Journal articles on the topic 'Under pressure decision model'
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1
Tamir, Emanuel, and Mirit K. Grabarski. "Under Pressure: Why School Managements Use Garbage Can Model of Decision?" Research in Educational Administration & Leadership 3, no. 1 (July 27, 2018): 1–28. http://dx.doi.org/10.30828/real/2018.1.1.
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Hwang, Mark I. "Decision making under time pressure: A model for information systems research." Information & Management 27, no. 4 (October 1994): 197–203. http://dx.doi.org/10.1016/0378-7206(94)90048-5.
3
Wu, Xinlin, and Daoxin Ding. "A Satisficing Heuristic Decision-Making Model under Limited Attention and Incomplete Preferences." Journal of Mathematics 2021 (December 3, 2021): 1–8. http://dx.doi.org/10.1155/2021/8951335.
Abstract:
Classical choice theory assumes that a decision-maker considers all feasible alternatives. However, a decision-maker in the real world can not consider all alternatives because of limited attention. In this paper, we propose a satisficing choice model to describe the choice procedure based on the incomplete preferences under the limited attention of the decision-maker. Moreover, the existence and rationality properties of the satisficing choice model on the different domains are studied combined with some proposed rationality conditions. Further, the proposed satisficing choice model is applied to a case of quality competition. Results show that the satisficing choice model of this paper is of a certain theoretical guiding significance to a kind of emergency decisions made by decision-makers under the circumstance of time pressure and limited information. It can also be the theoretical foundation for the study on the boundedly rational decision-making.
4
Goldsby, Elizabeth, Michael Goldsby, Christopher B. Neck, and Christopher P. Neck. "Under Pressure: Time Management, Self-Leadership, and the Nurse Manager." Administrative Sciences 10, no. 3 (June 28, 2020): 38. http://dx.doi.org/10.3390/admsci10030038.
Abstract:
Decision making by nurses is complicated by the stress, chaos, and challenging demands of the work. One of the major stressors confronting nurses is perceived time pressure. Given the potential negative outcomes on nurses due to perceived time pressures, it seems logical that a nurse manager’s ability to lead nurses in moderating this time pressure and in turn to make better decisions could enhance nurse well-being and performance. Paralleling research in the nursing literature suggests that, in order to improve patients’ judgement of the care they received, nurse managers should embrace ways to lower nurses’ perceived time pressure. In this conceptual paper, we propose a model to help mitigate time pressure on nurse managers and their frontline nurses based on the research regarding time pressure, psychosocial care, time management, and self-leadership. Three metaconjectures and suggested future studies are given for further consideration by organizational and psychological researchers.
5
Dai, Feng, Jingxu Liu, and Ling Liang. "INDUSTRY SEGMENTATION UNDER ENVIRONMENTAL PRESSURE: AN OPTIMAL APPROACH." Technological and Economic Development of Economy 19, Supplement_1 (January 28, 2014): S524—S543. http://dx.doi.org/10.3846/20294913.2014.880081.
Abstract:
Based on the Solow growth model, this paper builds a combinatorial model for economic growth under environmental pressure. Using the model, the optimal number of industries in an economy can be computed, and the “optimal number” can be regarded as a criterion for decision making concerning industry segmentation. This paper presents a critical value of the number of industries in an economy, which determines the economic output to grow (or not) after industry segmentation. The findings include the following: (1) technological progress and innovative growth cause an industry segmentation, and (2) industry segmentation is the primary approach to sustaining economic growth under environmental pressure.
6
Glöckner, Andreas, and Sara D. Hodges. "Parallel Constraint Satisfaction in Memory-Based Decisions." Experimental Psychology 58, no. 3 (November 1, 2011): 180–95. http://dx.doi.org/10.1027/1618-3169/a000084.
Abstract:
Three studies sought to investigate decision strategies in memory-based decisions and to test the predictions of the parallel constraint satisfaction (PCS) model for decision making (Glöckner & Betsch, 2008). Time pressure was manipulated and the model was compared against simple heuristics (take the best and equal weight) and a weighted additive strategy. From PCS we predicted that fast intuitive decision making is based on compensatory information integration and that decision time increases and confidence decreases with increasing inconsistency in the decision task. In line with these predictions we observed a predominant usage of compensatory strategies under all time-pressure conditions and even with decision times as short as 1.7 s. For a substantial number of participants, choices and decision times were best explained by PCS, but there was also evidence for use of simple heuristics. The time-pressure manipulation did not significantly affect decision strategies. Overall, the results highlight intuitive, automatic processes in decision making and support the idea that human information-processing capabilities are less severely bounded than often assumed.
7
Luini, Lorenzo P., and Francesco S. Marucci. "Prediction–Confirmation Hypothesis and Affective Deflection Model to account for split-second decisions and decision-making under pressure of proficient decision-makers." Cognition, Technology & Work 17, no. 3 (February 28, 2015): 329–44. http://dx.doi.org/10.1007/s10111-015-0328-0.
8
Kalaitzi, Dimitra, Aristides Matopoulos, Michael Bourlakis, and Wendy Tate. "Supply chains under resource pressure." International Journal of Operations & Production Management 39, no. 12 (December 10, 2019): 1323–54. http://dx.doi.org/10.1108/ijopm-02-2019-0137.
Abstract:
Purpose The purpose of this paper is to investigate the implications of supply chain strategies that manufacturing companies can use to minimise or overcome natural resource scarcity, and ultimately improve resource efficiency and achieve competitive advantage. The relationship between resource efficiency and competitive advantage is also explored. Design/methodology/approach The proposed research model draws on resource dependence theory. Data were collected from 183 logistics, purchasing, sustainability and supply chain managers from various manufacturing companies and analysed by applying the partial least squares structural equation modelling technique. Findings The results indicate that both buffering and bridging strategies improve resource efficiency; however, only bridging strategies seem to lead to firm’s competitive advantage in terms of ownership and accessibility to resources. The relationship between resource efficiency and competitive advantage is not supported. Research limitations/implications Future research could confirm the robustness of these findings by using a larger sample size and taking into account other supply chain members. Practical implications This research provides guidance to managers faced with the growing risk of resource scarcity to achieve a resource efficient supply chain and an advantage over competitors. Originality/value Studies have explored the appropriate strategies for minimising dependencies caused by the scarcity of natural resources in the field of supply chain management; however, there is limited empirical work on investigating the impact of these strategies on resource efficiency and competitive advantage.
9
Soshi, Takahiro, Mitsue Nagamine, Emiko Fukuda, and Ai Takeuchi. "Modeling Skin Conductance Response Time Series during Consecutive Rapid Decision-Making under Concurrent Temporal Pressure and Information Ambiguity." Brain Sciences 11, no. 9 (August 25, 2021): 1122. http://dx.doi.org/10.3390/brainsci11091122.
Abstract:
Emergency situations promote risk-taking behaviors associated with anxiety reactivity. A previous study using the Iowa Gambling Task (IGT) has demonstrated that prespecified state anxiety predicts moderate risk-taking (middle-risk/high-return) after salient penalty events under temporal pressure and information ambiguity. Such moderate risk-taking can be used as a behavioral background in the case of fraud damage. We conducted two psychophysiological experiments using the IGT and used a psychophysiological modeling approach to examine how moderate risk-taking under temporal pressure and information ambiguity is associated with automatic physiological responses, such as a skin conductance response (SCR). The first experiment created template SCR functions under concurrent temporal pressure and information ambiguity. The second experiment produced a convolution model using the SCR functions and fitted the model to the SCR time series recorded under temporal pressure and no temporal pressure, respectively. We also collected the participants’ anxiety profiles before the IGT experiment. The first finding indicated that participants with higher state anxiety scores yielded better model fitting (that is, event-related physiological responses) under temporal pressure. The second finding demonstrated that participants with better model fitting made consecutive Deck A selections under temporal pressure more frequently. In summary, a psychophysiological modeling approach is effective for capturing overlapping SCRs and moderate risk-taking under concurrent temporal pressure and information ambiguity is associated with automatic physiological and emotional reactivity.
10
Dutta, Neelotpal, Garvit Mathur, and Mohammad Talha. "Analysis of the Formation of an Aneurysm in Arteries Through Finite Element Modeling Using Hyper-Elastic Model." Advanced Science, Engineering and Medicine 12, no. 12 (December 1, 2020): 1456–61. http://dx.doi.org/10.1166/asem.2020.2599.
Abstract:
This paper presents the stress patterns and deformation in an artery with an aneurysm under a variation of pressure. An aneurysm occurs when a weak portion of artery wall bulges out under pressure. To make a proper treatment decision, one must understand the formation and growth of the condition. In this article, finite element analysis of the formation of fusiform aneurysms in arteries is presented using the hyperelastic material model by employing ANSYS. We present the effect of different blood pressures in the deformation of the weakened arterial wall and also compare it with a normal artery. This work can be used as a reference for research in this area.
11
Baldassi, Carlo, Simone Cerreia-Vioglio, Fabio Maccheroni, Massimo Marinacci, and Marco Pirazzini. "A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure." Management Science 66, no. 11 (November 2020): 5075–93. http://dx.doi.org/10.1287/mnsc.2019.3475.
Abstract:
In this paper, we provide an axiomatic foundation for the value-based version of the drift diffusion model (DDM) of Ratcliff, a successful model that describes two-alternative speeded decisions between consumer goods. Our axioms present a test for model misspecification and connect the externally observable properties of choice with an important neurophysiologic account of how choice is internally implemented. We then extend our axiomatic analysis to multialternative choice under time pressure. In a nutshell, we show that binary DDM comparisons of the alternatives, paired with Markovian exploration of the consideration set, approximately lead to softmaximization. This paper was accepted by Manel Baucells, decision analysis.
12
Fitrijati, Krisnhoe Rachmi, and Mahfud Sholihin. "The Effect of Ethical Sensitivity and Accountability Pressures on the Ethical Decision-Making." Management Accounting Frontiers 5-6 (April 30, 2023): 51–82. http://dx.doi.org/10.52153/prj0601006.
Abstract:
This study investigates the effect of ethical sensitivity on Ethical Decision Making (EDM) in an accounting context, with a laboratory experiment using a 2×2 between subjects involving 61 postgraduate students. It considers the person-situation interaction approach with rationalist-intuition as the basis of moral decisions. Our analysis reveals that ethical sensitivity affects EDM. Furthermore, accountability pressure interacts with ethical sensitivity to affect EDM. Our results are consistent with Rest (1986) model and Jones (1991) theory. EDM is affected by individual factors (ethical sensitivity) and organizational factors (anonymity and feedback accountability pressures). Our findings reveal that to improve EDM, organizations should increase the ethical sensitivity of their organizational members as well as set accountability pressures for them. Individu with high ethical sensitivity who are under the pressure of feedback accountability are more ethical in decision-making. This study employs students as subjects, the results should be interpreted cautiously. Future study should validate the findings using professional accountants as subjects or performing other research strategies, such as a qualitative approach, to answer why such phenomena exist.
13
Wex, Felix, Guido Schryen, and Dirk Neumann. "A Fuzzy Decision Support Model for Natural Disaster Response under Informational Uncertainty." International Journal of Information Systems for Crisis Response and Management 4, no. 3 (July 2012): 23–41. http://dx.doi.org/10.4018/jiscrm.2012070103.
Abstract:
Coordination deficiencies have been identified after the March 2011 earthquakes in Japan in terms of scheduling and allocation of resources, with time pressure, resource shortages, and especially informational uncertainty being main challenges. The authors address this issue of operational emergency response in natural disaster management (NDM) by suggesting a decision support model and a Monte Carlo heuristic which account for these challenges by drawing on fuzzy set theory and fuzzy optimization. Deriving requirements for addressing NDM situations from both practice and literature, they propose a decision model that accounts for the following phenomena: (a) incidents and rescue units are spatially distributed, (b) rescue units possess specific capabilities, (c) processing is non-preemptive, and (d) informational uncertainty occurs due to vague and linguistic specifications of data. The authors computationally evaluate their heuristic and benchmark the results with current best practice solutions. The authors’ results indicate that applying the new heuristic can substantially reduce overall harm.
14
Smith, K., and P. A. Hancock. "Managing Risk under Time Stress." Proceedings of the Human Factors Society Annual Meeting 36, no. 13 (October 1992): 1019–23. http://dx.doi.org/10.1177/154193129203601322.
Abstract:
The evolution of automated and semi-automated systems is rendering continuous regulation relatively obsolete, leaving periodic “management” interventions as the main way in which operators exercise control. Consequently, the human is now more frequently required to respond in uncertain, unusual, or “emergency” conditions. Such circumstances connote high stress environments. Consequently, the research reported here investigates expertise at decision making under stress. The source of stress is ubiquitous in occurrence, namely time pressure. We present a process model that explains and predicts the decision behavior of skilled operators as they manage risk under time stress. The model identifies three components of decision making, (1) attention, (2) assessment, and (3) intervention. Attention (1) scans widely among information displays and focuses action narrowly upon one of three procedures for (2) assessing the attended information. Separate procedures assess (α) the risks posed by the environment, (β) risks generated by interacting with the environment, and (α) uncertainty about those risks. The uniquely appropriate intervention (3) is selected by a small set of rules that match heuristically the assessments of risk and uncertainty to a short list of alternative actions. The model is validated with respect to the operation of skilled operators in the domain of currency exchange. In comparing performance versus simulation data, the model identifies the one procedure that resists automation - the assessment of risks posed by the environment. This assessment involves causal arguments that often rely upon extensive domain knowledge. In contrast, attention to displays, heuristic matching, and the procedures for assessing uncertainty and the risk of interaction can be delegated to an automated decision support system. This result has clear implications for the the design of systems to support skilled decision making under emergency conditions: decision support systems for dynamic environments like currency trading must notify the operator of the occurrence of system parameters that require assessments of environmental risk and incorporate these assessments into automated procedures that recommend appropriate interventions.
15
Dror, Itiel E., Beth Basola, and Jerome R. Busemeyer. "Decision making under time pressure: An independent test of sequential sampling models." Memory & Cognition 27, no. 4 (July 1999): 713–25. http://dx.doi.org/10.3758/bf03211564.
16
Raab, M. "T-ECHO: model of decision making to explain behaviour in experiments and simulations under time pressure." Psychology of Sport and Exercise 3, no. 2 (April 2002): 151–71. http://dx.doi.org/10.1016/s1469-0292(01)00014-0.
17
Niu, Zhongqi, Wenlong Song, Yantong Lu, and Xingyu Bao. "Merkel Government’s Refugee Policy: Under Bounded Rationality." Social Sciences 12, no. 3 (March 20, 2023): 187. http://dx.doi.org/10.3390/socsci12030187.
Abstract:
As the country hosting the most significant number of refugees in Europe, Germany’s Merkel government’s refugee policy has been repeatedly adjusted and plagued by inconsistencies and management failures. What factors have influenced the formation of the Merkel government’s refugee policy and its two shifts? The traditional rational decision-making model does not effectively explain government decisions’ motivation in complex challenges. This article develops a framework for analyzing “bounded rational decision-making”. It identifies the three factors that influenced the Merkel government’s refugee policy: (a) strict border controls, (b) welcome culture, and (c) welcome culture under restrained policies. It explains the process and logic of their development. Based on the theory of “bounded rational decision making”, the article examines the “key events and problem identification”, “goal selection”, “national interests”, and “political psychology”. We found that in the early stages of the refugee crisis, (a) public opinions and pressure, (b) the pursuit of a positive national image, and (c) a shift in the leader’s psychology led Merkel to shift away from a pragmatic and rational course. These factors led to the first shift in German refugee policy from “hesitation” to a “welcoming culture”. At the end of 2015, however, the shortcomings of the irrational policy were quickly revealed, and the frequency of refugee-related social problems reversed German public opinion, forcing Merkel’s government to seek a balance between humanitarianism and national interests. As a result, refugee policy was adjusted for the second time, returning to a strict refugee examination system. The development of the Merkel government’s refugee policy exposed the shortcomings of the traditional crisis decision-making model. Moreover, it provided a new perspective for rethinking the governance of the refugee crisis.
18
Zhou, Xiao, and Xiancong Wu. "Decisions for a Retailer-Led Low-Carbon Supply Chain Considering Altruistic Preference under Carbon Quota Policy." Mathematics 11, no. 4 (February 10, 2023): 911. http://dx.doi.org/10.3390/math11040911.
Abstract:
With the release of the national energy-saving emission reduction policy and the improvement of consumers’ awareness of environmental protection, the demand for low-carbon products is growing rapidly. In a retailer-led low-carbon supply chain, the increased cost of carbon emission reduction puts manufacturers at a disadvantage. Under the carbon quota policy, to improve manufacturers’ profits as well as enhance carbon emission reduction, this paper studies the players’ decisions in a low-carbon supply chain consisting of one dominant retailer and one manufacturer. To maintain the supply chain’s stability and sustainability, the dominant retailer tends to employ altruistic preference policies towards the manufacturer. The optimal decision, carbon emission reduction and supply chain profit are compared and analyzed under three decision models: (i) centralized decision, (ii) decentralized decision without altruistic preference and (iii) decentralized decision with altruistic preference. The results indicate that the carbon emission reduction rate, market demand and profit in the centralized model are higher than in the decentralized model. The retailer’s altruistic preference is beneficial to the improvement of carbon emission reduction, market demand and the profit of the manufacturer and the supply chain. Under certain conditions, carbon trading can effectively reduce the cost pressure of manufacturers and improve the level of carbon emission reduction and the overall profit of the supply chain. These results will guide low-carbon supply chain decision-making and provide insight into the research of irrational behaviors in supply chain decision-making under carbon policies.
19
Liu, Biyu, Zhongsheng Hua, Qinhong Zhang, Haidong Yang, and Athanasios Migdalas. "Optimal Operational Decision Making of Manufacturers and Authorized Remanufacturers with Patent Licensing under Carbon Cap-and-Trade Regulations." Complexity 2020 (July 22, 2020): 1–22. http://dx.doi.org/10.1155/2020/1864641.
Abstract:
Constrained by production capacity and the pressure to reduce emissions, many original equipment manufacturers (OEMs) authorize third-party remanufacturers (TPRs) to remanufacture patented products. We investigate the operational decisions of OEMs and authorized TPRs under carbon cap-and-trade regulations in a two-echelon supply chain. We first formulate an operational decision model for OEMs before a TPR enters. Then, for the cases of centralized and decentralized decision making, we formulate an operational decision-making model for the TPR and, subsequently, establish one for the OEM after the TPR enters. We further analyze the effects of carbon emissions cap, trading price of carbon permits, yield rate, and consumer willingness to pay (WTP) on optimal decisions. Our results indicate: whether TPRs accept authorization remanufacturing depending on the ratio of carbon emissions cap to carbon emissions for producing per remanufactured product; royalty rate is negatively affected by trading price of carbon permits and per remanufactured product’ carbon emissions other than that for per new product, and can offset the threat caused by TPRs; the implementation of carbon cap-and-trade regulations causes OEMs to charge TPRs lower royalty rate; centralized decision making increases the total profit of the supply chain and delivers superior environmental benefits. As yield rate and WTP increase, the total profit increases, increasingly sensitive to WTP.
20
Klein, Gary A., Roberta Calderwood, and Anne Clinton-Cirocco. "Rapid Decision Making on the Fire Ground." Proceedings of the Human Factors Society Annual Meeting 30, no. 6 (September 1986): 576–80. http://dx.doi.org/10.1177/154193128603000616.
Abstract:
The objective of this study was to examine the way decisions are made by highly proficient personnel, under conditions of extreme time pressure, and where the consequences of the decisions could affect lives and property. Fire Ground Commanders (FGCs), who are responsible for allocating personnel and resources at the scene of a fire, were studied using a critical incident protocol analysis. The major finding was that in less than 12% of the decision points was there any evidence of simultaneous comparisons and relative evaluation of two or more options. Instead the FGCs most commonly relied on their experience to directly identify the situation as typical and to identify a course of action as appropriate for that prototype. A Recognition Primed Decision (RPD) model is proposed which emphasizes the use of recognition rather than calculation or analysis for rapid decision making.
21
Du, Chenxu, Fumin Deng, Chaozhu Wang, Peng Luo, and Xuedong Liang. "A Study of Two-level Inventory Allocation for E-commerce Pre-sales Based on Two Return Methods." E3S Web of Conferences 409 (2023): 03010. http://dx.doi.org/10.1051/e3sconf/202340903010.
Abstract:
In recent years, e-commerce shopping festivals based on the presale model have become increasingly popular. Under the pre-sale model, some e-commerce companies have used inventory front-loading methods to give consumers higher time satisfaction for alleviating the pressure caused by order piling. At the same time a new return method has been generated, but existing studies have only considered the traditional return method. In this paper, a dualobjective decision model based on NSGA-II algorithm is developed for maximizing enterprise profit and maximizing consumer time satisfaction under the premise of considering both return methods, and the optimal two-level inventory allocation decision scheme is calculated for e-commerce enterprises. The results show that the existence of two return methods will have a more significant impact on enterprise profit, and therefore is more practical for e-commerce enterprises to make two-level inventory allocation decisions.
22
Haier, Joerg, Maximilian Mayer, Juergen Schaefers, Siegfried Geyer, and Denise Feldner. "A pyramid model to describe changing decision making under high uncertainty during the COVID-19 pandemic." BMJ Global Health 7, no. 8 (August 2022): e008854. http://dx.doi.org/10.1136/bmjgh-2022-008854.
Abstract:
The COVID-19 pandemic put healthcare systems, hospitals and medical personal under great pressure. Based on observations in Germany, we theorise a general model of rapid decision-making that makes sense of the growing complexity, risks and impact of missing evidence. While adapting decision-making algorithms, management, physicians, nurses and other healthcare professionals had to move into uncharted territory while addressing practical challenges and resolving normative (legal and ethical) conflicts. During the pandemic, this resulted in decisional uncertainties for healthcare professionals. We propose an idealised risk-based model that anticipates these shifts in decision-making procedures and underlying value frameworks. The double pyramid model visualises foreseeable procedural adaptations. This does not only help practitioners to secure operational continuity in a crisis but also contributes to improving the conceptual underpinnings of the resilience of healthcare during the next pandemic or similar future crises situations.
23
Yang, J. S., E. S. Chung, S. U. Kim, and T. W. Kim. "Prioritization of water management under climate change and urbanization using multi-criteria decision making methods." Hydrology and Earth System Sciences 16, no. 3 (March 12, 2012): 801–14. http://dx.doi.org/10.5194/hess-16-801-2012.
Abstract:
Abstract. This paper quantifies the transformed effectiveness of alternatives for watershed management caused by climate change and urbanization and prioritizes five options using multi-criteria decision making techniques. The climate change scenarios (A1B and A2) were obtained by using a statistical downscaling model (SDSM), and the urbanization scenario by surveying the existing urban planning. The flow and biochemical oxygen demand (BOD) concentration duration curves were derived, and the numbers of days required to satisfy the environmental flow requirement and the target BOD concentration were counted using the Hydrological Simulation Program-Fortran (HSPF) model. In addition, five feasible alternatives were prioritized by using multi-criteria decision making techniques, based on the driving force-pressure-state-impact-response (DPSIR) framework and cost component. Finally, a sensitivity analysis approach for MCDM methods was conducted to reduce the uncertainty of weights. The result indicates that the most sensitive decision criterion is cost, followed by criteria response, driving force, impact, state and pressure in that order. As it is certain that the importance of cost component is over 0.127, construction of a small wastewater treatment plant will be the most preferred alternative in this application.
24
Yang, J. S., E. S. Chung, S. U. Kim, T. W. Kim, and Y. D. Kim. "Prioritization of water management under climate change and urbanization using multi-criteria decision making methods." Hydrology and Earth System Sciences Discussions 8, no. 6 (November 10, 2011): 9889–925. http://dx.doi.org/10.5194/hessd-8-9889-2011.
Abstract:
Abstract. This paper quantifies the transformed effectiveness of alternatives for watershed management caused by climate change and urbanization and prioritizes five options using multi-criteria decision making techniques. The climate change scenarios (A1B and A2) were obtained by using a statistical downscaling model (SDSM), and the urbanization scenario by surveying the existing urban planning. The flow and biochemical oxygen demand (BOD) concentration duration curves were derived, and the numbers of days required to satisfy the environmental flow requirement and the target BOD concentration were counted using the Hydrological Simulation Program-Fortran (HSPF) model. In addition, five feasible alternatives were prioritized by using multi-criteria decision making techniques, based on the driving force-pressure-state-impact-response (DPSIR) framework and cost component. Finally, a sensitivity analysis approach for MCDM methods was conducted to reduce the uncertainty of weights. The result indicates that the most sensitive decision criterion is cost, followed by criteria response, driving force, impact, state and pressure in that order. Since it is certain that the importance of cost component is over 0.127, use of the groundwater collected by subway stations will be the most preferred alternative in this application.
25
Wang, Yihua, Mengke Yang, and Xiaoguang Zhou. "The path selection model of emergency logistics based on cumulative prospect theory." E3S Web of Conferences 136 (2019): 04067. http://dx.doi.org/10.1051/e3sconf/201913604067.
Abstract:
In recent years, sudden natural disasters occur frequently. Typical emergencies have the characteristics of great uncertainty, large-scale casualty risk, time pressure and urgency, which have a series of serious and sustained impacts on people's production and life. Therefore, after the emergencies, emergency rescue is particularly important for disaster-stricken areas, and the decision-making of emergency logistics is an important part of it. At present, the research on emergency logistics in China focuses on the shortest distribution time, multi-objective decision-making, dynamic path planning, and operational research. It is believed that people are completely rational in making decisions, ignoring people's subjective factors and risk attitudes. From the perspective of decision-makers' risk attitude, this paper studies people's decision-making bias under the condition of incomplete rationality. Based on previous studies, this paper determines the value coefficient and weight coefficient, and according to the characteristics of emergency logistics, time is selected as the reference point., and A path selection model based on cumulative prospect theory is established. According to the risk attitude, the decision maker is divided into risk preference type and risk avoidance type. Based on the established model, an example is simulated, and the parameters in the model are simulated, and the impact of risk attitude and parameter changes on the final decision-making is analyzed. The simulation results show that the cumulative prospect theory is applicable to the study of emergency logistics decision-making mechanism, and the parameter setting will also have an important impact on the path prospect.
26
Menn, Marie Le, Cyril Bossard, Bruno Travassos, Ricardo Duarte, and Gilles Kermarrec. "Handball Goalkeeper Intuitive Decision-Making: A Naturalistic Case Study." Journal of Human Kinetics 70, no. 1 (November 30, 2019): 297–308. http://dx.doi.org/10.2478/hukin-2019-0042.
Abstract:
Abstract Goalkeepers hold a key position for success in team sports competitions. They perform in dynamical contexts and are highly submitted to time pressure. The purpose of this naturalistic case study, therefore, was to explore how a handball expert goalkeeper deals with the uncertainty of the competition settings to make successful decisions. An individual self-confrontation interview was held with a goalkeeper while he watched duels with potential throwers in an official competition. A mixed method was used combining the first-person and third-person point of view. Verbal data were supplemented by observational data (distance measures between the goalkeeper and the potential thrower) in 83 short accounts of decision-making situations. Qualitative analysis resulted in 419 units of salient features, in three types of processes related to the Recognition-Primed Decision model, and in four micro-decisions. Non-parametrical statistical analysis indicated that there was a significant effect of distances between the potential thrower and the goalkeeper, on the micro-decision categories, but not on the recognition processes. These results provide insights into cognitive contents and processes an expert goalkeeper can use under uncertainty and time pressure. The mixed method furnishes a meaningful description and a subsequent understanding of expert performances in sport.
27
Schiozer, Denis José, Antonio Alberto de Souza dos Santos, Susana Margarida de Graça Santos, and João Carlos von Hohendorff Filho. "Model-based decision analysis applied to petroleum field development and management." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 74 (2019): 46. http://dx.doi.org/10.2516/ogst/2019019.
Abstract:
This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.
28
Pereira, Leonor, Antonio Valente, Bruno Soares, Carlos Costa, Salviano Soares, José Lima, and Igor Gonçalves. "Viticulture under climate change: A case study on a water scarcity model." BIO Web of Conferences 68 (2023): 01019. http://dx.doi.org/10.1051/bioconf/20236801019.
Abstract:
Changes in climatic patterns hinder the prediction of water availability, being imperative to develop new strategies to optimise water management in the agricultural sector. A multi-sensor network is being developed by ADVID/CoLAB VINES&WINES and University of Trás-os-Montes and Alto Douro (UTAD), aiming to determine water stress in vineyards, as a Decision Support System (DSS) for winegrowers. Remote wireless data transmission through LoRaWAN technology, will allow the development of a Machine Learning based model for water stress mapping. Measured parameters include soil, plant, and atmosphere data, given the importance of soil-plant-atmosphere continnum when evaluating water status. The pilot is installed in a commercial vineyard in the Douro Demarcated Region (DDR), and different sensor’s modules were distributed spatially in the parcel. Lower cost and higher range than WiFi or Bluetooth, LoRaWAN are especially important for applications in remote areas, where mobile networks have little coverage, allowing to benefit a larger number of producers. While overcoming the constraints of the current monitoring method (Scholander pressure bomb), this system will allow remote and continuous water monitoring, assisting the producer in decision making. Altogether, this solution will contribute to better management of water resources, as well to the sustainability and competitiveness of farms.
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Mackinnon, Lachlan, Liz Bacon, Gabriella Cortellessa, and Amedeo Cesta. "Using Emotional Intelligence in Training Crisis Managers." International Journal of Distance Education Technologies 11, no. 2 (April 2013): 66–95. http://dx.doi.org/10.4018/jdet.2013040104.
Abstract:
Multi-agency crisis management represents one of the most complex of real-world situations, requiring rapid negotiation and decision-making under extreme pressure. However, the training offered to strategic planners, called Gold Commanders, does not place them under any such pressure. It takes the form of paper-based, table-top exercises, or expensive, real-world, limited-scope simulations. The Pandora project has developed a rich multimedia training environment for Gold Commanders, based on a crisis scenario, timeline-based, event network, with which the trainees and their trainer interact dynamically. Pandora uses the emotional intelligence of the trainees, through a behavioural modelling component, to support group dynamic and decision-making. It applies systemic emotional intelligence, based on inferred user state and rule-based affective inputs, to impact the stress levels of the trainees. Pandora can impose variable stress on trainees, to impact their decision-making, and model their behaviour and performance under stress, potentially resulting in more effective and realisable strategies.
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Gao, Feng. "Time Pressure Impact on En-Route Choice Behavior under Guidance Information." Applied Mechanics and Materials 614 (September 2014): 539–42. http://dx.doi.org/10.4028/www.scientific.net/amm.614.539.
Abstract:
This paper investigates time pressure impact on en-route choice behavior under guidance information. The impact of time pressure constraints is quite evident in en-route choice decisions. Understanding en-route choice behavior under time pressure and predicting route choices are important components in the overall goal of building a more reliable and efficient advanced traffic information systems (ATIS). A hybrid model to predict en-route driver routing decisions under guidance information is proposed. The model accounts for the observed changes in choice probabilities, including preference reversals as a function of time limit.
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Betsch, Tilmann, Babette Julia Brinkmann, Klaus Fiedler, and Katja Breining. "When prior knowledge overrules new evidence: Adaptive use of decision strategies and the role of behavioral routines 1The paper is based on a presentation held at the 16th SPUDM conference in Leeds, 1997. The research was supported by a grant from the Deutsche Forschungsgemeinschaft to Klaus Fiedler and Tilmann Betsch, within the Sonderforschungsbereich 504 at the Universities of Mannheim and Heidelberg. We are grateful to Frank Siebler for programming the Mouselab. Thanks are due to Susanne Haberstroh, Arndt Bröder, Bronwyn Jones and Esther Goldbloom for many helpful comments on earlier versions of the paper." Swiss Journal of Psychology 58, no. 3 (September 1999): 151–60. http://dx.doi.org/10.1024//1421-0185.58.3.151.
Abstract:
This paper focuses on behavioral routines in adaptive decision making. In an experiment consisting of two phases, participants worked on recurrent, multiattribute choice problems. In the first phase, routines were induced by relying upon the human ability to adapt to situational changes by changing decision strategies. To induce strategy change, time pressure was varied as a within factor. Payoffs were manipulated so that an adaptive change in strategy led participants to maximize choice frequency for one out of three options (routine acquisition). After a one week time lapse, participants worked on similar problems, containing the previously preferred routine option. In this second phase, payoffs favored deviation from the routine option. Results showed that choices were almost perfectly calibrated to payoffs under low time pressure. However, if time pressure increased, participants were more likely to prefer the routine option, even though search strategies were still used adaptively and evidence discouraged routine selection. Results are discussed with reference to the model of adaptive decision making ( Payne, Bettman & Johnson, 1993 ), and the MODE model of attitude-behavior relation ( Fazio, 1990 ).
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Bopp, Christopher M., William Briggs, Catherine Orlando, and Raed Seetan. "Predicting cardiovascular disease using different blood pressure guidelines." International Journal of Public Health Science (IJPHS) 12, no. 2 (June 1, 2023): 794. http://dx.doi.org/10.11591/ijphs.v12i2.22188.
Abstract:
The criteria used to categorize patients as either hypertensive or normotensive were changed in 2017 by the American Heart Association and the American College of Cardiology (AHA/ACC). The updated guidelines lowered the criteria by which individuals are classified as hypertensive; systolic blood pressure (SBP) cut-off from ≥140 mmHg to ≥130 mmHg and diastolic blood pressure from ≥90 mmHg to ≥80 mmHg. The purpose of this study was to investigate what effect these changes in diagnostic criteria had on the ability of supervised learning to predict cardiovascular disease. Three models were developed and tested. Two models using a binned hypertension measure based on either the AHA/ACC new released guidelines or the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) original guidelines. The third model used SBP as a continuous variable. Data from 68,657 patients was processed through decision tree algorithm to determine which model offered the best accuracy. For both female and male subjects, the model with SBP returned the best area under the receiver operator characteristic curve and overall better sensitivity and specificity values. Our results showed that changing the criteria by which individuals are classified as hypertensive or normotensive negatively impacted the ability of decision tree to predict cardiovascular disease in both females and males.
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Yan, Wenjing, Hong Wang, Min Zuo, Haipeng Li, Qingchuan Zhang, Qiang Lu, Chuan Zhao, and Shuo Wang. "A Deep Machine Learning-Based Assistive Decision System for Intelligent Load Allocation under Unknown Credit Status." Computational Intelligence and Neuroscience 2022 (September 8, 2022): 1–8. http://dx.doi.org/10.1155/2022/5932554.
Abstract:
Nowadays, the banks are facing increasing business pressure in loan allocations, because more and more enterprises are applying for it and financial risk is becoming vaguer. To this end, it is expected to investigate effective autonomous loan allocation decision schemes that can provide guidance for banks. However, in many real-world scenarios, the credit status information of enterprises is unknown and needs to be inferred from business status. To handle such an issue, this paper proposes a two-stage loan allocation decision framework for enterprises with unknown credit status. And the proposal is named as TLAD-UC for short. For the first stage, the idea of deep machine learning is introduced to train a prediction model that can generate credit status prediction results for enterprises with unknown credit status. For the second stage, a dynamic planning model with both optimization objective and constraint conditions is established. Through such model, both the profit and risk of banks can be well described. Solving such a dynamic planning model via computer simulation programs, the optimal allocation schemes can be suggested.
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Naugle, Asmeret Bier, George A. Backus, Vincent C. Tidwell, Elizabeth Kistin-Keller, and Daniel L. Villa. "A Regional Model of Climate Change and Human Migration." International Journal of System Dynamics Applications 8, no. 1 (January 2019): 1–22. http://dx.doi.org/10.4018/ijsda.2019010101.
Abstract:
As climate change and human migration accelerate globally, decision-makers are seeking tools that can deepen their understanding of the complex nexus between climate change and human migration. These tools can help to identify populations under pressure to migrate, and to explore proactive policy options and adaptive measures. Given the complexity of factors influencing migration, this article presents a system dynamics-based model that couples migration decision making and behavior with the interacting dynamics of economy, labor, population, violence, governance, water, food, and disease. The regional model is applied here to the test case of migration within and beyond Mali. The study explores potential systems impacts of a range of proactive policy solutions and shows that improving the effectiveness of governance and increasing foreign aid to urban areas have the highest potential of those investigated to reduce the necessity to migrate in the face of climate change.
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Bracco, A., J. D. Neelin, H. Luo, J. C. McWilliams, and J. E. Meyerson. "High dimensional decision dilemmas in climate models." Geoscientific Model Development Discussions 6, no. 2 (May 8, 2013): 2731–67. http://dx.doi.org/10.5194/gmdd-6-2731-2013.
Abstract:
Abstract. An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Neelin et al. (2010) used a quadratic metamodel to objectively calibrate an atmospheric circulation model (AGCM) around four adjustable parameters. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g. how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.
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Zheng, Maoyong, and Cesar L. Escalante. "Banks' sustainable growth challenge under economic recessionary pressure." Agricultural Finance Review 80, no. 3 (February 25, 2020): 437–51. http://dx.doi.org/10.1108/afr-07-2019-0077.
Abstract:
PurposeThis is a comparative study of the nature of operating decisions made by agricultural and non-agricultural banks, affecting their actual growth plans in the years around and during the Great Recession of 2008. The main empirical question is whether banks under greater economic stress shortly before, during, and immediately after the recession made deliberate adjustments in their growth decisions vis-à-vis predetermined sustainable levels.Design/methodology/approachHiggins' sustainable growth challenge is employed to evaluate banks' growth decisions involving four growth levers (profitability, earnings retention, asset management, and financial leverage). Actual growth trends are related to business growth rates deemed sustainable given available financial capability as prescribed by Higgins' model.FindingsBoth banking groups made cautious growth decisions during the sample period. Actual growth rates were below sustainable levels. Agricultural banks registered steadily increasing sustainable growth rates from the pre-recession years until the recovery period, while non-agricultural banks were more constrained to grow given their declining sustainable growth levels. Notably, agricultural banks showed relatively more aggressiveness in raising slightly actual revenue growth to levels much closer to sustainable levels. This could have resulted from their less volatile profit margin trends and usual pressure to maintain acceptable liquidity conditions in order to gain access to external funds.Originality/valueThis study presents an additional application of Higgins' model to agricultural finance. The comparative analysis of banking groups becomes even more relevant these days as recent economic discussions focus on indicators of an imminent recessionary period.
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Ross, Karol G., and Linda G. Pierce. "Cognitive Engineering of Training for Adaptive Battlefield Thinking." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 11 (July 2000): 410–13. http://dx.doi.org/10.1177/154193120004401121.
Abstract:
Individual military decision-makers and the military staff, as a decision-making team, are currently under increased pressure to make quick assessments more often and under more unusual conditions than ever before. Advances in information technology, changing operational missions, and re-designed, “flattened” organizations all contribute to the new performance requirements. U.S. Army leadership has defined the general skill underlying the performance requirements as “adaptive thinking.” Our goal was to further the systematic and early development of that ability during officers' careers. We used a learning model derived from cognitive theory and a descriptive model of military expertise to engineer training interventions. We developed two training interventions that embody the learning model in this specific domain of expertise. We also identified research needed to further clarify how the learning model should be used in training development in the military as well as in other training settings.
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Bracco, A., J. D. Neelin, H. Luo, J. C. McWilliams, and J. E. Meyerson. "High dimensional decision dilemmas in climate models." Geoscientific Model Development 6, no. 5 (October 15, 2013): 1673–87. http://dx.doi.org/10.5194/gmd-6-1673-2013.
Abstract:
Abstract. An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM) to changes of four adjustable parameters, Neelin et al. (2010) used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.
39
Wang, Qiuji, Weiqi Feng, Wenhui Li, Shan Li, Qiuyi Wu, Zhichang Liu, Xin Li, et al. "Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection." Advances in Skin & Wound Care 37, no. 1 (January 2024): 1–6. http://dx.doi.org/10.1097/asw.0000000000000077.
Abstract:
ABSTRACT OBJECTIVE To establish a risk assessment model to predict postoperative National Pressure Injury Advisory Panel stage 2 or higher pressure injury (PI) risk in patients undergoing acute type A aortic dissection surgery. METHODS This retrospective assessment included consecutive patients undergoing acute type A aortic dissection surgery in the authors’ hospital from September 2017 to June 2021. The authors used LASSO (logistic least absolute shrinkage and selection operator) regression analysis to identify the most relevant variables associated with PI by running cyclic coordinate descent with 10-times cross-validation. The variables selected by LASSO regression analysis were subjected to multivariate logistic analysis. A calibration plot, receiver operating characteristic curve, and decision curve analysis were used to validate the model. RESULTS There were 469 patients in the study, including 94 (27.5%) with postoperative PI. Ten variables were selected from LASSO regression: body mass index, diabetes, Marfan syndrome, stroke, preoperative skin moisture, hemoglobin, albumin, serum creatinine, platelet, and d-dimer. Four risk factors emerged after multivariate logistic regression: Marfan syndrome, preoperative skin moisture, albumin, and serum creatinine. The area under the receiver operating characteristic curve of the model was 0.765. The calibration plot and the decision curve analysis both suggested that the model was suitable for predicting postoperative PI. CONCLUSIONS This study built an efficient predictive model that could help identify high-risk patients.
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Alberti, Alexandre Ramalho, Eduarda Asfora Frej, Lucia Reis Peixoto Roselli, Murilo Amorim Britto, Evônio Campelo, Adiel Teixeira de Almeida, and Rodrigo José Pires Ferreira. "Methodology to Support the Triage of Suspected COVID-19 Patients in Resource-Limited Circumstances." International Journal of Decision Support System Technology 14, no. 1 (January 1, 2022): 1–21. http://dx.doi.org/10.4018/ijdsst.309993.
Abstract:
COVID-19 pandemic has put health systems worldwide under pressure. Thus, establish a triage protocol to support the allocation of resources is important to deal with this public health crisis. In this paper, a structured methodology to support the triage of suspected or confirmed COVID-19 patients has been proposed, based on the utilitarian principle. A decision model has been proposed for evaluating three treatment alternatives: intensive care, hospital stay and home isolation. The model is developed according to multi-attribute utility theory and considers two criteria: the life of the patient and the overall cost to the health system. A screening protocol is proposed to support the use of the decision model, and a method is presented for calculating the probability of which of three treatment is the best one. The proposed methodology was implemented in an information and decision system. The originality of this study is using of the multi-attribute utility theory to support the triage of suspected COVID-19 and implement the decision model in an information and decision system.
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Kovaliuk, Dmytro, Ruslan Osipa, and Victoria Кondratova. "Decision making in control systems based on data analysis." Proceedings of the NTUU “Igor Sikorsky KPI”. Series: Chemical engineering, ecology and resource saving, no. 4 (December 24, 2021): 30–38. http://dx.doi.org/10.20535/2617-9741.4.2021.248902.
Abstract:
Technological processes are always accompanied by deviations from the set mode, which is due to the influence of many external and internal factors. The environmental parameters, the components of input raw materials, and the condition of technological equipment are constantly changing, which requires solving the problem of finding the optimal control parameters and, in some cases, the parameters of the process itself.
Most industries are focused on obtaining the final product with a given level of quality. Changes in parameters of the technological process may deteriorate the quality of production and cause defects or even emergency situations. To prevent this, forecasting methods are used.
The task of constructing predictive models based on experimental data is relevant for a wide range of technological processes. Today, predictive models are widely used in management, diagnosis and identification. The vast majority of these models are based on artificial intelligence technologies or methods of mathematical statistics.
The most widespread forecasting models find application in areas such as banking, insurance, business economics, medicine, diagnostics of technical components and equipment, and forecasting the parameters of technological processes.
Despite the well-developed algorithm for model development and application, the main problem that remains is to acquire data, select an appropriate model structure, and integrate the model into existing control systems.
The paper will predict the parameters of the technological process of methanol production under reduced pressure. The production of methanol under reduced pressure is a multi-stage process, and the emergence of problems at some stage will adversely affect further work and the end result.
Note that there are all problems related to the performance of technological processes in the production of methanol, which are described above. Therefore, another task is to forecast emergencies, taking into account the indicators of all stages in the process. The development of models for forecasting emergencies and controlling thermal regimes and their further integration into the existing automatic process control system is proposed to be performed according to the principles of industrial revolution – Industry 4.0.
Important components of Industry 4.0 are the Internet of Things, data analysis, and digital duplicates. Each of these components solves a partial problem and, collectively, they provide full automation of production, forecasting of real-time process indicators, and calculation of optimal control.
The process of methanol production under reduced pressure can be fully automated in accordance with the components of Industry 4.0. First, there is instrumentation that allows the values of technological process to be obtained over time. Second, given a moderate size of these data, one can obtain models of control objects, perform their software implementation, and use them to calculate optimal control or predict the state of the process.
The paper proposes a variant of constructing a virtual model based on experimental data and its further use with actual values of process parameters.
A regression model was chosen to develop a model for predicting the temperature regime. Regression analysis allows checking the statistical significance of the parameters, assessing the adequacy and accuracy of the model, and establishing the nature and closeness of the relationship between the studied phenomena.
It is also important to predict the occurrence of emergency (adverse) situations at the workplace. For this purpose, it is necessary to determine a list of these situations according to the technological regulations and develop a model for forecasting emergencies. There are various forms of presenting a model for forecasting emergencies. A decision tree is one of them. It will be developed for the production of methanol.
The resulting tree is a graphical structure of the verbal (semantic) model relying on the expert's reasoning in solving problems related to emergencies. This is a network structure, whose nodes indicate potential deviations of the control object from the normal mode of operation. The resulting tree is used to solve forecasting and diagnosing problems.
For practical use, the decision tree is implemented in software as an "if - then" set of rules. The software is used as an element of a higher-level system in relation to the existing automatic process control system.
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Nematian, Javad, and Seyed Salar Ghotb. "Mathematical Programming for Modelling Green Supply Chains Under Randomness and Fuzziness." International Journal of Fuzzy System Applications 6, no. 1 (January 2017): 56–85. http://dx.doi.org/10.4018/ijfsa.2017010104.
Abstract:
Nowadays by growing concerns about environmental problems, businesses and industries are under pressure to decrease their negative impact on environment, consequently firms and industries have to reconsider about their activities and make their business compatible with environment. So industries should green their supply chains to optimize economic and environmental concerns, but because of uncertainty in the real world like inconsistency of world economy, the process of greening supply chains can be more complex. To optimize total costs and the unfavourable sides of supply chains simultaneously in an uncertain situation, this paper presents a multi-objective mixed integer programming with fuzzy random variables (FRVs) and by using fuzzy theory and fuzzy random chance-constrained programming (FRCCP), the proposed model is converted to deterministic model. This paper can be also suitable for decision making with optimistic, pessimistic and realistic notion. Finally, a numerical example is presented to illustrate the model.
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de Lima e Silva, Leandro, Rodrigo Gomes de Souza Vale, Eduardo Borba Neves, Juliana Brandão Pinto de Castro, Erik Salum de Godoy, Jurandir Baptista da Silva, Magna Leilane Silva, and Rodolfo de Alkmim Moreira Nunes. "Hemodynamic and motion demands of soccer referees: a comparison between series A and B of the State Championship of Rio de Janeiro, Brazil." Archivos de Medicina del Deporte 40, no. 4 (August 14, 2023): 222–28. http://dx.doi.org/10.18176/archmeddeporte.00139.
Abstract:
Introduction: Soccer referees need excellent conditioning to withstand the physical and psychological demands of games. Objective: To compare the hemodynamic variables, speed, cadence, and distance coursed of referees during soccer games of series A and B in Rio de Janeiro, Brazil. Material and method: The total number of decisions made during the 10 soccer matches evaluated was 1,224 observable decisions of 10 professional Soccer referees (one per soccer match: 5 in series A and 5 in series B). We used a frequency meter (Polar, model V800, PolarFlow software) and video footage of the games (Sony, model PXW-Z150, 4K). The moments considered were: the decision, 15 seconds that preceded it, and the period from the beginning of each stage to each decision. Were studied the hemodynamic [average heat rate (mean HR), maximum heart rate (HRmax), and minimum heart rate (HRmin)] and motion variables [average speed (Vmed), maximum speed (Vmax), average cadence (cadencemed), maximum cadence (cadencemax), minimum cadence (cadencemin), and distance covered]. Descriptive measures were used to present the results of the variables studied and the Student’s T-Test for independent samples to test the study hypotheses. The significance level was set at 95% (P <0.05). Results: The matches of series A had a greater number of interventions and greater hemodynamic load at the exact moment of the decision than those of series B. significantly (P <0.05): mean HR, HRmax, HRmin, Vmax, Cadencemed, and Cadencemax in series A were higher compared to series B. In the 15 seconds before the decisions: mean HR, HRmax, and HRmin in series A were higher than in series B, and Vmed in series B was higher in relation to series A. At the exact moment of the decisions: mean HR in series A was higher in relation to series B. Conclusion: Referees’ interventions are generally carried out under high hemodynamic pressure. The matches played in the A series require a higher number of interventions and hemodynamic intensity than the matches in the series B under high hemodynamic pressure, other psychological factors may play a role; however, this needs to be studied in greater depth.
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Muharremi, Robert. "Establishing Institutions under International Administration." Hrvatska i komparativna javna uprava 20, no. 1 (March 31, 2020): 7–28. http://dx.doi.org/10.31297/hkju.20.1.1.
Abstract:
The international community, led by the United Nations, created Kosovo’s new post-war institutions and continues to influence them, even after Kosovo declared independence in 2008. One of the very first priorities of the United Nations Interim Administration Mission in Kosovo (UNMIK) was to establish the rule of law and to develop institutions and legal frameworks for a normally functioning economy. However, after almost two decades of internationally led institution-building, Kosovo is still, measured by European standards, a poor country with weak institutions. This paper shows that the creation of institutions does not follow a rational decision-making model, even when, like in Kosovo, institutions are created under direct international involvement and with the intention to develop the rule of law and facilitate economic development. The garbage can model approach to governance and decision-making provides a better explanation of the formation of governance institutions and why institutions, despite perhaps the best intentions, do not produce the desired results; failing to solve the underlying policy problems. The case studies on the privatisation of socially owned property and the development of contract law show that, in the case of Kosovo, adopting the best international and European standards almost always meant adopting a decontextualised solution promoted by an international actor. It did not really matter if that solution indeed solved the problem. In fact, in most cases the problem remained, with new problems being created because of the inadequacy of the imported ready-made solution. The conclusion is that sometimes less international assistance is more. In the absence of so much international financial and technical assistance, Kosovar leadership would have been required to assume more ownership of the policy-making for solving their problems. Less international assistance would also have meant less competition between international actors and less pressure to adopt ready-made decontextualised solutions.
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Bukljaš, Mihaela, Kristijan Rogić, and Vladimir Jerebić. "Distributionally Robust Model and Metaheuristic Frame for Liner Ships Fleet Deployment." Sustainability 14, no. 9 (May 5, 2022): 5551. http://dx.doi.org/10.3390/su14095551.
Abstract:
The container shipping industry market is very dynamic and demanding, economically, politically, legally, and financially. Considering the high cost of core assets, ever rising operating costs, and the volatility of demand and supply of cargo space, the result is an industry under enormous pressure to remain profitable and competitive. To maximize profits while maintaining service levels and ensuring the smooth flow of cargo, it is essential to make strategic decisions in a timely and optimal manner. Fleet deployment selection, which includes the profile of vessel hire, as well as their capacity and port rotation, is one of the most important strategic and tactical decisions container shipping operators must make. Bearing in mind that maritime business is inherently stochastic and uncertain, the key aims of this paper are to address the problem of fleet deployment under uncertain operating conditions, and to provide an integrated and optimized tool in the form of a mathematical model, metaheuristic algorithm, and computer program. Furthermore, this paper will show that the properties of the provided solutions exceed those offered in the literature so far. Such a solution will provide the shipping operator with a decision tool to best deploy its fleet in a way that responds more closely to real life situations and to meet the maximum demand for cargo space with minimal expense. The final goal is to minimize the operating costs while managing cargo flows and reducing the risks of unfulfilled customer demands.
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Lai, Lucy, and Samuel J. Gershman. "Human decision making balances reward maximization and policy compression." PLOS Computational Biology 20, no. 4 (April 26, 2024): e1012057. http://dx.doi.org/10.1371/journal.pcbi.1012057.
Abstract:
Policy compression is a computational framework that describes how capacity-limited agents trade reward for simpler action policies to reduce cognitive cost. In this study, we present behavioral evidence that humans prefer simpler policies, as predicted by a capacity-limited reinforcement learning model. Across a set of tasks, we find that people exploit structure in the relationships between states, actions, and rewards to “compress” their policies. In particular, compressed policies are systematically biased towards actions with high marginal probability, thereby discarding some state information. This bias is greater when there is redundancy in the reward-maximizing action policy across states, and increases with memory load. These results could not be explained qualitatively or quantitatively by models that did not make use of policy compression under a capacity limit. We also confirmed the prediction that time pressure should further reduce policy complexity and increase action bias, based on the hypothesis that actions are selected via time-dependent decoding of a compressed code. These findings contribute to a deeper understanding of how humans adapt their decision-making strategies under cognitive resource constraints.
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Stroh, J. N., Tellen D. Bennett, Vitaly Kheyfets, and David Albers. "Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales." JMIR Medical Informatics 9, no. 3 (March 22, 2021): e23215. http://dx.doi.org/10.2196/23215.
Abstract:
Background The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained. Objective This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data. Methods We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP. Results The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation. Conclusions Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.
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Ventikos, Nikolaos P., Alexandros Koimtzoglou, Konstantinos Louzis, Nikolaos Themelis, and Marios-Anestis Koimtzoglou. "A Smart Risk Assessment Tool for Decision Support during Ship Evacuation." Journal of Marine Science and Engineering 11, no. 5 (May 10, 2023): 1014. http://dx.doi.org/10.3390/jmse11051014.
Abstract:
In case of a ship emergency situation and during its evolvement that might result in an evacuation, the master and the bridge command team of a ship have to continuously assess risk. This is a very complex procedure, as crucial decisions concerning safety are made under time pressure. The use of a decision-support tool would have a positive effect on their performance, resulting in an improvement in the way ships are evacuated. The purpose of this paper is to present the PALAEMON smart risk assessment platform (SRAP). SRAP is a real-time risk assessment platform developed to assist the decision-making process of the master and bridge command team of a ship regarding the evacuation process. Its purpose is to provide decision support for the following aspects: (1) the decision to sound the general alarm (GA) following an accident, (2) monitoring the progress of the mustering process in order to take any additional actions, and (3) the decision to abandon the ship or not. SRAP dynamically assesses the risk to the safety of the passengers and crew members in the different phases of the evacuation process, so one model in the form Bayesian networks (BNs) was developed for each stage of the evacuation process. The results of a case study that was implemented reflect how various parameters such as injuries, congestion, and the functionality of the ship’s systems affect the outcome of each model.
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Wang, Junwu, Yipeng Liu, Mingyang Liu, Suikuan Wang, Jiaji Zhang, and Han Wu. "Multi-Phase Environmental Impact Assessment of Marine Ecological Restoration Project Based on DPSIR-Cloud Model." International Journal of Environmental Research and Public Health 19, no. 20 (October 15, 2022): 13295. http://dx.doi.org/10.3390/ijerph192013295.
Abstract:
In order to achieve a comprehensive evaluation of the environmental impact of ecological restoration projects (ERP) under the current destruction and restoration of coastal ecological areas, this paper takes into account the impact of positive and negative indicators on the environment; analyzes the positive and negative benefits of ERP; and establishes a comprehensive environmental impact index system for marine ERP from ecological, economic, and social perspectives through the DPSIR model. On this basis, the cloud model and Monte Carlo simulation are used to obtain the comprehensive assessment grade of the construction period, short-term operation, and long-term operation in the project life cycle. The results show that the benefits of ERP, considering the impact of negative factors, are significantly reduced, and the benefits of ERP will increase remarkably in the long-term operation period. In engineering practice, the environmental pressure factor caused by excessive human activities during construction and operation periods is a key negative factor affecting the overall benefits of ERP. For project decision makers and other stakeholders, the comprehensive assessment grade considering negative impacts is more practical. At the same time, decision makers should take active response measures in the framework of long-term sustainable development, set a tolerance threshold for negative pressure indicators, and strengthen the management of ERP.
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Bastardie, Francois, J. Rasmus Nielsen, O. R. Eigaard, H. O. Fock, P. Jonsson, and V. Bartolino. "Competition for marine space: modelling the Baltic Sea fisheries and effort displacement under spatial restrictions." ICES Journal of Marine Science 72, no. 3 (December 1, 2014): 824–40. http://dx.doi.org/10.1093/icesjms/fsu215.
Abstract:
AbstractMaritime spatial planning (MSP) and fishery management may generate extra costs for fisheries by constraining fishers activity with conservation areas and new utilizations of the sea. More energy-efficient fisheries are also likely to alter existing fishing patterns, which already vary from fishery to fishery and from vessel to vessel. The impact assessment of new spatial plans involving fisheries should be based on quantitative bioeconomic analyses that take into account individual vessel decisions, and trade-offs in cross-sector conflicting interests. We use a vessel-oriented decision-support tool (the DISPLACE model) to combine stochastic variations in spatial fishing activities with harvested resource dynamics in scenario projections. The assessment computes economic and stock status indicators by modelling the activity of Danish, Swedish, and German vessels (>12 m) in the international western Baltic Sea commercial fishery, together with the underlying size-based distribution dynamics of the main fishery resources of sprat, herring, and cod. The outcomes of alternative scenarios for spatial effort displacement are exemplified by evaluating the fishers's abilities to adapt to spatial plans under various constraints. Interlinked spatial, technical, and biological dynamics of vessels and stocks in the scenarios result in stable profits, which compensate for the additional costs from effort displacement and release pressure on the fish stocks. The effort is further redirected away from sensitive benthic habitats, enhancing the ecological positive effects. The energy efficiency of some of the vessels, however, is strongly reduced with the new zonation, and some of the vessels suffer decreased profits. The DISPLACE model serves as a spatially explicit bioeconomic benchmark tool for management strategy evaluations for capturing tactical decision-making in reaction to MSP.