In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other resources needed to execute them. Usually, this involves determining a function that relates the size of an algorithm's input to the number of steps it takes or the number of storage locations it uses. An algorithm is said to be efficient when this function's values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same size may cause the algorithm to have different behavior, so best, worst and average case descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an upper bound, determined from the worst case inputs to the algorithm.
Comparative advantage in an economic model is the advantage over others in producing a particular good. A good can be produced at a lower relative opportunity cost or autarky price, i.e. at a lower relative marginal cost prior to trade. Comparative advantage describes the economic reality of the work gains from trade for individuals, firms, or nations, which arise from differences in their factor endowments or technological progress.
Growth accounting is a procedure used in economics to measure the contribution of different factors to economic growth and to indirectly compute the rate of technological progress, measured as a residual, in an economy. Growth accounting decomposes the growth rate of an economy's total output into that which is due to increases in the contributing amount of the factors used—usually the increase in the amount of capital and labor—and that which cannot be accounted for by observable changes in factor utilization. The unexplained part of growth in GDP is then taken to represent increases in productivity or a measure of broadly defined technological progress.
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
X-inefficiency is a concept used in economics to describe instances where firms go through internal inefficiency resulting in higher production costs than required for a given output. This inefficiency is a result of various factors such as outdated technology, Inefficient production processes, poor management and lack of competition resulting in lower profits and higher prices for consumers. The concept of X-inefficiency was introduced by Harvey Leibenstein.
In economics, a production function gives the technological relation between quantities of physical inputs and quantities of output of goods. The production function is one of the key concepts of mainstream neoclassical theories, used to define marginal product and to distinguish allocative efficiency, a key focus of economics. One important purpose of the production function is to address allocative efficiency in the use of factor inputs in production and the resulting distribution of income to those factors, while abstracting away from the technological problems of achieving technical efficiency, as an engineer or professional manager might understand it.
Productivity is the efficiency of production of goods or services expressed by some measure. Measurements of productivity are often expressed as a ratio of an aggregate output to a single input or an aggregate input used in a production process, i.e. output per unit of input, typically over a specific period of time. The most common example is the (aggregate) labour productivity measure, one example of which is GDP per worker. There are many different definitions of productivity and the choice among them depends on the purpose of the productivity measurement and data availability. The key source of difference between various productivity measures is also usually related to how the outputs and the inputs are aggregated to obtain such a ratio-type measure of productivity.
In economics, an input–output model is a quantitative economic model that represents the interdependencies between different sectors of a national economy or different regional economies. Wassily Leontief (1906–1999) is credited with developing this type of analysis and earned the Nobel Prize in Economics for his development of this model.
In economics, the concept of returns to scale arises in the context of a firm's production function. It explains the long-run linkage of increase in output (production) relative to associated increases in the inputs.
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow. Modern feedforward networks are trained using the backpropagation method and are colloquially referred to as the "vanilla" neural networks.
The Solow–Swan model or exogenous growth model is an economic model of long-run economic growth. It attempts to explain long-run economic growth by looking at capital accumulation, labor or population growth, and increases in productivity largely driven by technological progress. At its core, it is an aggregate production function, often specified to be of Cobb–Douglas type, which enables the model "to make contact with microeconomics". The model was developed independently by Robert Solow and Trevor Swan in 1956, and superseded the Keynesian Harrod–Domar model.
In microeconomic theory, productive efficiency is a situation in which the economy or an economic system operating within the constraints of current industrial technology cannot increase production of one good without sacrificing production of another good. In simple terms, the concept is illustrated on a production possibility frontier (PPF), where all points on the curve are points of productive efficiency. An equilibrium may be productively efficient without being allocatively efficient — i.e. it may result in a distribution of goods where social welfare is not maximized.
Stochastic frontier analysis (SFA) is a method of economic modeling. It has its starting point in the stochastic production frontier models simultaneously introduced by Aigner, Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977).
Production is the process of combining various inputs, both material and immaterial in order to create output. Ideally this output will be a good or service which has value and contributes to the utility of individuals. The area of economics that focuses on production is called production theory, and it is closely related to the consumption theory of economics.
In signal processing, overlap–save is the traditional name for an efficient way to evaluate the discrete convolution between a very long signal and a finite impulse response (FIR) filter :
In computational complexity the decision tree model is the model of computation in which an algorithm is considered to be basically a decision tree, i.e., a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals rather than the typical residuals used in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. A gradient-boosted trees model is built in a stage-wise fashion as in other boosting methods, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function.
William Wager Cooper was an American operations researcher, known as a father of management science and as "Mr. Linear Programming". He was the founding president of The Institute of Management Sciences, founding editor-in-chief of Auditing: A Journal of Practice and Theory, a founding faculty member of the Graduate School of Industrial Administration at the Carnegie Institute of Technology, founding dean of the School of Urban and Public Affairs at CMU, the former Arthur Lowes Dickinson Professor of Accounting at Harvard University, and the Foster Parker Professor Emeritus of Management, Finance and Accounting at the University of Texas at Austin.
Edwardo Lao Rhodes is an American management science scholar and author. An Emeritus Professor at the Indiana University School of Public and Environmental Affairs, Rhodes is best known for his seminal work in data envelopment analysis, as well as his applications of management science to policy analysis and environmental policy.
Rajiv D. Banker(born 1953, deceased March 1, 2023) was an accounting researcher and educator, recognized by the Institute for Scientific Information as one of the 150 most influential researchers in economics and business. He was the Director of the Center for Accounting and Information Technology at the Fox School of Business and Management, Temple University. He was also President of the International Data Envelopment Analysis Society and Editor-in-Chief of the Data Envelopment Analysis Journal.
