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Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity, and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases have been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multiattribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis reveals that the decoy influence map is highly structured even beyond the three classic biases. We identify a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps us understand why the biases are typically correlated across participants and allows us to validate a prediction about their interrelationship. This work helps to clarify the origin of three of the most widely studied biases in human decision-making., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Signatures of confidence emerge during decision-making, implying confidence may be of functional importance to decision processes themselves. We formulate an extension of sequential sampling models of decision-making in which confidence is used online to actively moderate the quality and quantity of evidence accumulated for decisions. The benefit of this model is that it can respond to dynamic changes in sensory evidence quality. We highlight this feature by designing a dynamic sensory environment where evidence quality can be smoothly adapted within the timeframe of a single decision. Our model with confidence control offers a superior description of human behaviour in this environment, compared to sequential sampling models without confidence control. Using multivariate decoding of electroencephalography (EEG), we uncover EEG correlates of the model's latent processes, and show stronger EEG-derived confidence control is associated with faster, more accurate decisions. These results support a neurobiologically plausible framework featuring confidence as an active control mechanism for improving behavioural efficiency. Feelings of confidence may have a functional role in decision-making. Here, the authors develop a neurobiologically plausible framework featuring confidence as an active control mechanism for improving behavioural efficiency in dynamic environments. [ABSTRACT FROM AUTHOR]

Rapid adaptation to sudden changes in the environment is a hallmark of flexible human behaviour. Many computational, neuroimaging, and even clinical investigations studying this cognitive process have relied on a behavioural paradigm known as the predictive-inference task. However, the psychometric quality of this task has never been examined, leaving unanswered whether it is indeed suited to capture behavioural variation on a within- and between-subject level. Using a large-scale test–retest design (T1: N = 330; T2: N = 219), we assessed the internal (internal consistency) and temporal (test–retest reliability) stability of the task's most used measures. We show that the main measures capturing flexible belief and behavioural adaptation yield good internal consistency and overall satisfying test–retest reliability. However, some more complex markers of flexible behaviour show lower psychometric quality. Our findings have implications for the large corpus of previous studies using this task and provide clear guidance as to which measures should and should not be used in future studies. [ABSTRACT FROM AUTHOR]

Tasks of emergency evacuation based on transporting the maximum number of potentially affected people from hazard areas to the safe destinations are becoming more and more popular. However, hard computing approaches to solving these problems have limited capabilities due to such factors as complexity of the problems, environmental factors, human behavior. Uncertainty and fuzziness incorporated into emergency evacuation strategies require fuzzy emergency problem statements and decisionmaking. Another problem researchers tackle during emergency response is transporting potential victims by the path of the minimum time in a dynamic network, whose arc traversal time can vary due to the blocking of corridors with fire or smoke. This article integrates flow approaches in transportation networks and hesitant fuzzy decision-making to transport the maximum number of potential aggrieved to the safe destination in shortest time. Multi-attribute decision making is applied to find the order in which aggrieved must be transported to the sink. Hesitant fuzzy ELECTRE method is applied to find the ranks of intermediate nodes for transportation the potential victims. The purpose of the proposed algorithm is to shift the maximum possible number of aggrieved from origins to destinations that have different levels of priority due to the level of reachability, capacity of destination nodes, reliability (security) and affordability. A case study is conducted to illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

The purpose of this article is to understand the distinctively human behavior from Aristotelian ethics and evolutionary science to offer a perspective of what it means to act rationally. We argue that this way of acting is characterized by a decision informed by the analysis of whether or not it is worth pursuing an end, and by certain means, which takes place through a weighting of consequences from the body of knowledge that the person has so far We also argue that such a process can occur quickly (and requiring a less cognitive effort) or slowly (and demanding more cognitive effort), depending on whether or not the person has previous experiences of choices that have generated good consequences in the type of context presented; What does it mean for a person to have or not rational heuristics established in their minds, which are those that are connected to the most current network of "whys" and that has been consolidated precisely because they have proven effective in pointing out what is best to do in that kind of context. Finally, we apply the perspective we are offering to evidence three imprecise notions about "acting rationally". [ABSTRACT FROM AUTHOR]

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this research work, we have designed a mechanism to generate artificial somatic punishments in an autonomous system. An autonomous system is understood as a system capable of performing autonomous behavior and decision making. We incorporated this mechanism within a decision model oriented to support decision making on stock markets. Our model focuses on using artificial somatic punishments as a tool to guide the decisions of an autonomous system. To validate our proposal, we defined an experimental scenario using official data from Standard & Poor's 500 and the Dow Jones index, in which we evaluated the decisions made by the autonomous system based on artificial somatic punishments in a general investment process using 10,000 independent iterations. In the investment process, the autonomous system applied an active investment strategy combined with an artificial somatic index. The results show that this autonomous system presented a higher level of investment decision effectiveness, understood as the achievement of greater wealth over time, as measured by profitability, utility, and Sharpe Ratio indicators, relative to an industry benchmark. [ABSTRACT FROM AUTHOR]

Economics is frequently criticised for relying on a narrow and limited view of human beings. This may be particularly true of economic analyses of non-market decisions in which individuals often appear reduced to self-interested automata who maximise a given objective function. In this article, we show that the approach of one of the founders of public choice and constitutional political economy, James Buchanan, contradicts this view. Even though he assumed individuals were rational and self-interested, Buchanan nevertheless had a sophisticated view of human nature. He distinguished between a natural and artifactual man, but also between (what we term) symmetrical and asymmetrical man. This is not only important to demonstrate the richness of the ontology of an influential economist, but also because, we also show, Buchanan's public choice and constitutional economics cannot be understood without a reference to this ontology. [ABSTRACT FROM AUTHOR]

Emergency evacuations involve a complex interaction between different stress-related factors (encompassing both human behavior and physical infrastructure) and associated responses. Utilizing the unity real-time development platform, this research examines these dynamics by exploring diverse aspects of evacuation, such as psychological traits and their interaction with physical infrastructure elements such as building layout, exit options, and crowd density. A single-story workspace at Marshall University was used as a case study. The findings reveal that individual human traits, behaviors, and interactions significantly impact evacuation success, often more than the physical infrastructure features themselves. Moreover, there is potential for the coupling effect where multiple stress inducing and/or reducing factors may act together and increase and/or decrease evacuation efficiency. In essence, this study underscores the complexity of human behavior under stress, emphasizing the importance of considering related factors in evacuation models to simulate realistic scenarios accurately. These insights, validated by subject matter experts for general applicability across various workplace setups, can help organizations enhance their emergency management strategies, resulting in more effective systems, better-prepared personnel, and more adaptive responses to different stressors. [ABSTRACT FROM AUTHOR]

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation reversal in ferromagnetic nanostructures, we developed a model that closely reflects human decision-making dynamics. Tested against a spectrum of psychological data, our model adeptly captures the complexities inherent in individual choices. This blend of physics and psychology paves the way for fresh perspectives on understanding the imprecision of human decision-making processes, extending the reach of the current classical and quantum physical models of human behaviour and decision making. [ABSTRACT FROM AUTHOR]

Considering all of the criteria involved in vendor evaluation makes it a difficult Multi-Criteria Decision-Making (MCDM) problem. In this case, they factor in DMs that are knowledgeable about the suppliers and their needs. Rating, a multi-criteria assessment method based on optimum scoring is proposed in this work. To meet the requirements of the decision-making process, this method employs rating-Based Optimal-Point Sorting (RBOP). A tried-and-true MCDM method can consistently find the option that will make the DM happy by first modeling the decision-making process after how humans really behave. Additionally, they create a new subjective scoring mechanism for the MCDM that is based on human behavior; they call it the win-loss-draw (WLD) method. They apply their methods to a case study that is based on actual Iranian cheese company operations in order to find the best possible cheese supplier for a hypermarket in Iran. The WLD approach had an effect on the RBOP strategy's findings, which differ somewhat from the other MCDM methods. The WLD method increased spread and optimum regions throughout the choosing process. [ABSTRACT FROM AUTHOR]

Joiner and colleagues' Interpersonal Theory of Suicide (IPTS), a prominent "desire-capability" model of suicide—based on the common-sense idea that people take their own lives because they want to, and can—is critiqued from a biological perspective. Tinbergen's ethological "four questions" guide the analysis: evolution, survival value, ontogeny, and proximate causation, each addressing a different aspect of biological understanding. Problems for IPTS emerge with all four. As a parsimonious solution, the desire-capability hypothesis is reconceived as an ultimate, instead of proximate, mode of explanation. By this light, desire and capability for suicide combined in our species' ancestral past, thus making suicide a recurrent survival threat, and driving the evolution of special-purpose defensive adaptations. This stance tallies with the pain-brain theory of the evolution of suicide, and with Joiner and colleagues' own investigation into organismic anti-suicide defenses, which appears to conflict conceptually with IPTS. These defenses' evolved algorithm may make suicide an intrinsically aleatory phenomenon, opaque to usefully accurate prediction. Positive implications for prevention and research are proposed. [ABSTRACT FROM AUTHOR]

Objective Decision-making is responsible for the best and worst of human nature. The field of decision science has done much to elucidate the psychological process of decision-making, variables that affect decision-making, and outcomes of disadvantageous decision-making. However, understanding any psychological process requires creation of reliable measures. Few studies focus on the test–retest reliability of behavioral decision-making tasks despite their utility in repeated assessment batteries. Method The present study examined the extent to which common behavioral decision-making tasks are reliable across time. Across two samples and two time points, participants completed multiple decision-making assessments. Results Results revealed moderate at best evidence of test–retest reliability across a 10-week interval in any of the tasks assessed. Conclusions These findings raise large questions for the field of behavioral decision-making and the utility for tasks to track changes in decision-making across time in clinical populations. [ABSTRACT FROM AUTHOR]

Objective: We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background: Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Results: The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Conclusion: Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Application: Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles. [ABSTRACT FROM AUTHOR]

Intelligent computing has distinct cognitive characteristics, especially when dealing with some multi-attribute group decision-making questions, it is regarded as a human behavior based on cognition. Pythagorean fuzzy set (PFS) is not only an extension of intuitionistic fuzzy set (IFS), but also can handle some fuzzy decision-making problems of multi-attribute information on a larger scale, especially some new methods have been rapidly spread and developed in decision-making science. In this paper, some defects in the existing ranking criteria for Pythagorean fuzzy numbers (PFNs) were pointed out through some counterexamples, the main reasons of these flaws are analyzed, so that all IFNs are unified into PFNs space through coordinate transformation. Secondly, a novel improved score formula and ranking method are proposed by the residual sector area (RSA) and hesitancy degree of PFNs in a geometric background, and the rationality of this ranking criterion is further demonstrated through rigorous mathematical methods, and then the fundamental properties of the score function are discussed. Finally, the superiority of the novel score function was interpreted through comparison and analysis with other existing seven score formulas, and the new score formula was applied to multi-attribute group decision-making problems through an example, and the superiority of the novel method was fully displayed. In fact, the proposed method achieves a perfect ranking of all PFNs, especially for the equivalent PFNs, it can be achieved precise comparison or ranking, which overcomes some flaws of other methods, and ending the confusion caused by the independent ranking of IFNs and PFNs. [ABSTRACT FROM AUTHOR]

Some people say that expectation is an illusion, based on a betrayal mechanism. Yet the psychology of human behaviour shows that the concept of expectation is a very central one for the explanation of real-life behavioural phenomena. The expectations concept plays a major role in the field of economic psychology. The operational aspects of measuring expectations and of using them in behavioural forecasts and control are of primary concern. For Keynes an emphasis on expectations meant an emphasis on the uncertainty of decision making, the frequency of mistakes, the need for time in which to adjust to unanticipated events, and the disorder of economic systems. [ABSTRACT FROM AUTHOR]

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. [ABSTRACT FROM AUTHOR]

Many aspects of humans' dynamic decision-making (DDM) behaviors have been studied with computer-simulated games called microworlds. However, most microworlds only emphasize specific elements of DDM and are inflexible in generating a variety of environments and experimental designs. Moreover, despite the ubiquity of gridworld games for Artificial Intelligence (AI) research, only some tools exist to aid in the development of browser-based gridworld environments for studying the dynamics of human decision-making behavior. To address these issues, we introduce Minimap, a dynamic interactive game to examine DDM in search and rescue missions, which incorporates all the essential characteristics of DDM and offers a wide range of flexibility regarding experimental setups and the creation of experimental scenarios. Minimap specifically allows customization of dynamics, complexity, opaqueness, and dynamic complexity when designing a DDM task. Minimap also enables researchers to visualize and replay recorded human trajectories for the analysis of human behavior. To demonstrate the utility of Minimap, we present a behavioral experiment that examines the impact of different degrees of structural complexity coupled with the opaqueness of the environment on human decision-making performance under time constraints. We discuss the potential applications of Minimap in improving productivity and transparent replications of human behavior and human-AI teaming research. We made Minimap an open-source tool, freely available at https://github.com/DDM-Lab/MinimapInteractiveDDMGame. [ABSTRACT FROM AUTHOR]

The article emphasises the urgency of addressing the risks posed by AI-powered deceptive design strategies intricately woven into online platforms. These 'psychological patterns' mislead users into making decisions contrary to their intentions, exploiting psychological vulnerabilities. The article also critically examines the complex interplay between AI-powered deceptive design and legislative responses, mainly focusing on Article 5 of the European Union's AI Act. It underscores the importance of safeguarding user autonomy in the rapidly evolving digital landscape. Thus, the article discusses the dynamics of psychological manipulation, the need for effective regulation via Article 5, and the critical importance of maintaining user autonomy amidst technological advances in AI. [ABSTRACT FROM AUTHOR]

Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers' reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers' predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: it is easier for consumers to internalize a favorable decision outcome that is rendered by a human than by an algorithm, but it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances. [ABSTRACT FROM AUTHOR]

Should policymaking assume humans are irrational? Using empirical, theoretical, and philosophical arguments, we suggest a more useful frame is that human behavior is reasonable. Through identifying goals and systemic factors shaping behavior, we suggest that assuming people are reasonable enables behavioral science to be more effective in shaping public policy. [ABSTRACT FROM AUTHOR]

An introduction to articles in the issue which examine some of the challenges and most promising solutions for bringing nature into decision-making at scale is presented including an exploration of what values we assign to nature, an example of how governments have brought nature into decision-making, and enhancing nature connection.

In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts—which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people's sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough. Author summary: When explicitly asked to be random, people are not random enough. Previous accounts of these random generation tasks have argued that people are effortfully trying not to be predictable. In many other tasks, however, people also show random behavior, even when it is unnecessary or outright disadvantageous. Here, we try to bridge this apparent gap. We hypothesize that the randomness people produce when trying to be random and the randomness that they display when trying to make the best choice has the same common mechanism: drawing mental samples to make judgments and decisions. In two experiments, we compare previous random generation accounts, which are task-specific in nature, to the more general account of mental sampling that has been used to explain how people behave in many other domains. We find that the flexibility of human random generation in our data is better explained by the mental sampling account. We also find a novel empirical signature of momentum in random generation, which points to a new kind of mental sampling algorithm. If mental sampling governs behavior in random generation tasks and elsewhere, then this task has great promise in helping to understand wider human behavior. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic has made abundantly evident that human behavior is a critical factor in determining whether interventions intended to manage infection spread are effective. Human behavior is driven by decision processes about whether to comply with advice from public-health experts and instructions from officials charged with managing pandemic response within organizations and governmental regions. And guiding those decisions are personal values, often shared with others, which are understudied features of pandemic management. Here, we demonstrate the role of values in a pandemic simulation using The Artificial Organization (TAO), an existing, strongly validated, agent-based, decision-support tool for pandemic management. We enhance TAO by adding human values to create TAO-V, focusing particularly on values related to political ideology, the spread of those values, and the way political values and compliance decisions have interacted in the United States of America (and other nations, usually to a lesser extent). TAO-V confirms that human values are influential factors in a pandemic simulation, which invites testing against real-world data from pandemic-management efforts. Even before real-world testing, the results of this study suggest that publichealth messaging might be more effective if it were to engage values rather than only stress compliance with public-health recommendations. [ABSTRACT FROM AUTHOR]

To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g., fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within populations, can lead to substantial deviations and unanticipated effects of interventions. This paper presents a computational model of fisher agents’ decision-making that draws on theoretical and empirical psychological insights to enrich this critical component. The model includes mechanisms for information integration (learning), social comparisons, and thresholds for economic satisfaction. In offering this enriched account, the model captures how fishers may adapt behaviourally given changes in policy, economic conditions, or social pressures. Furthermore, the model can be parameterised to capture the effects of different socio-cultural contexts can be simulated. The model of fisher agents has been implemented as part of POSEIDON (an agent-based fisheries management model), showing that fishers imbued with the model learn and adapt when responding dynamically to changing conditions. The model is thus demonstrated in a fisheries environment, but we discuss how its architecture could be implemented for simulation in other human-environment systems, such as designing policies to combat the human-environment problems. [ABSTRACT FROM AUTHOR]

The data-driven techniques have utilized data mining and machine learning (ML) techniques in the biomedical and healthcare fields. The process of decision-making in uncertain contextual related to human addictions and emotions play an important role in the present research. The main aim of the research is to perform classification and generate a support system for uncertain addiction circumstances by proposing a technique for drug addiction treatment. The human behavior has majority shown challenges for the prediction of human behaviors that includes body poses estimation, movements and interaction with objects. This pose estimation has showed complexity with more pose aspects and the proposed research attempts to understand the human behaviors. The present research uses the local interpretable model-agnostic explanations (LIME) for finding the input features which are most important to generate a particular output based on decision service. LIME understands the model to perturb the data samples as an input and understands shows predictions change. Also, the ensemble classifier contains classifiers group that combines for performing the prediction of all unseen instances based on voting. The proposed LIME Feature-Ensemble classifier obtained 97.54% of accuracy when compared to the existing convolutional neural network (CNN) of 59.33% and Ensemble model of 93.33% accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ibero-Americana de Estratégia (RIAE) is the property of Revista Ibero-Americana de Estrategia/UNINOVE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The decisions made by social robots while they fulfill their tasks have a strong influence on their performance. In these contexts, autonomous social robots must exhibit adaptive and social-based behavior to make appropriate decisions and operate correctly in complex and dynamic scenarios. This paper presents a Decision-Making System for social robots working on long-term interactions like cognitive stimulation or entertainment. The Decision-making System employs the robot's sensors, user information, and a biologically inspired module to replicate how human behavior emerges in the robot. Besides, the system personalizes the interaction to maintain the users' engagement while adapting to their features and preferences, overcoming possible interaction limitations. The system evaluation was in terms of usability, performance metrics, and user perceptions. We used the Mini social robot as the device where we integrated the architecture and carried out the experimentation. The usability evaluation consisted of 30 participants interacting with the autonomous robot in 30 min sessions. Then, 19 participants evaluated their perceptions of robot attributes of the Godspeed questionnaire by playing with the robot in 30 min sessions. The participants rated the Decision-making System with excellent usability (81.08 out of 100 points), perceiving the robot as intelligent (4.28 out of 5), animated (4.07 out of 5), and likable (4.16 out of 5). However, they also rated Mini as unsafe (security perceived as 3.15 out of 5), probably because users could not influence the robot's decisions. [ABSTRACT FROM AUTHOR]

Fires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants' behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building's environmental conditions to generate contextual information, aiding in developing solutions to make the occupants' behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants' behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations. [ABSTRACT FROM AUTHOR]

That wealth has diminishing marginal utility is a fact of life, and that people be sensitive to their current level of wealth when deciding whether to pursue additional wealth is a requirement of rational choice. A series of experiments, spanning diverse contexts, reveal marginal-utility neglect —that people are rather insensitive to their current wealth when deciding how much effort to expend to acquire a monetary reward (e.g. how long to walk to claim a voucher). Moreover, the experiments demonstrate that a marginal-utility-prompting manipulation, which prompts people to consider their current wealth and their need for the reward given their current wealth, produces a significant sensitization effect—making financially richer (vs. less rich) individuals less (vs. more) willing to seek the reward. This manipulation is more effective than either prompting people to consider their current wealth alone or consider their need for the reward alone, suggesting that marginal-utility prompting does not merely draw people's attention to their current wealth or merely draw their attention to their need for the reward, but links the two elements. This research elucidates the psychology of marginal utility and yields implications beyond the pursuit of monetary rewards. [ABSTRACT FROM AUTHOR]

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal. Author summary: The way humans perform reaching movements is compatible with models considering that they result from the minimization of a task-related cost function. However, these models typically assume a cost function that does not change within movement, which is incompatible with experimental findings highlighting humans' ability to adjust reaching control online and change target flexibly. We hypothesized that this ability relied on continuous evaluation of the cost-to-go, which integrates task- and body-related information. We show that this model can optimally select and adjust control during movement in a way that reproduces human behavior in a set of tasks involving change in cost function and change in goal target. Our model predicted that decision-time to change target must be postponed when limb displacements and alternative rewards are smaller, which was borne out in our previous experimental dataset. To conclude, our model explains dynamic updates in reach control and suggests the cost-to-go as decision variable linking decision-making and motor control. [ABSTRACT FROM AUTHOR]

Many decision makers are thought to economize on attention by processing information at the simpler level of a category. We directly test whether such category focus reflects an adaptive response to attention constraints, in five preregistered experiments using an information sampling paradigm with mouse tracking. Consistent with rational principles, participants focus more on category-level information when individual differences are small, when the category contains more members, and when time constraints are more severe. Participants are sensitive to the statistical structure of the category even when it must be learned from experience, and they respond to a latent shift in this structure. Beliefs about category members tend to cluster together more when category focus is high—a key element of rational inattention. However, this is counteracted by greater weight placed on salient and idiosyncratic information when the category is large. Our results broadly substantiate influential theories of categorical thinking, giving us a clearer view on the drivers and consequences of inattention. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This work was supported by the Office of Naval Research (N00014-21-1-2170), the Pershing Square Fund for Research on the Foundations of Human Behavior, and an NWO Vidi grant (VI.Vidi.201.059). Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4803. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The concept of the homo oeconomicus is often used to model human behaviour in economic contexts. However, other factors like tradition or the preference to comply with social norms can play a role in decision‐making processes.To emphasize the need for incorporating non‐pecuniary values in economic models, we use data for participation in several agri‐environment schemes (AES) in Europe and show that dynamical patterns can not be simulated by models using the homo oeconomicus concept.The presented data show gradually increasing participation levels in AES even if payment levels are constant. Furthermore, low participation levels are sometimes observed despite appropriate incentive schemes.We propose and investigate a dynamic mathematical model to implement social norms in farmers' land‐use decision‐making in the face of AES. This socio‐economic model can help to explain the variety of observed behavioural patterns. It can generate multistable dynamics regarding the level of AES participation in the long‐term. We further assume that informative campaigns can modify farmers' perception of norms. Campaigns can have a stabilizing effect if strong enough.The attempt of this work is to gain a better understanding of how the integration of social human behaviour in economic models affects simulation patterns. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Background: Intertemporal decision-making, which involves making choices between outcomes at different time points, is a fundamental aspect of human behavior. Understanding the underlying mental processes is vital for comprehending the complexities of human decision-making and choice behavior. Objective: The main objective of this study is to investigate the interplay of mental processes, specifically cognitive evaluation, subjective valuation, and comparison, in the context of intertemporal decision-making, with a specific focus on understanding the discounting process. Methodology: Development of a mathematical representation of the discounting process that incorporates the mental processes associated with intertemporal decision-making. Result: Our findings indicate that hyperbolic discounting aligns well with the cognitive processes underlying intertemporal decision-making. Subsequent research will employ qualitative questionnaires to establish the discount function relevant to specific groups, thereby enhancing our comprehension of the discounting process within intertemporal decision-making. [ABSTRACT FROM AUTHOR]

Although mouse-tracking has been seen as a real-time window into different aspects of human decision-making processes, currently little is known about how the decision process unfolds in veridical and false memory retrieval. Here, we directly investigated decision-making processes by predicting participants' performance in a mouse-tracking version of a typical Deese–Roediger–McDermott (DRM) task through distributional semantic models, a usage-based approach to meaning. Participants were required to study lists of associated words and then to perform a recognition task with the mouse. Results showed that mouse trajectories were extensively affected by the semantic similarity between the words presented in the recognition phase and the ones previously studied. In particular, the higher the semantic similarity, the larger the conflict driving the choice and the higher the irregularity in the trajectory when correctly rejecting new words (i.e., the false memory items). Conversely, on the temporal evolution of the decision, our results showed that semantic similarity affects more complex temporal measures indexing the online decision processes subserving task performance. Together, these findings demonstrate that semantic similarity can affect human behavior at the level of motor control, testifying its influence on online decision-making processes. More generally, our findings complement previous seminal theories on false memory and provide insights into the impact of the semantic memory structure on different decision-making components. [ABSTRACT FROM AUTHOR]

The Value Learning Task (VLT; e.g., Raymond & O'Brien, 2009) is widely used to investigate how acquired value impacts how we perceive and process stimuli. The task consists of a series of trials in which participants attempt to maximize accumulated winnings as they make choices from a pair of presented images associated with probabilistic win, loss, or no-change outcomes. The probabilities and outcomes are initially unknown to the participant and thus the task involves decision making and learning under uncertainty. Despite the symmetric outcome structure for win and loss pairs, people learn win associations better than loss associations (Lin, Cabrera-Haro, & Reuter-Lorenz, 2020). This learning asymmetry could lead to differences when the stimuli are probed in subsequent tasks, compromising inferences about how acquired value affects downstream processing. We investigate the nature of the asymmetry using a standard error-driven reinforcement learning model with a softmax choice rule. Despite having no special role for valence, the model yields the learning asymmetry observed in human behavior, whether the model parameters are set to maximize empirical fit, or task payoff. The asymmetry arises from an interaction between a neutral initial value estimate and a choice policy that exploits while exploring, leading to more poorly discriminated value estimates for loss stimuli. We also show how differences in estimated individual learning rates help to explain individual differences in the observed win-loss asymmetries, and how the final value estimates produced by the model provide a simple account of a post-learning explicit value categorization task. [ABSTRACT FROM AUTHOR]

Objective: This research aims to investigate the effectiveness of wayfinding information (destination reference images) in real-time complex environmental settings through the syntheses of mobile and static information sources. This investigation will help in improving the wayfinding information design for complex institutional environments which will enhance the environmental legibility. Background: Complex environments are challenging for wayfinding efficiency. Identification of the location and orientation are considered to be important for wayfinding efficiency. This study investigated the user behavior in synthesizing the wayfinding information for identifying the users' location and orientation in a complex environment. Method: Twenty-five university students have participated in the desktop-based virtual reality experiment. Five experimental conditions were developed for the assessment of wayfinding efficiency and users' preferred information sources. Results: Reference images of destinations with user-matched angles were found more effective than the building's facade images. Participants synthesized the mobile wayfinding information with real environmental information to identify their location and orientation. Real environment information (road intersections and shape of the road) and mobile information (reference images of building facades) were found influential in the identification of the user's location and orientation. Conclusions: The study suggested a need for thorough investigation regarding the users' wayfinding behavior with different types of environmental information. Future studies have been recommended to investigate other complex institutional environments with larger sample groups for enhancing environmental legibility and institutional efficiency. [ABSTRACT FROM AUTHOR]

Visual Analytics approach allows driving informed and effective decision-making. It assists decision-makers to visually interact with large amount of data and to computationally learn valuable hidden patterns in that data, which improve the decision quality. In this article, we introduce an enhanced visual analytics model combining cognitive-based visual analysis to data mining-based automatic analysis. As emotions are strongly related to human behaviour and society, emotion prediction is widely considered by decision making activities. Unlike speech and facial expressions modalities, EEG (electroencephalogram) has the advantage of being able to record information about the internal emotional state that is not always translated by perceptible external manifestations. For this reason, we applied the proposed cognitive approach on EEG data to demonstrate its efficiency for predicting emotional reaction to films. For automatic analysis, we developed the Echo State Network (ESN) technique considered as an efficient machine learning solution due to its straightforward training procedure and high modelling ability for handling time-series problems. Finally, utility and usability tests were performed to evaluate the developed prototype. [ABSTRACT FROM AUTHOR]

Several situations cause a case to be moot. These include settlement agreements, party collusion, changes in litigant status, and extrinsic circumstances thwarting the court from granting any relief. The final reason is unilateral mootness—when a defendant ends a lawsuit against a plaintiff’s wishes by giving them everything for which they ask. In practice, this allows defendants to strategically stop lawsuits when it is clear they are not going to win. By doing so, they prevent the court from handing down adverse precedent and preserve the opportunity to engage in similar behavior with impunity. Courts have established a series of mootness exceptions to limit such gamesmanship. These exceptions are based on vague standards, which do little to guide judges making mootness decisions. The result is that some cases are heard on the merits, while other, nearly identical ones are dismissed. Unilateral mootness fails as a prudential doctrine. It struggles to limit disparate outcomes, prevent defendant gamesmanship, or save judicial resources, and alternative solutions do not fully address these three problems. This Note argues that the best recourse is to scrap unilateral mootness completely. Barring a settlement, collusion, or impossibility of relief, judges should never dismiss a case as moot. [ABSTRACT FROM AUTHOR]

The article analyzes the interaction between individuals in the company and the possibility of complementing the approaches of Pérez López and Wojtyla. While Pérez López focuses on systems theory, Wojtyla emphasizes the objective and subjective dimensions of human action. Despite their methodological differences, both approaches can complement each other, as Wojtyla's personalism provides a philosophical view of the person and action, while Pérez López's approach enriches the personalist perspective with key concepts about motivations, learning, trust generation, and decision-making in the company. [Extracted from the article]

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. [ABSTRACT FROM AUTHOR]

The problem we face today is that many people think that playing games only has a negative impact on a person's brain and behavior. but the fact is that playing games has a positive impact in many ways. The aim of this document is to prove whether video games can really influence human behavior on their decision-making skills. We will test 22 respondents directly who are teenagers and adults around 17 - 25 years old, and we will score them after they have finished playing games with the genre that we decided. The results proved that competitive First-person shooter (FPS) games increase human ability to make decisions quickly and correctly. Many of our participants agree that after playing competitive FPS games, they feel a positive impact on their cognitive skills. Our participants said that they can quickly compare the impact of the decisions they make and choose exactly which is the best course of action. [ABSTRACT FROM AUTHOR]

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Purpose: The paper provides evidence-based managerial advice for preparing the organizations to find successful pathways through crises by priming the managerial decision-making towards either entrepreneurial thinking or resource conservation, and hence cascading the inventive or rigid state of mind through all management levels. Design/methodology/approach: The general review is based on summarizing the peer-reviewed academic studies of organizational decision-making and acting in crisis situations, illustrated using the turnaround cases of Corticeira Amorim (reinventing itself when faced with emerging technological threats) and Kiddiegarten School (adjusting to the pandemic shock of social/human nature) Findings: This study reveals that a set of dimensions in the crisis situation's cognitive framing determines the firm's response to adversity, freezing it in a rigid state or unfreezing it to stimulate an organization-wide entrepreneurial search for turnaround strategies. If managers sense having a lack of time to deal with adversity, or a lack of predictability, they become paralyzed with threat-rigidity mechanisms, stubbornly pursuing the established methods of doing business, which often were the cause of crisis in the first place. Hence, in situations requiring an immediate response, the dual threats of urgency and unpredictability become cognitive blinders, preventing organizations from pursuing new opportunities, exposing firms to the risk of being too slow, eroding their competitive advantage and, ultimately, going out of business. Originality/value: Integrating the insights of three decades prior research of the topic of managerial decision making in crisis situations, this study proposes the novel leadership framework allowing to stimulate entrepreneurial behavior in adverse contexts. [ABSTRACT FROM AUTHOR]