Human Decision Research Articles

Identifying the bottleneck and its shifts for the nexus across water supply, power generation and environment systems: Hehuang, China

Identifying the bottleneck is the key to alerting and preventing unexpected failures of the nexus across water supply, power generation, and environment (WPE) systems. However, the bottleneck identification is challenged by the unclear concept of bottleneck as well as the difficulty of simulating complex emergent behaviors. Here, we develop a generic framework for identifying the bottleneck for the WPE nexus, which is defined as the subsystem that has the highest risk of leading to the failure of WPE nexus. First, the evolution of uncertainties of the state variables of WPE nexus is simulated by a generic stochastic dynamical system. Next, the propagation matrix, accumulation ratio (AR), and risk contribution ratio are defined as effective metrics and tools to identify the system bottleneck. Finally, the effects of human decisions and input stochasticity on the shifts of bottleneck are analyzed using sensitivity and scenario analysis. The WPE system in Hehuang Region, China, is selected as a case study. Results indicate that: (a) The effects of human decisions on ARs can be superimposed, suggesting a “superimposition method” for policy-making of Hehuang Region; (b) The subsystems can be classified as “endogenous” or “exogenous”, depending on the sources of accumulated uncertainties that are from local inputs, or inputs from the other subsystems; (c) There exist multiple bottlenecks at the same time that could shift from one subsystem to another, which complicates the prevention of the failures of WPE nexus. For the Hehuang Region, water and energy subsystems are more likely to be bottlenecks at the beginning stage, while society and environment subsystems are more likely to be bottlenecks at the steady stage. This study provides the first attempt to identify the bottleneck within the WPE nexus, which helps to provide realistic insights for preventing unexpected failures.

Artificial Intelligence in Marketing: From Computer Science to Social Science

This commentary explores the implications of artificial intelligence (AI) for marketing practice and society at large. It reviews recent findings demonstrating the importance of behavioral insights for safe AI deployment. As AI models increasingly demonstrate human-like capabilities, their deployment raises crucial concerns about bias due to unintended interactions between algorithmic and human decision-making. The discussion underscores the importance of incorporating behavioral science perspectives to understand and mitigate AI risks, moving beyond purely technical solutions.

Post hoc explanations improve consumer responses to algorithmic decisions

Algorithms are capable of assisting with, or making, critical decisions in many areas of consumers’ lives. Algorithms have consistently outperformed human decision-makers in multiple domains, and the list of cases where algorithms can make superior decisions will only grow as the technology evolves. Nevertheless, many people distrust algorithmic decisions. One concern is their lack of transparency. For instance, it is often unclear how a machine learning algorithm produces a given prediction. To address the problem, organizations have started providing post-hoc explanations of the logic behind their algorithmic decisions. However, it remains unclear to what extent explanations can improve consumer attitudes and intentions. Five experiments demonstrate that algorithmic explanations can improve perceptions of transparency, attitudes, and behavioral intentions – or they can backfire, depending on the explanation method used. The most effective explanations highlight concrete and feasible steps consumers can take to positively influence their future decision outcomes.

Applying Artificial Intelligence for Emotional Cognition in Game Testing for Quality Assurance

Artificial intelligence (AI) is a branch of computer science that tries to develop computational tools and systems that can carry out tasks comparable to human decision-making and learning. The subject of AI is expanding quickly, and AI technology is becoming more and more significant in a variety of IT specialties. When more automated and intelligent solutions are employed instead of outdated techniques, the quantity of manpower and resources needed for game testing will be greatly decreased. The aim of this study is to find new models that will make game testing easier by utilising state-of-the-art AI techniques. In an attempt to determine the model base and algorithmic foundation for the new approach, the examination and comparison of existing theories, models, and algorithms is used to infer and identify the viability of certain current mainstream AI models and algorithms in game testing sessions. Standard software testing is not the same as game testing. Further consideration should be given to the whole gaming experience and entertainment value in addition to functional testing. User questionnaires and in-game testing are used in the current standard experience testing methodology. Artificial intelligence eliminates human aspects in emotional cognition. It may produce more objective test results while spending less money for both human and material resources by using massive data sets and its own learning and processing capabilities. This paper examines the state of AI in software testing, game creation, and emotion perception using expertise in using and applying AI approaches in game testing for quality assurance. It also demonstrates how well AI methods work for predicting player emotions during game testing. The AI testing reduces testing expenses related to labour and material resources while guaranteeing total game confidentiality before release. It also increases test accuracy and reduces the impact of subjectivity.

Examining flight time, cognitive reflection, workload, stress and metacognition on decision making performance for pilots during flight simulation

Despite technological advancements, human decision errors still contribute to civil aviation accidents. This study investigated whether flight time, cognitive reflection, task-load, metacognition, and perceived stress predicted decision-making (DM) performance during two in-flight training simulations with 104 commercial pilots at Bogota International Airport. Hierarchical regression analysis revealed that the predictors accounted for 56% of the variance. Cognitive reflection, flight time and performance task load emerged as significant positive predictors. Cognitive reflection significantly moderated the relationship between flight time and DM performance, with pilots scoring lower on cognitive reflection showing improved DM with increased flight time, while controlling for performance task load. The study did not find significant relationships between stress metacognition and DM performance. The study emphasises the significance of advanced training methods in improving pilots’ DM, especially for those with low cognitive reflection. Future research should expand to multiple airlines, address gender balance, and incorporate direct measures of metacognitive monitoring.

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.

Development of an automated urban heritage monitoring tool: Between a techno-fix and fallacy

The preservation of urban heritage is one of the main challenges for contemporary society and an important task for governments. The administration of urban heritage preservation (UHP) requires effective methods, as well as sufficient material and human resources. The administrative practice of UHP is embedded in the relevant regulatory framework, which comprises legislative and normative acts at municipal, national, and international levels. The scale and range of heritage objects to be monitored, the complex statutory basis of the regulatory framework, and the traditionally low human and material resources of city administrations all contribute to the ineffectiveness of UHP practice in the UNESCO World Heritage Site of Vilnius.Advances in technologies such as 3D spatial scanning and AI-based image processing offer the promise of a technological fix to the ineffective administrative practice. In this paper, we present a grounded design study aimed at developing a technology prototype for an automated urban heritage risk monitoring tool. Originally conceived as a techno-fix to the inefficiency of administrative practice, the prototype development reveals tensions and stumbling blocks within digitalisation efforts. Using the analytical lens of the Trifecta model of IT-based regulation, the case study examines the interaction between technological and administrative domains in shaping technology design choices.Based on this case study, we critique the notion that IT is perceived as capable of providing “techno-fix” solutions to tasks requiring reflective human decision-making, especially when such solutions are expected to be effective without first redesigning the entire organisational system. The paper concludes with a discussion on the limits of automating administrative practices and identifies specific avenues for further research into the digitalisation of urban heritage preservation.

Pattern of occupational shift among Malabar migrants: a case study of Kallar grama panchayath in Kerala

The impact of the migration on the economy of Kerala in general and of Malabar in particular needs special mention. Large scale production of commercial crops like rubber, ginger, pepper, arecanut and coconut, advanced methods of cultivation and the introduction of new agricultural crops are some of the aspects to be highlighted. Migration to Malabar turned out to be a mass movement, as migration of half a million people took place without any design, organisation or leadership. Famine was a driving force behind large scale migration. Land prices were very low as compared with Travancore. The increasing tendency of cultivation of commercial crops helped the migration. It is a form of agriculture to agriculture migration, anticipating a long run improvement and can be perceived as a human capital decision. This often leads to change of traditional occupation to other and substantial socio economic development. The immigrant people change their occupation from agriculture or agricultural labour to industrial labour or some other jobs. The present study focuses the changes that take place in the occupational pattern of the Malabar migrants in Kallar Grama Panchayath in Kasaragod district of Kerala

Individual differences in fusing the face identification decisions of humans and machines

Individual differences in fusing the face identification decisions of humans and machines

Development of an Expert System for Diagnosing Musculoskeletal Disease

Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.

Research on shared control of robots based on hybrid brain-computer interface

BackgroundWith the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain–computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control. New methodsThis paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion. ResultsEight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50 %, improves the success rate of traversing obstacles by 25.00 %, and the navigation trajectory is more consistent with expectations. ConclusionsThe HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.

C-frame thinking: Embedding behavioral economics into ecological economics

This paper aims to explore opportunities for integrating Behavioral Economics (BE) into Ecological Economics (EE). By examining the frames of analysis for both disciplines, this study categorizes BE as operating at the individual level (i-frame), while EE addresses systemic aspects of society (s-frame) and extends its considerations to the biosphere (n-frame), advocating for collective action through bottom-up intermediate-level interventions (c-frame).The study posits that EE can benefit from BE’s rich insights into human behavior and decision-making, especially for c-frame action strategies. However, integrating these disciplines requires finding common ontological and epistemological ground to avoid eclecticism and methodological flaws. The integration is approached in two steps: first, adapting BE epistemology to the systems thinking approach of EE, and second, addressing the ontological gap in BE regarding the world surrounding the individual. This paper argues that embedding BE within EE’s ontology points to the necessity of c-frame thinking for human decision-making.A case study of the ex-GKN factory in Italy demonstrates the practical benefits of c-frame thinking in a complex decision process. An alliance of workers, researchers, and civil society movements collaboratively developed a future plan that considered the needs of all stakeholders, showcasing the effectiveness of collective action.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world.

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Michael is better than Mehmet: exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring.

Artificial intelligence algorithms are increasingly adopted as decisional aides in many contexts such as human resources, often with the promise of being fast, efficient, and even capable of overcoming biases of human decision-makers. Simultaneously, this promise of objectivity and the increasing supervisory role of humans may make it more likely for existing biases in algorithms to be overlooked, as humans are prone to over-rely on such automated systems. This study therefore aims to investigate such reliance on biased algorithmic advice in a hiring context. Simulating the algorithmic pre-selection of applicants we confronted participants with biased or non-biased recommendations in a 1 × 2 between-subjects online experiment (n = 260). The findings suggest that the algorithmic bias went unnoticed for about 60% of the participants in the bias condition when explicitly asking for this. However, overall individuals relied less on biased algorithms making more changes to the algorithmic scores. Reduced reliance on the algorithms led to the increased noticing of the bias. The biased recommendations did not lower general attitudes toward algorithms but only evaluations for this specific hiring algorithm, while explicitly noticing the bias affected both. Individuals with a more negative attitude toward decision subjects were more likely to not notice the bias. This study extends the literature by examining the interplay of (biased) human operators and biased algorithmic decision support systems to highlight the potential negative impacts of such automation for vulnerable and disadvantaged individuals.

Transfer Learning-Based Steering Angle Prediction and Control with Fuzzy Signatures-Enhanced Fuzzy Systems for Autonomous Vehicles

This research introduces an innovative approach for End-to-End steering angle prediction and its control in electric power steering (EPS) systems. The methodology integrates transfer learning-based computer vision techniques for prediction and control with fuzzy signatures-enhanced fuzzy systems. Fuzzy signatures are unique multidimensional data structures that represent data symbolically. This enhancement enables the fuzzy systems to effectively manage the inherent imprecision and uncertainty in various driving scenarios. The ultimate goal of this work is to assess the efficiency and performance of this combined approach by highlighting the pivotal role of steering angle prediction and control in the field of autonomous driving systems. Specifically, within EPS systems, the control of the motor directly influences the vehicle’s path and maneuverability. A significant breakthrough of this study is the successful application of transfer learning-based computer vision techniques to extract respective visual data without the need for large datasets. This represents an advancement in reducing the extensive data collection and computational load typically required. The findings of this research reveal the potential of this approach within EPS systems, with an MSE score of 0.0386 against 0.0476, by outperforming the existing NVIDIA model. This result provides a 22.63% better Mean Squared Error (MSE) score than NVIDIA’s model. The proposed model also showed better performance compared with all other three references found in the literature. Furthermore, we identify potential areas for refinement, such as decreasing model loss and simplifying the complex decision model of fuzzy systems, which can represent the symmetry and asymmetry of human decision-making systems. This study, therefore, contributes significantly to the ongoing evolution of autonomous driving systems.

THE ROLE OF AI AND WEB 2.0 TOOLS IN DEVELOPING PRAGMATIC COMPETENCE OF L2 ENGLISH LEARNERS

Artificial Intelligence (AI) and web 2.0 tools has transformed various sectors, like healthcare and finance, by imitating human intelligence and decision-making. However, AI still struggles with understanding and effectively participating in human communication, which is a crucial challenge. Pragmalinguistics, a branch of linguistics focused on the relationship between language and context, addresses this issue. It examines pragmatic aspects such as speaker intention, context, and shared knowledge to grasp the intended meaning behind communication. By integrating pragmalinguistics into AI systems, researchers and developers aim to narrow the gap between machines and human communication. This article explores how AI and web 2.0tools are helpful in pragmalinguistics collaborate to improve AI capabilities.

Interpersonal strategy for controlling unpredictable opponents in soft tennis

Competition in sports, unlike cooperation in everyday life, does not involve a single solution because individuals aim to behave unpredictably, thereby preventing others from predicting their actions. This study determined how individuals in court-based sports attempted to control others’ unpredictable behaviors, addressing the gap in previous studies regarding the lack of clarity around strategies employed by individuals in competitive situations. We achieved this by applying a switching hybrid dynamics model, considering external inputs to analyze individual behaviors. Consequently, the study indicates that skilled individuals, in contrast to intermediate players, exhibit greater consistency in their behaviors. These skilled individuals lead others to anticipate their consistency and subsequently employ strategies to disrupt these expectations. This strategy exploits the principles of active human inference, implying that competition involves cooperation. This was revealed by an analysis of both human decision-making and behavior in actual matches as discrete and continuous dynamical systems. This interpersonal strategy could assist policymakers in the field of everyday life to enhance competitiveness. This strategy enables policymakers to adopt new policies that promote cooperation with competitors, ultimately increasing competitiveness in various aspects of our daily lives.

Hybrid reward-punishment in feedback-evolving game for common resource governance.

How to maintain the sustainability of common resources is a persistent challenge, as overexploiters often undermine collective efforts by prioritizing personal gain. To mitigate the overexploitation of resources by violators, previous theoretical studies have revealed that the introduction of additional incentives, whether to reward rule-abiding cooperators or to punish those who overexploit, can be beneficial for the sustainability of common resources when the resource growth rate is not particularly low. However, these studies have typically considered rewarding and punishing in isolation, thus overlooking the role of their combination in common resource governance. Here, we introduce a hybrid incentive strategy based on reward and punishment within a feedback-evolving game, in which there is a complex interaction between human decision making and resource quantity. Our coevolutionary dynamics reveal that resources will be depleted entirely, even with cooperative strategies for prudent exploitation, when resource growth is slow. When the rate of resource growth is not particularly low, we find that the coupled system can generate a state where resource sustainability and cooperation can be maintained. Furthermore, when the rate of resource growth is moderate, we find that achieving this state cannot simply allocate all incentive budgets to reward. In addition, the increase in per capita incentives significantly promotes the stability of this state.

Good decisions in an imperfect world: a human-focused approach to automated decision-making

ABSTRACT Legal rules are based on an imagined regulatory scene that contains presumptions about the reality a regulation addresses. Regarding automated decision-making (ADM), these include a belief in the ‘good human decision’ that is mirrored in the cautious approach in the GDPR. Yet the ‘good human decision’ defies psychological insight into human weaknesses in decision-making. Instead, it reflects a general unease about algorithmic decisions. Against this background I explore how algorithms become part of human relationships and whether the use of decision systems causes a conflict with human needs, values and the prevailing socio-legal framework. Inspired by the concept of Human-Centered AI, I then discuss how the law may address the apprehension towards decision systems. I outline a human-focused approach to regulating ADM that focuses on improving the practice of decision-making. The interaction between humans and machines is an essential part of the regulation. It must address socio-legal changes caused by decision systems both to integrate them into the existing value system and adapt the latter to changes brought forth by ADM. A human-focused approach thus connects the benefits of technology with human needs and societal values.