Decision Domain Research Articles

Critical decisions at the early stage of start-ups: a systematic literature review

AbstractThis systematic literature review (SLR) identifies and ranks the decisions that can be considered critical for entrepreneurs at the early stage of the start-up process. The sample (N = 56) derived from the SLR contained primary publications from the A + and A journals of the VHB-Journal ranking. Deductive and inductive approaches were used to define the critical decision domains. Then, the relevance of the domains was evaluated using the frequency of the selected studies. A key implication for founders is to understand that not all decisions that could be taken in the early stage may be critical for success. Most critical seems to invest time and effort in the, what we label the internal business and human core (Core People and Product Definition) and the external business and human core (Market segment selection and Partnerships). This study depicts an integrated view of the fragmented field of critical decisions in the context of early-stage start-ups by using a SLR. Thus, it creates value for theory-building and practitioners in entrepreneurship and innovation.

Navigating uncertainty: Exploring consumer acceptance of artificial intelligence under self-threats and high-stakes decisions

In an era of transformation fueled by Artificial Intelligence (AI), human resistance to adopt this powerful technology has emerged as one of its most critical barriers. In a series of four studies involving almost 4,000 consumers, this research explores factors that contribute to consumer reluctance toward AI through theories related to algorithm aversion, decision-making under risk, and compensatory decision-making. The results underscore the impact of decision stakes and their adverse outcomes on AI service agent adoption across decision domains. These effects can be attributed to the self-threat experienced by consumers in high-stakes decision scenarios. Together, the current findings advance our understanding of consumer responses in the context of AI adoption, illustrating how perceived stakes and self-threats foster reluctance to rely on AI agents for advice. From a practical standpoint, the results emphasize the need of a hybrid approach—combining AI and human agents—for a successful transition toward AI-powered service industries.

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.

Foundation models in robotics: Applications, challenges, and the future

We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In contrast, foundation models pretrained on internet-scale data appear to have superior generalization capabilities, and in some instances display an emergent ability to find zero-shot solutions to problems that are not present in the training data. Foundation models may hold the potential to enhance various components of the robot autonomy stack, from perception to decision-making and control. For example, large language models can generate code or provide common sense reasoning, while vision-language models enable open-vocabulary visual recognition. However, significant open research challenges remain, particularly around the scarcity of robot-relevant training data, safety guarantees and uncertainty quantification, and real-time execution. In this survey, we study recent papers that have used or built foundation models to solve robotics problems. We explore how foundation models contribute to improving robot capabilities in the domains of perception, decision-making, and control. We discuss the challenges hindering the adoption of foundation models in robot autonomy and provide opportunities and potential pathways for future advancements. The GitHub project corresponding to this paper can be found here: https://github.com/robotics-survey/Awesome-Robotics-Foundation-Models .

Generalized Encoding of the Relative Subjective Value of Cognitive Effort in the Dorsal ACC.

Making choices about whether and when to engage cognitive effort are a common feature of everyday experience, with important consequences for academic, career, and health outcomes. Yet, despite their hypothesized importance, very little is understood about the underlying mechanisms that support this form of human cost-benefit decision-making. To investigate these mechanisms, we used the Cognitive Effort Discounting Paradigm (Cog-ED) during fMRI scanning to precisely quantify the neural encoding of varying cognitive effort demands relative to reward outcomes, within two distinct cognitive domains (working memory, speech comprehension). The findings provide strong evidence that the dorsal anterior cingulate cortex (dACC) plays a central and selective role in this decision-making process. Trial-by-trial modulations in dACC activation tracked the relative subjective value of the low-effort, low-reward option, with the strongest activity occurring when this was of greater value than the high-effort, high-reward option. In contrast, dACC activity was not modulated by decision difficulty, though such effects were found in other frontoparietal regions. Critically, dACC activity was also strongly correlated across the two decision-making task domains and further predicted subsequent choice behavior in both. Together, the results suggest that dACC activity modulation reflects a domain-general valuation comparison mechanism, which acts to bias participants away from decisions to engage in cognitive effort, when the perceived subjective costs of such engagement outweigh the reward-related benefits. These findings complement work in other cost domains and species by pointing to a clear role of the dACC in representing subjective value differences between choice options during cost-benefit decision-making.

Elements of episodic memory: insights from artificial agents.

Many recent artificial intelligence (AI) systems take inspiration from biological episodic memory. Here, we ask how these 'episodic-inspired' AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory while differing in important respects and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory's operations and function. However, further work is needed to validate them as models of episodic memory and isolate the contributions of their memory systems to their behaviour. More immediately, we propose that these systems have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy. In this vein, we propose that the evidence reviewed here highlights two pursuit-worthy hypotheses about episodic memory's function: that it plays a role in planning that is independent of future-oriented simulation, and that it is adaptive in virtue of its contributions to fast learning in novel, sparse-reward environments. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

The Factors Influencing Supply Chain Performance in Tanzanian Public Procuring Entities: The Mediating Role of Supplier-Buyer Relationships

Academia's current emphasis on supply chain performance highlights the significance of information sharing as a mediator and the supplier-buyer relationship as a moderator. Nevertheless, the examination into the intermediate effects of the supplier-buyer relationship has been relatively neglected. This research examined the factors affecting supply chain performance, emphasizing the mediating role of the supplier-buyer relationship to address the existing knowledge gap in the literature. The research included both descriptive and explanatory methodologies. The positivist research paradigm and stratified sampling method were employed to obtain a sample size of 167 respondents. The data were collected through a questionnaire and an analysis of relevant documents. The collected data was examined using Partial Least Squares Structural Equation Modeling, facilitated by SmartPLS 4, a tool for conducting inferential statistical analysis. The analyzed data on respondents' profiles underwent descriptive statistical analysis using IBM SPSS Statistics Version 26. The research findings indicate that supply chain performance is affected by information sharing, strategic procurement planning, and the relationship between suppliers and buyers. The statistical significance of strategic procurement planning and information sharing indicates that buyers and suppliers rely on these practices to improve supply chain performance. The results provide definitive evidence validating the accuracy of the proposed research model within the domain of decision-making, specifically regarding the prioritization of investments to improve supply chain performance.

Women's decision-making power can influence modern contraceptive use: Evidence from Bangladesh.

It is generally believed that gender inequality and women's lack of decision-making power may restrict women's use of modern contraception, leading to high rates of unwanted pregnancies, abortions, and deaths. Evidence shows that empowered women are more likely to use modern contraception methods, but few studies have investigated this across multiple domains of empowerment. This study examined the associations between women's empowerment and modern contraception use in Bangladesh. Data from a sample of 16,834 married women aged 15-49years from the Bangladesh Demographic and Health Survey 2017-2018 were analysed. Complex survey weight adjusted logistic models were fitted to evaluate the associations after adjusting for clusters, strata, and sampling weights. indicate that just over half of the married women (55.7%) had used modern contraception methods. Women's empowerment was associated with contraceptive use, especially decision-making power. Women who had medium or high autonomy of household decision making were likely to have 20% (AOR=1.20; 95% CI: 1.04-1.39) and 27% (AOR=1.27, 95% CI: 1.11-1.45) increased odds of using modern contraceptives compared to those who scored low in the decision-making domain. The findings demonstrated strong evidence of direct influence of women's decision-making power on modern contraception use. The results also found influence of several socio-demographic factors including area of residence, husband's age, wealth index andmobile phone ownership on the use of modern contraceptives. Future interventions can focus on integrating women's empowerment into family planning programming, with a particular focus on enhancing women's autonomy in decision making.

Optimized model involving forward and inverse problem formulation for the assessment of environmentally sustainable transportation alternatives based on intuitionistic linguistic weighted average

Intuitionistic fuzzy set theory provides a versatile framework for conveying information acquired during the decision-making process, particularly adept at handling indeterminate data. This study aims to devise a robust α-cut approach for intuitionistic linguistic weighted average (ILWA) applied specifically to intuitionistic fuzzy numbers (IFN). The utilization of an extended Jaccard Similarity Measure (EJSM) within the intuitionistic fuzzy context will yield valuable insights into the accuracy of representing linguistic data through the ILWA method. These advancements hold significant implications for selecting environmentally sustainable transportation alternatives and furnish analytical techniques for addressing uncertainty and imprecision across diverse decision-making domains.

Women's empowerment, household dietary diversity, and child anthropometry among vulnerable populations in Odisha, India.

Women's empowerment has been promoted by researchers and development practitioners as one of the most promising strategies to address widespread hunger and malnutrition. However, the relationship between women's empowerment and dietary diversity and child nutrition has rarely been studied among vulnerable populations or individuals at greater risk of poor physical and social health status. Moreover, the effects of different domains of women's empowerment on nutritional outcomes, including dietary diversity and child anthropometry, have rarely been examined, especially with panel data. Using two rounds of panel data from 1900 households and fixed effects regression models, we analyze the effect of women's empowerment on household dietary diversity score (HDDS) and child anthropometry among the particularly vulnerable tribal groups in Odisha, India. We also estimate the effects of various decision-making domains of women's empowerment on HDDS and child anthropometry to understand which empowerment domains matter for nutrition. Results show that women's empowerment is positively associated with HDDS (coef. 0.41 food groups; p < 0.1) and reduces the prevalence of underweight (coef. 39%; p < 0.05) and wasting (coef. 56%; p < 0.1) in children but has no effect on the prevalence of child stunting. Women's empowerment in agricultural input use; output sales; income; food purchases; and credit, group membership, and employment contribute to improved dietary diversity and child nutrition. We conclude that women's empowerment contributes to improved dietary diversity and child nutrition and is a promising strategy to improve farm household diets and child nutrition among vulnerable populations. Strengthening women's empowerment through the promotion of women's access to land and other agricultural inputs, market participation, access to information, capital, and credit is important.

Facilitating Seamless Integration of Game-based Assessments in Medical Education.

A health-care professional is expected to hone multiple skills that are not limited to possessing medical knowledge and clinical skills. Game-based assessments are an innovative type of assessment method that combines the features of gamification and simulation to revolutionize the delivery of medical education. These assessments have the potential to promote active engagement of students by ensuring sustained interest, which is not possible to accomplish with conventional assessments. Even though game-based assessments have immense scope in medical education delivery and have been linked with multiple merits, there are potential concerns with regard to their implementation in medical institutions. In conclusion, game-based assessments offer an immersive approach to assessing the performance of medical students, especially in the domain of critical thinking and decision-making. However, the process of planning and implementation of game-based assessment has its own share of challenges, and we must adopt a systematic approach to overcome the challenges and benefit students.

Behavioral Biases in Investment: Overconfidence, Disposition Effect, and Herding Behavior

This section introduces the research by outlining the importance of understanding behavioral biases in investment decisions, particularly within the context of retail investors in Pakistan. This research work primarily intends to find out the influence of behavioral biases over investors' investment decisions. The population of this research are individual investors investing in Pakistan Stock Exchange. The research design of this study is quantitative. The sample size of current research was 400 individual investors. The survey assessed several types of biases, including overconfidence, disposition effect, and herding behavior. The result outcome indicated that with an increased number of such biases present, it significantly influences the investment decisions of investors. This study specifically indicates that respondents are often subject to overconfidence, the disposition effect, and herding behavior, and finally the factors that lead to relatively poor investment decisions. These results underline the identification and need for tackling these biases within the financial decision-making domain. Financial professionals, comprising advisors and educators, are better placed with skills on how to design strategies that deter more adverse consequences of these biases. They can enhance investment quality in decision-making by well-directed training programs and advisory services. Therefore, this study underscores the pivotal role that behavioral finance plays in improving investor outcomes and calls for the rectification of behavioral bias to attain more informed and rational investment decisions.

Personal career decisions during medical training are not complicated, they are complex.

For medical training to be deemed successful, in addition to gaining the skills required to make appropriate clinical decisions, trainees must learn how to make good personal decisions. These decisions may affect satisfaction with career choice, work-life balance, and their ability to maintain/improve clinical performance over time-outcomes that can impact future wellness. Here, the authors introduce a decision-making framework with the goal of improving our understanding of personal decisions. Stemming from the business world, the Cynefin framework describes five decision-making domains: clear, complicated, complex, chaotic, and confusion, and a key inference of this framework is that decision-making can be improved by first identifying the decision-making domain. Personal decisions are largely complex-so applying linear decision-making strategies is unlikely to help in this domain. The available data suggest that the outcomes of personal decisions are suboptimal, and the authors propose three mechanisms to explain these findings: (1) Complex decision is susceptible to attribute substitution where we subconsciously trade these decisions for easier decisions; (2) predictions are prone to cognitive biases, such as assuming our situation will remain constant (linear projection fallacy), believing that accomplishing a goal will deliver lasting happiness (arrival bias), or overestimating benefits and underestimating costs of future tasks (planning fallacy); and (3) complex decisions have an inherently higher failure rate than complicated decisions because they are the result of an ongoing, dynamic person-by-situation interaction and, as such, have more time to fail and more ways to do so. Based upon their view that personal decisions are complex, the authors propose strategies to improve satisfaction with personal decisions, including increasing awareness of biases that may impact personal decisions. Recognising that the outcome of personal decisions can change over time, they also suggest additional interventions to manage these decisions, such as different forms of mentoring.

Considering personalized individual semantics with ordinal and cardinal consensus reaching processes via three-way decision and regret theory

The seamless integration of computerized methodologies into industrial engineering problem-solving is pivotal for optimizing efficiency. In the specific domain of multi-attribute group decision-making (MAGDM) with probabilistic linguistic term sets (PLTSs), these methodologies offer systematic approaches to consensus building, ensuring effective decision processes in intricate scenarios. Within the realm of PLTSs, the consensus-reaching process (CRP) for MAGDM is gaining prominence. This paper addresses this evolving area by proposing ordinal and cardinal CRPs within the framework of PLTSs, specifically incorporating the regret theory (RT) of three-way decisions (TWD). The paper introduces an initial distance formula under PLTSs, providing a complementary approach to assess similarity relations among decision-makers (DMs). To account for diverse semantics across DMs, personalized individual semantics (PIS) is integrated into the CRP, recognizing variations in DMs’ alternatives and attributes. To enhance realism, the paper introduces the concepts of individual alternative sets and individual attribute sets. Additionally, the paper integrates ordinal and cardinal consensus, establishing a dynamic feedback adjustment mechanism grounded in the principles of RT and TWD. The method’s reasonableness is validated through a real case study, and a comparative analysis with the existing methods underscores the superiority of the approach presented in this paper.

Health care and contraceptive decision-making autonomy and use of female sterilisation among married women in Malawi.

Female sterilisation is the most common contraceptive method used globally. The use of female sterilisation is disproportionately low in sub-Sahara Africa (SSA) at just 1%. Nonetheless, the prevalence of sterilisation among married women in Malawi is quite high at about 11%. While a few recent studies in SSA have examined the relationship between women's decision-making autonomy and use of long-acting contraceptives, very few have investigated whether different dimensions of decision-making autonomy predict the use of female sterilisation differently. The objective of this study was therefore to examine the relationship between health care and contraceptive decision-making autonomy and the use of female sterilisation in Malawi. The study relied on secondary data from the 2015-16 Malawi Demographic and Health Survey. The sample comprised 9,164 married women in Malawi that were using a modern contraceptive. Multinomial logistic regression analysis was used to examine the association between health care and contraceptive decision-making autonomy and the use of female sterilisation, controlling for key socio-demographic characteristics. The study revealed that the percentage of married women that made health care and contraceptive decisions independently was quite low. The main finding of this study was that contraceptive decision-making autonomy increased the relative likelihood of using female sterilisation while health care autonomy was associated with a lower likelihood of being sterilized. The socio- demographic characteristics that significantly predicted the use of female sterilisation included age, place of residence, household wealth and the number of children a woman had. This study demonstrates that health care and contraceptive decision making have different effects on the use of female sterilisation among married women in Malawi. Specifically, women with autonomy in health care decision making had a relatively lower likelihood of using female sterilisation while those with contraceptive decision-making autonomy had a higher likelihood of using female sterilisation. This suggests that intervention aimed at increasing the uptake of female sterilisation in Malawi need to focus on empowering women in the contraceptive decision-making domain.

Transferring Black-Box Decision Making to a White-Box Model

In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.

A quantum group decision model for meteorological disaster emergency response based on D-S evidence theory and Choquet integral

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual influence of group opinions. To overcome this challenge, this paper proposes a novel quantum framework for group decision-making, which is used to deal with the emergency situation of meteorological disaster. The model initially characterizes attribute correlations using 2-additive Choquet integrals and employs Dempster-Shafer evidence theory to both integrate information and ascertain attribute weights, and decision makers' weights are calculated based on grey relative correlation. On this basis, a quantum-like Bayesian network is developed to capture the interference among decision-makers' opinions. The alternatives are ranked by quantum probabilities computed based on Bayesian principle. Finally, a case study on meteorological disaster emergency scenario assessment is conducted to validate the proposed model's effectiveness and superiority. Additionally, its stability and practicality are confirmed through sensitivity analysis and comparative analysis.

A novel fuzzy multi-criteria decision-making for enhancing the management of medical waste generated during the coronavirus pandemic

The coronavirus pandemic significantly increased the use of essential medical supplies, resulting in a surge in medical waste generation. This surge has spurred extensive research into sustainable disposal methods for safe and environmentally responsible medical equipment management. Addressing this multifaceted issue falls within the domain of multi-criteria decision-making. This study presents a comprehensive framework for selecting optimal medical waste treatment methods, considering economic, technological, environmental, and social factors. This is the first study to address the problem of selecting a medical waste disposal technology using the Fuzzy Dombi Bonferroni. The mean operator to combine expert opinions, the fuzzy preference selection index method to evaluate the criteria and the fuzzy compromise ranking of alternatives from distance to ideal solution method to rank the alternatives. According to the weightings, the social dimension holds the highest significance at 0.3217. Disinfection efficiency ranks as the most critical criterion, weighing in at 0.0823. The autoclave is rated as the top disposal technique, with a utility function value of 5.4579. Sensitivity analyses ensured the stability and reliability of the models. The adaptability of the applied model to sustainable practices such as energy conversion, material recycling, and resource recovery represents an essential aspect of policymaking in waste management. This assessment can guide policy formulation or improvement processes for waste disposal.

Big Data Analytics

Abstract: In today's digital era, decision-makers are presented with an abundance of data. Big data encompasses datasets that are not only extensive but also diverse and rapidly evolving, posing challenges for conventional management tools and methods. With the escalating volume of such data, it's crucial to explore and implement solutions for effectively handling and deriving insights from these datasets. Decision-makers need to glean meaningful insights from a variety of data sources, including daily transactions, consumer interactions, and social media activity . Big data analytics involves employing advanced analytical techniques on large datasets to unlock valuable insights. This paper will explore various analytics approaches and tools suitable for big data analysis, as well as the potential benefits offered by leveraging big data analytics across different decision-making domains

It's not a lie … If you believe it: Narrative analysis of autobiographical memories reveals over-confidence disposition in patients who confabulate

Humans perceive their personal memories as fundamentally true, and although memory is prone to inaccuracies, flagrant memory errors are rare. Some patients with damage to the ventromedial prefrontal cortex (vmPFC) recall and act upon patently erroneous memories (spontaneous confabulations). Clinical observations suggest these memories carry a strong sense of confidence, a function ascribed to vmPFC in studies of memory and decision making. However, most studies of the underlying mechanisms of memory overconfidence do not directly probe personal recollections and resort instead to laboratory-based tasks and contrived rating scales. We analyzed naturalistic word use of patients with focal vmPFC damage (N = 18) and matched healthy controls (N = 23) while they recalled autobiographical memories using the Linguistic Inquiry and Word Count (LIWC) method. We found that patients with spontaneous confabulation (N = 7) tended to over-use words related to the categories of ‘certainty’ and of ‘swearwords’ compared to both non-confabulating vmPFC patients (N = 11) and control participants. Certainty related expressions among confabulating patients were at normal levels during erroneous memories and were over-expressed during accurate memories, contrary to our predictions. We found no elevation in expressions of affect (positive or negative), temporality or drive as would be predicted by some models of confabulation. Thus, erroneous memories may be associated with subjectively lower certainty, but still exceed patients' report criterion because of a global proclivity for overconfidence. This may be compounded by disinhibition reflected by elevated use of swearwords. These findings demonstrate that analysis of naturalistic expressions of memory content can illuminate global meta-mnemonic contributions to memory accuracy complementing indirect laboratory-based correlates of behavior. Memory accuracy is the result of complex interactions among multiple meta-mnemonic processes such as monitoring, report criteria, and control processes which may be shared across decision-making domains.