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Analysis Of Competing Hypotheses
The analysis of competing hypotheses (ACH) is a methodology for evaluating multiple competing hypotheses for observed data. It was developed by Richards (Dick) J. Heuer, Jr., a 45-year veteran of the Central Intelligence Agency, in the 1970s for use by the Agency. ACH is used by analysts in various fields who make judgments that entail a high risk of error in reasoning. ACH aims to help an analyst overcome, or at least minimize, some of the cognitive limitations that make prescient intelligence analysis so difficult to achieve. ACH was a step forward in intelligence analysis methodology, but it was first described in relatively informal terms. Producing the best available information from uncertain data remains the goal of researchers, tool-builders, and analysts in industry, academia and government. Their domains include data mining, cognitive psychology and visualization, probability and statistics, etc. Abductive reasoning is an earlier concept with similarities to ACH. Pro ...
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Methodology
In its most common sense, methodology is the study of research methods. However, the term can also refer to the methods themselves or to the philosophical discussion of associated background assumptions. A method is a structured procedure for bringing about a certain goal. In the context of research, this goal is usually to discover new knowledge or to verify pre-existing knowledge claims. This normally involves various steps, like choosing a sample, collecting data from this sample, and interpreting this data. The study of methods involves a detailed description and analysis of these processes. It includes evaluative aspects by comparing different methods to assess their advantages and disadvantages relative to different research goals and situations. This way, a methodology can help make the research process efficient and reliable by guiding researchers on which method to employ at each step. These descriptions and evaluations of methods often depend on philosophical background ...
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Backtracking
Backtracking is a class of algorithms for finding solutions to some computational problems, notably constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a valid solution. The classic textbook example of the use of backtracking is the eight queens puzzle, that asks for all arrangements of eight chess queens on a standard chessboard so that no queen attacks any other. In the common backtracking approach, the partial candidates are arrangements of ''k'' queens in the first ''k'' rows of the board, all in different rows and columns. Any partial solution that contains two mutually attacking queens can be abandoned. Backtracking can be applied only for problems which admit the concept of a "partial candidate solution" and a relatively quick test of whether it can possibly be completed to a valid solution. It is useless, for example, ...
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Bayesian Analysis
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem: P(H\ ...
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Mercyhurst College Institute For Intelligence Studies
The Ridge College of Intelligence Studies and Applied Sciences at Mercyhurst University (RIAP), located on the campus of Mercyhurst University in Erie, Pennsylvania, offers undergraduate and graduate studies programs in intelligence analysis. The program also offers graduate certificates in Applied Intelligence, Counterintelligence, and Law Enforcement Intelligence. The Intelligence Studies program "promotes the study of Intelligence in higher academic settings, while seeking to identify, promote, and employ best practices in the study and application of intelligence studies throughout its various disciplines (national security, law enforcement, business intelligence and academia)." History The Institute for Intelligence Studies (IIS) was founded in February 2004 via help from a grant from the U.S. Department of Education's Fund for the Improvement of Post-Secondary Education (FIPSE). It was named after Tom Ridge, an Erie native and the first Secretary of Homeland Security. For a do ...
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Oxford University Press
Oxford University Press (OUP) is the university press of the University of Oxford. It is the largest university press in the world, and its printing history dates back to the 1480s. Having been officially granted the legal right to print books by decree in 1586, it is the second oldest university press after Cambridge University Press. It is a department of the University of Oxford and is governed by a group of 15 academics known as the Delegates of the Press, who are appointed by the vice-chancellor of the University of Oxford. The Delegates of the Press are led by the Secretary to the Delegates, who serves as OUP's chief executive and as its major representative on other university bodies. Oxford University Press has had a similar governance structure since the 17th century. The press is located on Walton Street, Oxford, opposite Somerville College, in the inner suburb of Jericho. For the last 500 years, OUP has primarily focused on the publication of pedagogical texts and ...
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Argument Map
An argument map or argument diagram is a visual representation of the structure of an argument. An argument map typically includes the key components of the argument, traditionally called the '' conclusion'' and the ''premises'', also called ''contention'' and ''reasons''. Argument maps can also show co-premises, objections, counterarguments, rebuttals, and lemmas. There are different styles of argument map but they are often functionally equivalent and represent an argument's individual claims and the relationships between them. Argument maps are commonly used in the context of teaching and applying critical thinking. The purpose of mapping is to uncover the logical structure of arguments, identify unstated assumptions, evaluate the support an argument offers for a conclusion, and aid understanding of debates. Argument maps are often designed to support deliberation of issues, ideas and arguments in wicked problems. An argument map is not to be confused with a concept map or ...
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Tim Van Gelder
Tim van Gelder is the co-founder of Austhink Software, an Australian software development company, and the Managing Director of Austhink Consulting. He was born in Australia, and was educated at the University of Melbourne (BA, 1984). He went on to receive his PhD from the University of Pittsburgh (1989). He has held academic positions at Indiana University and the Australian National University before returning to Melbourne as an Australian Research Council QEII Research Fellow. In 1998, he transitioned to part-time academic work allowing him to pursue private training and consulting, and in 2005 began working full-time at Austhink Software. In 2009 he transitioned to Managing Director of Austhink Consulting. He co-leads The SWARM Project at the University of Melbourne. Research Van Gelder's research has had three main phases, corresponding to his PhD research on distributed representation, his subsequent research on dynamics & cognition, and his current phase, research int ...
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Argumentation Theory
Argumentation theory, or argumentation, is the interdisciplinary study of how conclusions can be supported or undermined by premises through logical reasoning. With historical origins in logic, dialectic, and rhetoric, argumentation theory, includes the arts and sciences of civil debate, dialogue, conversation, and persuasion. It studies rules of inference, logic, and procedural rules in both artificial and real-world settings. Argumentation includes various forms of dialogue such as deliberation and negotiation which are concerned with collaborative decision-making procedures. It also encompasses eristic dialog, the branch of social debate in which victory over an opponent is the primary goal, and didactic dialogue used for teaching. This discipline also studies the means by which people can express and rationally resolve or at least manage their disagreements. Argumentation is a daily occurrence, such as in public debate, science, and law. For example in law, in courts by the ju ...
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Confirmation Bias
Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one's prior beliefs or values. People display this bias when they select information that supports their views, ignoring contrary information, or when they interpret ambiguous evidence as supporting their existing attitudes. The effect is strongest for desired outcomes, for emotionally charged issues, and for deeply entrenched beliefs. Confirmation bias cannot be eliminated, but it can be managed, for example, by education and training in critical thinking skills. Biased search for information, biased interpretation of this information, and biased memory recall, have been invoked to explain four specific effects: # ''attitude polarization'' (when a disagreement becomes more extreme even though the different parties are exposed to the same evidence) # ''belief perseverance'' (when beliefs persist after the evidence for them is shown to be false) # the ''irr ...
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Root Cause Analysis
In science Science is a systematic endeavor that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Science may be as old as the human species, and some of the earliest archeological evidence for ... and engineering, root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. It is widely used in IT operations, manufacturing, telecommunications, Process control, industrial process control, accident analysis (e.g., in aviation, rail transport, or nuclear plants), medicine (for medical diagnosis), healthcare industry (e.g., for epidemiology), etc. Root cause analysis is a form of deductive inference since it requires an understanding of the underlying causal mechanisms of the potential root causes and the problem. RCA can be decomposed into four steps: * Identify and describe the problem clearly * Establish a timeline from the normal situation until the problem ...
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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'' ...
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Bayesian Network
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (''e.g.'' speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Graphical mode ...
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