CITS 3001 - 2022
Project: A game on operations in the information environment
Due Date: 13th October
Implementation: Java or Python

Game Scenario
There are four teams involved: Red, Blue, Green and Grey.
The scenario has been deliberately designed to represent the uneven playing field of the
contested environment between the various teams. The scenario highlights the
vulnerabilities of blue team in the contested information environment. The concept of blue
and red teams is prevalent in cybersecurity related serious games or wargames. If you wish
to get some background knowledge about the functioning of teams, you can read this article:
https://csrc.nist.gov/glossary/term/red_team_blue_team_approach. However, this game is
not related to cyber security, rather we are modelling the information environment in a
country.
Red and Blue teams are the major geopolitical players in this fictitious country.
Red team is seeking geopolitical influence over Blue team. Of particular interest to Red team
is influence over Green population and the Government. Blue is seeking to resist the Red
teams growing influence in the country, and promote democratic government in the Green
country.
A key challenge faced by the Blue team, that will become apparent in the exercise, is that
their democratic values are leveraged against them. They are vulnerable to some forms of
manipulation, yet their rules-of-engagement do not allow them to respond in equal measure:
there are key limitations in the ways in which they respond and engage in this unique
battlespace. The Blue team is bound by legal and ethical restraints such as free media,
freedom of expression, freedom of speech.
The Green team lacks a diverse media sector, it is confused and there is a wide range of
foreign news broadcasting agencies Green’s population has subscribed to. The Green
population suffers from poor internet literacy, and the internet literacy can be modelled via
pareto distribution. The government lacks resources to launch a decisive response to foreign
influence operations and a lack of capability to discover, track and disrupt foreign influence
activity.
The Red team, an authoritarian state actor, has a range of instruments, tactics and
techniques in its arsenal to run influence operations. The Green government can block
websites and social media platforms and censor news coverage to its domestic population
whilst maintaining the capability to run sophisticated foreign influence operations through
social media.
The Grey team constitutes foreign actors and their loyalties are not known.
Election day is approaching and the Red team wants to keep people from voting.

Population Model:
An underlying network model that define the probability of nodes interacting with each other.
Majority of the nodes, over 90%, will belong to green team and they depict the population of
the country. A small percentage of nodes will be red, blue and grey. At the beginning grey
nodes are not part of the network.
Each green node/agent has an opinion and an uncertainty associated. In every simulation
round nodes will interact with each other and affect each others’ opinions. The more
uncertain an agent is, the more likely their opinion would change. The probability of
interaction is not uniform across all nodes. Some nodes (for instance those in a household),
may have a higher probability to interact.
How teams are going to take turns:
Teams are going to take turns one by one.
1. Red Team: You need to create function where red team (only 1 agent) is able to
interact with all members of the green team. The agent affects the opinions and
uncertainty of the green team during the interaction. The catch is that you need to
select from 5 levels of potent messaging. If the red team decides to disseminate a
potent message, during the interaction round, the uncertainty variable of the red team
will assume a high value. A highly potent message may result in losing followers i.e.,
as compared to the last round fewer green team members will be able to interact with
the red team agent. However, a potent message may decrease the uncertainity of
opinion among people who are already under the influence of the red team (meaning
they are skeptical about casting a vote). You need to come up with intelligent
equations so that red team improves the certainity of opinion in green agents, but at
the same time does not lose too many green agents. Think of it as a media channel
trying to sell their narrative to people. However, if they may big, claim, lie too much,
they might lose some neutral followers which they could indoctrinate with time.

2. Blue Team: Similarly, blue team can push a counter-narrative and interact with green
team members. However, if they invest too much by interacting with a high certainty,
they lose their “energy level”. If they expend all their energy, the game will end. You
need to model this in way that the game keeps going on while the blue team is
changing the opinion of the green team members. Blue team also has an option to let
a grey agent in the green network. That agent can be thought of as a life line, where
blue team gets another chance of interaction without losing “energy”. However, the
grey agent can be a spy from the red team and in that case, there will be a round of
an inorganic misinformation campaign. In simple words, grey spy can push a potent
message, without making the red team lose followers.

Rubric:
Criteria Excellent Good Satisfactory Inadequate Comments Max
Marks
Selection and
design of
appropriate AI
technology

Good choice of
design, with
clear
explanation and
complete
justification

Suitable
choice of
design with
clear
explanation
and some
justification

Suitable design
chosen and
explained, but
justification lacking

Design is
unsuitable, or not
clearly explained.

10pts
Validation of
Agent,
including
answers to the
questions

Comprehensive
tests, with
insightful
metrics

Good
coverage of
performance
and
complexity.

Some tests given,
but results
inconclusive or
incomplete

Few or no tests,
or no meaningful
metrics used.

20pts
Implementation
of agent

Complete and
correct
implementation
with best
practice data
structures used.

Complete
and well
formatted
code with
suitable data
structures

Mostly correct code
with adequate data
structures and
algorithms

Not formatted or
incomprehensible
code, with
flawed data
structure choice

10pts
Agent Design Best practise
data structures
and algorithms,
challenging,
non- trivial
code and ability
to learn
strategies from
the past
simulations

Best practise
data
structures
and
algorithms,

Sound choice of
data structures and
algorithms,
pragmatic design
decisions

Substandard data
structures and
algorithms, or
trivial basic code.

10 pts
Performance of
Agent when
playing with a
human

Runs and
performs at an
excellent level
with
challenging
play

Runs and
generates
realistic
strategic play

Runs with some
non-trivial strategic
play

Does not run or
trivial strategy


10 pts
Extra credit Excellent
Visualisation
on a grid, along
with current
parameters
Good
visualisation
and display
of parameters
Weak attempt at
visualisation/display
of parameters
Not a satisfactory
attempt at
visualisation or
display of
parameters
10pts


Input:
In order to run automatic simulations you only need the following inputs:
1. Number of agents in the green team and probability of connections (n,p)
2. Number of agents in the grey team and proportion of agents who are spies from the
red team
3. Uncertainty interval (e.g, a tight interval such as [-0.9, 0.1], or a broad interval such as
[-0.5, 0.5]). To make it simple, the more positive a value is the more uncertain the
agent is and the more negative the value is the more certain the agent is.
4. Percentage of agents (green) who want to vote in the election, at the start of the game.

In order to learn more about your agents, you can run multiple simulations on varying
parameters and analyse the results.

For a human to play as a red or a blue agent, you need to display a list of options to the player
at their turn. For a blue agent, the options will consist of a) - 10 correction messages (Please
come up with some fictitious messages), uncertainty value and associated energy loss, b) a
choice to introduce a grey agent in the game play. For a red agent, the options will consist of
a) - 10 pieces of misinformation (Please come up with some fictitious messages), uncertainty
value and followers loss.

Output:
- Working Code
- Snap shot of all parameters at any given time, should we desire to have a look.
- An approximately 3000 words essay describing your game, addressing the rubric and
explicitly answering the following questions:

1. How does the game change if you have a tight uncertainty interval at the
beginning of the game?
2. How does the game change if you have a broad uncertainty interval at the
beginning of the game?
3. What effect a zero uncertainty has on the game play.

Plot distribution of uncertainties for each of the above question.

4. In order for the Red agent to win (i.e., a higher number of green agents with
opinion “not vote”, and an uncertainty less than 0 (which means they are
pretty certain about their choice)), what is the best strategy?
a. Discuss and show with simulation results how many rounds red agent
needs in order to win?
5. In order for the Blue agent to win (i.e., a higher number of green agents with
opinion “vote”, and an uncertainty less than 0 (which means they are pretty
certain about their choice)), what is the best strategy?
a. Discuss and show with simulation results how many rounds blue
agent needs in order to win?
b. What impact did grey agents have on the simulation?