ISEN 609 Lecture 6

# ISEN 609 Lecture 6 - Practical Use of Probability Practical...

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Practical Use of Probability

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Practical Use of Probability: 5 Easy Pieces 1. It is better to be wise than to be lucky.
Practical Use of Probability: 5 Easy Pieces 2. The purpose of a probability model is not to understand the universe; it is to make a decision.

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Practical Use of Probability: 5 Easy Pieces 3. To make good decisions in the face of uncertainty, you must know the rules. Failure to do so may lead to . .. bad decisions.
Practical Use of Probability: 5 Easy Pieces 4. Many paths that lead to a probability model.

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Practical Use of Probability: 5 Easy Pieces 5. Information changes what you think about the future. Information is valuable if and only if it leads you to change your decision.
6. Uncertainty, Risk, Decisions, and Information In engineering, we are interested in making decisions (committing scarce resources) in the face of uncertain outcomes. Decision: (1) a choice among alternatives; (2) an irrevocable allocation of resources Engineering decisions are associated with: product design and development designing, building, operating large infrastructure projects operating and maintaining manufacturing capacity etc., etc., etc. ...

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Engineering Decisions Engineering decisions have the flavor of high-stakes wagers: -- they are made in the presence of significant uncertainty about the reward associated with the decision; -- they entail a commitment of financial resources, with high potential gain or loss. Engineering projects can result in great benefits but may also entail significant risks. Decision analysis is used to understand benefits and risks.
In case we need reminders that we make decisions in an uncertain environment. .. BHOPAL, INDIA CHERNOBYL REACTOR, U.S.S.R CHALLENGER SPACE SHUTTLE, USA THREE MILE ISLAND, USA

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Uncertainty in engineering arises from . .. Material properties, usage, demand, deterioration, manufacturing processes, human elements, yield, output, sales, . ... (Perhaps it is easier to list features that we know are “certain”)
Where does Probability come in? We need a rigorous, quantitative method to characterize the uncertainty that governs the outcome of our commitment of resources. Probability is the common language that forms the basis of decision-making involving system design and operation. Two closely related fields involving decision-making -- Decision Analysis and Probabilistic Risk Assessment - have probability at their core

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Descriptive vs. Normative Models Descriptive models characterize key features of a physical system. So far, we have discussed descriptive models of uncertainty (i.e., probability) Normative (or prescriptive) models both characterize a physical system and direct actions to meet an objectives (e.g., linear programming) Our goal here is to develop a normative theory (or model of decision-making) that guides our choice among alternatives.
Axiomatic Approach to Decision Making First proposed by von Neuman and Morgenstern (

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## This note was uploaded on 04/28/2011 for the course ISEN 609 taught by Professor Klutke during the Spring '08 term at Texas A&M.

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ISEN 609 Lecture 6 - Practical Use of Probability Practical...

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