Decision-Making Under Uncertainty
Author: Vitaly - mr. Koteo (Brisbane Mafia Club)
Mafia is a game of incomplete information.
You never know the full truth. You never act with certainty. Every decision is made under ambiguity, pressure, and limited time.
This chapter explains how to make correct decisions when certainty is impossible.
It is the bridge between:
mathematical thinking (previous chapters)
real gameplay under pressure
Risk vs Reward
Decision-making in Sports Mafia is not about being right. It is about making the best possible decision with the information available.
A correct decision can lead to a loss. An incorrect decision can win the game.
What matters is not the outcome. What matters is whether the decision was logically justified at the moment it was made.
Decision Quality vs Outcome
Players are not “Red” or “Dark” in your mind. They are probabilities.
Example:
Player #4 → more likely Dark
Player #7 → less likely Dark
You vote #4. #4 flips Red.
This does not make your decision wrong.
Decisions are evaluated before the reveal, not after.
Risk Depends on Structure
Risk is not just probability — it is also game state.
The same decision has different risk depending on player count.
At 9 players → wrong vote is recoverable
At 8 players → wrong vote may lead to immediate loss (via 7 → night → 6)
Critical rounds amplify risk.
A 60/40 decision:
acceptable at 9
dangerous at 8
Expected Value (Practical Form)
You do not calculate EV (Expected Value) explicitly at the table.
But you act according to it:
Choose the action that maximises your team’s chance to win.
This includes:
eliminating the most likely Dark
preserving likely Red players
avoiding moves that lead to parity loss
Risk vs Consequence
Not all risks are equal.
Two decisions may have similar probability, but different consequences.
Example:
Voting wrong at 9 → low consequence
Voting wrong at 8 → catastrophic consequence
Risk must always be evaluated together with consequence.
Information vs Elimination
Not all decisions aim to eliminate correctly.
Some aim to generate information.
Examples:
free voting
creating splits
delayed voting
forcing nominations
These actions:
may not remove a Dark immediately
but improve future decisions
Information EV vs Elimination EV
Strong Red play balances both.
Red vs Dark Risk Profiles
Red Team
minimise long-term error
maximise information
avoid catastrophic mistakes
Dark Team
increase uncertainty
force critical errors
accelerate losing transitions for Red
Dominated Decisions
Some decisions are always worse than alternatives.
Examples:
voting a less suspicious player when a stronger candidate exists
following votes without reasoning
avoiding responsibility in critical rounds
These should be eliminated entirely.
Regret Minimisation
When uncertain, ask:
“Can I justify this decision after the reveal?”
A correct decision should:
be explainable
match your reads
fit the structure of the game
Core Rule
Choose the decision that maximises team advantage while minimising structural risk.
Decision Trees
Every decision creates multiple possible futures.
This can be visualised as a decision tree.
In theory, trees are large and complex. In practice, strong players simplify them.
Two-Level Thinking
Instead of full trees, use:
Level 1: What happens immediately after the decision
Level 2: What happens after the next night/day cycle
That is enough for practical play.
Why Full Trees Are Impractical
A full tree includes:
all players
all votes
all future interactions
This is impossible to compute in real time.
Strong players reduce trees to key forks only.
World-Based Thinking
Instead of trees, think in:
World A → assumption 1
World B → assumption 2
Then test:
does behaviour match this world?
Decision Rule
Choose the branch that:
is most likely
leads to a better structure if correct
is least damaging if wrong
Information Paths
Every action generates information.
Strong players think not only:
“What happens if I’m right?”
But also:
“What do I learn from this action?”
Information Value
Information is a resource.
Some actions increase it:
free voting
forcing nominations
delaying decisions
creating splits
Red vs Dark Objectives
Red
play to learn
increase clarity over time
Dark
reduce clarity
create noise
force premature conclusions
Information vs Speed
Fast decisions:
reduce information
increase risk of error
Slow decisions:
increase information
but may allow Dark to manipulate
Balance is required.
Information Traps
Dark players can create false information.
Examples:
staged disagreements
fake conflicts
controlled vote patterns
These produce:
information that looks real
but leads to incorrect conclusions
Connection to Patterns
Information is not raw — it must be interpreted.
Patterns:
convert actions into meaning
help distinguish signal from noise
Core Principle
The best decision is not always the fastest or most confident — it is the one that improves future decisions.
Psychological vs Mathematical Choices
Mafia is played by humans, not equations.
Even perfect logic can fail if it ignores human behaviour.
When Mathematics Should Dominate
critical rounds (8, 7 players)
parity risks
sheriff-based decisions
clear probability advantages
In these cases:
Logic must override psychology.
When Psychology Matters More
early game
unclear structures
In these cases:
The table’s behaviour affects outcomes more than pure probability.
Table Influence and Leadership
Players do not act independently.
Some players:
lead decisions
influence votes
shape narratives
Ignoring this is a mistake.
Example:
correct vote mathematically
but table will not follow → decision fails
Common Psychological Factors
fear of being wrong
avoiding conflict
trusting confident players
Red vs Dark Use of Psychology
Red
stabilise the table
reduce chaos
guide decisions logically
Dark
increase chaos
create doubt
exploit confidence gaps
Core Rule
A correct decision that the table will not follow is not a complete decision.
You must consider:
not only what is correct
but what can be executed
Mixing Intuition Into a Logic-Driven Game
Intuition exists — but it must be understood correctly.
What Intuition Really Is
Intuition is not guessing.
It is:
fast pattern recognition based on experience
Examples:
tone changes
hesitation
unnatural reactions
emotional inconsistencies
Beginner vs Advanced Use
Beginners:
rely heavily on intuition
often without structure
Strong players:
use logic first
use intuition when logic is incomplete
Intuition Must Be Verbalised
As a Red player:
Intuition should be expressed, not hidden.
Example:
“I don’t have structural proof, but behaviour feels inconsistent”
This:
adds information to the table
allows others to evaluate it
Calibration of Intuition
Strong players:
track when intuition is correct
adjust trust in their reads over time
Danger: Overconfidence (“Power Play” 💪)
One of the most dangerous states:
Believing you have solved the game completely
This leads to:
ignoring contradictions
forcing incorrect decisions
collapsing team structure
Balanced Use
Correct approach:
use logic as foundation
use intuition as a supplement
never allow intuition to override clear structure
Core Rule
Intuition is useful only when it supports logic — not when it replaces it.
Chapter Summary
Decision-making under uncertainty requires:
probabilistic thinking
structural awareness
controlled risk-taking
information management
psychological understanding
disciplined use of intuition
The strongest players are not those who guess correctly — but those who consistently make the best possible decisions in unclear situations.
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