what are agent based modelling?
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In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules.
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The Solow–Swan model is an economic model of long-run economic growth set within the framework of neoclassical economics. It attempts to explain long-run economic growth by looking at capital accumulation, labor or population growth, and increases in productivity, commonly referred to as technological progress.
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The main principle of the Spiral model is risk handling. The Agile model focuses on the delivery of an increment to the customer after each Time-box, so customer interaction is more frequent. Spiral model mainly deals with various kinds of unanticipated risks but customer interaction is less.
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The particle model has four main tenets: All substances are made of particles. The particles are attracted to each other (some strongly, others weakly). The particles move around (have kinetic energy).
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In macro economics field (primarily national / state economics), logic, mathematics, and statistics are used for modeling. In fact, it is better to say that all models are based on logic, mathematics, and statistics - nothing else except perhaps experience.
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Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty.
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Data model is an abstract model that organizes elements of data and standardizes how they relate to one another. Database model is a logical structure of a database that determines in which manner data can be stored, organized and manipulated.
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['Linear Model of Communication. ... ', 'Transactional Model of Communication. ... ', 'Interactive Model of Communication. ... ', "Dance's Helical Spiral of Communication. ... ", "Westley and MacLean's Conceptual Model."]
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Enterprise modeling is a term for the modeling of various processes, infrastructures, asset groups, or other elements of a business or organization. Enterprise modeling helps leaders to visualize what is going on within a business and how to make changes.
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Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph.
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"A domain model is a representation of real-world conceptual classes, not of software components." ... The software components are commonly implemented in an object oriented programming language. A domain model contains conceptual classes, associations between conceptual classes, and attributes of a conceptual class.
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Definition. The use of a mathematical model to recreate a situation, often repeatedly, so that the likelihood of various outcomes can be more accurately estimated.
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['Formal versus Informal Models. ... ', 'Physical Models versus Abstract Models. ... ', 'Descriptive Models. ... ', 'Analytical Models. ... ', 'Hybrid Descriptive and Analytical Models. ... ', 'Domain-Specific Models. ... ', 'System Models. ... ', 'Simulation versus Model.']
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Scientific models are used to explain and predict the behaviour of real objects or systems and are used in a variety of scientific disciplines, ranging from physics and chemistry to ecology and the Earth sciences.
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The basic building blocks of all data models are entities, attributes, and relationships. An entity is anything, such as a person, place, thing, or event, about which data are to be collected and stored. Entities may be physical objects such as customers or products.
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"A domain model is a representation of real-world conceptual classes, not of software components." Domain modeling is a technique used to understand the project problem description and to translate the requirements of that project into software components of a solution. ... The model is shown as a class diagram.
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The mixture model (and HMM) is a model of the data generating process, sometimes called a likelihood or 'forward model'. ... In contrast to the mixture model (and HMM) the neural network learns a posterior distribution over the output categories directly (a discriminative approach).