358,063 research outputs found

    Human Decision-Making Behavior and Modeling Effects

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    Previous research indicates that the human decision-making process is somewhat nonlinear and that nonlinear models would be more suitable than linear models for developing advanced decision-making models. In our study, we tested this generally held hypothesis by applying linear and nonlinear models to expert\u27s decision-making behavior and measuring the predictive accuracy (predictive validity) and valid nonlinearity. As a result, we found that nonlinearity in the decision-making process is positively related to the predictive validity of the decision. Secondly, in modeling the human decision-making process, we found that valid nonlinearity is positively related to the predictive validity of nonlinear models. Thirdly, we found that the more nonlinearity is inherent in the decision-making process, the more nonlinear models are effective. Therefore, we suggest that a preliminary analysis of the characteristics of an expert’s decision-making is needed when knowledge-based models such as expert systems are being developed. We also verify that the lens model is effective in evaluating the predictive validity of human judgment and in analyzing the validity and nonlinearity of the human decision-making process

    Principles of Human Learning

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    What are the general principles that drive human learning in different situations? I argue that much of human learning can be understood with just three principles. These are generalization, adaptation, and simplicity. To verify this conjecture, I introduce a modeling framework based on the same principles. This framework combines the idea of meta-learning -- also known as learning-to-learn -- with the minimum description length principle. The models that result from this framework capture many aspects of human learning across different domains, including decision-making, associative learning, function learning, multi-task learning, and reinforcement learning. In the context of decision-making, they explain why different heuristic decision-making strategies emerge and how appropriate strategies are selected. The same models furthermore capture order effects found in associative learning, function learning and multi-task learning. In the reinforcement learning context, they resemble individual differences between human exploration strategies and explain empirical data better than any other strategy under consideration. The proposed modeling framework -- together with its accompanying empirical evidence -- may therefore be viewed as a first step towards the identification of a minimal set of principles from which all human behavior derives

    Confirmation Bias Estimation from Electroencephalography with Machine Learning

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    Cognitive biases are known to plague human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, behavioral methods are employed to measure the level of bias in a decision. However, these measures can be hindered by a multitude of subjective factors and cannot be collected in real-time. This work investigates enhancing the current measures of estimating confirmation bias with additional behavior patterns and physiological variables to explore the viability of real-time bias detection. Confirmation bias in decisions is estimated by modeling the relationship between Electroencephalography (EEG) signals and behavioral data using machine learning methods

    A Fuzzy Inference Model for Predicting Irregular Human Behaviour During Stressful Missions

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    AbstractIn this paper a hybrid fuzzy inference and transfer function modeling is used to predict the irregular human behavior during hard and stressful tasks such as dangerous military missions. A set of affecting factors such as missioner's experience, fatigue, sunshine intensity, hungriness, thirstiness, psychological characteristics, affright, etc. may be taken to account. In this regard a dynamic system model is used to predict the convolution of the timed effects of different factors on irregular behavior of personnel during the mission. This approach of predicting irregular behavior or erroneous decision making of staff have serious usages in aerospace, military, social and similar projects where a wrong decision can have catastrophic outcome such as attempting to suicide by a pilot or killing civilians by a soldier in stressful situations. The effect of such behavior and decisions may even cause the failure of the overall project or mission. For example, killing civilians by a soldier can result to the overall failure of human terrain missions where the main objective is gaining trust between the local civilian population

    Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study

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    Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers.HighlightsOrganizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19.Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies.Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level

    A Demonstration of the PMF-Extraction Approach: Modeling The Effects of Sound on Crowd Behavior

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    The vast majority of psychology, sociology, and other social-science literature describing human behavior and performance does not reach the eyes of those of us working in the modeling and simulation community. Our recent work has been concerned with the extraction and implementation of Human Behavior Models (HBMs)/Performance Moderator Functions (PMFs) from this literature. This paper demonstrates how our methodology was applied to extract models of the effects of music and sound on both individuals and groups and to implement them in a simulated environment. PMFs describing how several classes of sound affect decision-making and performance were constructed based on well-established psychological models. These PMFs were implemented in a simulation of protesters and security guards outside a prison that demonstrates how the presence of chanting and music changes the response of protesters to police aggression. The extraction of PMFs from the literature, the synthesis of a coherent, cohesive model, and the implementation and results of the simulation are discussed
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