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1,032 results for “Decision making”
Additional Material for Debiasing Architectural Decision-Making: Teaching Software Practitioners
<p>Additional material for the paper subsmission "Debiasing Architectural Decision-Making: Teaching Software Practitioners".<br><br>Coding_info.xlsx - Code description as well as code measurements for all coded values.<br>Experiment-plan.docx - Instructions used by authors which performed the experiment.<br>Debiasing-workshop-plan.docx - Instruction for the teacher conducting the workshop, specifying what should be done while showcasing particular presentation slides.<br>Debiasing-workshop-slides.pptx - Slides used during the debiasing workshop.<br>Questionaire_and_test.xlsx - Questionnaire used to gather information about the participants + Simple test used as the last step of the workshop.</p>
Replication Package: Temperature and decision-making
<p>Escobar Carias, M., Johnston, D., Knott, R., Sweeney, R. 2024. Replication package: Temperature and Decision-Making</p>
Learn from Human-driving Accidents to Attack Autonomous Driving_Practical Traffic Flow Attacks on Decision-making
<p>We provide some demo videos of attack patterns</p>
Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain
<p>The dataset and code provided correspond to Manuscript Number: ISCIENCE-D-23-09408R1, titled "Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain."</p> <p>This dataset comprises delta and alpha filtered data from a total of 19 participants. Each .mat file contains 800 cells, representing the number of trials. Each cell contains a matrix of size 128 x 1750. Here, 128 denotes the number of EEG channels, and 1750 represents the number of time points. Time points are sampled from -1 s to 2.5 s relative to the onset of the first stimulus, with a 2ms interval.<br><br></p> <p>The participants' behavioral data are stored in separate .mat files for each run (or block). Upon loading these files, a struct named "data" is loaded, containing six variables, each representing a 1 x 200 vector:</p> <ol> <li> <p>Cat1: Represents the category of the first stimulus. It takes a value of 1 for animate words and 2 for inanimate words.</p> </li> <li> <p>Cat2: Denotes the category of the second stimulus. It is assigned 1 for animate words and 2 for inanimate words.</p> </li> <li> <p>Same: Indicates the correct response for each trial. A value of 1 signifies a match between the categories of Stimulus 1 and Stimulus 2, while 2 indicates a non-match.</p> </li> <li> <p>Resp: Records the participant's decision for each trial. A value of 1 denotes a match between the categories of Stimulus 1 and Stimulus 2, whereas 2 represents a non-match.</p> </li> <li> <p>RT: Represents the participant's response time in seconds for each trial.</p> </li> <li> <p>Corr: Indicates the correctness of the participant's response. A value of 1 signifies a correct response, while 0 indicates an incorrect response.</p> </li> </ol> <p> </p>
Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks
<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>
DECISION MAKING THEORIES IN BOTH HEALTH RELATED BEHAVIORS AND ENVIRONMENTAL BEHAVIORS
<p>DECISION MAKING THEORIES IN BOTH HEALTH RELATED BEHAVIORS AND ENVIRONMENTAL BEHAVIORS</p>
The Decision-Making Process Behind Library Adoption
<p>This is the Factors/Process Book for the research paper "The Decision-Making Process Behind Library Adoption", submitted to: The 35th IEEE International Conference on Software Maintenance and Evolution (ICSME).</p>
Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins
<p>Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making.<br> Despite intensive research on models of<br> collective decision-making, the implementation in multi-robot systems is still challenging.<br> Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the task difficulty.<br> Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence.<br> We investigate a dynamic environment that requires constant collective monitoring of option qualities.<br> Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly.<br> This is only possible by preventing lock-ins, a global consensus state of no return.<br> In addition, we introduce a task of increased difficulty as the robots must locate themselves to assess the quality of an option.<br> Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame.<br> We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.</p>
Importance of Data Science in Decision Making
<p>Data Science is a rapidly growing field of study that not only focuses on understanding the data but also, on predicting the future outcomes by the method of data gathering and data analysis. Usingstatistical methods to find patterns in data, we can turn an outcome in our favor by adjusting our actions and responses accordingly. In this paper, we investigate how decision-making process is leveraged with the help of data science in various fields and the ethical issues involved for the same. We also discuss how data science can foster better controlling, administering, regulating, directing and executing aspects of decision-making. This paper would ultimately serve as a reference material for individuals and organizations willing to understand the importance of data science in decision making process.</p> <p> </p>
Distinct neural population code and causal roles of primate caudate nucleus in multimodal decision-making
<p>To replicate the results in the paper, you should:</p> <ol> <li>Download and then gunzip the dataset.</li> <li>Download the analysis code at https://github.com/ZacZeng/CN-causally-contributes-to-MSDM.</li> <li>Run code as the README in Git says to get figures in the preprint paper (<a href="https://www.biorxiv.org/content/10.1101/2024.09.03.610907v1">Distinct neural manifolds and critical roles of primate caudate nucleus in multimodal decision-making | bioRxiv</a>).</li> </ol>
Social life cycle sustainability assessment of dried tomato products based on material and process selection through multi-criteria decision making
<h3>BACKGROUND</h3> <p>Tomatoes are a significant product of the Mediterranean region and a crucial component of the Mediterranean diet. The formulation of dried tomato products enriched with proteins and bioactive compounds could be a strategic approach to promote adherence to the Mediterranean diet. Six different novel tomato products were analyzed using different protein enrichment sources (pea proteins and leaf proteins) and drying technologies (hot-air dryer, microwave vacuum dryer, and conventional dryer). The novelty of this approach lies in combining product-specific criteria with global societal factors across their life cycles. Using 21 criteria and an analytic hierarchy process (AHP) survey of experts, the social sustainability score for each product was determined through a multi-criteria assessment.</p> <h3>RESULTS</h3> <p>The tomato product's life cycles have minimal regional impacts on unemployment, access to drinking water, sanitation, or excessive working hours. However, they affect discrimination, migrant labor, children's education, and access to hospital beds significantly. The study identified nutritional quality as the top criterion, with the most sustainable design being a tomato bar enriched with pea protein and processed using microwave vacuum drying.</p> <h3>CONCLUSION</h3> <p>The study revealed that integrating sensory and nutrient compounds into social sustainability assessments improves food sustainability and provides a practical roadmap for social life cycle assessments of food products. It emphasized the importance of considering global social issues when reformulating Mediterranean products to ensure long-term adherence to the Mediterranean diet. Incorporating social factors into sustainability scores can also enhance the effectiveness of product information for conscious customers. © 2024 The Author(s). <em>Journal of the Science of Food and Agriculture</em> published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.</p>
Early social deprivation shapes neuronal programming of the social decision-making network in a cooperatively breeding fish
The early social environment an animal experiences may have pervasive effects on its behaviour. The Social Decision-Making network (SDMN), consisting of interconnected brain nuclei from the forebrain and midbrain, is involved in the regulation of behaviours during social interactions. In species with advanced sociality such as cooperative breeders, offspring are exposed to a large number and a great diversity of social interactions every day of their early life, which may have life-long consequences on the development of several neurophysiological systems within the SDMN, although these effects are largely unknown. We studied these life-long effects in a cooperatively breeding fish, Neolamprologus pulcher, focusing on the expression in the SDMN of genes involved in the monoaminergic and stress response systems. N. pulcher fry were raised until an age of two months either with their parents, subordinate helpers and same clutch siblings (+F), or with same clutch siblings only (-F). Analysis of the expression of glucocorticoid receptor (GR), mineralcorticoid receptors (MR), corticotropin releasing factor (CRF), dopamine receptors 1 and 2, serotonin transporter (SERT) and DNA methyltransferase I (DNMT1) genes showed that early social experiences altered the neurogenomic state of the preoptic area (POA) of the hypothalamus. The dopamine receptor 1 gene was up-regulated in the POA of -F fish, compared to +F fish. -F fish also showed up-regulation of GR1 expression in the dorsal medial telencephalon (homologous to the basolateral amygdala). Our results suggest that early social environment has life-long effects on the development of several neurophysiological systems within the SDMN. --
Data from: Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland
1. Floodplain vegetation states (communities) exhibit spatiotemporal dynamics in vegetation structure and composition, which reflect unique hydrological and connectivity patterns. Shifts in inundation regimes can drive succession and establish new stable states, determined by the magnitude and duration of the hydrological perturbation. 2. We aimed to develop a modelling approach that is able to capture ecosystem dynamics, identify and quantify the main drivers of change, and provide a tool for conservation decision-making. We developed state and transition models for floodplain vegetation states based on surveys in 1991 and 2008 in the Macquarie Marshes (Australia), a Ramsar wetland of international importance. We used a Bayesian logistic regression approach to model state and transitions between vegetation states and investigated how flood frequency, distance to stream and fire frequency were associated with vegetation dynamics during this period. 3. During 1991–2008, significant transitions have occurred towards drier states. Semi-permanent wetland vegetation had the lowest persistence probability (ppsis = 0·456) and a significant threshold response of transitioning to terrestrial vegetation (ptran = 0·505). Transition to drier states was driven by lower inundation probabilities followed by increased fire probability, and distance to nearest stream. 4. Using developed models, we predicted persistence probabilities of vegetation states under an unregulated (i.e. no dams or diversions) and regulated water availability system. Under a regulated system, semi-permanent wetland vegetation had an average persistence of ppsis = 0. 67 and 0·08 in the northern and southern sections of the nature reserve, respectively. Under an unregulated system, the predicted persistence of semi-permanent wetland vegetation was considerably higher: ppsis = 0·87 and 0·38, respectively. 5. Synthesis and applications. Developing quantitative models of state transitions significantly improved our understanding of ecosystem dynamics, identifying sensitive indicators for monitoring and thus supporting conservation decision-making. This helps managers understand potential trajectories of change in ecosystems in response to management options. For example, increasing environmental flows in the Macquarie Marshes is predicted to shift the community towards more of a wetland than the terrestrial state, resulting from river regulation. State and transition models identified how key ecological assets respond to drivers of change, particularly where these can be managed. This is critical for ensuring that all ecosystem components are managed and that these do not shift into undesirable states.
Data from: Hierarchical decision-making balances current and future reproductive success
Parental decisions in animals are often context-dependent and shaped by fitness trade-offs between parents and offspring. For example, the selection of breeding habitats can considerably impact the fitness of both offspring and parents, and therefore parents should carefully weigh the costs and benefits of available options for their current and future reproductive success. Here we show that resource-use preferences are shaped by a trade-off between parental effort and offspring safety in a tadpole-transporting frog. In a large-scale in-situ experiment, we investigated decision-strategies across an entire population of poison frogs that distribute their tadpoles across multiple water bodies. Pool use followed a dynamic and sequential selection process and transportation became more efficient over time. Our results point to a complex suite of environmental variables that are considered during offspring deposition, which necessitates a highly dynamic and flexible decision-making process in tadpole-transporting frogs.
Behavioral and electrocortical effects of transcranial alternating current stimulation during advice-guided decision-making - raw data
<p>Raw EEG and behavioral data of the study "Behavioral and electrocortical effects of transcranial alternating current stimulation during advice-guided decision-making"</p>
Correlated decision making across multiple phases of olfactory guided search in Drosophila
<p>All motile organisms must search for food, often requiring the exploration of heterogeneous environments across a wide range of spatial scales. Recent field and laboratory experiments with the fruit fly, <i>Drosophila</i>, have revealed that they employ different strategies across these regimes, including kilometer scale straight-path flights between resource clusters, zig-zagging trajectories to follow odor plumes, and local search on foot after landing. However, little is known about the extent to which experiences in one regime might influence decisions in another. To determine how a flies' odor plume tracking during flight is related to their behavior after landing, I tracked the behavior of individually labelled fruit flies as they explored an array of three odor emitting, but food-barren, objects. The distance flies travelled on the objects in search of food was correlated with the time elapsed between their visits, suggesting that their in-flight plume tracking and on-foot local search behaviors are interconnected through a lossy memory-like process.</p>
Spatially explicit models for decision-making in animal conservation and restoration
<p>Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79 %), towards the species and population level (80 %) and towards conservation (rather than restoration) applications (71 %). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10 % of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration, and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by (1) developing a toolbox with multiple, easier-to-use methods, (2) improving calibration and validation of dynamic modelling approaches, and (3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by (4) combining multiple modelling approaches to assess uncertainty, and (5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.</p>
Factors Affecting Managerial Decision-Making
<p>Coded segments - factors affecting managerial decision-making (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).</p>
Big Data Analytics: A key driver for decision making and business success
<p>This repository contains all the data related to the research conducted on the use of Big Data Analytics and the competitiveness of companies in the retail sector. </p>
Components related to ethical decision making in medical science students: A structural equation model
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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.