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1,032 results for “decision making”
Additional Material for Debiasing architectural decision-making: a workshop-based training approach
<p>This additional material was created to enable full replication of the experiment described in the paper:<br> "Klara Borowa, Maria Jarek, Gabriela Mystkowska, Weronika Paszko and Andrzej Zalewski: <strong>Debiasing architectural decision-making: a workshop-based training approach</strong>".<br> The paper was accepted for publication at ECSA 2022 (The 16th European Conference on Software Architecture).<br> <br> The folder contains the following files:<br> Coding-details.xlsx - the coding scheme, as well as coding results. Contains all code counts for each team participating in the experiment.<br> Experiment-meeting-plan.pdf - contains the plan for the 3-hour meeting during which the experiment was conducted, as well as a timetable of when each meeting took place and who organised it.<br> Debiasing-workshop-slides.pdf - slides used during the debiasing workshop.<br> Debiasing-workshop-plan.pdf - detailed instruction for workshop organisers, what should be said and done during the workshop.<br> Participants_questionnaire_and_results.xlsx - questionnaire questions used to obtain information about participants, as well as questionnaire results for all 44 participants.</p>
Factorial Survey: Decision Making for Extracorporeal life support (ECLS)
<p>Extracorporeal life support (ECLS) provides support to patients with cardiopulmonary failure refractory to conventional therapy. While ECLS is potentially life-saving, it is associated with severe complications; decision making to initiate ECLS must, therefore, carefully consider which patients ECLS potentially benefits despite its consequences.</p> <p>Data of a factorial survey among 420 physicians from 111 hospitals in Switzerland and Germany.</p>
Magnetoencephalographic spectral fingerprints differentiate evidence accumulation from saccadic motor preparation in perceptual decision-making
<p>This repository contains the dataset of the paper "Magnetoencephalographic spectral fingerprints differentiate evidence accumulation<br> from saccadic motor preparation in perceptual decision-making".</p> <p>The dataset contains the MEG power spectrum of 16 subjects in the source space of a perceptual decision making experiment.</p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (Decision-Making Model)
<p>When considering the complex challenges faced by cultural heritage organisations in collecting, preserving and sharing born digital and hybrid objects, it becomes clear that the process of defining solutions as a community of practice is in its early probing phase: characterised as tentative, exploratory, questioning, experimental. The workshops within this Preserving and sharing born-digital and hybrid objects from and across the National Collection project, which examined the case studies from multiple angles, yielded a richly discursive examination of the main considerations.</p> <p>This Decision Model represents an attempt to create a structured representation of those main considerations and the discourse from the workshops, to codify the main decision-making processes that an organisation may go through when assessing an acquisition of such an object, categorised into high level areas. It attempts to create a traversable system that could be used by collections professionals in their work - policy makers, managers, collections management or digital preservation practitioners, conservators.</p>
Data and code for "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making" (CHI 2017)
<p>This repository contains data and analysis code for the following paper:</p> <p>Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay. "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making", Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI 2018. DOI: 10.1145/3173574.3173718<br> </p>
Figure 2. Decision Making Model based on Emotions, Drives, and Different Memory Types
<p>The first model that is briefly introduced is an approach inspired by research findings in<br> neuro-psychology and psychoanalysis (see figure 2). The model considers the environment, the<br> body, and the brain/mind of an artificial being. The environment and the body are perceived via<br> sensors. (Re-)actions can be carried out via actuators. The model focuses on the decision-making<br> process of how to (re-)act according to situations currently perceived. In this decision-making<br> process, there are involved concepts like emotions, desires, drives, and different types of memory as<br> well as concepts like the Ego-Id-Superego model of Sigmund Freud. There are considered fast<br> reactions in form of reflexes and slower reactions that need reflection, thinking, and planning. For a<br> detailed model description, see [14, 17, 18].</p>
Exploring the generational influence on social media based tourist decision making in India
Open the record for dataset details and reuse information.
Figure 3 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 3. Total multi-attribute utility including all weighted objectives for responding to smallmouth bass in Cultus Lake, BC, across all management activities. Each utility was on a scale from 0-1, so the multi-attribute scale also ranges from 0–1.
Figure 1 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 1. Steps involved in structured decision-making. Steps can be re-evaluated at any point; responses should be re-evaluated once new data is gathered following implementation. Adapted from Liu and Cook (2016).
Figure 2 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 2. Calculated utility of avoiding fish species at-risk and not at-risk of extirpation in Cultus Lake, BC, across all combinations of smallmouth bass control. Utility is presented on a scale from 0–1.
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 19. AnyLogic Implementation of Decision-Making Architechture in an Virtual Autonomous Agent Environment
<p>Figure 19 shows a screenshot of the test implementation. The picture in the middle shows the modules and interfaces<br> of the decision-making architecture which were realized by so-called “active objects” and “ports”.<br> On the left side, the implemented modules are listed. In the right lower corner of the figure, the<br> agents and the virtual environment are displayed. The environment comprises different “objects”<br> (food sources, obstacles, predators, other agents, etc.). In order to survive, the agents have to access<br> food sources. However, the accessing of food sources bears difficulties and risks.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 17. Autonomous Decision-Making Architecture
<p>An overview of the decision-making architecture is presented in Figure 17. The architecture was<br> guided by two core concepts. The first core concept is that human intelligence bases on a<br> combination of low-level and high-level mechanisms. Low-level mechanisms are mainly<br> predefined. They are not in all situations completely accurate but have the advantage of being fast.<br> High-level mechanisms are not predefined and thus slower but more accurate. The second core<br> concept concerns the use of so-called emotions as mechanism for the evaluation of information</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 18. Different Modules involved in the Autonomous Decision-Making Process
<p>The basic functioning of this architecture is now described in the following step by step<br> using Figure 18a-f, where always the relevant modules of the model are highlighted for better<br> comprehension.</p>
Photomorphogenesis for Robot Self-assembly: Adaptivity, Collective Decision-making, and Self-repair
<p>Self-assembly in biological systems is an inspiration for engineered large-scale multi-modular systems with desirable characteristics, such as robustness, scalability, and adaptivity. Previous works have shown that simple mobile robots can be used to emulate and study self-assembly behaviors. However, many of these studies were restricted to rather static and inflexible aggregations in predefined shapes, and were limited in adaptivity compared to that observed in nature. We propose a photomorphogenesis approach for robots using our vascular morphogenesis model---a light-stimuli directed method for multi-robot self-assembly inspired by the tissue growth of trees. Robots in the role of `leaves' collect a virtual resource that is proportional to a real, sensed environmental feature. This resource is then shared throughout the whole robot aggregate and determines where it grows or shrinks as a reaction to the dynamic environment. In our approach the robots use supplemental bioinspired models to collectively select a seed robot to decide who starts to self-assemble (and where), or to assemble static aggregations. The robots then use our vascular morphogenesis model to aggregate in a directed way preferring bright areas, hence resembling natural phototropism (growth towards light). In this assembly, they are adaptive and able to react to a dynamic environment by collectively and autonomously rearranging the aggregate, discarding outdated parts and growing new ones. In representative experiments, the self-assembling robots collectively make rational decisions on where to grow. Cutting off parts of the aggregate triggers a self-organizing repair process in the robots, and the parts regrow. All these capabilities of adaptivity, collective decision-making, and self-repair in our robot self-assembly originate directly from self-organized behavior of the vascular morphogenesis model. Our approach opens up opportunities for self-assembly with reconfiguration on short time-scales with high adaptivity of dynamic forms and structures.</p>
Data Template for exchanging information between Decision Making System & Simulator
<p>Data Template for exchanging information between Decision Making System & Simulator. Under the prism of optimizing design/production and checking circularity of parts.</p>
Fig. 2 in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 2. Decision time (s) spent by the A. subterraneus target worker according to number of trips (n) made by the respective target worker and the concentration of pheromone manipulated on the branch that does not lead to the food, estimated by total flow of foragers.
Fig. 1. Y in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 1. Y-trail system with branches of equal length (225 mm). Branches arranged at an angle of 60◦ connected to a bifurcation. Decision lines (LD) established at fixed points 140 mm far from the bifurcation center on the right and left branches, and 25 mm from the base branch to calculate the A. subterraneus workers' frequency of passage when half of their bodies had crossed each LD.
Data from: Distinct developmental trajectories for risky and impulsive decision-making in chimpanzees
<p>Human adolescence is characterized by a suite of changes in decision-making and emotional regulation that promote risky and impulsive behavior. Accumulating evidence suggests that behavioral and physiological shifts seen in human adolescence are shared by some primates, yet it is unclear if the same cognitive mechanisms are recruited. We examined developmental changes in risky choice, inter-temporal choice, and emotional responses to decision outcomes in chimpanzees, our closest-living relatives. We found that adolescent chimpanzees were more risk-seeking than adults, as in humans. However, chimpanzees showed no developmental change in inter-temporal choice, unlike humans, although younger chimpanzees did exhibit elevated emotional reactivity to waiting compared to adults. Comparisons of cortisol and testosterone indicated robust age-related variation in these biomarkers, and patterns of individual differences in choices, emotional reactivity, and hormones also supported a developmental dissociation between risk and choice impulsivity. These results show that some but not all core features of human adolescent decision-making are shared with chimpanzees.</p>
Intraparietal stimulation disrupts negative distractor effects in human multi-alternative decision-making
<p>There has been debate about whether addition of an irrelevant distractor option to an otherwise binary decision influences which of the two choices is taken. We show that disparate views on this question are reconciled if distractors exert two opposing but not mutually exclusive effects. Each effect predominates in a different part of decision space: 1) a positive distractor effect predicts high-value distractors improve decision-making; 2) a negative distractor effect, of the type associated with divisive normalisation models, entails decreased accuracy with increased distractor values. Here, we demonstrate both distractor effects coexist in human decision making but in different parts of a decision space defined by the choice values. We show disruption of the medial intraparietal area (MIP) by transcranial magnetic stimulation (TMS) increases positive distractor effects at the expense of negative distractor effects. Furthermore, individuals with larger MIP volumes are also less susceptible to the disruption induced by TMS. These findings also demonstrate a causal link between MIP and the impact of distractors on decision-making via divisive normalisation.</p>
A habitat connectivity reality check for fish physical habitat model results and decision making for river restoration
<ol> <li>Fish physical habitat models are a tool for guiding restoration efforts in lotic ecosystems but often they overestimate restoration outcomes because currently they do not incorporate habitat connectivity. This persistent issue can, in extreme cases, result in little or no improvement to fish populations after the restoration, wasting valuable conservation resources.</li> <li>We present a case study where practitioners applied a fish habitat model for multiple life history stages of gravel spawning fishes to a 52 kilometer stretch of the Iller River but did so at a microscale implementation (every 200 meters). This approach provided an opportunity to assess the connectivity of gravel spawning fishes to find suitable habitats for all life history stages and seasonal movements.</li> <li>We used the assessed habitat estimates (availability of distinct habitat types within the 200 m reaches) to calculate the minimum distance a fish would need to go as it hypothetically “grew up” from egg to full spawning adult. We call this technique a reality check as it results in a decisive understanding of which areas were ultimately necessary to fulfill the life cycle of gravel spawning fishes, which standard assessments do not show.</li> <li>Our results show that complete connectivity still require long movement distances for vulnerable life stages to find suitable habitat. This contradicts standard practice, as restoration schemes and decision making often assume that connectivity inherently leads to more fish production without added habitat restoration.</li> <li>We recommend practitioners should perform this habitat connectivity approach when assessments implement fish habitat suitability models at similar scales. As a result, decision makers can evaluate proposed restoration sites and measures more realistically.</li> </ol>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.