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Welcome. We'll explore
a foundational concept
in decision and game theory,
perfect information.
This refers to a decision
making environment
where every player knows
all moves previously made by
every participant
and all elements
relevant to the situation.
Each participant is aware of
the entire decision
history up to any point.
Unlike complete
information, which
refers to knowledge of
the rules and payoffs,
perfect information
means every action and
outcome so far is
transparent to all involved.
To clarify the concept,
let's explore some
classic examples.
Chess is a quintessential
game of perfect information.
All pieces are visible
and every move is public,
allowing players to observe
the entire state of play.
Games like tick tack toe or
checkers also fit
this description.
In contrast, poker is a game of
imperfect information where
players cards are hidden.
Many real world
decisions fall between
these extremes as analysts
rarely have perfect information.
This leads to uncertainty
and more complex
strategic choices.
The distinction between perfect
and imperfect information
has significant consequences
for decision analysis.
With perfect
information, decisions
can be modeled using
straightforward techniques,
as planners know the
entire current state
and the consequences
of every action.
Decision trees for perfect
information scenarios are
simple with unknowns
limited to random events.
However, as uncertainty arises,
such as unknown competitor
actions or market changes,
