Decision Trees Skills

Decision Trees

+ DT01: Apply basic concepts of probability

• outcomes & events
• mutually exclusive & exhaustive
• independence
• P(A or B)
• P(A and B)
• P(A|B)

+ DT02: Calculate expected monetary value

• $P(x) \times \mbox{value of outcome X}$
• Expected value of a set of mutually exclusive alternatives (e.g., branches coming out of a chance node) is sum of expected values.

+ DT03: Translate verbal description of decision process into descriptive decision tree and vice versa

• Read a description, draw the tree
• See a tree, write the description

+ DT04: Use simple tree to assess a set of alternatives under uncertainty

• Work backwards to calculate values of chance nodes and pick decisions at choice nodes.

+ DT05: Use decision tree to calculate the value of information provided by a perfect test (how much should we be willing to pay for information?)

• Take an ordinary DT and add a test/no test choice node, calculate value of test
• Expected value of perfect information (EVPI)

+ DT06: Solve a problem that incorporates risk aversion

+ DT07: Solve a problem that incorporates imperfect tests

• Define false positive and false negative
• "Flip" a tree to calculate expectations of test outcomes

+ DT08: Explain relationship of decision trees and utility theory

• TBA

+ DT09: Explain/distinguish related concepts: choice/chance nodes, exhaustive/mutually exclusive, folding-back, imperfect tests, risk-aversion, the value of information, utility theory

* choice/chance

• exhaustive/mutually exclusive
• folding-back
• imperfect tests
• risk-aversion
• the value of information
• utility theory

+ DT10: Describe indications and contraindications

• TBA

+ DT11: Identify and respond to typical concerns and caveats

• TBA