Decisions In Dynamic Environment Research Articles

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.

An Empirical Study on Strategic Technological Decisions In Dynamic Environment

An Empirical Study on Strategic Technological Decisions In Dynamic Environment

A Defeasible Logic-based Framework for Contextualizing Deployed Applications

In human to human communication, context increases the ability to convey ideas. However, in human to application and application to application communication, this property is difficult to attain. Context-awareness becomes an emergent need to achieve the goal of delivering more user-centric personalized services, especially in ubiquitous environments. However, there is no agreed-upon generic framework that can be reused by deployed applications to support context-awareness. In this paper, a defeasible logic-based framework for context-awareness is proposed that can enhance the functionality of any deployed application. The nonmonotonic nature of defeasible logic has the capability of attaining justifiable decisions in dynamic environments. Classical defeasible logic is extended by meta-rules to increase its expressiveness power, facilitate its representation of complex multi-context systems, and permit distributed reasoning. The framework is able to produce justified decisions depending on both the basic functionality of the system that is itself promoted by contextual knowledge and any cross-cutting concerns that might be added by different authorities or due to further improvements to the system. Active concerns that are triggered at certain contexts are encapsulated in separate defeasible theories. A proof theory is defined along with a study of its formal properties. The framework is applied to a motivating scenario to approve its feasibility and the conclusions are analyzed using argumentation as an approach of reasoning.

Paramedic Judgement, Decision-Making and Cognitive Processing: A Review of the Literature

Background Paramedics routinely perform multiple time-sensitive decisions in dynamic environments, often with limited information and equipment. Paramount to patient safety, how paramedics make judgements and decisions within their uncertain environment is important. The primary aim of this review was to identify, examine and synthesise the published literature on how paramedics working in the out-of-hospital environment use judgement and make decisions. Methods Databases Medline, PubMed, CINAHL, Embase were searched and common themes pertaining to paramedic decision-making were identified. Full text original research articles that focussed on how paramedics perform decision-making in the out-of-hospital environment were included. Papers excluded were non-English; those examining emergency medical technicians, nurse- or physician-led ambulances; paramedics operating in hospital or clinic-based environment; and studies of purely paramedic student populations. Data were managed using the ‘preview, question, read, summarise’ approach. Results A total of 362 abstracts and titles were reviewed; six were found to address the research aim. Of those six, four were qualitative in approach, one quantitative and one was mixed-methods. Overall, paramedics displayed the application of subconscious (intuitive) and conscious (analytical) thought processes – consistent with dual-process theory – with experience and formal education influencing factors. Paramedics gathered cues, problem solved, critically analysed, reasoned and displayed aptitude at rapid clinical impressions in critically ill patients with minimal information. Expert paramedics collected, processed and utilised information differently to novices portraying an interconnectedness of conscious and sub-conscious processing. Conclusion Paramedic judgement and decision-making is complex and multifaceted with multiple layers of knowledge interwoven. Implications for practice include better cognitive performance; educational course structure guidance; encouraging implementation of routine reflection and feedback, thus promoting continued improvement and better patient outcomes. Despite its importance, research was lacking.

EmoWare: A Context-Aware Framework for Personalized Video Recommendation Using Affective Video Sequences

With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to make optimal decisions in dynamic environments has been proven and very well conceptualized by reinforcement learning (RL). The learning characteristics of deep-bidirectional recurrent neural networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and propose EmoWare (emotion-aware), a personalized, emotionally intelligent video recommendation engine, employing a novel context-aware collaborative filtering approach, where the intensity of users' spontaneous non-verbal emotional response toward the recommended video is captured through interactions and facial expressions analysis for decision-making and video corpus evolution with real-time feedback streams. To account for users' multidimensional nature in the formulation of optimal policies, RL-scenarios are enrolled using on-policy (SARSA) and off-policy (Q-learning) temporal-difference learning techniques, which are used to train DBRNN to learn contextual patterns and to generate new video sequences for the recommendation. System evaluation for a month with real users shows that the EmoWare outperforms the state-of-the-art methods and models users' emotional preferences very well with stable convergence.

Modeling cognitive load and physiological arousal through pupil diameter and heart rate

This study investigates individuals’ cognitive load processing abilities while engaged on a decision-making task in serious games, to explore how a substantial cognitive load dominates over the physiological arousal effect on pupil diameter. A serious game was presented to the participants, which displayed the on–line biofeedback based on physiological measurements of arousal. In such dynamic decision-making environment, the pupil diameter was analyzed in relation to the heart rate, to evaluate if the former could be a useful measure of cognitive abilities of individuals. As pupil might reflect both cognitive activity and physiological arousal, the pupillary response will show an arousal effect only when the cognitive demands of the situation are minimal. Evidence shows that in a situation where a substantial level of cognitive activity is required, only that activity will be observable on the pupil diameter, dominating over the physiological arousal effect indicated by the pupillary response. It is suggested that it might be possible to design serious games tailored to the cognitive abilities of an individual player, using the proposed physiological measurements to observe the moment when such dominance occurs.

Evolutionary multi-objective multi-architecture design space exploration methodology

The design of revolutionary aerospace vehicles is characterized by large design spaces, a lack of established baselines, and some uncertainty in the design and regulatory requirements that such vehicles will need to meet. A new evolutionary multi-architecture multi-objective optimization algorithm is presented to support design concept selection when faced with such challenges. The proposed approach allows designers to efficiently and exhaustively generate variable-oriented architectures that can be further optimized and compared. It provides a dynamic decision-making environment able to identify trends and trade-offs, and prioritize designs. The application of the proposed methodology to suborbital vehicles highlights key promising technological enablers, which can be leveraged to design high-performance and robust concepts.

An Innovative Cognitive Architecture for Humanoid Robot

Humanoid robot is appearing as most popular research tool and emerging research field. The greatest challenge in the development of robot is cognition, advancement and the understanding in the human like cognition. Humanoid robot requires a self-learning behavior like the humans that is able to get the experience from environment. Based on experience, it can modify their actions, or having conscious intellectual capability to reduce empirical factual knowledge. In this regard, we propose a novel framework called an Innovative Cognitive Architecture for Humanoid Robot (ICAHR) that is capable to develop cognitive through social interaction and autonomous exploration. It combines the modules of active memory, decision processor, and sensor listener that has capability to perform self-learning behavior like human, to make decisions in dynamic environment, and perform more valid and intelligent actions with better precision. The proposed architecture may result in safe, robust, flexible, and reliable machines that can be substitute of human beings in different tasks. The feasibility of new proposed ICAHR design has been examined through real-world case studies.

Reward and Feedback in the Control over Dynamic Events

On what basis do we learn to make effective decisions when faced with intermittent feedback from actions taken in a dynamic decision-making environment? In the present study we hypothesize that reward information (financial, social) may provide useful signals that can guide decision-making in these situations. To examine this, we present three experiments in which people make decisions directly towards controlling a dynamically uncertain output. We manipulate the framing of incentives (gains and losses) and the form of the incentives (financial, social), and measure their impact on decision-making performance (both in frequent and intermittent output feedback conditions). Overall, performance suffered under intermittent output feedback. Relative to social rewards, financial rewards generally improved control performance, and a gains framing (financial, social) leading to better performance than a losses framing (financial, social). To understand how rewards affect behavior in our tasks, we present a reinforcement learning model to capture the learning and performance profiles in each of our experiments. This study shows that information regarding incentives impacts the levels of exploration, the optimality of decisions-making, and the variability in the strategies people develop to control a dynamic output experienced frequently and intermittently.

Behavioral and Neural Signatures of Reduced Updating of Alternative Options in Alcohol-Dependent Patients during Flexible Decision-Making.

In addiction, patients maintain substance use despite devastating consequences and often report having no choice but to consume. These clinical observations have been theoretically linked to disturbed mechanisms of inference, for example, to difficulties when learning statistical regularities of the environmental structure to guide decisions. Using computational modeling, we demonstrate disturbed inference on alternative choice options in alcohol addiction. Patients neglecting "what might have happened" was accompanied by blunted coding of inference regarding alternative choice options in the medial prefrontal cortex. An impaired integration of alternative choice options implemented by the medial prefrontal cortex might contribute to ongoing drug consumption in the face of evident negative consequences.

Group size advantages to decision making are environmentally contingent in house-hunting Myrmecina ants

Pooling information can allow groups to make better decisions than individuals, an idea that underlies the use of groups to make important decisions in human society. Group size is also thought to influence the accuracy of decision making in nonhuman animals, but despite the enormous variation in the size and composition of animal societies, very few studies have explored this question outside of humans. Furthermore, although both humans and animals need to make decisions in dynamic environments and models suggest that environmental conditions can alter or even invert the advantage of group size, no empirical study has addressed how the advantage of group size may vary with environmental context. In this study I investigated how group size and environmental context influence decision making using an ant model system in which colonies use a quorum-based process to decide collectively among new nesting sites. Decision making unfolded in a similar manner in colonies of different size, as quorum thresholds and task allocation scaled with colony size. Larger colonies, however, made more accurate decisions than small colonies, and in most cases did so more rapidly. There was also an influence of environmental conditions, as whereas the decision accuracy of small groups was comparable to that of large groups in benign conditions, decision making was largely ineffective in small colonies in more challenging conditions. Colonies adapted to different environmental conditions by adjusting quorum thresholds, shifting thresholds down to emphasize speed when under stress and showing a pattern of higher thresholds in complex environments. The advantage to larger colonies probably stems from their greater information collecting and processing ability, which also serves to buffer them from the negative influences of more challenging environments.

Dynamic Decision Making Using the Balanced Scorecard Framework

ABSTRACTThis study examines the effects that two balanced scorecard framework (BSF) elements, causal linkages between strategic objectives (“causal linkages”) and time delay information (“delays”) in a strategy map, have on long-term profit performance in a dynamic decision-making environment. Using a computer-based simulation task, we conduct a 3 × (4) experiment (control group; causal linkages without delays; causal linkages with delays; four simulation rounds) and find that managers presented with causal linkages without delays generate greater long-term profit compared to a control group. For managers presented with causal linkages with delays, long-term profit generation is higher than the control group, but is not significantly different from the causal linkages without delays treatment. Those managers presented with causal linkages with delays, however, demonstrate learning across the four simulation rounds. In contrast, learning is found to plateau for the causal linkages without delays treatment and is not present for the control group. We also examine the cognitive mechanism through which these two BSF elements impact performance, by measuring the accuracy of two components of managers' mental models.Data Availability: Experimental materials are available upon request from the authors.

The application of recognition-primed decision theory to decisions made in an outdoor education context

Introduction Outdoor leaders take people into the outdoors in a myriad of ways. Included are activities such as rock-climbing, kayaking and mountain biking, through diverse domains such as outdoor education, adventure tourism, and club trips. Much of the learning and welfare of group members are in the leader's hands. There is widespread agreement that outdoor programme implementation relies on effective decision making (DM) (Martin, Cashel, Wagstaff & Breunig, 2006; Shooter & Furman, 2011). It is well recognised that the outdoors is a complex and dynamic decision-making environment (Galloway, 2002; Martin, Schmid & Parker, 2009; Tozer, Fazey & Fazey, 2007). The natural environment has intrinsic elements of risk and uncontrolled variables presented by elements such as weather, height, water and speed (New Zealand Department of Labour, 2010). Adding to this complexity is the uniqueness of individuals and the dynamics of groups. Decision circumstances for outdoor leaders vary greatly, with some situations pressured and changeable and others stable and consistent. The dynamic interactions between and among participants, leaders and the environment means that leaders of outdoor activities make countless decisions during an excursion. Examples of outdoor decisions include: (a) place (e.g., where to camp); (b) time (e.g., when to stop for lunch); (c) the needs of individuals (e.g., comfort, safety); and (d) the group process (e.g., is everyone happy). Depending on context, these could be simple or complex decision situations that affect the quality of an adventure experience and the safety of participants (e.g., to cross a river or not, and if so, where, how and when). Consequently, the importance of leader decisions varies from inconsequential to paramount. Drury, Bonney, Berman and Wagstaff (2005) proposed a classification of decisions on a continuum from simple to complex (see also Martin et al, 2006). Here, decisions could be classified depending on a combination of contextual factors including degrees of challenge, uncertainty, predictability of outcome, time pressure, threat, emotionality and situational understanding. Consideration of these arrays appears in several text books (see Drury et al., 2005; Martin et al., 2006; Priest & Gass, 2005). Some empirical research has been conducted to explore different outdoor decision situations. These include: outdoor pursuits leaders (Beare & Lynch, 2005); outdoor education leaders (Boyes & O'Hare, 2003); medical decision making by Outward Bound staff (Galloway, 2007); and outdoor leaders on a Wilderness Education Association course (Martin et al., 2009). Their decision problems include emergency situations such as hypothermia, a lost person, and a spinal injury, as well as whether to cross an alpine pass and a river. Also included are decisions about extending a cross country ski trip to increase the level of challenge. Despite the universality of decisions, researchers have yet to develop a taxonomy of outdoor leadership decisions based on empirical evidence. A deeper understanding would be useful as a basis for understanding more about how different contexts and levels of complexity influence decision processes. The implications of this approach may be useful to inform the education of new and existing leaders and contribute to better practice. The present research explores the range of decisions made by six highly experienced outdoor leaders and presents their data in taxonomies. Some of their key decisions are then explored through the lens of Naturalistic Decision Making (NDM). Naturalistic decision making Hitherto, a modest amount of empirical research investigates outdoor leader decision making and a comprehensive understanding has yet to be agreed upon (Furman, Shooter & Schumann, 2010). Some researchers have focussed on classical DM models where multiple options for action are generated, whereby in a rational, comparative process, the best perceived solution is chosen and implemented (e. …

Newsvendor Problems with Demand Shocks and Unknown Demand Distributions

ABSTRACTIn today's competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range; no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithm's performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta‐algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta‐algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent

A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making

Consensus reaching models are widely applied in group decision making problems to improve the group's consensus level before making a common decision. Within the context of the group Analytic Hierarchy Process (AHP), a novel consensus reaching model in a dynamic decision environment is proposed. A Markov chain method can be used to determine the decision makers’ weights of importance for the aggregation process with respect to the group members’ opinion transition probabilities. The proposed group consensus reaching model facilitates a peer to peer opinion exchange process which relieves the group of the need for a moderator by using an automatic feedback mechanism. Moreover, as the elements in the group decision framework change in a dynamic decision making problem, this model provides feedback suggestions that adaptively adjust for each of the decision makers depending on his credibility in each round. The full process of the dynamic adaptive consensus reaching model is presented and its properties are discussed. Finally, a numerical example is given to demonstrate the effectiveness of our model.

A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives.

Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.