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Decision Theory.PPT

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Decision Theory - Learning Objectives ã Express a decision situation in terms of decision alternatives, states of nature, and payoffs. ã Differentiate between non-Bayesian and Bayesian decision criteria. ã Determine the expected payoff for a decision alternative. ã Calculate and interpret the expected value of perfect information. ã Express and analyze the decision situation in terms of opportunity loss and expected opportunity loss. Key Terms ã Levels of doubt – Risk – Uncertainty – Certain
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   Decision Theory  - Learning Objectives ã Express a decision situation in terms of decision alternatives, states of nature, and payoffs. ã Differentiate between non-Bayesian and Bayesian decision criteria. ã Determine the expected payoff for a decision alternative. ã Calculate and interpret   the expected value of perfect information. ã Express and analyze the decision situation in terms of opportunity loss and expected opportunity loss.  Key Terms ã Levels of doubt – Risk – Uncertainty – Certainty ã Decision situation – Decision alternatives – States of nature – Probabilities – Expected payoff ã Maximin criteria ã Maximax criteria ã Minimax regret ã Expected value of perfect information ã Expected opportunity loss  The Decision Situation ã The decision maker can control which decision alternative ( row ) is selected but cannot determine which state of nature ( column ) will occur. ã The decision alternative is selected prior to knowing the state of nature.
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