CMfg) is an advanced service-oriented manufacturing paradigm that breaks physical space constraints to integrate distributed resources across enterprises, and provides on-demand configuration of manufacturing services for personalized consumer needs in real-time. The focus of this paper is to achieve dynamic configuration of distributed resources in CMfg considering stochastic order arrival, while reducing overall completion time and improving resource utilization. First, a dynamic knowledge graph for distributed resources is constructed, and its definition and construction methods are introduced. Secondly, semantic matching between massive optional manufacturing resources and multiple types of subtasks is achieved through knowledge extraction, thereby obtaining a candidate set of manufacturing resources that meet basic requirements for each subtask. An artificial intelligence (AI) scheduler based on deep reinforcement learning is developed, and order urgency is incorporated into the design of state observation vectors. AI scheduler can generate optimal decision results based on environmental observations, select high-quality manufacturing service compositions over candidate sets, and ultimately achieve efficient distributed resources configuration. Finally, Dueling DQN-based training method is put forward to optimize AI scheduler, enabling adaptable decision-making performance in dynamic environment. In the experiment, a simulation environment with 18 different settings is designed that considers stochastic processing time, random order compositions and various order arrival patterns. The proposed graph-based matching method, scheduling policy learning method and dynamic decision-making method are tested in the simulation environment. The experiment results demonstrate that the cognitive and AI joint driven distributed manufacturing resource configuration method is superior to traditional methods in terms of policy learning speed and scheduling solution quality.

Moral Considerations and Layoffs Dilemma: Analysis of Empathetic Ability in Decision-making

Empathy, the perceptive grasp and comprehension of someone else's emotional state, holds immense significance in social interactions. Its manifestation, nonetheless, undergoes fluctuations across diverse scenarios, making it a fascinating and complex aspect of human behaviour. To delve into this captivating phenomenon, this dissertation explores the ethical quandary faced by company managers when deciding whether to lay off employees during financial crises. In this scenario, empathy's role becomes even more critical. Employing the sensitive topic of employee layoffs as a prime example, the study unravels the intricacies of empathy disparity among individuals, delving into the psychological, cultural, and personal factors contributing to varying levels of empathetic responses. While universally recognised as essential, this meticulous analysis highlights how empathy can sometimes be elusive or inconsistent. Furthermore, the research sheds light on the cascading impact of leadership empathy on employee empathy and the subsequent influence of individual employee empathy on group empathy. Understanding the interconnected nature of empathy within an organizational context becomes pivotal for nurturing a compassionate and supportive work environment. Moreover, this study delves into the far-reaching implications of empathy within organizations. The intricate interplay between empathy, decision-making, conflict resolution, and team dynamics is examined. The research identifies specific boundary conditions that may affect the effectiveness of empathy, shedding light on when empathy might be less impactful and when it can genuinely foster positive outcomes.

Three-Party Evolutionary Game Analysis of IoT Platform Knowledge Hiding Under Organization Participation from the Perspective of Stakeholders

The 21st century is an era of rapid development of high-tech industry. Through continuous collection and sharing of data, IoT technology connected by things has penetrated into every aspect of human life. Modern organizations find that they increasingly rely on knowledge and information sharing and interconnection to enhance their innovation and development capabilities. Therefore, this paper studies the strategic decision-making of knowledge sharing among employees on the IoT platform drawing on evolutionary game theory, this paper constructs a three-party game model composed of “organization-knowledge sharers-knowledge seekers” from the perspective of stakeholders, and discusses the strategic choice of organization and employee behavior under the dynamic decision mechanism and when the game reaches equilibrium and stability. Moreover, this paper uses MATLAB 2016a to simulate the model. The results show that under the premise of higher organizational rewards, synergistic benefits, high-shared extra income, knowledge-sharing preferences, incentive preferences of the organization, the system is easier to reach the ideal state. With the smaller cost of sharing and incentive, organization and employees are more willing to be motivators and sharers, while the incentive degree of organization should be controlled within a reasonable range. This paper can provide specific theoretical and practical guidance for the practice of organizational knowledge management.

Millennial Heterosexual Couples’ Sustainable Consumption Choices: An Exploratory Study into Decision-Making

This research sought to explore the influences and motives that lead to decision-making of, and behavioral changes toward, sustainable consumption in heterosexual married couples. Understanding how different factors affect couples’ consumption decision-making dynamics can help provide successful methods to promote behavior towards sustainable consumption. For this study, semi-structured interviews were employed to explore couples’ sustainable consumption behaviors. A total of six heterosexual couples (12 individual participants) provided in-depth narratives regarding their sustainable consumption and behavioral patterns. Four major themes that emerged include: (1) the meaning of sustainable consumption for the couples and their lack of confidence in their knowledge, (2) shopping decisions regarding sustainable consumption, (3) life events that influenced the couples’ views and behaviors towards sustainable consumption, and (4) the motivations behind sustainable consumption. In spite of the small sample size, the findings of the study can be used by businesses to develop more effective marketing strategies. The study results can help businesses understand heterosexual married couples’ sustainable consumption decision-making processes and persuade them to make decisions about the company’s sustainable product offerings.

Unveiling Maternal Narratives and Experiences in Lesotho: Insights into Breastfeeding Choices, Practices, And Hurdles

This research investigated maternal and infant care in Lesotho, with an emphasis on exclusive breastfeeding practices during the first year of life. A quantitative, descriptive survey included 97 mothers attending healthcare facilities. The findings highlighted a prevalent preference for exclusive breastfeeding in the initial three months, which decreases during the following 3 months. Yet around 7% introduced complementary foods or fluids early on and many did not breastfeed exclusively until the 6-month recommended period. The majority began breastfeeding within the recommended one-hour post-delivery window. Distinct decision-making dynamics emerged: nearly half of the mothers made autonomous feeding decisions, around a quarter collaborated with the child’s father or partner, and 11.3% involved extended family in these choices. Colostrum's recognized value as a new-born’s first nutrient source aligned with global health recommendations. However, the early introduction of other foods indicates a need to explore mothers’ beliefs and knowledge about feeding practices further. Common breastfeeding challenges encompassed issues like sore nipples and perceived milk insufficiency, underlining the importance of educational support. Healthcare professionals, especially nurses, played a key role in offering guidance and support on breastfeeding. The frequent practice of immediate skin-to-skin contact post-birth emphasized the trend towards promoting early bonding and breastfeeding initiation. Furthermore, the community’s positive reception to public breastfeeding signalled societal acceptance. In conclusion, this study illuminates the nuances of breastfeeding practices and decision-making in Lesotho, providing insights for interventions and policies to enhance maternal and child health in the country.

Role of Decision-Making Dynamics and Farm Participation in Succession: The Case of Philippine Coffee Farming Households

Succession is crucial to ensure agricultural sustainability, food security, and rural development in the agriculture sector. Existing succession studies analyze how demographics, farms, and social characteristics affect the likelihood of having a successor. There is scarce literature on how household members’ decision-making involvement and farm participation affect the likelihood of succession. The effect of access to agricultural services was also examined in this study. A Probit model was used in the analysis using information from 755 coffee farming households in nine (9) regions of the Philippines. One of the key findings is that the likelihood of farm succession is more strongly influenced by gender-specific farm activity engagement than by the extent of involvement in farm decision-making. To increase the likelihood of farm succession, any intervention and extension should incorporate a component that actively engages youth in the farm’s day-to-day operations. Active membership of the household head in an agricultural organization and women’s access and participation in agricultural training can significantly increase the likelihood of succession. The study supplements theoretical literature on farm succession by providing measurements on inclusive farm activity participation, decision-making within the household, and how it affects farm succession likelihood, a critical factor that should be considered in analyzing farm succession.

Quantifying Levels of Influence and Causal Responsibility in Dynamic Decision Making Events

Intelligent systems support human operators’ decision-making processes, many of which are dynamic and involve temporal changes in the decision-related parameters. As we increasingly depend on automation, it becomes imperative to understand and quantify its influence on the operator’s decisions and to evaluate its implications for the human’s causal responsibility for outcomes. Past studies proposed a model for human responsibility in static decision-making processes involving intelligent systems. We present a model for dynamic, non-stationary decision-making events based on the concept of causation strength. We apply it to a test case of a dynamic binary categorization decision. The results show that for automation to influence humans significantly, it must have high detection sensitivity. However, this condition is insufficient since it is unlikely that automation, irrespective of its sensitivity, will sway humans with high detection sensitivity away from their original position. Specific combinations of automation and human detection sensitivities are required for automation to have a major influence. Moreover, the automation influence and the human causal responsibility that can be derived from it are sensitive to possible changes in the human’s detection capabilities due to fatigue or other factors, creating a “Responsibility Cliff.” This should be considered during system design and when policies and regulations are defined. This model constitutes a basis for further analyses of complex events in which human and automation sensitivity levels change over time and for evaluating human involvement in such events.

A hybrid computational approach to anticipate individuals in sequential problem solving.

Human-awareness is an ever more important requirement for AI systems that are designed to assist humans with daily physical interactions and problem solving. This is especially true for patients that need support to stay as independent as possible. To be human-aware, an AI should be able to anticipate the intentions of the individual humans it interacts with, in order to understand the difficulties and limitations they are facing and to adapt accordingly. While data-driven AI approaches have recently gained a lot of attention, more research is needed on assistive AI systems that can develop models of their partners' goals to offer proactive support without needing a lot of training trials for new problems. We propose an integrated AI system that can anticipate actions of individual humans to contribute to the foundations of trustworthy human-robot interaction. We test this in Tangram, which is an exemplary sequential problem solving task that requires dynamic decision making. In this task the sequences of steps to the goal might be variable and not known by the system. These are aspects that are also recognized as real world challenges for robotic systems. A hybrid approach based on the cognitive architecture ACT-R is presented that is not purely data-driven but includes cognitive principles, meaning heuristics that guide human decisions. Core of this Cognitive Tangram Solver (CTS) framework is an ACT-R cognitive model that simulates human problem solving behavior in action, recognizes possible dead ends and identifies ways forward. Based on this model, the CTS anticipates and adapts its predictions about the next action to take in any given situation. We executed an empirical study and collected data from 40 participants. The predictions made by CTS were evaluated with the participants' behavior, including comparative statistics as well as prediction accuracy. The model's anticipations compared to the human test data provide support for justifying further steps built upon our conceptual approach.