Choices by one agent in which background conditions are independent of what other agents are doing. We usually represent these decisions with a decision matrix or decision table. Sometimes called evidential decision theory.

See also: causal decision theory

e.g. Pascal’s Wager


  • Rows are possible acts
    • Acts are functions that map states to outcomes
  • Columns are possible states of the world
    • Probabilities are sometimes included for decisions under risk.
    • Should not depend on agent action
    • States should be
      • Mutually exclusive
      • Exhaustive: no possibility is left out
      • Relevant partition: distinctions that actually have impact on probability or utility of outcomes
      • Independence: (optional) each state should be causally and probabilistically independent of the acts
        • Dominance principle only holds if independent holds
  • Cells are outcomes.
    • Can be described using
      • Verbal description
      • Preference ranking on an ordinal scale
        • Defines a partial ordering of outcomes
          • is a weak preference
          • is a strong preference
          • is indifference between and
      • Utility (numerical value) using an interval scale

Decision tables

  1. Art: providing a good formalization of a decision into a table
  2. providing a justified recommendation based off of the formalization

They can also be represented using decision trees

We can transform decision tables between each other using “reasonable transformations”

  1. PIR: assign equal probabilities to all states (if we have no knowledge of probabilities, aka DUI)
  2. Merger: if two states yield identical columns, then we can merge them into one state and add probabilities if we know them