Agent-based Simulation of Socially-inspired Model of Resistance against

Unpopular Norms

Arshad Muhammad, Kashif Zia and Dinesh Kumar Saini

Faculty of Computing and Information Technology, Sohar University, Oman

Keywords:

Agent-based Modeling and Simulation, Unpopular Norms, Emperors Dilemma, Norm Aversion.

Abstract:

People lives in the society adhering to different norms. Some of these norms are unpopular. Sometimes,

for the overall societal good, it is necessary to oppose and possibly avert unpopular norms. To achieve this

goal, it is necessary to know the conditions, which enable persistence of the unpopular norms and models

that support possible aversion of them. This study attempts to elaborate the conditions and reasons for the

emergence, spreading and aversion of unpopular norms in society, using theory-driven agent-based simulation.

The simulation results reveal that in addition to agents actively participating in averting the unpopular norm,

incorporating a rational decision-making model in the population of agents is necessary to achieve a dominant

norm aversion. The signiﬁcance of these results concerning digital societies is enormous. In the new social

landscape dominated by digital contents (particularly of social networking), it can be argued that careful

amalgamation of social media contents can not only educate the people but also be useful in aversion of

undesirable behavior, for example, retention and spreading of unpopular norms.

1 INTRODUCTION

Social norms (Manning, 2013) are concepts and prac-

tices prevalent in society. They play a vital role in

development of social order (Ostrom, 2014). Norms

can change, create and affect the behaviors and vice

versa (Neighbors et al., 2015).

Social norms have a historical perspective, which

evolves into traditions and standards to which a so-

ciety can relate and act. An individual in a com-

munity is expected to behave according to the soci-

etal norms. However, the equation is not that simple.

Even following a societal norm do not meant to accept

it. There may be other conditions and incentives that

force an individual to follow a social norm (Morrow,

2015).

Social norms can be unpopular; a situation in

which majority of people do not agree. In fact, peo-

ple personally do not conform to these so-called “un-

popular norms”, but follow them and may be uninten-

tionally enforce others to follow them. In sociology,

such situations are dealt through a dilemma, named as

Emperors Dilemma as given in (Nkomo, 1992). It

relates to a tale in which everyone shows fake admi-

ration for new gown worn by an emperor even though

the emperor was naked. The cunning gown designers

announced that the (non-existent) gown would not be

visible to those who are not loyal to the emperor or

who are dumb. The fear of being punished and identi-

ﬁed as having inferior societal traits, no one spoke the

truth. The truth that the emperor was in fact naked.

In many places around the world, manifestations

of Emperors dilemma are evident. Whether it is

foot-binding in neo-Confucian China or inter-cousin

marriages and dowry in Asia (indicated by Blake in

(Blake, 1994) and Hughes in (Hughes, 1978), respec-

tively). People do not reveal what they believe due to

the fear of being identiﬁed as ignorant or anti-social.

However, there are evidences that a minority of ac-

tivists can make a big difference if the environment

is conducive as indicated by Khondker in (Khondker,

2011). Hence, the question “Can a minority of ac-

tivists change an unpopular norm adopted by the ma-

jority?” becomes relevant.

Silently following an unpopular norm at an indi-

vidual level is one thing. But, when a large popula-

tion adopts it, following an unpopular norm becomes

a kind of default behavior and inﬂuence the section of

the population, which does not follow or remains neu-

tral. As a consequence, it has been observed that peo-

ple even start enforcing unpopular norm to which they

personally disapprove. This behavior can be termed

as false enforcement. (Merdes et al., 2017) have fo-

cused on discovering the reason of wrong enforce-

Muhammad, A., Zia, K. and Saini, D.

Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms.

DOI: 10.5220/0006735501330139

In Proceedings of the 10th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2018) - Volume 1, pages 133-139

ISBN: 978-989-758-275-2

133

ment. The authors opinion that people falsely en-

force unpopular norms to create an illusion of sincer-

ity rather than conviction. The study has been tested

in two experiments of wine tasting and text evalua-

tion. Both experiments reveal that people who en-

forced a norm, even against their actual belief, in fact,

criticized deviants of the norm (the alternates of the

unpopular norm). These outcomes indicate how so-

cial pressure can lead to false enforcement of an un-

popular norm.

Essentially, norms propagation and transforma-

tion are co-relate to each other. Norms propagate

through diffused inﬂuence. Since the subjects being

inﬂuenced may have their perspective, they may de-

cide to adhere or reject it. As a consequence, recip-

rocating inﬂuence of the subjects may transform the

norm itself. Exploration of the scenarios of such na-

ture (“being inﬂuenced and inﬂuencing reciprocally”)

has been a subject of complex adaptive systems using

agent-based modeling as given by Macy and Flache

in (Macy and Flache, 2009; Macal and North, 2014).

Understanding the emergence of norms in a society of

agents is a challenge and an area of ongoing research

(Vouros, 2015).

To avert unpopular norms, it is necessary to un-

derstand the conditions that help to stop propaga-

tion of these norms. Especially, it is imperative to

ﬁnd the conditions necessary to establish the alterna-

tive norm (a reciprocal norm of prevailing unpopu-

lar norm) and the conditions that enforce others (peo-

ple other than activists) to follow the alternative norm.

Towards this, the social interaction model of unpop-

ular norm, proposed in (Centola et al., 2005) is cus-

tomized and extended.

Studying norms in society has been one of the re-

search focus of agent-based modeling community. A

lot of theoretical work has been done, in which agents

are supposed to comply with the social norms as given

in (Conte and Castelfranchi, 2001) and (Meneguzzi

et al., 2015). The fear of punishment from the society

is evidenced as the predominant factor behind com-

pliance of norms as presented in (Briggs and Cook,

1995). There are other examples, which focused on

the emergence of the norms and described strategies

that shows how norms prevail in any society explained

in (Sanchez-Anguix et al., 2013) and (Sato, 2012),

mainly governed by societal inﬂuence. Agents set

their goals and frequently change their behavior based

on societal inﬂuence (Vouros, 2015), until balanced.

By contrast there is limited work on how unpopular

norms can be averted. To the best our knowledge, we

found not a single agent-based model on this topic

except for our previous work (Zareen et al., 2016). In

this paper, we propose a model of (unpopular) norm

aversion. The agent-based model is simulated asking

important “what-if” questions to elaborate the condi-

tions and reasons for the emergence, spreading and

aversion of unpopular norms. Such conditions can

be analyzed and mapped onto the behavioral progres-

sions of real people and patterns of their interactions

to achieve improved societal traits especially using

the new social landscape dominated by digital con-

tents and social networking. Hence, it can argue that

careful amalgamation of social media contents, can

not only educate the people but also be useful in the

aversion of undesirable behavior, such as retention

and spreading of unpopular norms.

The rest of the paper is organized as follows. In

section 2, the motivation of the proposed model is pre-

sented, followed by the proposed model. In Section

3, the simulation scenarios and analysis of simulation

results is presented. The paper ends with conclusions

of the study.

2 MODELS

2.1 Motivation

(Centola et al., 2005) state the Emperors Dilemma

as: “Hans Christian Andersen tells the story of three

rogues who sell a foolish monarch a nonexistent robe

that they claim cannot be seen by those who are “unﬁt

for ofﬁce” or “incorrigibly stupid.” Fear of exposure

leads the emperor, and in turn, each of the citizens,

to express admiration for the new clothes, which then

reinforces the illusion of widespread support for the

norm. The spell is broken when a child, innocent of

the norm, laughs at the naked old man.”

The authors consider two type of agents; True Be-

lievers (TB) are those agents who follow or comply

with and enforce the unpopular norm, and Disbeliev-

ers (DB) are agents who do not genuinely believe in

the sanctity of the norm. The belief of an agent corre-

sponds to its true feeling about the unpopular norm; 1

for TB and -1 for DB. Based on their beliefs, agents

adopt a two-state compliance behavior (to comply or

not to comply). The initial value of compliance of

the norm is also set to 1 for TB, and -1 for DB. The

strength of an agent is directed by the relationship of

belief and compliance; hence, its value is equal to 1

for TB and a low random ﬂoating point number (0:0

to 1:0) for DB. A complying agent can also enforce

a norm. The enforcement is a by-product of agents

interaction in their proximity and dependent on en-

forcement need.

Each agent’s decision to comply with the norm is

given by a two-state value, which is dependent on the

ICAART 2018 - 10th International Conference on Agents and Artiﬁcial Intelligence

134

social pressure exerted by the neighbors and strength

of its belief.

(

−B

i f

−B

∑

j=1

> S

otherwise

(1)

In equation 1, C

is compliance of agent i, assigned a

two-state value either equal to the negation of its be-

lief or equal to its belief as it is. The compliance is

opposite of i’s original belief (B

) if aggregated en-

forcement (E) exerted by its neighbors (N

for all j’s

from 1 to count of N

) is more than its own strength

). Otherwise, it does not change.

An agent’s willingness to comply with the unpop-

ular norm can lead to norm enforcement (E

). Agent

enforcement can be true enforcement or false enforce-

ment, depending upon the situation.











−B

i f (

−B

∑

j=1

> (S

+ k))

6= C

)

i f (S

> k)

(Bi = Ci)

0 otherwise

(2)

In equation 2, E

is enforcement of agent i which is

assigned a three-state value either equal to negation of

its belief or opposite of it (the belief set through equa-

tion 1 above) or 0. The enforcement is negation of be-

lief set through equation 1 if aggregated enforcement

) exerted by its neighbors (N

for all j’s from 1 to

count of N

) is more than its strength (S

) plus a con-

stant k AND compliance and belief of the agent are

opposite to each other. Otherwise, if compliance and

belief of the agent are equal AND strength (S

) of the

agent weighted with enforcement need (W

) is greater

than constant k, the enforcement is equal to the belief.

Otherwise, there is no enforcement. The factor need

for enforcement (W

) is amount of agent’s neighbors

whose behaviors does not match with agent’s belief,

calculated by equation 3.

Wi =

1 −





∑

j=1

C j

(3)

2.2 The Proposed Model

As it is showed in the model presented above that a

TB is not a normal agent; i.e., it would never be af-

fected by whats happening in its surroundings. Our

model is based on reciprocity of this behavior. In the

proposed model, a notion of an activist is introduced.

An activist (ACT) is a DB who is ambitious and aims

to avert the unpopular norm. Like TBs, these ACTs

will never be affected by their surroundings. Hence,

they act as reciprocating inﬂuence to TBs.

Figure 1: Extended decision-making model of DBs.

The ACTs role will be triggered by the presence

of TBs in the surroundings, especially who are en-

forcing. An activist would change its belief from -1

to 1 after encountered by the enforcement of norms

from its neighborhood. This is accomplished by the

progressive increment of the value of S

by a constant

k. If this value reaches to 1 or greater, the belief of

the agent is changed from -1 to 1, which means that

now the agent believes in the aversion of the unpopu-

lar norm and acts to avert it.

In our recent work (Zareen et al., 2016), it is evi-

denced that high density conditions of agent popula-

tion with a high percentage of norm aversion activists,

the aversion of unpopular norms can be achieved. In

this paper, the model is further extended to incorpo-

rate the decision-making of a DB because of neigh-

borhood condition. It is proposed that DBs (who

are not ACTs) should not be considered as entirely

a numb entity. We propose a probabilistic decision-

making model. Figure 1 outlines the algorithm of the

proposed model.

Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms

135

Figure 2: NetLogo Simulation. (a) Setup of 1000 agents with 5% TBs and 5% ACTs. Agents represented as ﬁlled blue

triangles are TBs, whereas agents represented as blue persons are ACTs. The rest are DBs (in green). (b) Application of

model Centola proposed (Centola et al., 2005). The situation after equilibrium with respects to state changes is achieved. All

DBs comply with the unpopular norm B against their belief.

2.3 The Model Extension

Model is invoked by all the DBs which are not com-

plying with the unpopular norm. These are the DBs

which do not comply with the “unpopular norm”, but

still follow it (due to inﬂuence of the neighborhood).

These are the DBs which are mere followers and in

such a cognitive state which can be termed as “un-

sure”. It is assumed that these followers would be af-

fected by inﬂuence of the surrounding. If N is count of

all the neighbors, RLikes represents the ratio of neigh-

boring DBs which are of same kind. RComp is the

ratio of neighboring DBs which comply to unpopu-

lar norm. Whereas, RCompAll is the ratio of neigh-

boring DBs which comply to unpopular norm includ-

ing ACTs. Hence, RCompAll also includes inﬂuence

of ACTs in the surrounding. The attribute payoff is

a calculated probabilistic factor which would avert a

follower so that it starts complying to the “alternate

norm”. The following are the rules of change:

• R1: If majority of agents in the neighborhood of

a follower are also followers, and also there is a

signiﬁcant number of complying DBs, the value

of payoff is 20%. The incentive to deviate in this

case is quite low as majority is either followers or

DBs which comply to the unpopular norm.

• R2: If majority of agents in the neighborhood of

a follower are also followers, and there is not a

signiﬁcant number of complying DBs, the value

of payoff is 40%. The incentive to deviate in this

case is a little bit high because there are not many

complying DBs in the neighborhood.

• R3: If majority of agents in the neighborhood of a

follower are not followers but complying DBs, the

value of payoff is 50%. The incentive to deviate

in this case is purely random.

• R4: If majority of agents in the neighborhood of

a follower are not followers and complying DBs,

then there must be signiﬁcant number of ACTs in

the neighborhood. Hence, the payoff increases;

more in case of more ACTs (90%), and less in

case of less ACTs (70%).

3 SIMULATIONS AND RESULTS

3.1 Results

The simulation is performed in Netlogo (Wilensky

and Rand, 2015), a popular agent-based simulation

tool with grid space support. The agents reside on

cells of a spatial grid. We have used Moores neigh-

bourhood to represent the surrounding of an agent

which has been a popular strategy in many cell-based

spatial conﬁgurations (Weidmann et al., 2014). The

simulation space consist of a torus of 33 × 33 cells.

1000 agents are placed on cells without overlapping.

Figure 2 (a) illustrates simulation setup. The simula-

tion results are analyzed based on four quantities:

• DBComplBCount: The count of disbelievers

which comply with the unpopular norm B against

their belief.

• DBFollBCount: The count of disbelievers which

do not comply with the unpopular norm B, but fol-

ICAART 2018 - 10th International Conference on Agents and Artiﬁcial Intelligence

136

Figure 3: NetLogo Simulation. (a) Setup of 1000 agents with 5% TBs and 5% ACTs. Agents represented as ﬁlled blue trian-

gles are TBs, whereas agents represented as blue persons are ACTs. The rest are DBs. Application of model Zareen proposed

(Zareen et al., 2016). The situation after equilibrium with respects to state changes is achieved. DBs in the neighborhood of

ACTs start following (blue) and complying (red) norm A against their belief. (b) Application of proposed model extension.

The situation after equilibrium with respects to state changes is achieved. In addition to DBs in the neighborhood of ACTs

start following (blue) and complying (red) norm A against their belief, most DBs start complying norm A with a belief in it

(represented in pink color).

low it against their belief.

• DBComplACount: The count of disbelievers

which comply with the alternate norm A, but still

do not believe in it.

• DBBelACount: The count of disbelievers which

comply with the alternate norm A, and believe in

it.

The purpose and intention of the proposed model

is to reduce the value of DBFollBCount, because

these agents are unsure and their belief can potentially

be averted. The possible aversion may transform

agents from following status to those which are com-

plying with the alternate norm (DBComplACount).

The model Centola proposed (Centola et al., 2005)

only formulates the spread of unpopular norm. The

results of application of the model settles in an equi-

librium after 5

iteration. The graph corresponding to

visualization of Figure 2 (b) is shown in Figure 4 (a).

It is evident from the graph that all DBs after started

following the unpopular norm, quickly, start comply-

ing it.

After all DBs start complying with norm B, a

change in strategy is tested. The ACTs start playing

their role as proposed in (Zareen et al., 2016). The re-

sults of application of the model settles in an equilib-

rium after 10 − 12

iteration. The graph correspond-

ing to visualization of Figure 3 (a) is shown in Figure

4 (b). It is evident from the graph that DBs start com-

plying with alternate norm A under the inﬂuence of

ACTs. The number of DBs which are merely follow-

ing again goes up in the start but it does not drop to

0, after transforming to compliance state. The DBs

following and complying to norm B stabilizes with

followers more than agents which are complying. As

shown in Figure 3 (a), DBs in the neighborhood of

ACTs start following and complying norm A, against

their belief.

The proposed model extension again achieve an

equilibrium. The graph corresponding to visualiza-

tion of Figure 3 (b) is shown in Figure 5 (a). It is

evident from the graph that DBs start complying with

alternate norm A under the inﬂuence of ACTs. How-

ever, a large majority of DBs start complying norm

A with a belief in it. And number of DBs following

norm B reduces to almost nothing.

Finally, we have compared the above situation

with a situation in which there are more TBs and more

ACTs than before. By comparing the two graphs (Fig-

ure 5 (a) to (b)) it is evidenced that the pattern and

rate of state changes is similar, but, aggregate num-

ber of DBs in state DBBelACount and DBFollBCount

has decreased substantially when compared with ag-

gregate count of DBComplACount and DBComplB-

Count. It means the DBs which believed in norm A

has decreased to almost half as the number of TBs and

ACTs is doubled.

3.2 Discussion

The main idea is to reduce the disbelievers complying

with unpopular norm B. This is achieved by simulat-

Agent-based Simulation of Socially-inspired Model of Resistance against Unpopular Norms

137

Figure 4: Graphs of Number of DBs in different states vs. Simulation Time. (a) Results of model Centola proposed (Centola

et al., 2005). (b) Results of model proposed in (Zareen et al., 2016).

Figure 5: Graphs of Number of DBs in different states vs. Simulation Time. (a) Results of proposed model extension with

5% TBs and 5% ACTs. (b) Results of proposed model extension with 10% TBs and 10% ACTs.

ing model proposed from 95% agents to almost 25%

agents. However, 50% agents still follow norm B, and

the only 20% start complying with alternate norm A.

These results are evident from graphs in Figure 4. The

credit goes to introduction of ACTs in the population

of agents.

The introduction of rational-decision making

while making a choice to follow / comply / believe

in unpopular norm (as proposed in this paper) sig-

niﬁcantly improves the results. While the disbeliev-

ers complying with unpopular norm does not change

much, but, the majority of disbelievers start believ-

ing in alternate norm instead of following unpopular

norm. This is evident when graphs of Figures 4 (b)

and 5 (a) are compared with each other. Hence ratio-

nal thought process changes even the beliefs of ma-

jority of the agents. Increasing the percentage of TBs

and ACTs in the population also signiﬁcantly change

the situation when applied in conjunction with ratio-

nal thought process.

4 CONCLUSION

In this work, it is argued that for societal good, it is

necessary to oppose and possibly avert the unpopular

norms. Hence, an attempt is made to realize the con-

ditions that result in emergence of unpopular norms

and deﬁne situations under which these norms can be

changed and averted.

In this paper, an agent-based simulation model of

unpopular norm aversion is presented. The recipro-

cal nature of persistence and aversion of norms is uti-

lized to deﬁne situations under which these norms can

be changed and averted. The simulation results re-

vealed that, in addition to agents actively participat-

ing in averting the unpopular norm, incorporating a

rational decision making model for normal agents is

necessary to achieve a dominant norm aversion. It

was also evidenced that the percentage of true believ-

ers and activist play a signiﬁcant role in norm aver-

sion dynamics. Overall, the simulation results reveal

that more educated and socially active individuals are

the key to reduce undesirable norms in a society. The

signiﬁcance of this fact is also applicable to digital

societies primarily created by social networks now-a-

days.

ICAART 2018 - 10th International Conference on Agents and Artiﬁcial Intelligence

138

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