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Value Of Information
Value of information (VOI or VoI) is the amount a decision maker would be willing to pay for information prior to making a decision. Similar terms VoI is sometimes distinguished into value of perfect information, also called value of clairvoyance (VoC), and value of imperfect information. They are closely related to the widely known expected value of perfect information (EVPI) and expected value of sample information (EVSI). Note that VoI is not necessarily equal to "value of decision situation with perfect information" - "value of current decision situation" as commonly understood. Definitions Simple A simple example best illustrates the concept: Consider the decision situation with one decision, for example deciding on a 'Vacation Activity'; and one uncertainty, for example what will the 'Weather Condition' be? But we will only know the 'Weather Condition' after we have decided and begun the 'Vacation Activity'. :* The Value of perfect information on ''Weather Condition'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clairvoyance
Clairvoyance (; ) is the claimed ability to acquire information that would be considered impossible to get through scientifically proven sensations, thus classified as extrasensory perception, or "sixth sense". Any person who is claimed to have such ability is said to be a clairvoyant () (). Claims for the existence of paranormal and psychic abilities such as clairvoyance have not been supported by scientific evidence.Robert Todd Carroll, Carroll, Robert Todd. (2003)"Clairvoyance" Retrieved 2014-04-30. Parapsychology explores this possibility, but the existence of the paranormal is not accepted by the scientific community. The scientific community widely considers parapsychology, including the study of clairvoyance, a pseudoscience. Usage Pertaining to the ability of clear-sightedness, clairvoyance refers to the paranormal ability to see persons and events that are distant in time or space. It can be divided into roughly three classes: precognition, the ability to perceive o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Game Tree
In the context of combinatorial game theory, a game tree is a graph representing all possible game states within a sequential game that has perfect information. Such games include chess, checkers, Go, and tic-tac-toe. A game tree can be used to measure the complexity of a game, as it represents all the possible ways that the game can pan out. Due to the large game trees of complex games such as chess, algorithms that are designed to play this class of games will use partial game trees, which makes computation feasible on modern computers. Various methods exist to solve game trees. If a complete game tree can be generated, a deterministic algorithm, such as backward induction or retrograde analysis can be used. Randomized algorithms and minmax algorithms such as MCTS can be used in cases where a complete game tree is not feasible. Understanding the game tree To better understand the game tree, it can be thought of as a technique for analyzing adversarial games, whi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Economics
Information economics or the economics of information is the branch of microeconomics that studies how information and information systems affect an economy and economic decisions. One application considers information embodied in certain types of commercial products that are "expensive to produce but cheap to reproduce." Samuelson, Paul A., and William D. Nordhaus (2001). Economics, p.194. Examples include computer software (e.g., Microsoft Windows), pharmaceuticals and technical books. Once information is recorded "on paper, in a computer, or on a compact disc, it can be reproduced and used by a second person essentially for free." Without the basic research, initial production of high-information commodities may be too unprofitable to market, a type of market failure. Government subsidization of basic research has been suggested as a way to mitigate the problem. The subject of "information economics" is treated under ''Journal of Economic Literature'' classification code ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Theory
Information theory is the mathematical study of the quantification (science), quantification, Data storage, storage, and telecommunications, communication of information. The field was established and formalized by Claude Shannon in the 1940s, though early contributions were made in the 1920s through the works of Harry Nyquist and Ralph Hartley. It is at the intersection of electronic engineering, mathematics, statistics, computer science, Neuroscience, neurobiology, physics, and electrical engineering. A key measure in information theory is information entropy, entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a Fair coin, fair coin flip (which has two equally likely outcomes) provides less information (lower entropy, less uncertainty) than identifying the outcome from a roll of a dice, die (which has six equally likely outcomes). Some other important measu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Influence Diagram
An influence diagram (ID) (also called a relevance diagram, decision diagram or a decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network, in which not only probabilistic inference problems but also decision making problems (following the maximum expected utility criterion) can be modeled and solved. ID was first developed in the mid-1970s by decision analysts with an intuitive semantic that is easy to understand. It is now adopted widely and becoming an alternative to the decision tree which typically suffers from exponential growth in number of branches with each variable modeled. ID is directly applicable in team decision analysis, since it allows incomplete sharing of information among team members to be modeled and solved explicitly. Extensions of ID also find their use in game theory as an alternative representation of the game tree. Semantics An ID is a directed acyclic graph wit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Value Of Structural Health Information
The value of structural health information is the expected utility gain of a built environment system by information provided by structural health monitoring (SHM). The quantification of the value of structural health information is based on decision analysis adapted to built environment engineering. The value of structural health information can be significant for the risk and integrity management of built environment systems. Background The value of structural health information takes basis in the framework of the decision analysis and the value of information analysis as introduced by Raiffa and Schlaifer and adapted to civil engineering by Benjamin and Cornell. Decision theory itself is based upon the expected utility hypothesis The expected utility hypothesis is a foundational assumption in mathematical economics concerning decision making under uncertainty. It postulates that rational agents maximize utility, meaning the subjective desirability of their actions. Rationa .. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expected Value Of Sample Information
In decision theory, the expected value of sample information (EVSI) is the expected increase in utility that a decision-maker could obtain from gaining access to a sample of additional observations before making a decision. The additional information obtained from the sample may allow them to make a more informed, and thus better, decision, thus resulting in an increase in expected utility. EVSI attempts to estimate what this improvement would be before seeing actual sample data; hence, EVSI is a form of what is known as ''preposterior analysis''. The use of EVSI in decision theory was popularized by Robert Schlaifer and Howard Raiffa in the 1960s. Formulation Let : \begin d\in D & \mbox D \\ x\in X & \mbox X \\ z \in Z & \mbox n \mbox \langle z_1,z_2,..,z_n \rangle \\ U(d,x) & \mbox d \mbox x \\ p(x) & \mbox x \\ p(z, x) & \mbox z \end It is common (but not essential) in EVSI scenarios for Z_i=X, p(z, x)=\prod p(z_i, x) and \int z p(z, x) dz = x, which is to say that each ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expected Value Of Including Uncertainty
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Expected may refer to: *Expectation (epistemic) *Expected value *Expected shortfall *Expected utility hypothesis *Expected return *Expected loss See also *Unexpected (other) *Expected value (other) Expected value is a term used in probability theory and statistics. It may also refer to: Physics * Expectation value (quantum mechanics) In quantum mechanics, the expectation value is the probabilistic expected value of the result (measurement ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expected Value Of Perfect Information
In decision theory, the expected value of perfect information (EVPI) is the price that one would be willing to pay in order to gain access to perfect information. A common discipline that uses the EVPI concept is health economics. In that context and when looking at a decision of whether to adopt a new treatment technology, there is always some degree of uncertainty surrounding the decision, because there is always a chance that the decision turns out to be wrong. The expected value of perfect information analysis tries to measure the expected cost of that uncertainty, which “can be interpreted as the expected value of perfect information (EVPI), since perfect information can eliminate the possibility of making the wrong decision” at least from a theoretical perspective. Equation The problem is modeled with a payoff matrix ''Rij'' in which the row index ''i'' describes a choice that must be made by the player, while the column index ''j'' describes a random variable that th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Analysis
Decision analysis (DA) is the Academic discipline, discipline comprising the philosophy, methodology, and professional practice necessary to address important Decision making, decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision; for prescribing a recommended course of action by applying the maximum Expected_utility_hypothesis, expected-utility action axiom, axiom to a well-formed representation of the decision; and for translating the formal representation of a decision and its corresponding recommendation into insight for the Decision making, decision maker, and other corporate and non-corporate Stakeholder (corporate), stakeholders. History In 1931, mathematical philosopher Frank_Ramsey_(mathematician), Frank Ramsey pioneered the idea of Probability_interpretations#Subjectivism, subjective probability as a Expected utility hypothesis#Ramsey-theore ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Tree
A decision tree is a decision support system, decision support recursive partitioning structure that uses a Tree (graph theory), tree-like Causal model, model of decisions and their possible consequences, including probability, chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in Decision tree learning, machine learning. Overview A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. In decision analysis, a de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Set (game Theory)
In game theory, an information set is the basis for decision making in a game, which includes the actions available to players and the potential outcomes of each action. It consists of a collection of decision nodes that a player cannot distinguish between when making a move, due to incomplete information about previous actions or the current state of the game. In other words, when a player's turn comes, they may be uncertain about which exact node in the game tree they are currently at, and the information set represents all the possibilities they must consider. Information sets are a fundamental concept particularly important in games with imperfect information. In games with perfect information (such as chess or Go (game), Go), every information set contains exactly one decision node, as each player can observe all previous moves and knows the exact game state. However, in games with imperfect information—such as most Card game, card games like poker or Bridge (card game), bri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
