Relevant bibliographies by topics / Computational making

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computational making'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Computational making"

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Knight, Terry, and Theodora Vardouli. "Computational making." Design Studies 41 (November 2015): 1–7. http://dx.doi.org/10.1016/j.destud.2015.09.003.

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Mujica-Parodi, Lilianne R., and Helmut H. Strey. "Making Sense of Computational Psychiatry." International Journal of Neuropsychopharmacology 23, no. 5 (March 27, 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.

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Abstract In psychiatry we often speak of constructing “models.” Here we try to make sense of what such a claim might mean, starting with the most fundamental question: “What is (and isn’t) a model?” We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building—suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.
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Li, Tianrui, Pawan Lingras, Yuefeng Li, and Joseph Herbert. "Computational Intelligence in Decision Making." International Journal of Computational Intelligence Systems 4, no. 1 (February 2011): i—iv. http://dx.doi.org/10.1080/18756891.2011.9727758.

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Espinilla, Macarena, Javier Montero, and J. Tinguaro Rodríguez. "Computational intelligence in decision making." International Journal of Computational Intelligence Systems 7, sup1 (October 11, 2013): 1–5. http://dx.doi.org/10.1080/18756891.2014.853925.

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Bankes, Steven, Robert Lempert, and Steven Popper. "Making Computational Social Science Effective." Social Science Computer Review 20, no. 4 (November 2002): 377–88. http://dx.doi.org/10.1177/089443902237317.

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Gottwald, Sebastian, and Daniel Braun. "Bounded Rational Decision-Making from Elementary Computations That Reduce Uncertainty." Entropy 21, no. 4 (April 6, 2019): 375. http://dx.doi.org/10.3390/e21040375.

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In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited by the amount of available computational resources. Here, we introduce the notion of elementary computation based on a fundamental principle for probability transfers that reduce uncertainty. Elementary computations can be considered as the inverse of Pigou–Dalton transfers applied to probability distributions, closely related to the concepts of majorization, T-transforms, and generalized entropies that induce a preorder on the space of probability distributions. Consequently, we can define resource cost functions that are order-preserving and therefore monotonic with respect to the uncertainty reduction. This leads to a comprehensive notion of decision-making processes with limited resources. Along the way, we prove several new results on majorization theory, as well as on entropy and divergence measures.
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French, Robert M. "The computational modeling of analogy-making." Trends in Cognitive Sciences 6, no. 5 (May 2002): 200–205. http://dx.doi.org/10.1016/s1364-6613(02)01882-x.

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Bossaerts, Peter, and Carsten Murawski. "Computational Complexity and Human Decision-Making." Trends in Cognitive Sciences 21, no. 12 (December 2017): 917–29. http://dx.doi.org/10.1016/j.tics.2017.09.005.

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Giles, Jim. "Computational social science: Making the links." Nature 488, no. 7412 (August 2012): 448–50. http://dx.doi.org/10.1038/488448a.

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Pal, Nikhil R., and Rajani K. Mudi. "Computational intelligence for decision-making systems." International Journal of Intelligent Systems 18, no. 5 (April 22, 2003): 483–86. http://dx.doi.org/10.1002/int.10098.

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Dissertations / Theses on the topic "Computational making"

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Sanders, Tom. "Sensory computation and decision making in C. elegans : a computational approach." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15442/.

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In Caenorhabditis elegans (C. elegans) and in neuroscience generally, a hierarchical view of nervous systems prevails. Roughly speaking, sensory neurons encode the external environment, interneurons encode internal state and decisions, and motor neurons encode muscle activation. Here, using an integrated approach to model sensory computation and decision making in C. elegans, I show a striking phenomenon. Via the simplest modulation possible, sensitization and desensitization, sensory neurons in C. elegans can also encode the animal’s internal state. In this thesis, I present a modeling framework, and use it to implement two detailed models of sensory adaptation and decision making. In the first model I consider a decision making task, in which worms need to cross a lethal barrier in order to reach an attractant on the other side. My model captures the experimental results, and predicts a minimal set of requirements. This model‘s mechanism is reminiscent of similar top-down attention modulation motifs in mammalian cortex. In the second model, I consider a form of plasticity in which animals alternate their perception of a signal from attractive to repulsive. I show how the model encodes high and low-level behavioral states, balancing attraction and aversion, exploration and exploitation, pushing the ‘decision making’ into the sensory layer. Furthermore, this model predicts that specific sensory neurons may have the capacity to selectively control distinct motor programs. To accomplish these results, the modeling framework was designed to simulate a full sensory motor pathway and an in silico simulation arena, allowing it to reproduce experimental findings from multiple assays. Hopefully, this allows the model to be used by the C. elegans community and to be extended, bringing us closer to the larger aim of understanding distributed computation and the integrated neural control of behavior in a whole animal.
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Heller, Collin M. "A computational model of engineering decision making." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50272.

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The research objective of this thesis is to formulate and demonstrate a computational framework for modeling the design decisions of engineers. This framework is intended to be descriptive in nature as opposed to prescriptive or normative; the output of the model represents a plausible result of a designer's decision making process. The framework decomposes the decision into three elements: the problem statement, the designer's beliefs about the alternatives, and the designer's preferences. Multi-attribute utility theory is used to capture designer preferences for multiple objectives under uncertainty. Machine-learning techniques are used to store the designer's knowledge and to make Bayesian inferences regarding the attributes of alternatives. These models are integrated into the framework of a Markov decision process to simulate multiple sequential decisions. The overall framework enables the designer's decision problem to be transformed into an optimization problem statement; the simulated designer selects the alternative with the maximum expected utility. Although utility theory is typically viewed as a normative decision framework, the perspective in this research is that the approach can be used in a descriptive context for modeling rational and non-time critical decisions by engineering designers. This approach is intended to enable the formalisms of utility theory to be used to design human subjects experiments involving engineers in design organizations based on pairwise lotteries and other methods for preference elicitation. The results of these experiments would substantiate the selection of parameters in the model to enable it to be used to diagnose potential problems in engineering design projects. The purpose of the decision-making framework is to enable the development of a design process simulation of an organization involved in the development of a large-scale complex engineered system such as an aircraft or spacecraft. The decision model will allow researchers to determine the broader effects of individual engineering decisions on the aggregate dynamics of the design process and the resulting performance of the designed artifact itself. To illustrate the model's applicability in this context, the framework is demonstrated on three example problems: a one-dimensional decision problem, a multidimensional turbojet design problem, and a variable fidelity analysis problem. Individual utility functions are developed for designers in a requirements-driven design problem and then combined into a multi-attribute utility function. Gaussian process models are used to represent the designer's beliefs about the alternatives, and a custom covariance function is formulated to more accurately represent a designer's uncertainty in beliefs about the design attributes.
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Findling, Charles. "Computational learning noise in human decision-making." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS490.

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Dans des environnements incertains et changeants, prendre des décisions nécessite l’analyse et la pondération des informations passées et présentes. Pour modéliser le comportement humain dans de tels environnements, des approches computationnelles de l’apprentissage ont été développées basées sur l’apprentissage par renforcement ou de l’inférence bayésienne. Afin de mieux prendre en compte la variabilité comportementale, ces approches supposent un bruit dans la sélection de l’action. Dans la première partie de mon travail, j’argumente que le bruit dans la sélection de l’action est insuffisant pour expliquer la variabilité comportementale et je montre la présence de bruit d'apprentissage reflétant des imprécisions de calcul. À cette fin, j’introduis du bruit dans l'algorithme d'apprentissage en donnant la possibilité d'écarts aléatoires de la règle de mise à jour sans bruit. L'ajout de ce bruit permet de mieux expliquer les performances comportementales humaines (Findling C., Skvortsova V., et al., 2018a, en préparation). Dans la deuxième partie de mon travail, je montre que ce bruit possède des propriétés adaptatives vertueuses dans les processus d’apprentissage suscités dans des environnements changeants (volatiles). En utilisant le cadre de modélisation Bayésien, je démontre qu’un modèle d’apprentissage simple, faisant l’hypothèse de contingences externes stables, mais avec du bruit dans l’apprentissage, est aussi performant que le modèle Bayésien optimal qui infèrent la volatilité de l’environnement. En outre, j’établis que ce modèle de bruit explique mieux le comportement humain dans des environnements changeants (Findling C. et al., 2018b, en préparation)
In uncertain and changing environments, making sequential decisions requires analyzing and weighting the past and present information. To model human behavior in such environments, computational approaches to learning have been developed based on reinforcement learning or Bayesian inference. To further account for behavioral variability, these computational approaches assume action selection noise, usually modeled with a softmax function. In the first part of my work, I argue that action selection noise is insufficient to explain behavioral variability and show the presence of learning noise reflecting computational imprecisions. To this end, I introduced computational noise in the standard reinforcement learning algorithm through random deviations in the noise-free update rule. Adding this noise led to a better account of human behavioral performances in reward-guided tasks (Findling C., Skvortsova V., et al., 2018a, in prep). The presence of learning noise led me to investigate whether this noise could have a functional role. In the second part of my work, I argue that this learning noise actually has virtuous adaptive properties in learning processes elicited in changing (volatile) environments. Using the Bayesian modeling framework, I demonstrate that a simple learning model assuming stable external contingencies with learning noise performs virtually as well as the optimal Bayesian adaptive process based on inferring the volatility of the environment. Furthermore, I establish that this learning noise model better explains human behavioral performances in changing environments (Findling C. at al., 2018b, in prep)
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D'Ambrosio, Catherine P. "Computational representation of bedside nursing decision-making processes /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/7266.

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Yuan, Fan. "Modeling and computational strategies for medical decision making." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54857.

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In this dissertation, we investigate three topics: predictive models for disease diagnosis and patient behavior, optimization for cancer treatment planning, and public health decision making for infectious disease prevention. In the first topic, we propose a multi-stage classification framework that incorporates Particle Swarm Optimization (PSO) for feature selection and discriminant analysis via mixed integer programming (DAMIP) for classification. By utilizing the reserved judgment region, it allows the classifier to delay making decisions on ‘difficult-to-classify’ observations and develop new classification rules in later stage. We apply the framework to four real-life medical problems: 1) Patient readmissions: identifies the patients in emergency department who return within 72 hours using patient’s demographic information, complaints, diagnosis, tests, and hospital real-time utility. 2) Flu vaccine responder: predicts high/low responders of flu vaccine on subjects in 5 years using gene signatures. 3) Knee reinjection: predicts whether a patient needs to take a second surgery within 3 years of his/her first knee injection and tackles with missing data. 4) Alzheimer’s disease: distinguishes subjects in normal, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) groups using neuropsychological tests. In the second topic, we first investigate multi-objective optimization approaches to determine the optimal dose configuration and radiation seed locations in brachytherapy treatment planning. Tumor dose escalation and dose-volume constraints on critical organs are incorporated to kill the tumor while preserving the functionality of organs. Based on the optimization framework, we propose a non-linear optimization model that optimizes the tumor control probability (TCP). The model is solved by a solution strategy that incorporates piecewise linear approximation and local search. In the third topic, we study optimal strategies for public health emergencies under limited resources. First we investigate the vaccination strategies against a pandemic flu to find the optimal strategy when limited vaccines are available by constructing a mathematical model for the course of the 2009 H1N1 pandemic flu and the process of the vaccination. Second, we analyze the cost-effectiveness of emergency response strategies again a large-scale anthrax attack to protect the entire regional population.
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Bacharidou, Maroula. "Active prototyping : a computational framework for designing while making." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118501.

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Thesis: S.M. in Architecture Studies, Massachusetts Institute of Technology, Department of Architecture, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 89-92).
In the wake of an increased accessibility of rapid prototyping tools in design education and practice, designers still face a series of challenges related to their use, one of them being the way in which they use these machines to actively explore and enhance their ideas. At the same time, the concepts of continuous interaction with computational fabrication tools and design exploration through physical prototyping are gaining impetus in computational design research and human-computer interaction. Stimulated by these inquiries, the hypothesis of this thesis is that physical prototyping tools can be used as tools for active design exploration and evaluation. Towards this goal, I introduce Active Prototyping, a framework for enhancing physical engagement with design objects by aiding the designer to project the impact of tools on design outcomes and explore a range of possible design solutions while making. Active Prototyping integrates the following operations: (a) physical control of a fabrication device, (b) recording of designer actions while using the device (c) visual exploration of possible design solutions while developing a physical prototype and (d) machine feedback on the prototyping of selected design solutions. To demonstrate the Active Prototyping framework, I develop Fabcorder, a technical apparatus that implements a number of the above operations. Through application examples, I demonstrate how Active Prototyping can render physical prototyping processes more exploratory and digital fabrication processes more intuitive. I conclude by proposing action recording and generative methods as two novel additions to existing frameworks for computational design and fabrication that can bring future tool-making strategies into a more creative context.
by Maroula Bacharidou.
S.M. in Architecture Studies
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Mancinelli, Federico. "Models of decision making and behavioural control in computational psychiatry." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10046131/.

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Computational psychiatry, which is a recent area of research, involves the use of statistical and computational methods to investigate human psychopathology and the brain. Here, I present models of human behaviour in tasks where subjects expressed preferences between options characterised by orthogonal amounts and odds of monetary rewards and different degrees and demands on behavioural control. We first model an experiment which examined subjects’ propensity to gamble for different odds and amounts of reward in the face of Acute Tryptophan Depletion (ATD). Our computational approach supported existing statistical evidence that specific serotonin receptor types might mediate the effects of ATD on the sensitivity of subjects to rewards. We also showed that subjects’ choices were influenced by the weighted sum of the probability and the amount, rather than by their interaction, as required by conventional prospect theory. In the remainder of the thesis, we present the design, realisation, and analysis of a novel task for humans in which choice was tied to perceived behavioural control (as measured by Hannah Levenson’s multi-dimensional Locus of Control scales). The genesis of this task was an attempt to capture aspects of controllability relevant to psychopathology in depression. In the task, the winning amounts were made explicit to subjects, but the odds, and the extent to which these depended on their decisions and efforts, were learned. We used computational modelling to interpret various measures of choice, choice evolution, and indecision in the task.
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Cartwright, Daniel R. "Digital decision-making : using computational argumentation to support democratic processes." Thesis, University of Liverpool, 2011. http://livrepository.liverpool.ac.uk/2993/.

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One of the key questions facing governments around the world is that of how to increase and maintain the engagement of citizens in democratic processes. Recent thought, both within academia and government itself, has turned to the use of modern computational technology to provide citizens with access to democratic processes. Access to computer and Internet technology by the general public has vastly increased over the past decade, and this wide access is one of a number of motivations behind research into the provision of democratic tasks and processes online. The particular democratic process that forms the focus of this thesis is that of online opinion gathering in order to aid government decision making. The provision of mechanisms to gather and analyse public opinion is important to any government which claims to promote a fair and equal democracy, as decisions should be made in consideration of the views and opinions of the citizens of such a democracy. The work that comprises this thesis is motivated by existing research into harvesting opinion through a variety of online methods. The software tools available largely fall into one of two categories: Those which are not based on formal structure, and those which are based on an underlying formal model of argument. The work presented in this thesis aims to overcome the shortfalls inherent to both of these categories of tool in order to realise a software suite to support both the process of opinion gathering, and analysis of the resulting data. This is achieved through the implementation of computational models of argument from the research area of argumentation, with special consideration as to how these models can be used in implemented systems in a manner that allows laypersons to interact with them effectively. A particular model of argument which supports the process of practical reasoning is implemented in a web-based computer system, thus allowing for the collection of structured arguments which are later analysed according to formal models of argument visualisation and evaluation. The theories underlying the system are extended in order to allow for added expressivity, thus providing a mechanism for more life-like argument within a system which supports comprehensive computational analysis. Ultimately, the contributions of this thesis are a functional system to support an important part of the democratic process, and an investigation into how the underlying theories can be built upon and extended in order to promote expressive argumentation.
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Huang, He. "Decision-making and motor control| computational models of human sensorimotor processing." Thesis, University of California, San Diego, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3673994.

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To survive and effectively interact with the environment, human sensorimotor control system collects sensory information and acts based on the state of the world. Human behavior can be considered and studied at discrete time or continuous time. For the former, human makes discrete categorical decisions when presented with different alternative choices (e.g. choose Left or Right at an intersection). For the later, humans plan and execute continuous movements when instructed to perform a motor task (e.g. drive to a destination). In this dissertation we examine human behavior at both levels. Part I focuses on understanding decision-making at discrete time using Bayesian Models. We start by investigating the influence of environmental statistics in a saccadic visual search ask, in which we use a dynamic belief model to describe subjects' learning process of the environment statistics cross-trials. Then we look at a special effect of decision- making, the sequential effect, and apply the dynamic belief model to explain subjects' cross-trial learning and a drift diffusion model to explain their within-trial decision- making process. Part II focuses on examining motor control at continuous time using Optimal Control Theory. We start by investigating the objective functions in oculomotor control (saccadic eye movement, smooth pursuit, and applications in eye-hand coordination) with an infomax model. Then we apply inverse optimal control model to study impaired motor behavior in depressed individuals. In particular, we present a framework based on optimal control theory, which can distinguish the effects of sensorimotor speed, goal setting and motivational factors in goal-directed motor tasks. Finally, we propose to use facial expression as another measure of the emotional state in depressed individuals, which can be used to provide further understanding of the behavior and model parameters estimated from the proposed inverse framework.

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Keel, Paul E. (Paul Erich). "Knowledge trading : computational support for individual and collaborative sense-making activities." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28807.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2004.
Includes bibliographical references (leaves 127-132).
(cont.) outlined. 2. Demonstration that computer systems can use the discovered relations among data items to help users search for relevant information, prioritize the data exchange between collaborating users, and visualize data in various ways. This investigation looks at how a human's increasing knowledge about a problem space is influential in the subsequent accumulation of new data. The findings are converted into computational equivalents that can support individual and collaborative sense-making processes.
This dissertation explores the potential for computational systems to analyze and support individual and collaborative human sense-making activities. In this context human sense-making refers to the act of mentally and physically relating pieces of information so as to develop an understanding of a particular situation. Human sense-making activities such as brainstorming, decision-making, and problem solving sessions often produce a lot of data such as notes, sketches, and documents. The participants of sense-making activities usually develop a good understanding of the relations among these individual data items. These relations define the context. Because the relations remain within the minds of the participants they are neither accessible to outsiders and computational systems nor can they be recorded or backed up. This dissertation outlines a first set of computational mechanisms that construct relations from the spatial arrangement, use, and storage of data items. A second set of computational mechanisms takes advantage of these relations by helping users to keep track of, search for, exchange, arrange, and visualize data items. The computational mechanisms are both adaptive and evocative, meaning that the computational mechanisms dynamically adapt to users and changing circumstances while also trying to influence the human sense-making process. Contributions: 1. Demonstration that computer systems can discover probable relations among data items from their spatial arrangement and use by users. This work identifies and analyzes various human mental processes involved in the determination of possible relations among data items such as documents on a work desk or files in a computer system. A computational equivalent is proposed for every mental process
by Paul Erich Keel.
Ph.D.
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Books on the topic "Computational making"

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Madureira, Ana, Cecilia Reis, and Viriato Marques, eds. Computational Intelligence and Decision Making. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-4722-7.

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Doumpos, Michael, Constantin Zopounidis, and Panos M. Pardalos, eds. Financial Decision Making Using Computational Intelligence. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3773-4.

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Constantin, Zopounidis, Pardalos, P. M. (Panos M.), 1954-, and SpringerLink (Online service), eds. Financial Decision Making Using Computational Intelligence. Boston, MA: Springer US, 2012.

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Madureira, Ana. Computational Intelligence and Decision Making: Trends and Applications. Dordrecht: Springer Netherlands, 2013.

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Lytvynenko, Volodymyr, Sergii Babichev, Waldemar Wójcik, Olena Vynokurova, Svetlana Vyshemyrskaya, and Svetlana Radetskaya, eds. Lecture Notes in Computational Intelligence and Decision Making. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-26474-1.

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Babichev, Sergii, Volodymyr Lytvynenko, Waldemar Wójcik, and Svetlana Vyshemyrskaya, eds. Lecture Notes in Computational Intelligence and Decision Making. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54215-3.

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Kontoghiorghes, Erricos John, Berc Rustem, and Stavros Siokos, eds. Computational Methods in Decision-Making, Economics and Finance. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3613-7.

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Babichev, Sergii, and Volodymyr Lytvynenko, eds. Lecture Notes in Computational Intelligence and Decision Making. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82014-5.

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Ruan, Da. Computational intelligence in complex decision systems. Paris: Atlantis Press, 2010.

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Ringuest, Jeffrey L. Multiobjective optimization: Behavioral and computational considerations. Boston: Kluwer Academic Publishers, 1992.

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Book chapters on the topic "Computational making"

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Meisel, Stephan. "Computational Study." In Anticipatory Optimization for Dynamic Decision Making, 119–57. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0505-4_8.

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Insua, David Ríos. "Computational experience." In Sensitivity Analysis in Multi-objective Decision Making, 127–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51656-6_4.

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Słowiński, Roman, Salvatore Greco, and Benedetto Matarazzo. "Rough Sets in Decision Making." In Computational Complexity, 2727–60. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_168.

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Salinas, Emilio. "Decision Making: Overview." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_248-6.

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Salinas, Emilio. "Decision-Making: Overview." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7320-6_248-7.

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Miller, Paul. "Decision Making, Models." In Encyclopedia of Computational Neuroscience, 1–18. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_312-3.

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Yu, Angela J. "Decision-Making Tasks." In Encyclopedia of Computational Neuroscience, 1–8. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_314-1.

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Miller, Paul. "Decision Making, Threshold." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_315-5.

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Miller, Paul. "Decision Making, Threshold." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_315-6.

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Hauser, Christopher K., and Emilio Salinas. "Perceptual Decision Making." In Encyclopedia of Computational Neuroscience, 1–21. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_317-1.

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Conference papers on the topic "Computational making"

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Rode, Jennifer A., Jennifer Booker, Andrea Marshall, Anne Weibert, Konstantin Aal, Thomas von Rekowski, Houda El mimouni, Akshay Sharma, Jordan Jobs, and Alexis Schleeter. "From computational thinking to computational making." In the 2015 ACM International Joint Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2800835.2800926.

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Rode, Jennifer A., Anne Weibert, Andrea Marshall, Konstantin Aal, Thomas von Rekowski, Houda El Mimouni, and Jennifer Booker. "From computational thinking to computational making." In the 2015 ACM International Joint Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2750858.2804261.

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Rode, Jennifer A., Andrea Marshall, Houda El Mimouni, and Jennifer Booker. "Computational Making (Abstract Only)." In the 47th ACM Technical Symposium. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2839509.2850522.

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Monajemi, Hatef, David L. Donoho, and Victoria Stodden. "Making massive computational experiments painless." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840870.

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Johnson, Chris. "Toward Computational Making with Madeup." In SIGCSE '17: The 48th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3017680.3017703.

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Scheppegrell, Lindsey, Elyse Hiatt, Johanna Okerlund, and David Wilson. "Computational Thinking in the Making." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287324.3293833.

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Wallom, D. "Computational frameworks for HiPerDNO." In IET Conference on Smart Grid 2010: Making it a reality. IET, 2010. http://dx.doi.org/10.1049/ic.2010.0079.

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Rode, Jennifer A., and Veronica Cucuiat. "Computational making, binary gender and LEGO." In the 4th Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3196839.3196854.

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Lean, Stephen, Hans W. Guesgen, Inga Hunter, and Kudakwashe Dube. "Computational Confidence for Decision Making in Health." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_d-033.

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Cotrell, David, Tobias Hoeink, Sachin Ghorpade, and Elijah Odusina. "Making Frac Hits History with Computational Physics." In Unconventional Resources Technology Conference. Tulsa, OK, USA: American Association of Petroleum Geologists, 2019. http://dx.doi.org/10.15530/urtec-2019-332.

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Reports on the topic "Computational making"

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Cooke, Nancy, Paul E. Keel, Matthew Sither, and Patrick Winston. Ewall: Electronic Card Wall: Computational Support for Collaborative Decision-Making. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada549952.

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Just, Marcel A., Patricia A. Carpenter, Cleotilde Gonzalez, and Javier Lerch. Integrated Cognitive Computational and Biological Assessment of Workload in Decision Making. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada418079.

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Richards, Whitman. Computational Models for Belief Revision, Group Decision-Making and Cultural Shifts. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada567102.

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SENGLAUB, MICHAEL E., DAVID L. HARRIS, and ELAINE M. RAYBOURN. Foundations for Reasoning in Cognition-Based Computational Representations of Human Decision Making. Office of Scientific and Technical Information (OSTI), November 2001. http://dx.doi.org/10.2172/789585.

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Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, May 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.

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The COVID-19 pandemic has accelerated research worldwide and resulted in a large number of computational models and initiatives. Models were mostly aimed at forecast and resulted in different predictions partially since models were based on different assumptions. In fact the idea that a computational model is just an assumption attempting to explain a phenomenon has not been sufficiently explored. Moreover, the ability to combine models has not been fully realized. The Reference Model for disease progression was performing this task for years for diabetes models and recently started modeling COVID-19. The Reference Model is an ensemble of models that is optimized to fit observed disease phenomenon. The ensemble has the ability to include model components from different sources that compete and cooperate. The recent advance in this model is the ability to include models calculated in different scales, making the model the first known multi-scale ensemble model. This manuscript will review these capabilities and show how multiple models can improve our ability to comprehend the COVID-19 pandemic.
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Roschelle, Jeremy, James Lester, and Judi Fusco. AI and the Future of Learning: Expert Panel Report. Digital Promise, November 2020. http://dx.doi.org/10.51388/20.500.12265/106.

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This report is based on the discussion that emerged from a convening of a panel of 22 experts in artificial intelligence (AI) and in learning. It introduces three layers that can frame the meaning of AI for educators. First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths. Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion that the most impactful uses of AI in education have not yet been invented. The report enumerates important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning. Through discussions among experts about these layers, we observed new design concepts for using AI in learning. The panel also made seven recommendations for future research priorities.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.

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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.
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