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A Dynamic Model of Integration

Joseph D. Johnson* 1

Marisa C. Eisenberg 2

1 Department of Mathematics and Statistics, Carleton College, Northfield,
Minnesota, United States 2 Center for the Study of Complex Systems, University
of Michigan, Ann Arbor, Michigan, United States

*<EMAIL_ADDRESS>
## Abstract

Thomas Schelling introduced his agent-based model of segregation in 1971 and
concluded that even when there is a low amount of intolerance within society
that segregation will develop if people follow their individual preferences. A
large body of literature building of this framework has been built and has
bolstered this claim. This paper aims to take the same framework but instead
look for ways to get to an integrated state. We focus on Allport’s contact
hypothesis that states that if there is equal status among groups, common
goals among groups, and an institutional mechanism supporting intergroup
contact then intergroup contact can reduce prejudice. We incorporate the
contact hypothesis by having individuals adjust their intolerance based on
their current neighborhood composition and the ease of conforming to their
surroundings. Furthermore, we add in positive and negative media effects, as
individuals are likely to get information about an outgroup from the media
(e.g., news, TV, movies, etc.) that they consume. We find that having a
society composed of individuals who do not easily conform to their
surroundings and displaying positive examples of both groups in media promote
integration within society.

## Introduction

Thomas Schelling created an agent-based model (ABM) of neighborhood
segregation in 1971 [1]. The model showed how individual preferences can lead
to larger scale segregation within a population. Schelling’s research revealed
that segregation can occur even when individuals can tolerate being in the
minority if agents move according to their individual preferences.

Many extensions to Schelling’s segregation model have been implemented, adding
components such as housing markets [2, 3], networks [4], reinforcement
learning [5] and external meeting places [6]. Many extensions of Schelling’s
work land on the idea that segregation is quite stable, with only a handful of
papers exploring factors that lead to integration [7].

Segregation has been proven to have negative effects on society[8]. For
instance, it limits economic opportunities for Black individuals[9] and is
associated with a reduction of access to healthy food options for Black
individuals[10, 11]. Therefore, avoiding segregation should be a priority, and
seeking potential solutions would benefit the public.

In this paper, we build off Schelling’s segregation model in order to find
situations where integration occurs. The main extensions included in this
model are the effects of intergroup contact and media influence. Intergroup
contact in this model is based on Allport’s contact hypothesis [12]. Allport
postulated a hypothesis that intergroup contact reduces prejudice when there
is equal status among groups, common goals among groups, and an institutional
mechanism supporting intergroup contact. In our model we will be considering
contact by itself, assuming the other factors are already in place.

This paper is not the first paper to incorporate adaptive tolerance and the
contact hypothesis into their analysis of segregation. Urselmans and Phelps
[13] developed a model where agents became more tolerant if they were
satisfied with the percentage of their ingroup and if that agent comes into
contact at least one agent from their outgroup. Agents will decrease in
tolerance if they are surrounded by agents from the outgroup. The amount the
tolerance changes is fixed being equivalent for increases or decreases in
tolerance and is set before the simulation begins.

The authors also included migration into their analysis, controlling the final
proportion of migrants to native agents and the number of migrations waves (if
the number of waves were greater than one then the amount of migrants each
wave were adjusted to hit the desired migrant-to-native proportion).

Their work focused on migration and showed that under their framework that the
tolerance will polarize to the extremes with agents becoming extremely
tolerant or extremely intolerant. Here, the options for the equlibrium (ex:
extremely tolerant, extremely intolerant or a mix of intolerant and tolerant)
is determined by the amount that tolerance changes and proportion of native
and migrant agents.

We believe our work adds to the body of work that analyzes segregation via
agent-based modeling by varying the amount that agents adjust their tolerance
based on that agent’s neighborhood composition. We believe that this link
between agents reaction and the neighborhood composition is plausible due to
the survey results in the 1976 and 1992 Detroit Area Survey which find
people’s willingness to move to an area is dependent on the neighborhood
current composition [14, 15].

Frequently, individuals from different groups do not interact with one another
directly. As a result, media has become a significant influence on people’s
perceptions of out-groups. Research has demonstrated that media can either
increase or decrease an individual’s prejudice levels, depending on how the
out-group is portrayed[16, 17] . To account for this, we have included
mechanisms that can adjust an agent’s tolerance level based on the type of
media they consume.

Individuals move for various reasons outside of dissatisfaction with the
racial or ethnic composition of their neighborhood. The Joint Center for
Housing Studies (JCHS) [18] states “40 percent of movers did so for housing-
related reasons in 2019, 27 percent moved for family-related reasons, 21
percent for job-related reasons, and 12 percent for other reasons”. Hence, the
model also incorporates movement that is due to factors outside of the
composition of an agent’s neighborhood composition.

We recognize that the Schelling framework emphasizes individual mechanisms
that cause segregation rather than societal factors that contribute to it,
such as zoning laws, infrastructure that separates racial groups, and local
racial covenants[19, 20]. Our current investigation is introductory and lays
the foundation for future work that builds upon this analysis.

## Materials and Methods

We create agents on a 51 $\times$ 51 grid. During the initial phase of the
simulation, each grid cell has a probability, determined by the density
parameter, of having one agent placed on it. The agent that is generated also
has a probability of being either red or green, which is controlled by the
red-percent parameter. The color of the agent indicates its “racial” group.

We take inspiration from the original Schelling model and assume agents have
an intolerance level at timestep $t$, $I^{t}_{j}$, represented as the desired
percentage of their neighbors who are from the same group. At the beginning of
the simulation all agents are given the same intolerance is given by $I^{0}$.
We define a neighborhood using the Moore neighborhood, which consists of the
eight grid cells surrounding an agent. If the proportion of neighbors from the
same group is lower than an agent’s desired percentage, they will move to
another location that meets their desired percentage. In our model, if no
empty grid cells satisfy an agent’s desired neighborhood composition, the
agent will move to the empty space with the highest percentage of agents from
the same group.

We have also incorporated a parameter, non-racial-move, which allows for a
small chance of agents moving for reasons other than neighborhood composition,
such as job changes, the housing market, or school systems. This addition
reflects the reality that individuals do not exclusively base their moving
decisions on neighborhood demographics. Alongside the probability of agents
moving due to dissatisfaction with their neighborhood, there is an additional
probability, at each time step, that agents will randomly move to an empty
grid cell, disregarding neighborhood composition.

We borrow measures of segregation from Wilensky’s NetLogo Segregation Model
[21]. The level of segregation—assigned to variable percent-similar—is given
by the mean number of agents that are similar to a given agent on a grid
divided by average number of neighbors an agent has. This means that if one
starts with a population split evenly between the two groups, an integrated
equilibrium should have an average percent similar value close to 50 percent.

The stopping condition is determined by tracking percent-similar and the mean
intolerance $\overline{I^{t}}=\sum_{j}I^{t}_{j}/N$—$N$ is the number of
agents. We keep only the last 100 timesteps. If the standard deviation of
percent-similar is within 1 and the standard deviation mean intolerance is
within 0.5 then the simulation stops. There is a timescale $\tau$ called the
adaptibility.

Below, we lay out how intergroup contact and media effects will be implemented
in the simulation.

### Intergroup Contact

As a starting point we borrow from Sabin-Miller and Abrams’s work on political
polarization [22]. In their work, they develop an opinion model where
individuals move towards political opinions that are close their own, but will
be repulsed by opinions that are far away. We assume similar dynamics hold for
intolerance values of the model’s agents. In our model, agents compute the
absolute difference of the intolerance $I^{t}_{j}$ to the current percentage
of individuals in its Moore neighborhood that are from the same group
$\%^{t}_{j}$ (this includes the agent itself). Specifically, the agents check
if the absolute difference, $|\%^{t}_{j}-I^{t}_{j}|$, is bigger or smaller the
“conformity” parameter $\lambda$.

Conformity, represented by the parameter $\lambda$, determines the degree of
tolerance an agent has for the difference between its own intolerance and the
neighborhood composition it desires. If the absolute difference between the
neighborhood percentage and its intolerance value is smaller than the
conformity parameter, i.e., $|\%^{t}_{j}-I^{t}_{j}|<\lambda$ then the the
intolerance value will: (1) increase if the neighborhood percentage is larger
or (2) decrease if the neighborhood percentage is smaller. If the absolute
difference between the neighborhood percentage and its intolerance value is
larger than the conformity parameter, i.e., $|\%^{t}_{j}-I^{t}_{j}|>\lambda$,
the intolerance value will: (1) increase if the neighborhood percentage is
larger or (2) decrease if the neighborhood percentage is smaller. The way that
the intolerance changes in response to neighborhood composition is outlined in
Table 1.

Sign of $\%^{t}_{j}-I^{t}_{j}$ |  $|\%^{t}_{j}-I^{t}_{j}|<\lambda$ or $|\%^{t}_{j}-I^{t}_{j}|>\lambda$? | Sign of $f(\%^{t}_{j},I^{t}_{j})$
---|---|---
Negative | Less Than | Negative
Negative | Greater Than | Positive
Positive | Less Than | Positive
Positive | Greater Than | Negative
Table 1: How the reaction function $f(\%^{t}_{j},I^{t}_{j})$ works. Non-
boundary cases are illustrated in this table. If the absolute difference
between the actual neighborhood composition and the desired neighborhood
composition (intolerance) is smaller than the conformity then the change
intolerance has the same sign.

An agent’s reactions to it’s given surroundings will be given by the cubic
function $f(\%^{t}_{j},I^{t}_{j})$, illustrated in Fig. 1 . I define
$f(\%^{t}_{j},I^{t}_{j})$ as follows,

$f(\%^{t}_{j},I^{t}_{j})=(\%^{t}_{j}-I^{t}_{j})\left(1-\left(\frac{\%^{t}_{j}-I^{t}_{j}}{\lambda}\right)^{2}\right).$
(1)

-1-0.500.51-1-0.500.51Neighborhood-Intolerance Difference $\%^{t}_{j}-I^{t}_{j}$Agent Reaction $f(\%^{t}_{j},I^{t}_{j})$ Fig 1: Sample reaction function. Two cases of the reaction function, $f(\%^{t}_{j},I^{t}_{j})$ with different conformity values: $\lambda=0.25$ (red, dashed) and $\lambda=0.75$ (blue, solid).

We alter the reaction function so that intolerance values are kept within the
range $I^{t}_{j}\in[0,1]$ by multiplying $f(\%^{t}_{j},I^{t}_{j})$ by
$I^{t}_{j}(1-I^{t}_{j})$. This gives the final expression for the change in
intolerance due to intergroup contact $\Delta I^{t}_{j}$,

$\Delta
I^{t}_{j}=I^{t}_{j}(1-I^{t}_{j})f(\%^{t}_{j},I^{t}_{j})=I^{t}_{j}(1-I^{t}_{j})(\%^{t}_{j}-I^{t}_{j})\left(1-\left(\frac{\%^{t}_{j}-I^{t}_{j}}{\lambda}\right)^{2}\right).$
(2)

Since intolerance values are bounded within the range $I^{t}_{j}\in[0,1]$, the
magnitude of the differences between intolerance and neighborhood composition
cannot be larger than 1 and hence the conformity values are bounded within the
range $0<\lambda<1$ through each simulation. We present two examples of
$\Delta I^{t}_{j}$ in Fig. 2 when $\lambda=0.75$ (blue, solid) and
$\lambda=0.25$ (red, dashed) assuming that 50 percent of the $i^{th}$ agent’s
neighborhood is of the same type as agent $j$ (i.e, $\%^{t}_{j}=0.5$).

00.51-0.1-0.0500.050.1Intolerance $I^{t}_{j}$Intolerance Change $\Delta
I^{t}_{j}$ Fig 2: Example change function. The change in the tolerance value
$\Delta I^{t}_{j}$ given by Eq. is plotted here. Here, half of the $i^{th}$
agent’s neighborhood is composed of individuals from its own group, i.e.,
$\%^{t}_{j}=0.5$. The conformity values are set to $\lambda=0.25$ (red,
dashed) and $\lambda=0.75$ (blue, solid).

It is unlikely that we have formulated the exact function for how individuals
adjust their tolerances. However, we aim to explore various other functions in
future work. Nonetheless, if we assume that the Contact Hypothesis holds some
validity, our reaction function might align with the results given in the
Detroit Area Study and research conducted by Farley et al. [14, 15]. The
survey results are portrayed in Fig. 3 . These studies suggest that Black
individuals are open to living in integrated areas and may even prefer
integration over a purely Black neighborhood. In contrast, White individuals
tend to live in almost purely White neighborhoods and have less contact with
Black individuals. As a result, integration may be less appealing to them. We
conjecture that this may be due to the fact that Black individuals come into
contact with White individuals more frequently compared to White people.

Fig 3: Results from the Detroit Area Survey. We plot the response data from
the 1976 and 1992 Detroit Area Survey. Participants in this survey were asked
if they were willing to move to a prospective house given the percentage of
the “other” race—Black for White individuals and White for Black
individuals—in the neighborhood. We can see that the percentage of White
respondents quickly drops as the percentage of the other race rises. The
responses from Black individuals were U-shaped, where a neighorhood with a
50-50 split between White and Black people were the most preferred and all-
Black neighborhoods or all-White neighborhoods were not as desired.

### Media Effect

Media bias towards certain groups of people is a common phenomenon. For
example, Black and Latin individuals in America are overly represented as
criminals in media and White individuals are overly represented as upholders
of the law [23]. We implement the effect of this bias on the $i^{th}$ agent’s
intolerance as a multiplicative factor, $g(j)$, in the following fashion

$I^{t+1}_{j}=(1-g(j))(I^{t}_{j}+\Delta I^{t}_{j}).$ (3)

If $0<g(j)\leq 1$, the agent is consuming positive (tolerant) media about the
outgroup and hence their intolerance is decreasing. If $g(j)<0$, the agent is
consuming negative (intolerant) media about the outgroup and hence their
intolerance is increasing. Finally, if $g(j)=0$, the media is neutral and thus
their intolerance does not change. It is important to note also that the media
effect modulates the impact of an individuals local neighborhood change as
well (as $g(j)$ is implemented as a multiplicative rather than additive
effect).

Parameter | Description
---|---
density | The expected proportion of the grid that will be occupied by an agent
red-percent | The percentage of the population from the red group
non-racial-move | The probability that an agent will move to a random empty square at each time step. Ignores neighborhood composition.
$I^{t}_{j}$ | The minimum proportion from the same group in an agent’s neighborhood that satisfies that agent
$I^{0}$ | Initial minimum proportion from the same group in an agent’s neighborhood that satisfies that agent
$\%^{t}_{j}$ | The proportion from the same group in an agent’s neighborhood
$\lambda$ | The willingness for an agent to conform to its neighborhood
$g_{(}j)$ | Media consumed by agent $j$

Table 2: Parameter definitions. Table of parameters used in the model with
descriptions.

## Results

### Conformity

We investigate how the agents’ conformity affects equilibrium values for
segregation. In the following simulations, we set the split between the groups
to be 50-50, the non-racial-move probability to 5 percent and the density to
87.5 percent.

The effect of conformity $\lambda$ on segregation is given in panel (a) of
Fig. 4 . For any initial value of intolerance small values of conformity lead
to integrated equilibria. Integration persists until a critical level of
conformity, $\lambda^{*}(I^{0})$, that depends on the initial intolerance.
This threshold appears to be nonlinear in nature when starting in a random
configuration.

Fig 4: Heatmap of Segregation and Average Final Intolerance. (a) Equilibrium
values of segregation measured by percent-similar, and (b) final average
intolerance as the conformity $\lambda$ and initial intolerance $I^{0}$
varies. For all values of $I^{0}$, fully segregated equilibria occur for past
some threshold $\lambda^{*}(I^{0})$. Integrated equlibria occur when
conformity values below $\lambda^{*}(I^{0})$. Here, red-percent = 50, density
= 0.875, and non-racial-move = 0.05.

We performed the same experiment with a completely segregated initial
condition. This segregated initial condition was generated by spawning all red
agents at the bottom of grid and then spawning green agents above the red
agents. This threshold becomes linear, $\lambda^{*}=0.99-I^{0}$ when the
simulation starts in completely segregated state.

The reason why the relationship is linear is because agents initial
neighborhood configuration is $\%^{0}_{j}=1$ for all $j$ aside from the ones
at the boundary of the red and green agents for both kinds of agents. This
means that if $|1-I^{0}|>\lambda$ then the most agents will have an adverse
reaction to the segregated condition and their intolerance will decrease.
Since there are only random swaps at the beginning many agents will spend time
in relatively segregated neighborhoods, leading to a further decrease in
intolerance. Then, the random movement mixes the population resulting in an
integrated equilibrium.

Fig 5: Heatmap of Segregation and Average Final Intolerance for Segregated
Initial Condition. (a) Equilibrium values of segregation measured by percent-
similar, and (b) final average intolerance as the conformity $\lambda$ and
initial intolerance $I^{0}$ varies. Agents start in a segregated state, silo-
ed in neighborhoods of all red agents or all green agents (except for agents
at the boundary). For all values of $I^{0}$, fully segregated equilibria occur
for past some threshold $\lambda^{*}(I^{0})=0.99-I^{0}$ (black line).
Integrated equlibria occur when conformity values below $\lambda^{*}(I^{0})$.
Here, red-percent = 50, density = 0.875, and non-racial-move = 0.05.

The final average intolerance of the agents match the segregation results.
When $\lambda<\lambda(I^{0})$ the intolerance drops near zero and will accept
neighorhood composition, matching the integrated state. When $\lambda$ is
above this threshold the agents desire neighborhoods composed purely of their
own kind. Therefore, segregation develops when individuals largely conform to
their surroundings and integration develops when individuals do not readily
conform to their surroundings.

### Population Ratio

We next investigated whether the results of the previous section are robust to
differences in the population ratio of agent types.

In real world settings, populations are often not equally sized, but rather
there may be differing population sizes that can affect the patterns of
segregation observed [24, 25]. We evaluated whether the results of the
previous subsection are robust as the population ratio varies. In the
following simulations we set the initial intolerance to $I^{0}=25,50$.

The effect of population ratio is illustrated in Fig. 6 . An imbalanced
population ratio leads to an increase in segregation. This result is not
surprising as the even when agents are placed randomly there is a larger
chance for an agent from the larger group to have neighbors from the same
group.

Fig 6: The effect of Population Ratio on Segregation. Equilibrium values of
segregation measured by percent similar as the percent of red agents and
conformity $\lambda$ varies for initial intolerance values $I^{0}=25,50$. As
the population becomes more imbalanced the amount segregation rises.
Furthermore, the threshold into a fully segregated equilibrium is consistent
across all population ratios. These thresholds can be matched to the
thresholds in panel (a) of Fig. 4 . The parameters for the simulations
pictured above are $I^{0}=50$, density = 0.875, and non-racial-move = 0.05.

As shown in Fig. 4 the threshold for the transition to a completely segregated
state when $I^{0}=25,50$ is $\lambda\approx 0.75$ and $\lambda\approx 0.5$
respectively. Panels (a) and (b) demonstrates that this conformity threshold
is consistent for all population ratios. Therefore while the population ratio
affects the degree of integration/segregation for lower conformity levels, the
population ratio does not affect where the transition to completely segregated
occurs.

### Media Influence

Next, we examined the effect that media has on the development of segregation.
In this section, we explore adjusting the media influence values $g(j)$ for
both the red and green populations to see the effect that purely positive
(tolerant) and purely negative (intolerant) media has on segregation.
Additionally, we investigate the effect that heterogeneous media consumption
(one population consuming negative media and the other positive, and vice-
versa) has on segregation.

We analyze the effect of media influence $g(j)$ on segregation and average
intolerance. In the following simulations, here we set the media value to
$g(j)=g_{R}$ for red agents and $g(j)=g_{G}$ for green agents. We set the
initial intolerance to $I^{0}=50$ in across all following runs. The population
is split evenly between red and green agents and the density is set to 87.5
percent.

Fig 7: Media Effect on Segregation and Intolerance When $\lambda=0.5$.
Equilibrium values for (a) segregation and (b) final intolerance. When the
media values for both the red and green populations consume tolerant media,
the integrated equilibrium arises and the intolerance levels approach zero.
Here, $I^{0}=50$, density = 0.875, and non-racial-move = 0.05.

The effect of media influence on segregation and intolerance is given in Fig.
7 . Integration develops when at both group’s media is positive ($g_{G}>0$ and
$g_{R}>0$). We can investigate further and check how this result changes if we
change the conformity value. We set the conformity value at two values clearly
below and above the threshold for initial intolerance $I^{0}=50$ given by Fig.
4 . In the following simulations we set $\lambda=0.25,0.75$.

Fig 8: Media Effect on Segregation and Intolerance for Conformity Values
$\lambda=0.25,0.75$. Panel (a) and (c) show the equlibrium segregation and the
equlibrium average tolerance when the conformity is 0.25. As seen in Fig. 4 ,
when both sets of agents consume neutral media then the equilibrium is an
integrated state. This lower value of conformity also allows mitigates the
effect that intolerant media has on equilibrium segregation.

The integrated equilibria persists even when both populations consume negative
media when $\lambda=0.25$ as seen in panel (a) of Fig. 8 . Furthermore,
transition from an integrated equlibria to a segregated equilibria is far
smoother when compared to the case when $\lambda=0.5$ or $\lambda=0.75$. This
demonstrates that when agents do not broadly conform to their neighborhood
integration can persist in the presence of negative media. Panel (b)
illustrates the case when $\lambda=0.75$. Here, the segregated state can occur
even when both groups are consuming positive media. So, it appears that larger
values of conformity reduce the effectiveness of positive media to develop
integration.

### Non-Racial-Move Probability

Throughout the previous simulations we set an agent’s chance to move for
reasons outside of it being unsatisfied with its neighborhood composition to
five percent. In this section, we vary the non-racial-move probability to
explore the affect moving for reasons outside of neighborhood composition has
on segregation and intolerance. In the following simulations, the agents’
initial intolerance value are set to $I^{0}=50,25$ and the density is set to
87.5 percent. In the following simulations we vary the non-racial-move
probability from 0 to 100.

Fig 9: The Effect of Moving Due to Non Neighborhood Factors. Equilibrium
segregation and final average intolerance values given the conformity
$\lambda$ and probability of moving due to non racial factors when the initial
intolerance is (a) $I^{0}=50$ and (b) $I^{0}=25$. The bottom row gives the
equilibria with no random movement. The results are distinct from the rest of
the simulations that include randomness.

As illustrated in Fig. 9 , increasing non-racial-move probability reduces
segregation. Panels (a) and (b) demonstrate that the conformity threshold
marking the transition from the segregated state to the integrated state
demonstrated in 4 is consistent across all nonzero values of non-racial-move
probability. The transition at this threshold becomes less stark as one
increases the non-racial-move probability until it is imperceptible.

Note, the bottom row of the heatmap give the results of simulation when there
is no random movement. This implies that introducing randomness into
simulation regardless of how small changes the results. There is a negative
relationship between non-racial-move probability and segregation, there is not
a notable difference in the equilibrium for small values of non-racial-move
probability (1 to 10 percent). Therefore, setting the non-racial-move
probability to five percent does not have a perceptible effect

## Discussion and Conclusions

In this paper, we extended Schelling’s Model to include sociological factors
that could lead to integration, such as intergroup contact, media influence,
or movement due to factors outside of neighborhood composition. We
demonstrated that integration develops when conformity is low—i.e. when
individuals tend not to not to adapt their preferences to their neighborhood
population. The conformity threshold is dependent on the society’s initial
intolerance (e.g. perhaps reflecting the potential effect of previous history
on current patterns), however when the agents broadly conform to their
neighborhood composition segregated equilibria arise regardless of their
initial intolerance. This threshold appears whether the initial state is well-
mixed or segregated, although when the initial state is segregated
relationship between the critical value of the conformity and initial
intolerance appears to be linear.

We found that when both groups consume media that paints the out-group
positively segregation is eliminated. When agents broadly conform to their
surroundings, segregation can persist even when both groups consume positive
media—up to a certain threshold. When agents are broadly repelled by their
neighborhood composition, integration seemingly persists even when both groups
consume negative media until a critical threshold. We also note that adding in
a relatively small amount random movement can lead to integration if
conformity is below a critical threshold.

There are several improvements that can be made to this model. This model does
not attempt to integrate the conditions laid out by Allport for when
intergroup contact leads to reduction in prejudice—equality in status, groups
having common goals, and mechanisms assisting in integroup contact—rather
these conditions are assumed to already be in place. Further research will be
loosening this assumption. It would be of interest to collect data for We
leave these alterations to the model for further research. Additionally,
adding elements such as physical barriers or spots that do not accept certain
groups are next step that one could incorporate into this model. These
elements would active steps in segregating races such as parallel bulldozing
neighborhoods to place highways to separate ethnic groups and redlining.

Within our framework, it appears that how willing people are to conform is the
critical component in the formation of segregation. This might point to a
further analysis of the kinds of people who choose to self-segregate and the
various methods of altering people’s desire to conform so that integration can
develop. Furthermore, our work suggests that focusing our supplying positive
media of ethnic groups would lead to positive steps in moving toward an
integrated populace. We hope that our work leads to more investigation into
factors that could drive the population into a more integrated society.

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