経営課題にAIを!ビジネスインフォマティクス研究会 (第17回)
JSAI Special Interest Group on Business Informatics (SIG-BI #17)

Bias-generating Agent-based Simulation and its
Application to Election Systems
Jiateng Pan1 , Atsushi Yoshikawa1 , Masayuki Yamamura1
1

School of Computing, Tokyo Institute of Technology, Japan

Abstract: Agent-based-Simulation can be used to study the impact of individual decisions on the overalloutcome. There has been a lot of research focused on giving the agent the ability to learn, especially there
are studies that analyze corporate organizations using reinforcement learning. We focus on the electionphenomena that there are often cases where popular candidates are suddenly overtaken by others in theelection campaign. We try to use the characteristic of overfitting of neural networks and conditional reflex
learning, such as the “Pavlov’s dog” effect, to explain the phenomena.
It is these biases that make people tend to make1 Research Backgroundemotional decisions.

Agent-based simulation, or ABS for short, is aTherefore, it is difficult to use models to explain

method that allows a plural number of agents that

why there are often cases where popular candidateswill interact with each other according to a pre-set

are suddenly overtaken by others in the election

process

campaign.

andfinallyobserve

theoverall[1,2,3,4,5,6]

changes.

To better perform ABS with human subjects[7,8,11,12,13,14]

There are also many studies

applying

ABS for voting simulation.

propose an agent learning model that generatesJiménez-Rolland M et al. study
computersimulations

such as voting simulation, it is important to[9]suggested thatcan provide

insights into the performance of voting methods on
different collective decision problems. Meanwhile
M.A Wiering et al. simulated the majority voting[10]

results using cellular automata"bias" and apply it to ABS.

valuable.

Anaëlle Wilczynski[7] considered the effect ofheuristic algorithms on voting results andperformed simulations.
But in either case, the thrust of ABS-based
research focuses on how to make the "optimal"

2 Purpose
The purpose of this study is to develop an agent
learning model that generates "bias" and apply it toABS.
Finally, apply this new ABS to a voting systemand observe its effect.
Therefore, this study can be divided into threeparts.

decision for the agent at all times. This is

Building an agent learning model that generates

applicable in many contexts. But in experiments"bias", building an ABS-based voting model, andwith humans or other intelligence, it may not be so

combining the two for simulation observations.

applicable.
Because, human beings are not always "rational",
people tend to have some "biases" in the process oflearning about the environment, such as treatingsome random and accidental events as inevitable.

3 Bias-generating Agent
3.1 Bias
To build an agent learning model that generates

経営課題にAIを!ビジネスインフォマティクス研究会 (第17回)
JSAI Special Interest Group on Business Informatics (SIG-BI #17)

"bias", it is first necessary to understand "What isbias, so this study would prefer a model that isbias?".

prone to overfitting.

Lucy Suchman, a professor of anthropology of

3.3 Bias-generating Neural network

science and technology at the Department ofSociology at Lancaster University in the United
Kingdom, put forward the view that "all humanbehavior is based on improvisation under certainconditions”.
This view implies that people perform actions
that are immediate and based on conditioned
reflexes in a situation.
Therefore, certain conditioned reflexes that do
not correspond to the actual situation in terms ofresults can be considered as a kind of bias.
From this point of view, the "Pavlov's dog"

Figure2experiment is a good illustration of the creation of

In this section, we build a neural network model

a bias:that uses the overfitting property of neural

Give the dog food along with something special(like a bell). After many times, the dog will salivateeven if no food is given, but only the bell is heard.
This is a reflection of a bias (ringing = food ->drooling) that the dog has developed because of aparticular circumstance.

networks to simulate the "Pavlov's dog" effect.
The dog model is shown in Figure2, divided into
two parts, the reflex nerve, and the brain.
The brain simply does the mechanical step of"drooling at the sight of food".
The main focus is on the reflex nerve part.

I call this phenomenon the "Pavlov's dog" effect.

3.2 Overfitting

Figure3: neural networkThe neural network has 3 inputs, corresponding
Figure1: Overfitting

to the presence or absence of food, bells, andOverfitting is a common problem in artificialpictures. The output is the presence or absence ofintelligence learning.

drool.

It refers to the fact that excessive learning allows

The structure of a single neuron is shown in

the AI to learn something unnecessary andFigure4. It will accept the input passed from therandomly occurring by chance along with it asprevious layer and compute the output to the next

correct knowledge.

layer, and will also accept the return value from theUsually, in the design of artificial intelligence,
we want to avoid the overfitting phenomenon as
much as possible.
But learning "random things by chance" ascorrect knowledge is the same as our perceived

next layer and use it to update its weights.
The return value used for neural network
backtracking is then the difference between theoutput of the brain and the output of the neuralnetwork.

経営課題にAIを!ビジネスインフォマティクス研究会 (第17回)
JSAI Special Interest Group on Business Informatics (SIG-BI #17)

4 ABS-based voting model
In this chapter, an ABS-based voting model will
be built, in which all agents as voters will vote forthe candidates, and the environment will makepublic the results of counting all agents’ voting
objects as polls. At the same time, there is an option
to tamper with the results of the polls for votemanipulation.

4.1 Agent
Agents will vote based on the results of theirsocial networks and public polls, combined withFigure4

their confidence level and ranking of their

At the same time, we not only let the learning

preferences for candidates. The model proposed byrate (LR) be 1 but also each backtracking will beDr. Anaëlle Wilczynski [7] is used here.

repeated several times for faster overfitting.

4.1.1 Social networks

3.4 Simulation results

Social network refers to the set of other agents

Similarto

thereal-life"Pavlov's

dog"

experiment.

that an agent can see, where " can see" means thatthe agent can know the voting objects of otherFor the first input, we gave both food and bell,

agents in the social network.

and after 60 repetitions, we gave both food andThe sparseness of social networks affects thepicture 100 times, and finally re-given food andagent's reliance on the results of public opinion

bell several times, observing at each moment

surveys.

whether the simulated dog would drool if the bell

In this study, for convenience, and contrary to Dr.

or the picture were given alone. The results areAnaëlle Wilczynski's view, the density of social

shown in the Figure5

networks is shown in Figure6.

Figure6
Figure5This will be a two-way network, using theAs can be seen from Figure5, the simulated dog

concept of distance instead of density. When thewill indeed be given "bell" + "food" at the samedensity is p, the agent's social network is the set oftime several times, and finally do only hear theall agents within p distance from itself.

"bell", will also drool.
And this phenomenon can be changed.
Therefore, it is believed that this neural network
can produce the "Pavlov's dog" effect.

4.1.2 Confidence level
As with the density of social networks,
confidence level also affects the agent’s votingresults.
Each agent combines the results of polls and the経営課題にAIを!ビジネスインフォマティクス研究会 (第17回)
JSAI Special Interest Group on Business Informatics (SIG-BI #17)

votes of people around them to have a guess, called
TC[ci], about the number of candidate ci votes.
And each agent has a preference ranking R[ci]Figure7.1

for each candidate.
Confidence is used if the value of TC[ci] +confidence is greater than all other TC[ci], then ci
is the possible-winner PW.
After identifying all possible winners, the agent

Figure7.2

will vote for the top possible-winner in thepreference ranking R[ci].
4.1.3 Behavior pattern
The behavior pattern of the agent is shown inFigure7.3

Argo1.

Figure7.4

4.2 Environment
The environment will count all agent's voting
targets and make the poll results public and will
Figure7.5

tamper with the poll results if necessary, as shownin Argo2.

Figure7.6
Comparing 7.1 and 7.5, it can be concluded that
Table1 Parameter table
Parameter

the confidence level has an impact on the voting
Valueresults.
Comparing 7.1 and 7.2, it can be concluded that

Number of voters

IntegerNumber of candidates

Integer

Density

IntegerComparing 7.3 and 7.4, it can be concluded that

Confidence levelInteger

tampering with polls to manipulate voters' votes

Whether to tamper

Booleanbecomes difficult in the case of a high density of

Availability of neural networks

Booleansocial networks.

4.3 Simulation results
Based on the above steps, six simulations wereperformed and the results are shown in Figure7.

the heuristic algorithm can induce voters' votes.

5 Bias-generating ABS-based
voting model
Differences between this model and the ABS-経営課題にAIを!ビジネスインフォマティクス研究会 (第17回)
JSAI Special Interest Group on Business Informatics (SIG-BI #17)

based voting model ：-> Each agent has a Bias-generating Neuralnetwork
Figure8.1-> The agent learns after each new poll result
is obtained by the neural network.

5.1 Bias-generating Neural network
This neural network is a 3*3 neural network
Figure8.2

with 3 inputs and 1 output.
The inputs are respectively.
1. Was the candidate he voted elected? 0;1
2.Was the candidate he liked the mostelected?0;1
3. Was the candidate with the most votes in his
social network elected? 0;1

Comparing the results of the two simulations,
we can see that
Even with a neural network, the heuristic
algorithm is still effective in manipulating votes bytampering with the poll results.
However, this sometimes leads to unexpected

The output is the confidence level.

situations, such as the one on the right, where theThe range of self-confidence is between 1 andghost candidate J is elected once instead.

10, but the output of the neural network is between
0 and 1.
Thus, we have:

I call this situation "tampering that gets out ofhand".
Of course, due to the time problem, the number

confidence= ⌊ output*10 + 1 ⌋of simulations is small, so it is hard to say that thisAs mentioned earlier, the confidence level is theis not a "special case".

agent's prediction of the value of its vote.
So, if the agent voting object does not get to thispredicted increase in votes, the confidence should
be reduced, and conversely, the confidence should
be increased.
We make the change in the number of votes of
the agent voter from the last result as △.
So, the return value = (△-confidence)/105.2 Voting Process
The process of Bias-generating ABS-based
voting model is shown in Argo3.

6. Summary
In this study, a Bias-generating Neural network
is built and applied to a simple dog model toreplicate the "Pavlov's dog" experiment by
exploiting the overfitting property of neuralnetworks.
A simple voting model was also built. Thismodel can be used to simulate the winning of avote under various scenarios (tampering or not,density size of social networks, the number ofvoters/candidates, etc.).
This voting model shows that heuristicalgorithms to manipulate voter voting are effective.
This is in line with the results of the model

5.3 Simulation results
Based on Argo3, simulations were performed
and the results are as figure8：

proposed by Dr. Anaëlle Wilczynski.
At the same time, I applied the neural networkmodel that produces the "Pavlov's dog effect" tothis voting model and performed simulations.
Although there is not much data yet, it seems

経営課題にAIを!ビジネスインフォマティクス研究会 (第17回)
JSAI Special Interest Group on Business Informatics (SIG-BI #17)

that the heuristic algorithm has the potential to "getおける流行の流布と初期採用者の配置に関す

out of hand".

る研究」追手門経営論文集、Vo.20, No.1, pp.27-

In future work, I will continue to improve the48,( 2014)

algorithm next while increasing the number of[6] Alan Kirman: Learning in Agent-Based Models,simulations and increasing the randomness of theEastern Economic Journal, Vol. 37, No. 1, pp.20-

simulations to get more data.

27 (2011)[7] Wilczynski A. Poll-confident voters in iterative
voting[C]//Proceedings of the AAAI Conference

Acknowledgment

on Artificial Intelligence. 2019, 33: 2205-2212.

Special thanks to Mr. Takahashi and Mr.

[8] Muñoz D F, Gardida H, Velázquez H, et al.

Kitazawa. They provided a lot of advice and help

Simulation models to support the preliminary

in this research. Thanks also to everyone in theelectoral results program for the Mexican Electoral

Yoshikawa Research Office for their help.

Institute[J]. Annals of Operations Research, 2020:
1-16.
[9]Jiménez-Rolland M, Macías-Ponce J C, Martínez-ReferencesÁlvarez L F. Using simulation in the assessment of[1] Hossein Nasrazadani, Mojtaba Mahsuli. Probabilistic

voting procedures: An epistemic instrumental

Framework for Evaluating Community Resilience:approach[J].

Integration of Risk Models and Agent-Based0037549720923031.

Simulation[J].

Journalof

StructuralEngineering,2020,146(11).
Behavior

and2020:[10] Dominguez Arroyo P. Artificial Life Simulation ofthe Majority Vote[D]. , 2020.

[2] Hwang Yi. Visualized Co-Simulation of Adaptive
HumanSIMULATION,

Dynamic[11] Allen T T. Introduction to discrete event simulation

Building

and agent-based modeling: voting systems, healthPerformance: An Agent-Based Model (ABM) andcare, military, and manufacturing[M]. SpringerArtificial Intelligence (AI) Approach for Smart

Science & Business Media, 2011.

ArchitecturalDesign[J].

Sustainability,2020,12(16).

[12] Kononovicius A. Empirical analysis and agentbased modeling of the Lithuanian parliamentary[3]. COVID-19/SARS-CoV-2 News from Preprints;

elections[J]. Complexity, 2017, 2017.

Impact Assessment of Full and Partial Stay-at-[13] Riddle R. 2020 US Elections: ModellingHome Orders, Face Mask Usage, and Contact

Subjectivity in Voting Behaviours Using Active

Tracing: An Agent-Based Simulation Study ofInference[J]. 2020.

COVID-19 for an Urban Region[J]. Medical Letteron the CDC &amp; FDA,2020.

to predict the outcome of a two-round election[J].

[4]. Sociology - Artificial Societies and Social
Simulation; Investigators at Technical Universityof Crete Report Findings in Artificial Societies andSocial Simulation (An Agent-based Model for
Simulating

Inter-settlement

Societies)[J].

Politics[14] Charcon D Y, Monteiro L H A. A multi-agent system

Trade&amp;

InPastGovernment

Business,2020.
[5] 中野統英、周慧：「Q 学習を含むエージェントによエージェントベースシミュレーションにApplied Mathematics and Computation, 2020,386: 125481.