Decision Strategies Research Articles

Spatial Big Data and Analysis Strategies Supporting Geographic Information System for Transportation (GIS-T) in Conceptual Design, Modelling, and Decision-making: A Review

Abstract. Transportation is a key component in urban design for cities’ efficiency and residents’ life quality, and GIS has the capability of handling data management, model design, and scientific decision-making for transportation studies. This article reviews how spatial big data and analysis strategies help GIS-T studies from the phases of conceptual design, modelling, and decision-making. In this research, we firstly summarized categorizes of data objects and relevant information for real-world issues from transportation applications in the conceptual design phase. In the modelling phase, optimization strategies for transport planning and accessibility measures through network data were also summarized. Finally, we reviewed spatial analysis methods in supporting transport decision-making, and how spatial methods take advantage of geography features of transport variables in previous studies. Our research primarily focuses on geography research with transportation topics as applications, and this work can help transport experts to have a better understanding of GIS values for transportation modelling and planning.

Cargo Volume Forecasting for Logistics Sorting Centers Based on Random Forest Model

With the rapid development of logistics industry, logistics sorting center, as a key node in e-commerce logistics network, its cargo volume prediction and personnel scheduling strategy are of great significance to improve operational efficiency and reduce costs. The purpose of this study is to deeply analyze the cargo volume data of 57 sorting centers through machine learning method, establish an accurate cargo volume prediction model, and optimize the personnel scheduling strategy based on the prediction results. In this study, we preprocess the historical cargo data, including missing value filling, time series conversion and feature construction. Then, the random forest model was used to carry out fitting analysis on the cargo volume data of different sorting centers. By comparing the evaluation indexes such as mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE), the superiority of random forest model compared with neural network, support vector regression and linear regression models was verified. This study not only improves the accuracy of cargo volume forecast, but also realizes the reasonable allocation of human resources, and provides scientific decision support for the operation and management of logistics sorting center.

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

A Distance Measure for Intuitionistic Fuzzy Multicriteria Decision Making in Pattern Recognition and Medical Diagnosis

ABSTRACT In artificial intelligence, decision-making is a widely studied field, and distance measure plays a crucial role in decision science. Several distance measures for Intuitionistic Fuzzy sets (IFSs) have been developed to handle uncertain situations. However, some of these measures lack properties of distance measures or produce unreasonable results. This study introduces a new distance measure between IFSs that satisfies the necessary properties and resolves counter-intuitive cases. The proposed distance measure is verified to be more reasonable through numerical examples, and it is applied to pattern recognition and medical diagnosis problems.

DEEP LEARNING APPROACH FOR ASPECT CATEGORY DETECTION: A BIBLIOMETRIC ANALYSIS

This article presents a quantitative and qualitative assessment of current research trends by conducting a bibliometric analysis of the sentiment analysis literature from 2020 to March 2024 using the Scopus database. Our focus is on the review of scientific documents, the arrangement of subject categories, the research trend in aspect category detection, the top 10 scholars that write the most number articles in aspect category detection and keyword trends. Our research shows that specialists in computer science, engineering, mathematics, medicine, decision science, material science, social sciences, business and management accounting, energy, and health are the most common topic groups in this industry. A study of keywords shows that terms like “BERT” and “deep learning” are frequently used together. This highlights the use of sophisticated models like BERT in this field and suggests a tendency to use innovative architecture to achieve better results. Conversely, although terms such as “sentiment analysis” and “aspect-based sentiment analysis” have modest frequency, their link strengths indicate a significant correlation with the main theme, emphasising the relationship between aspect category detection and sentiment analysis in research projects. we also provide the deep-learning technique used by the author for aspect category detection.

Cold Coastal City Neighborhood Morphology Design Method Based on Multi-Objective Optimization Simulation Analysis

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities.

Spatial-temporal characteristics of port infrastructures on sulfur-oxide concentrations of coastal port in China

Port infrastructure is an important guarantee to support the development of shipping trade and a main link to connect shipping routes, where its construction and expansion promote economic progress whereas exacerbating regional pollution. Based on the panel data of 18 above-scale coastal ports in China from 2005 to 2020, the spatial characteristics and correlation of sulfur-oxide (SOX) concentrations are first analyzed, then the spatial Durbin model (SDM) is established to evaluate the extent of port infrastructure's impact on the concentrations, further the intermediation effects of three dimensions on the concentrations are explored. From the outcome, the influences of port infrastructures on the SOX concentrations are from decreasing the SOX concentrations through improving the operational efficiencies and increasing the SOX concentrations by adding the operational equipment, which also shows significant spatial spillover effects. In addition, the mechanical dimension as a mediation makes the trade dimension have an indirect effect on the SOX concentrations. This paper employs a spatial econometric model to analyze panel data from 18 coastal ports and ports of higher tiers in China, spanning from 2005 to 2020. It uncovers the intricate mechanism by which port infrastructure influences SOX concentration, thus providing a scientific foundation and decision support for environmental protection and sustainable port development.

A-207 Enhancing Healthcare Delivery Network Laboratories: The Role of Third-Party Consultants in Benchmarking and Reviewing RFP Results

Abstract Background Healthcare delivery networks, particularly laboratory services, play a critical role in patient care and diagnosis. However, ensuring the quality and efficiency of these services can be challenging due to the complex and dynamic nature of healthcare systems. Third-party consultants offer valuable expertise in benchmarking and reviewing Request for Proposal (RFP) results, which can improve the quality and cost-effectiveness of laboratory services within healthcare networks. Methods To evaluate the role of third-party consultants in enhancing healthcare delivery network laboratories, the author conducted surveys to 50 laboratory leaders with a 50% response rate and analyzed the following aspects: Benchmarking: Comparing laboratory performance metrics with industry best practices and peer institutions to identify areas for improvement. RFP Review: Utilizing subject matter experts to assess the technical and financial proposals submitted by vendors to ensure compliance with RFP requirements and to identify the most suitable vendor for the network. Emotional Procurement: Minimizing the influence of emotional factors, such as personal relationships or brand loyalty, during the RFP process. Results Improved Quality: 65% said they can save >50 hours of labor utilizing a 3rd party for RFP, thus able to focus on patient results and quality. Cost Savings: 76% of respondents said consultants can help networks reduce expenses and improve their financial performance by 10-20% above and beyond incumbent negotiations. Emotional Procurement Mitigation: While the specific statistics supporting the influence of consultants on mitigating emotional factors in procurement decisions are not readily available, the broader impact of emotions on decision-making is well-documented. Therefore, while direct statistics may be limited, the influence of emotions on procurement decisions is a recognized factor in the field of decision science and consumer behavior. Turn around time: 70% of respondents said utilizing a 3rd party can reduce the turnaround time of a sourcing event from >3 months to < 1months. Conclusions Third-party consultants can provide valuable insights and expertise in benchmarking and reviewing RFP results, leading to several benefits. They can play a crucial role in enhancing healthcare delivery network laboratories by providing valuable expertise in benchmarking, reviewing RFP results, communication and reducing turnaround time. By improving quality, reducing costs, mitigating emotional procurement based on brand loyalty, and enhancing collaboration, consultants can help networks deliver high-quality services to patients and improve their overall performance. Consultants can facilitate more focused communication and partnership development between laboratories, vendors, and other stakeholders, ensuring a seamless transition and optimal performance without scope creep. As healthcare networks continue to evolve and face new challenges, the role of third-party consultants will become increasingly important in ensuring the quality and efficiency of laboratory services, potentially resulting in $100’s of millions in cost takeout across the country.

Research and application of the method of measuring the effective utilization coefficient of irrigation water on farmland

Abstract Under the guidance of the new water management idea of “water conservation priority, spatial balance, system management, and two-handed effort,” the National Water Conservation Action Plan released in 2019 puts forward the key task requirements of “double control of total intensity,” in which the effective utilization coefficient of irrigation water in farmland is one of the indicators of water intensity control. The effective utilization coefficient of irrigation water is one of the water intensity control indicators. At present, there are practical difficulties in measuring the effective utilization coefficient of irrigation water in farmland, such as the low measurement rate of water consumption in the current irrigation area, the high input equipment and manpower requirements, and the difficulty in accurately measuring the net irrigation water absorbed by crops. Therefore, this paper proposed the method of “field monitoring - remote sensing inversion - a comprehensive analysis of data.” The method of “field monitoring - remote sensing inversion - econometric model construction - comprehensive analysis” was proposed in this paper, which can obtain gross irrigation water utilizing field monitoring, overcome the difficulty of accurate monitoring of net irrigation water using remote sensing, and carried out a comprehensive analysis, to realize the scientific, effective and rapid calculation of effective utilization coefficient of irrigation water. This method was applied to the Meixian District, Guangdong Province, and the research results show that it not only generates reliable measurement data but also alleviates workload. Furthermore, it offers scientific measurement techniques for water use efficiency indicators, facilitating the achievement of “dual control over total volume and intensity.” This paper provides an important technical basis for the rational allocation of water consumption and scientific approval of water use plans and supports the assessment of the effectiveness of water-saving irrigation development and the reasonable evaluation of the water-saving potential of farmland irrigation, thus providing a basis for local government departments at all levels to make planning, scientific decision-making, and macro-management.

How bad becomes good: A neurocomputational model of affect-informed choice.

People often draw on their current affective experience to inform their decisions, yet little is known about the underlying mechanisms of this process. Understanding them has important implications for many big questions in both the affective and decision sciences. Do the same neural circuits that generate affect generate value? What differentiates people who have greater contextual flexibility in their reliance on affect? Do affective choices invoke processes that are distinct from less affective choices? To investigate these questions, we developed a neurocomputational model of affect-informed choice, in which people convert subjective affect into context-sensitive decision value through a process of weighted evidence accumulation. We then tested model predictions by recording electroencephalography and facial electromyography during a novel affective choice paradigm in a sample of racially diverse undergraduate participants (data collected in 2018-2019). In addition to validating our model, we found that generation of affective responses occurs earlier than, and is neurally distinct from, valuation of that affect. Moreover, individual differences in contextual flexibility of affective weighting correlated only with later valuation processes, not earlier affect generation processes. Our results have important theoretical implications for emotion, emotion regulation, and decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

LSTM Recurrent Neural Network Based Method for Identification of PDC Drill Bit Operating Conditions and Its Application

Abstract PDC drill bit underground operating conditions identification is one of of the difficulties during the drilling operation. It is of great significant for the drilling improvement and accurate construction formation to accurately identify the complex operating conditions during drilling the operation such as PDC bit wearing and stick-slip vibration, etc. In this paper, data of relationship between torque, rate of penetration (ROP) signal and weight on bit (WOB), rev were obtained through lab simulation experiment, after which, a relationship database of torque, ROP signal and WOB, rev was established, a single-layer LSTM recurrent neural network (LSTM-RNN) was constructed, the number of neurons, batch size and optimizer type of LSTM layer were optimized. With BPTT algorithm, LSTM model was trained and tested on the training set containing 6000 groups of data, and achieved the goal of identifying the bit operation conditions against the input of WOB, torque, rev and ROP signal parameters. Based on this model, the PDC bit underground operating conditions identification software was compiled, and was connected to the logging unit to realize real-time identification of bit underground operating conditions, and field test application was successfully carried out in Mahu 1 Well Region. The research showed that the convergence speed was fast and the time cost was at the lowest when the number of neurons of LSTM model was 50, and that the optimized Adam optimizer model could meet the requirements of convergence speed and prediction accuracy with the lowest loss value. The classification accuracy of this model on the test set reached 94% and the accuracy of the prediction method was also verified by field test application. To sum up, the method of LSTM-RNN based PDC bit underground operating conditions real-time identification can better meet the requirements of accurate identification of PDC bit underground operating conditions in the practical drilling, and has guiding significance for the judgement of PDC bit operating conditions on site and formulation of scientific construction decisions.

Incorporating q-Rung Picture Fuzzy Frank Prioritized Weighted Aggregators with Multimoora Strategy for Decision Making

The Frank norm operators and ˇq-rung picture fuzzy sets offer a versatile framework due to their adjustable parameters. Conversely, prioritized weighted operators are crucial for indicating the relative significance of alternatives and attributes. In this research, we have devised Frank’s prioritized weighted procedure for ˇq-rung picture fuzzy numbers. Thus, the ˇq-rung picture fuzzy prioritized weighted Frank averaging (ˇq-RPFPWFA) and geometric (ˇq-RPFPWFG) operators are presented and analyzed along with some of their properties. Utilizing these proposed operators, a novel approach is established, combining the enhanced MULTIMOORA (MM) procedure with unspecified weight data. A numerical example regarding the multiple attribute group decision making (MAGDM) problem of selecting the financial director of a residential society is handled based upon the aforementioned procedure. Furthermore, we have compared the results with those obtained using available operators. Evidently, the novel ˇq-RPFPWFA and ˇq-RPFPWFG aggregators with the MM technique yield reasonable and consistent outcomes for solving MAGDM.

Proximal Hamstring Avulsions: Surgical Versus Conservative Treatment Using a Shared Decision-Making Strategy

Background: Surgical treatment of patients with proximal hamstring avulsions provides good results; however, less is known about the outcome in patients who are offered conservative treatment. Purpose: To investigate the effect of surgical or conservative treatment (decided by a shared decision strategy) of proximal hamstring avulsions. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 24 patients with magnetic resonance imaging–verified proximal hamstring avulsion were included and had either surgical treatment (11 patients, 45% women; mean age, 50 ± 16 years) or conservative treatment (13 patients, 46% women; mean age, 50 ± 17 years). At baseline, 6 months and 12 months, all patients answered the Perth Hamstring Assessment Tool (PHAT) (0-100 scale) and Hip Sports Activity Scale (0-8 scale). Patients had their maximal hip extension strength and maximal strength at 30° and 90° of knee flexion measured in newton meters per kilogram using a handheld dynamometer. A minimal important change in PHAT was considered >7 points and a minimal important change in strength was considered >0.15 N·m/kg, respectively. Results: The surgical group had a shorter time from injury to initiation of treatment compared with the conservative group (median: 15 vs 64 days; P = .02). The surgical group had a greater amount of retraction of the tendons compared with the conservative group (3 vs 2 cm; P = .04). From baseline to 12-month follow-up, the surgical and conservative groups improved their mean PHAT scores (35 points [95 CI, 24-45 points] and 20 points [95% CI, 9-31 points], respectively) reaching a median of 79 points (interquartile range [IQR], 66-95 points) in the surgical group and 75 points (IQR, 66-85 points) in the conservative group at the 12-month follow-up. Their Hip Sports Activity Scale levels at 12 months were 3 points (95% CI, 1-4 points) and 1 point (95% CI, 0-3 points) (not significant). Furthermore, the surgical and conservative groups improved their maximal hip extension strength by 0.61 N·m/kg (IQR, 0.42-0.80 N·m/kg) and 0.62 N·m/kg (IQR, 0.13-1.10 N·m/kg), respectively. Their maximal knee flexion strength at 30° improved by 0.52 N·m/kg (IQR, 0.29-0.74 N·m/kg) and 0.32 N·m/kg (IQR, 0.12-0.52 N·m/kg) and their maximal knee flexion strength at 90° improved by 0.28 N·m/kg (IQR, 0.19-0.37 N·m/kg) and 0.22 N·m/kg (IQR, 0.02-0.41 N·m/kg). At the 12-month follow-up, the side-to-side difference in maximal muscle strength was 6% and 7%, respectively, during hip extension and 19% to 25% and 16% to 17%, respectively, during knee flexion. Conclusion: Twelve months after treatment of proximal hamstring avulsion, good clinical outcomes were seen when using a shared decision strategy regardless of whether the strategy led to surgical or conservative treatment.

Impact of government’s support policy on decision-making of platform participants under ESG

Blockchain-based token platform economy is a new branch of digital platform economics. Constructing a continuous time dynamic model of token platform economy, this paper analyzes what kind of ESG policy is appropriate for the government, meanwhile the token platform participants (developers, users and speculators) make optimal investments and decisions under ESG policy. Simulation result shows neutral ESG policy is optimal. Based on the given neutral ESG policy, we have done the research on ESG investment and decision strategies for platform participants. Our research shows that the tokens selling rate and efforts of green platform (ESG score greater than 0) developers are lower than the ones of brown platform (ESG score less than 0). Consequently, when developers’ token retention is about half of the initial amount, users should invest more brown tokens. Speculators should invest brown tokens for developers’ high token retention. Green token investments of speculators and users are needed in other cases. Next, the impact of the government’s three ESG policies on the maturity or termination of the platform also been analyzed. An important conclusion occurred: the government’s aggressive or conservative ESG policy cannot make the development of the green platform better; Therefore, we suggest a neutral ESG policy which means that the government could adopt high tax incentive and high tax burden on the green and brown platform while it is not necessary to implement the extra subsidy and punishment policy on the green and brown platform

Continuous measures of decision-difficulty captured remotely: Mouse-tracking sensitivity extends to tablets and smartphones

As decisions require actions to have an effect on the world, measures derived from movements such as using a mouse to control a cursor on a screen provide powerful and dynamic indices of decision-making. By adapting classic reach-decision paradigms and deploying them across computers, tablets, and smartphones, we show that portable touch-devices can sensitively capture decision-difficulty. We see this in pre- and during-movement temporal and motoric measures across diverse decision domains. We found touchscreen interactions to more sensitively reflect decision-difficulty during movement compared to computer interactions, and the latter to be more sensitive before movement initiation. Paired with additional evidence for the flexibility and unique utility of pre- and during-movement measures, this substantiates the use of widely available touch-devices to massively extend the reach of decision science.

Assessing benefits of computer-based video training and tools on learning outcomes and motivation in mechanical engineering education: digitalized intervention and approach

Traditionally, laboratory work has been a common approach to facilitate the acquisition of practical skills and familiarize the undergraduate students, particularly engineering students, with specialized tools and equipment. However, the conventional in-person labs often experience challenges such as limited resources and space, instructor availability, and inflexible schedules. The emergence of digital tools and the recent COVID-19 pandemic have prompted educators to reconsider their teaching methods. To this effect, this paper introduces an innovative approach and teaching methodology to address the challenges in traditional engineering education within the laboratory. It presents a methodology that combines cost-effective instructional videos and portable kits to promote autonomous development of practical skills in undergraduate engineering students. The proposed teaching methodology was grounded on the Descriptive Decision theory and Learning-Oriented Assessment (LOA), which are theoretical framework and type of students’ learning outcome (SLO) model that studies the rationality behind the decisions that the users are disposed to make, as well as level of outcome of the students’ learning process or performance. Motivation among the students was assessed using the Model of Academic Motivation Inventory (MUSIC Inventory) to evaluate the impact of the proposed teaching method and learning intervention on the interest of the students. The method was implemented in four independent courses at two different campuses of Tecnologico de Monterrey. The results show that the proposed learning approach was effective in helping students develop hard skills with reduced instructor intervention. Moreover, high levels of motivation was reported through the MUSIC methodology and test administered to the participating students at the end of the courses. The outcome of this study can be used to inform and support the curriculum design by the educators, promote effective policy and decision making by the university leaders, and encourage wide adoption of the digitalized-education.

Dynamic spatial–temporal conflict quantification of construction machinery for pouring blocks in arch dams

PurposeThe spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).Design/methodology/approachFirst, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.FindingsThe quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.Originality/valueThis study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.

Efficient and robust sequential decision making algorithms

AbstractSequential decision‐making involves making informed decisions based on continuous interactions with a complex environment. This process is ubiquitous in various applications, including recommendation systems and clinical treatment design. My research has concentrated on addressing two pivotal challenges in sequential decision‐making: (1) How can we design algorithms that efficiently learn the optimal decision strategy with minimal interactions and limited sample data? (2) How can we ensure robustness in decision‐making algorithms when faced with distributional shifts due to environmental changes and the sim‐to‐real gap? This paper summarizes and expands upon the talk I presented at the AAAI 2024 New Faculty Highlights program, detailing how my research aims to tackle these challenges.

A Study of Leadership during Crises: Strategies for Effective Decision Making and Organizational Resilience

This research explores the role of leadership in shaping an organization’s response to crises, with a focus on decision-making and strategies for building resilience. By analyzing real-world examples, the study highlights how effective leaders manage challenges such as uncertainty, stress, and disruption, while maintaining clear communication and providing strong direction for their teams. Key leadership traits like adaptability, transparency, and timely decision-making are emphasized as crucial elements in minimizing damage and fostering recovery. The goal is to provide actionable insights and practical guidance for organizations aiming to enhance their crisis management capabilities and navigate future crises with greater confidence and sustainability.

A pandemic-related affect gap in risky decisions for self and others

ABSTRACT The early stages of the COVID-19 pandemic exposed large portions of the global populations to increased daily stressors. Research on risky choice in medical contexts suggests that affect-rich choice options promote less-advantageous decision strategies compared with affect-poor options, causing an “affect gap” in decision making. The current experiments (total N = 437, age range: 21–82) sought to test whether negative pandemic-related affect would lower expected-value (EV) maximisation within individuals. In Experiment 1, participants indicated how much they would be willing to pay to avoid specific pandemic experiences (e.g. “not being able to gather in groups”), and then chose among pairs of risky prospects that involved pandemic experiences or subjectively-equivalent monetary losses. EV maximising was lower for pandemic experiences than for equivalent monetary losses. Experiment 2 replicated this finding, and further demonstrated a moderating role of decision perspective. EV maximising was greater in decisions made for another person than in decisions made for oneself. These findings highlight potential strategies for boosting decision making under affect-rich real-world conditions.