Maximisation (other)
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Maximisation (other)
Maximization or maximisation may refer to: * Maximization in the sense of exaggeration * Entropy maximization * Maximization (economics) ** Profit maximization ** Utility maximization problem ** Budget-maximizing model ** Shareholder value, maximization * Maximization (psychology) * Optimization (mathematics) * Expectation–maximization algorithm In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent varia ... See also * Minimization (other) {{disambiguation ...
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Exaggeration
Exaggeration is the representation of something as more extreme or dramatic than it is, intentionally or unintentionally. It can be a rhetorical device or figure of speech, used to evoke strong feelings or to create a strong impression. Amplifying achievements, obstacles and problems to seek attention is an everyday occurrence Inflating the difficulty of achieving a goal after attaining it, can be used to bolster self-esteem. In the arts, exaggerations are used to create emphasis or effect. As a literary device, exaggerations are often used in poetry, and is frequently encountered in casual speech. Many times the usages of hyperbole describes something as better or worse than it really is. An example of hyperbole is: "The bag weighed a ton." Hyperbole makes the point that the bag was very heavy, though it probably does not weigh a ton. Exaggerating is also a type of deception,Guerrero, L., Anderson, P., Afifi, W. (2007). Close Encounters: Communication in Relationships ( ...
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Entropy Maximization
The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information). Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function. Consider the set of all trial probability distributions that would encode the prior data. According to this principle, the distribution with maximal information entropy is the best choice. History The principle was first expounded by E. T. Jaynes in two papers in 1957, where he emphasized a natural correspondence between statistical mechanics and information theory. In particular, Jaynes argued that the Gibbsian method of statistical mechanics is sound by also arguing that the entropy of statistical mechanics and the information entropy of information theory are the same ...
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Maximization (economics)
Capitalism is an economic system based on the private ownership of the means of production and their use for the purpose of obtaining profit. This socioeconomic system has developed historically through several stages and is defined by a number of basic constituent elements: private property, profit motive, capital accumulation, competitive markets, commodification, wage labor, and an emphasis on innovation and economic growth. Capitalist economies tend to experience a business cycle of economic growth followed by recessions. Economists, historians, political economists, and sociologists have adopted different perspectives in their analyses of capitalism and have recognized various forms of it in practice. These include ''laissez-faire'' or free-market capitalism, state capitalism, and welfare capitalism. Different forms of capitalism feature varying degrees of free markets, public ownership, obstacles to free competition, and state-sanctioned social policies. The degree o ...
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Profit Maximization
In economics, profit maximization is the short run or long run process by which a firm may determine the price, input and output levels that will lead to the highest possible total profit (or just profit in short). In neoclassical economics, which is currently the mainstream approach to microeconomics, the firm is assumed to be a " rational agent" (whether operating in a perfectly competitive market or otherwise) which wants to maximize its total profit, which is the difference between its total revenue and its total cost. Measuring the total cost and total revenue is often impractical, as the firms do not have the necessary reliable information to determine costs at all levels of production. Instead, they take more practical approach by examining how small changes in production influence revenues and costs. When a firm produces an extra unit of product, the additional revenue gained from selling it is called the marginal revenue (\text), and the additional cost to produce ...
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Utility Maximization Problem
Utility maximization was first developed by utilitarian philosophers Jeremy Bentham and John Stuart Mill. In microeconomics, the utility maximization problem is the problem consumers face: "How should I spend my money in order to maximize my utility?" It is a type of Optimal decision, optimal decision problem. It consists of choosing how much of each available good or service to consume, taking into account a Natural borrowing limit, constraint on total spending (income), the prices of the goods and their Preference (economics), preferences. Utility maximization is an important concept in consumer theory as it shows how consumers decide to allocate their income. Because consumers are modelled as being Rational choice theory, rational, they seek to extract the most benefit for themselves. However, due to bounded rationality and other biases, consumers sometimes pick bundles that do not necessarily maximize their utility. The utility maximization bundle of the consumer is also not ...
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Budget-maximizing Model
The budget-maximizing model is a stream of public choice theory and rational choice analysis in public administration inaugurated by William Niskanen. Niskanen first presented the idea in 1968, and later developed it into a book published in 1971.William A. Niskanen, 9711994. ''Bureaucracy and Public Economics'', Elgar. Expanded ed. Description and reviewlinks and revieexcerpts According to the budget-maximizing model, rational bureaucrats will always and everywhere seek to increase their budgets in order to increase their own power, thereby contributing strongly to state growth and potentially reducing social efficiency Efficiency is the often measurable ability to avoid making mistakes or wasting materials, energy, efforts, money, and time while performing a task. In a more general sense, it is the ability to do things well, successfully, and without waste. .... The bureau-shaping model has been developed as a response to the budget-maximizing model. Niskanen's inspirati ...
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Shareholder Value
Shareholder value is a business term, sometimes phrased as shareholder value maximization. The term expresses the idea that the primary goal for a business is to increase the wealth of its shareholders (owners) by paying dividends and/or causing the company's stock price to increase. It became a prominent idea during the 1980s and 1990s, along with the management principle value-based management or managing for value. Definition The term ''shareholder value'', sometimes abbreviated to ''SV'', can be used to refer to: * The market capitalization of a company; * The view that the primary goal for a company is to increase the wealth of its shareholders (owners) by paying dividends and/or causing the stock price to increase (i.e. the Friedman doctrine introduced in 1970); * The more specific concept that planned actions by management and the returns to shareholders should outperform certain bench-marks such as the cost of capital concept. In essence, the idea that shareholders' money ...
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Maximization (psychology)
Maximization is a style of decision-making characterized by seeking the best option through an exhaustive search through alternatives. It is contrasted with satisficing, in which individuals evaluate options until they find one that is "good enough". Definition The distinction between "maximizing" and "satisficing" was first made by Herbert A. Simon in 1956.Simon, H. A. (1955). A behavioral model of rational choice. ''Quarterly Journal of Economics, 59'', 99–118.Simon, H. A. (1956). Rational choice and the structure of the environment. ''Psychological Review, 63''(2), 129–138. Simon noted that although fields like economics posited maximization or "optimizing" as the rational method of making decisions, humans often lack the cognitive resources or the environmental affordances to maximize. Simon instead formulated an approach known as bounded rationality, which he also referred to as satisficing. This approach was taken to be adaptive and, indeed, necessary, given our ...
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Optimization (mathematics)
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries. In the more general approach, an optimization problem consists of maxima and minima, maximizing or minimizing a Function of a real variable, real function by systematically choosing Argument of a function, input values from within an allowed set and computing the Value (mathematics), value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. Optimization problems Opti ...
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Expectation–maximization Algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the ''E'' step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. It can be used, for example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem. History The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. They pointed out that the method had been "proposed man ...
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