Decisions In Dynamic Environment Research Articles

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.

Turing-like Experiment in a Cyber Defense Game

During the past decade, researchers of behavioral cyber security have created cognitive agents that are able to learn and make decisions in dynamic environments in ways that assimilate human decision processes. However, many of these efforts have been limited to simple detection tasks and represent basic cognitive functions rather than a whole set of cognitive capabilities required in dynamic cyber defense scenarios. Our current work aims at advancing the development of cognitive agents that learn and make defense-dynamic decisions during cyber attacks by intelligent attack agents. We also aim to evaluate the capability of these cognitive models in ``Turing-like'' experiments, comparing the decisions and performance of these agents against human cyber defenders. In this paper, we present an initial demonstration of a cognitive model of the defender that relies on a cognitive theory of dynamic decision-making, Instance-Based Learning Theory (IBLT); we also demonstrate the execution of the same defense task by human defenders. We rely on OpenAI Gym and CybORG and adapt an existing CAGE scenario to generate a simulation experiment using an IBL defender. We also offer a new Interactive Defense Game (IDG), where \textit{human} defenders can perform the same CAGE scenario simulated with the IBL model. Our results suggest that the IBL model makes decisions against two intelligent attack agents that are similar to those observed in a subsequent human experiment. We conclude with a description of the cognitive foundations required to build autonomous intelligent cyber defense agents that can collaborate with humans in autonomous cyber defense teams.

Internationalization and Sustainable Hotel Competitiveness: Resilience and Network Ties to Increase Tourism Sustainability

The tourism industry, which is exposed to a turbulent environment, is one of the sectors that are the most vulnerable to any change (such as political, economic, environmental, or social). This makes it necessary to study firm resilience in this industry in order to identify the factors that can enhance companies’ capacity for resilience in turbulent environments. Moreover, the strategies pursued by tourism companies to become more resilient and more competitive are closely related to tourism sustainability. Among the factors that can affect resilience, we analyze the roles of the degree of internationalization and network ties. Moreover, we explore the influence of resilience on the sustainable competitiveness of hotel firms. For this purpose, we propose a variance-based structural equation modeling analysis where resilience acts as a mediating variable between the degree of internationalization and sustainable competitiveness. Our results allow us to draw important theoretical implications, which shed light on a field of study that is currently much debated, and practical implications, which will help hotel entrepreneurs to make timely decisions in dynamic environments.

Deep learning hybridization for improved malware detection in smart Internet of Things

The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

Managerial decision-making: exploration strategies in dynamic environments

PurposeWe argue that a fundamental issue regarding how to search and how to switch between different cognitive modes lies in the decision rules that influence the dynamics of learning and exploration. We examine the search logics underlying these decision rules and propose conceptual prompts that can be applied mentally or computationally to aid managers’ decision-making.Design/methodology/approachBy applying Multi-Armed Bandit (MAB) modeling to simulate agents’ interaction with dynamic environments, we compared the patterns and performance of selected MAB algorithms under different configurations of environmental conditions.FindingsWe develop three conceptual prompts. First, the simple heuristic-based exploration strategy works well in conditions of low environmental variability and few alternatives. Second, an exploration strategy that combines simple and de-biasing heuristics is suitable for most dynamic and complex decision environments. Third, the uncertainty-based exploration strategy is more applicable in the condition of high environmental unpredictability as it can more effectively recognize deviated patterns.Research limitations/implicationsThis study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.Practical implicationsThis study offers insights that there are different possibilities for exploration strategies for managers to apply conceptually and that the adaptability of cognitive-distant search may be underestimated in turbulent environments.Originality/valueDrawing on insights from machine learning and cognitive psychology research, we demonstrate the fitness of different exploration strategies in different dynamic environmental configurations by comparing the different search logics that underlie the three MAB algorithms.

Employment of time-varying sensory evidence to test the mechanisms underlying flexible decision-making.

To make flexible decisions in dynamic environments, the brain must integrate behaviorally relevant information while simultaneously discarding irrelevant information. This study aimed to investigate the mechanisms responsible for discarding irrelevant information during context-dependent decision-making. We trained two macaque monkeys to switch between direction and depth discrimination tasks in successive trials. During decision-making, the strength of the motion or depth signal changes transiently at various times, introducing a brief pulse. We assessed the effects of pulse on behavioral choices. Consistent with previous findings, early relevant pulses, such as motion pulses during direction discrimination, had a significantly larger effect compared to late pulses. Critically, the effects of irrelevant pulses, such as motion pulses during depth discrimination, exhibited an initial minimal effect, followed by an increase and subsequent decrease as a function of pulse timing. Gating mechanisms alone, aimed at discarding irrelevant information, did not account for the observed time course of pulse effects. Instead, the observed increase in the effects of irrelevant pulses with time suggested the involvement of a leaky integration mechanism. The results suggested that the brain controls the amount of disposal in accumulating sensory evidence during flexible decision-making.

Secure and Privacy-Preserving Intrusion Detection and Prevention in the Internet of Unmanned Aerial Vehicles.

In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model's effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%).

Communicating Uncertainty Information in a Dynamic Decision Environment

Abstract The likelihood of threatening events is often simplified for members of the public and presented as risk categories such as the “watches” and “warnings” currently issued by National Weather Service in the United States. However, research (e.g., Joslyn and LeClerc) suggests that explicit numeric uncertainty information—for example, 30%—improves people’s understanding as well as their decisions. Whether this benefit extends to dynamic situations in which users must process multiple forecast updates is as yet unknown. It may be that other likelihood expressions, such as color coding, are required under those circumstances. The experimental study reported here compared the effect of the categorical expressions “watches” and “warnings” with both color-coded and numeric percent chance expressions of the likelihood of a tornado in a situation with multiple updates. Participants decided whether and when to take shelter to protect themselves from a tornado on each of 40 trials, each with seven updated tornado forecasts. Understanding, decision quality, and trust were highest in conditions that provided percent chance information. Color-coded likelihood information inspired the least trust and led to the greatest overestimation of likelihood and confusion with severity information of all expressions.

Emotion Regulation and Strategic Decision Making: A Depletion of Resources Perspective

A growing body of evidence shows that making strategic decisions is emotionally draining and requires managers to devote significant effort to regulating emotions. However, theoretical models of strategic decision have not kept pace with these developments and largely overlook the role of emotion regulation. In this paper, we adopt a resource depletion perspective to examine how executives’ emotion regulation capabilities influence strategic decision processes and outcomes in dynamic environments. We propose that executive emotion regulation resources are vital for developing and sustaining novel representations of strategic opportunities, searching for distant solutions, and utilizing social systems for evaluating and selecting alternatives. Hence, the depletion of regulatory resources inhibits these processes and reduces the effectiveness of strategic decision making. By identifying interdependencies among executive emotion regulation capacity and the cognitive and social resources required for strategic decision making, our analysis extends understanding of the requirements for making strategic decisions in dynamic environments. We discuss implications for research on strategic decision making, executive job demands and dynamic managerial capabilities.

Choice adaptation to changing environments: trends, feedback, and observability of change.

Making successful decisions in dynamic environments requires that we adapt our actions to the changingenvironmental conditions. Past research has found that people are slow to adapt their choices when faced with change, they tend to be over-reliant on initial experiences, and they are susceptible to factors such as feedback and the direction of change (trend). We build on these findings using two experiments that manipulate feedback and trend in a binary choice task, where decisions are made from experience. Feedback was either partial (providing only the outcome of the selected choice) or full (providing outcomes of the selected and the forgone choice) and the expected value of one option either increased, decreased, or remained constant. Crucially, although the two choice options had equal expected value averaged across all trials, their expected values on individual trials differed, and halfway through 100 choice trials the choice option with higher expected value switched, requiring participants to adapt their choices in order to maximize their outcomes. In Experiment 1, the probability of receiving the high-value outcome changed over time. In Experiment 2, the outcome value changed over time. Generally, we found that participants had trouble adapting to change: full feedback led to more maximization than partial feedback before the switch but did not make a difference after the switch, suggesting stickiness and poor adaptation. Slightly better adaptation was found for changing outcome values over changing probabilities, implying that the observability of the element of change influences adaptation.

Generating Research Questions from Digital Trace Data: A Machine-Learning Method for Discovering Patterns in a Dynamic Environment

Digital trace data derived from organizations’ information systems represent a wealth of possibilities for analyzing decision-making processes and organizational performance. While data-mining methods have advanced considerably over recent years, organizational process research has rarely analyzed this type of trace data with the objective of better understanding organizations’ decision-making processes. However, accurately tracking decision-making actions via digital trace data can produce numerous applications that represent new and unexplored opportunities for IS research. The paper presents a novel method developed to combine quantitative process mining approaches with a variance perspective. Its viability is demonstrated by looking at teams’ decision patterns from a dynamic business-simulation game. This exploratory data-driven method represents a promising starting point for translating complex raw process data into interesting research questions connected with dynamic decision-making environments.

Learning and Dynamic Decision Making.

Humans make decisions in dynamic environments (increasingly complex, highly uncertain, and changing situations) by searching for potential alternatives sequentially over time, to determine the best option at a precise moment. Surprisingly, the field of behavioral decision making has little to offer in terms of theoretical principles and practical guidelines on how people make decisions in dynamic situations. My research program aims to fill in this gap by developing theoretical understandings of decision processes as well as practical demonstrations of how these theoretical developments can improve human dynamic decision making. Throughout my research career, I have helped create, test, and improve a general theory of dynamic decision making, instance-based learning theory, IBLT. The methods I have used to contribute to IBLT are (1) laboratory experiments that rely on dynamic games in which humans make choices over time and space, individually and in teams, and from which we extrapolate robust phenomena and behavioral insights; and (2) computational, actionable cognitive models, which specify the decision-making process and the cognitive mechanisms involved into a computational algorithm. The combination of these methods spawned novel applications in areas such as cybersecurity, phishing, climate change, and human-machine interactions. In this paper, I will take you through my own intellectual exploratory experience of computational modeling of human decision processes, and how the integration of experimental work and cognitive modeling helped in discovering and uncovering the field of dynamic decisionmaking.

The effectiveness of 3D multiple object tracking training on decision-making in soccer

ABSTRACT Background Soccer requires athletes to make quick decisions in dynamic environments. Several off-court technology-based interventions have been developed to train these perceptual cognitive skills. However, the evidence for training transfer using technologies to athletic performance has been sparse. Previous research found 3-dimensional multiple object tracking (3D MOT) training to cause a significant increase in quality of passing decision-making. Limitations to the research warrant further investigation of this association. Purpose To re-examine the effectiveness of 3D MOT on training decision-making. Methods Thirty-one NCAA Division III soccer players (female n = 16) were randomized to 3D MOT training or a control task. The experimental group received 10 training sessions over a span of 4 weeks. Results The manipulation check indicated a significant training effect in 3D MOT performance for the intervention but not the control group (F(1,29) = 21.46, p < .001, np 2 = .43). Non-significant changes with small effect sizes (np 2 = .01-.03) in decision-making and measures of near-transfer were found. Conclusion The findings challenge the association between 3D MOT training and increased quality of decision-making in soccer.

A heterogeneous GRA-CBR-based multi-attribute emergency decision-making model considering weight optimization with dual information correlation

The emergency response in an engineering operating entails a heterogeneous multi-attribute emergency decision-making (HMAEDM) problem containing crisp, interval, linguistic variables, etc. which is a dynamic process due to the continuous changes of engineering environment. However, few of researches on engineering emergency decision involved with heterogeneous multi attributes, and the static and non-inferential analytical methods ignored the linkage of the dual information between objective characteristics and preference information, which failed to effectively quantify the attribute weights and the similarity between cases and thus are difficult to make a valid decision for the dynamic decision-making environment. Based on case-based reasoning (CBR), a novel emergency decision model embedding with the grey relational analysis (GRA) and grey wolf optimization (GWO) algorithms, is proposed to help engineering emergency decision. Specially, the global indicator is derived by grey incidence degree for the similarity measure between heterogeneous multi-attribute cases from the perspective of system space, along with the GWO-based relative entropy method considering dual information correlation is designed for more reasonable weight allocation. Hence, the proposed model can exploit previous experiences and retrieve the optimal alternative for the future engineering emergency with heterogeneous multi attributes by imitating human thinking process. Finally, a real case in a water transfer project and comparison with three popular methods are conducted to demonstrate the applicability and effectiveness of the proposed model.

A Flexible Decision-Making Method for Green Supplier Selection Integrating TOPSIS and GRA Under the Single-Valued Neutrosophic Environment

The deteriorating environment and growing environmental awareness have created an urgency for selecting green suppliers. Although scholars have put forward many methods for selecting an appropriate green supplier, most of the existing methods are based on the fixed weight system, which causes these methods to fail to cope with complex and dynamic decision-making environment. To solve this problem, we develop a flexible decision model for green supplier selection where entropy, Technique for order preference by similarity to ideal solution (TOPSIS), and grey relational analysis (GRA) are embedded in the single-valued neutrosophic environment. The single-valued neutrosophic set is a superior tool in dealing with uncertainties, while the integration of single-valued set and classic methods for green supplier selection is limited. In this paper, entropy is integrated with the single-valued neutrosophic set to determine the weights of the criteria for green supplier evaluation; the weighted distance of TOPSIS and the weighted similarity of GRA are combined to select the optimal green supplier. The weights of TOPSIS and GRA are flexible; variable weights are assigned to TOPSIS and GRA based on different decision-making environments. A case study, along with comparative analysis, is carried out to prove the availability of our proposed decision model. Results show that our proposed decision model is flexible to accommodate dynamic decision-making issues and has good practicality.

A random intuitionistic fuzzy factor analysis model for complex multi-attribute large group decision-making in dynamic environments

The challenge of complex multi-attribute large group decision-making (CMALGDM) is reflected from three perspectives: interrelated attributes, large group decision makers (DMs) and dynamic decision environments. However, there are few decision techniques that can address the three perspectives simultaneously. This paper proposes a random intuitionistic fuzzy factor analysis model, aiming to address the challenge of CMALGDM from the three perspectives. The proposed method effectively reduces the dimensionality of the original data and takes into account the underlying random environmental factors which may affect the performances of alternatives. The development of this method follows three steps. First, the random intuitionistic fuzzy variables are developed to deal with a hybrid uncertain situation where fuzziness and randomness co-exist. Second, a novel factor analysis model for random intuitionistic fuzzy variables is proposed. This model uses specific mappings or functions to define the way in which evaluations are affected by the dynamic environment vector through data learning or prior distributions. Third, multiple correlated attribute variables and DM variables are transformed into fewer independent factors by a two-step procedure using the proposed model. In addition, the objective classifications and weights for attributes and DMs are obtained from the results of orthogonal rotated factor loading. An illustrative case and detailed comparisons of decision results in different environmental conditions are demonstrated to test the feasibility and validity of the proposed method.

Risk mechanisms of large group emergency decision-making based on multi-agent simulation

There are many critical sources of risk in large group emergency decision-making. In this paper, we systematically identify individual and group sources of risk in large group emergency decision-making and explore the relationship between each risk factor and two group effects, namely cognitive conflict and relationship conflict. Based on this analysis, we model a large group emergency decision-making risk-causing system. The model allows examination of the effect of key risk factors. These variables include individual acceptance, group structure, interaction mode, decision strategy, and decision-making environment, and variable values are set. A multi-agent simulation model of large group emergency decision-making risk-causing is developed using the Netlogo tool based on opinion dynamics. Through case simulation, we obtain the generalization rule of the causal mechanisms for each risk factor. The simulation shows that controlling the proportion of highly accepted decision-making subjects, increasing the interaction between clusters, and taking necessary anticipatory measures can noticeably reduce decision-making risks and help large teams function in highly dynamic decision-making environments. By highlighting the composition and collective influence of risk factors in large group emergency decision-making, this research offers reference and guidance for strategy selection in emergency decision-making.

Designing recycling networks for construction and demolition waste based on reserve logistics research field

Construction and demolition waste (CDW) has a significant effect on the environment. A well-designed recycling network is a prerequisite of CDW recycling, which is favourable for improving the recycling rate. However, the lack of quantitative tools to measure the characteristics of the construction industry has a serious effect on the effective design of CDW recycling network. Extensive studies on reverse logistics network design over the past decades have provided a solid foundation for recycling network design. Thus, in this study, reverse logistics research is explored to determine possible solutions to the issue of CDW recycling network design. This study aims to provide a model for CDW treatment plants location planning and capacity expansion planning based on the reverse logistics research in the construction industry. In our model, the conflicts between the objectives of different stakeholders and the dynamic decision-making environment in the construction industry were considered. Then, a multi-period multi-objective mixed-integer linear programming model was developed to minimize the CDW disposal costs of contractors and to maximize the profits of recycling firms and recycling rate of CDW. Chongqing, China was selected as the case to validate the proposed model and derive managerial insights. Results show our model provides an efficient tool for CDW recycling network design.

Maintaining ethical resolution in distributed constraint reasoning

Multi-agent systems (MAS) consist of autonomous agents that parcel out different tasks and make decisions in dynamic environments. Distributed constraint satisfaction problem (DisCSP) is the most effective and applicative MAS framework. In DisCSP, each agent is connected to the other agents via constraints and holds its own local constrained problem. Those agents find solutions satisfying their own constraints and the linking ones too by collaborating and exchanging messages holding their instantiations. This formalism does not take into consideration the possibility of the presence of unethical agents which can make irrelevant or even dangerous decisions, especially when human agents are involved in the resolution. In this paper, we propose an extension of the DisCSP into an ethical formalism “E-DisCSP”. It allows to control agents, detect intrusions and apply the convenient actions when an unethical agent is picked up. All these functionalities are done via control framework, in order to maintain the DisCSP resolution as normal as possible. Experimental results show the efficiency of our contribution. The detection rate of unethical agents achieves up to 100%. And the convenient actions’ application allows, to go from 45 to 0% of wrong solutions.

MIFCM: MICE imputed fuzzy C means clustering approach for effective skyline query processing on partially complete data

Skyline query is being widely adopted by the database community in today’s technological era. In a dynamic decision-making environment, the skyline query helps in producing effective results. Data produced in a dynamic decision-making scenario is prone to be affected by incomplete data. Executing the skyline query in a partially complete environment or incomplete data involves excessive pairwise comparisons which incurs degradation in the performance. This also affects the properties of skyline query. To address this challenge, we propose MIFCM (MICE Imputed Fuzzy C Means Clustering). Multivariate Imputation by Chained Equations (MICE) deals with data which has missing values. This proposed approach is a combination of MICE imputation and FCM clustering in order to improve the efficiency of skyline queries by reducing excess pairwise comparisons and search-time complexity of Skyline query. This manuscript captures our theoretical analysis and experimental results. From the experimental analysis, it is observed that the proposed approach significantly reduces the execution time for an incomplete dataset with an optimal result set size.