Model based cellularautomataonspreadofrumours

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Model-Based Cellular Automata on Spread of Rumours
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International Journal of Computer & Organization Trends –Volume 3 Issue 11 – Dec 2013
ISSN: 2249-2593 http://www.ijcotjournal.org Page 504
Model-Based Cellular Automata on Spread of Rumours
Maitanmi Olusola Stephen
Computer Science, Babcock University, Ilisan Remo, Ogun State, Nigeria
Adekunle Yinka
Agbaje Michael
Abstract
This paper illustrates that cellular automata can drive
the spread of gossips in our environment using
stochastic model. The spread of rumour is
encouraged by the use of homogeneous cells which
can either be allowed or disallowed which is further
influenced by a parity model in modelling physical
science which comprises four cells signifying the
white cell as never heard and the black cell as having
heard. This generates a rule which determines if a
cell lives or dies. We can observe that if the cell is
white, and it has one or more black neighbours,
consider each black neighbour in turn. For each black
neighbour, change to black with some speciﬁc
probability, otherwise remain white. While on the
other hand, once a cell becomes black, the cell
remains black.
Keywords: Automata, Automation, Cell
1. Introduction
The signal has two states either 1 (on) or 0 (off). This
can be proven in situation when having a grid of light
bulbs, such as those you can see displaying scrolling
messages in shops and airports. Each light bulb can
be either on or off. Suppose that the state of a light
bulb depended only on the state of the other light
bulbs immediately around it, according to some
simple rules. Such an array of bulbs would be a
cellular automaton (CA).
We start by deﬁning what a CA is and then consider
some standard examples, mainly developed within
the physical sciences. These can be adapted to model
phenomena such as the spread of gossip and the
formation of cliques. This leads us to a more detailed
consideration of some social science models, on
ethnic segregation, relations between political states
and attitude change.
1.1 Features of Cellular Automata
The following consist of automata features:
1. It consists of a number of identical cells (often
several thousand or even millions) arranged in a
regular grid. The cells can be placed in a long line (a
one-dimensional CA), in a rectangular array or even
occasionally in a three-dimensional cube. In social
simulations, cells may represent individuals or
collective actors such as countries.
2. Each cell can be in one of a few states – for
example, ‘on’ or ‘off’, or ‘alive’ or ‘dead’. We
shall encounter examples in which the states
represent attitudes (such as supporting one of
several political parties), individual characteristics
(such as racial origin) or actions (such as
cooperating or not cooperating with others).

3. Time advances through the simulation in steps. At
each time step, the state of each cell may change.
4. The state of a cell after any time step is determined
by a set of rules which specify how that state depends
on the previous state of that cell and the states of the
cell’s immediate neighbours. The same rules are used
to update the state of every cell in the grid. The
model is therefore homogeneous with respect to the
rules.
5. Because the rules only make reference to the states
of other cells in a cell’s neighbourhood,
cellular automata are best used to model situations
where the interactions are local
1.2 Objectives of the paper
The broad objective of the study is to show that
cellular automata can drive the spread of rumours.
Specifically, the study will address the following
major issues:-
i. If the cell is white, and it has one or
more black neighbours, consider
each black neighbour in turn. For
each black neighbour, change to
black with some speciﬁc
probability, otherwise remain
white.
ii. If the cell is black, the cell remains
black.
To summarize, cellular automata model, a world in
which space is represented as a uniform grid, time
advances by steps, and the laws of the world are
represented by a uniform set of rules which compute
each cell’s state from its own previous state and those
of its close neighbours.
Cellular automata have been used as models in many
areas of physical science, biology and mathematics,
as well as social science. As we shall see, they are
good at investigating the outcomes at the macro scale
of millions of simple micro-scale events. One of the
simplest examples of cellular automata, and certainly
the best-known, is Conway’s Game of Life [1].
2. The Game of Life
In the Game of Life, a cell can only survive if there
are either two or three other living cells in its
immediate neighbourhood that is, among the eight
cells surrounding it as shown in Figure 1. Without
these companions, it dies, either from overcrowding
if it has too many living neighbours, or from
loneliness if it has too few. A dead cell will burst into
life provided that there are exactly three living
neighbours. Thus, for the Game of Life, there are just
two rules:
1. A living cell remains alive if it has two or three
living neighbours, otherwise it dies.
2. A dead cell remains dead unless it has three living
neighbours, and it then becomes alive.
Figure 1: Black cells as neighbours of central cells
Figure 2: An example of the evolution of a pattern using
the rules of the Game of Life
Meanwhile, it is worthy to note that, with just these
two rules, many ever-changing patterns of live and
dead cells can be generated. Figure 2 shows the
evolution of a small pattern of cells over 12 time
steps. To form an impression of how the Game of

Life works in practice, let us follow the rules by hand
for the first step as demonstrated in an enlarge form
in Figure 2.
The black cells are ‘alive’ and the white ones are
‘dead’ see Figure 3. The cell at b3 has three live
neighbours, so it continues to live in the next time
step. The same is true of cells b4, b6 and b7. Cell c3
has four live neighbours (b3, b4, c4 and d4), so it dies
from overcrowding. So do c4,
Figure 3. Explanation of living and Dead Cells
c6 and c7. Cells d4, d6 and e3each have three
neighbours and survive. Figure 3: The initial
arrangement of cells e8 die because they only have
one living neighbour each, but e4 and e6, with two
living neighbours, continue. Cell f1, although dead at
present, has three live neighbours at e2, f2 and g2,
and it starts to live. Cell f2 survives with three living
neighbours, and so do g2 (two neighbours alive) and
g3 (three neighbours alive). Gathering all this
together gives us the second pattern in the sequence
shown in Figure 3.
It is clear that simulating a CA is a job for a
computer. Carrying out the process by hand is very
tedious and one is very likely to make mistakes The
eighth pattern in Figure 3 is the same as the first, but
inverted. If the sequence is continued, the fifteenth
pattern will be seen to be the same as the first pattern,
and thereafter the sequence repeats every 14 steps.
There are a large number of patterns with repeating
and other interesting properties and much effort has
been spent on identifying these. For example, there
are patterns that regularly ‘shoot’ off groups of live
cells, which then march off across the grid [1]
2.1 Other cellular automata models
The Game of Life is only one of a family of cellular
automata models. All are based on the idea of cells
located in a grid, but they vary in the rules used to
update the cells’ states and in their definition of
which cells are neighbours. The Game of Life uses
the eight cells surrounding a cell as the
neighbourhood that influences its state. These eight
cells, the ones to the north, north-east, east, south-
east, south, south-west, west and northwest, are
known as its Moore neighbourhood, after an early
CA pioneer (Figure 4).
Figure 4
3. Methodology of the System
The parity model
A model of some significance for modelling physical
systems is the parity model. This model uses just four
cells, those to the north, east, south and west, as the
neighbourhood (the von Neumann neighbourhood,

shown in Figure 4). The parity model has just one
rule for updating a cell’s state: the cell becomes
‘alive’ or ‘dead’ depending on whether the sum of the
number of live cells, counting itself and the cells in
its von Neumann neighbourhood, is odd or even.
Figure 5 shows the effect of running this model for
124 steps from a starting configuration of a single
filled square block of five by five live cells. As the
simulation continues, the pattern expands. After a
few more steps, it returns to a simple arrangement of
five blocks, the original one plus four copies, one at
each corner of the starting block. After further steps,
a richly textured pattern is created once again, until
after many more steps, it reverts to blocks, this time
consisting of 25 copies of the original [5].
The regularity of these patterns is due to the
properties of the parity rule. For example, the rule is
‘linear’: if two starting patterns are run in separate
grids for a number of time steps and the resulting
patterns are superimposed, this will be the same
pattern one finds if the starting patterns are run
together on the same grid.
Figure 5: The pattern produced by applying the parity rule
to a square block of live cells after 124 time steps
As simulated time goes on, the parity pattern
enlarges. Eventually it will reach the edge of the grid.
We then have to decide what to do with cells that are
on the edge. Which cell is the west neighbour of a
cell at the left-hand edge of the grid? Rather than
devise special rules for this situation, the usual choice
is to treat the far right row of cells as the west
neighbour of the far left row and vice versa, and the
top row of cells as the south neighbours of the bottom
row. Geometrically, this is equivalent to treating the
grid as a two-dimensional projection of a torus (i.e, a
doughnut-shaped surface). The grid now no longer
has any edges which need to be treated specially, just
as a doughnut has no edges [2].
3.1 One-dimensional models
The grids we have used so far have been two-
dimensional. It is also possible to have grids with one
or three dimensions. In one-dimensional models, the
cells are arranged along a line (which has its left-
hand end joined in a circle to its right-hand end in
order to avoid edge effects). Wolfram (1986) devised
a classification scheme for the rules of one-
dimensional automata [4].
Figure 7. The pattern produced after 120 steps by rule 22
starting from a single live cell at the top centre
For example, Figure 6 shows the patterns that emerge
from a single seed cell in the middle of the line, using
a rule that Wolfram classifies as rule 22. This rule
states that a cell becomes alive if and only if one of
four situations applies: the cell and its left
neighbour are alive, but the right neighbour is dead;
and its right neighbour are dead, but the left
neighbour is alive; the left neighbour is dead, but the
cell and its right neighbour are alive; or the cell and
its left neighbour are dead, but the right neighbour is

alive. Figure 6 shows the changing pattern of live
cells after successive time steps, starting at time 0 at
the top and moving down to step 120 at the bottom.
Further time steps yield a steadily expanding but
regular pattern of triangles as the influence of the
initial live cell spreads to its left and right.
4. Models of interaction
These examples have shown that cellular automata
can generate good patterns, but for us their interest
lies in the extent to which they can be used to model
social situations. We shall begin by examining two
very simple models that can be used to draw some
possibly surprising conclusions before describing a
more complex simulation which illustrates a theory
of the way in which national alliances might arise.
4.1 Applications of CA
The Gossip Model
Most commonly, individuals are modelled as cells
and the interaction between people is modelled using
the cell’s rules. For instance, one can model the
spread of knowledge or innovations or attitudes in
this way. Consider, for example, the spread of a
discussion from a single originator to an interested
audience. Each person hears of the gossip from a
neighbour who has already heard the news, and may
then pass it on to his or her neighbour (but if they
don’t happen to see their neighbour that day, they
will not have a chance to spread the news). Once
someone hears the gossip once, he or she remembers
it and does not need to hear it again.
This scenario can be modelled with a CA. Each cell
in the model has two states: ignorance about the item
of gossip (the equivalent of what in the previous
discussion we have called a ‘dead’ cell) or knowing
the gossip (the equivalent of being ‘alive’). We will
colour white a cell that does not know the gossip
and black one that does. A cell can only change
state from white to black when one of its four von
Neumann neighbours knows the gossip (and so is
coloured black) and passes it on. There is a constant
chance that within any time unit a white cell will pick
up the gossip from a neighbouring black cell and turn
black. Once a cell has heard the gossip, it is never
forgotten, so in the model, a black cell never reverts
to being white. Thus the rules that drive the cell state
changes are as follows:
1. If the cell is white, and it has one or more
black neighbours, consider each black
neighbour in turn. For each black neighbour,
change to black with some specific
probability, otherwise remain white.
2. If the cell is black, the cell remains black.
The rules we have mentioned previously have all
been deterministic. That is, given the same situation,
the outcomes of the rule will always be the same.
However, the gossip model is stochastic: there is
only a chance that a cell will hear the gossip from
a neighbour. We can simulate this stochastic
element with a random number generator. Suppose
the generator produces a random stream of integer
numbers between 0 and 99. A 50 per cent probability
of passing on gossip can be simulated by
implementing the first rule as follows:
1. If the cell is white, then for each neighbour
that is black, obtain a number from the
random number generator. If this number is
less than 50, change state from white to
black.

Figure 7: The spread of gossip: (a) with a 50 per cent
probability of passing on the news; (b) with a 5 per cent
probability; (c) with a 1 per cent probability
Figure 7.(a) shows the simulation starting from a
single source, using a 50 per cent probability of
passing on gossip. The gossip spreads roughly
equally in all directions. Because there is only a
probability of passing on the news, the area of black
cells is not a perfect circle but deviations from a
circular shape tend to be smoothed out over time.
With this model, we can easily investigate the effect
of different probabilities of communicating the
gossip by making an appropriate change to the rules.
Figure 7(b) shows the result of using a 5 per cent
probability (the first rule is rewritten so that a cell
only changes to black if the random number is less
than 5, rather than 50). Surprisingly, the change
makes rather little difference. The shape of the black
cells is a little more ragged and of course the news
travels more slowly because the chance of
transmission is much lower meaning that Figure 7(b)
required about 250 time steps, compared with 50
steps for Figure 7(a). However, even with this rather
low probability of transmission, gossip stills spreads.
We can go lower still: Figure 7(c) shows the outcome
of a 1 per cent probability of transmission. The shape
of the black cells remains similar to the previous two
simulations; although the rate of transmission is even
slower figure 7 (c) shows the situation after 600 time
steps. The model demonstrates that the spread of
gossip (or of other ‘news’ such as technological
innovations or even of infections transmitted by
contact) through local, person-to-person interactions
is not seriously impeded by a low probability of
transmission on any particular occasion, although low
probabilities will result in slow diffusion.
The model has assumed that once individuals have
heard the news, they never forget it. Black cells
remain black for ever. This assumption may be
correct for some target situations, such as the spread
of technological acquisition.
But it is probably unrealistic for gossip itself. What
happens if we build a chance of forgetting into the
model? This can be done by altering the second rule
to:
2. If a cell is black, it changes to white with a fixed
small probability.
Figure 8: The spread of gossip which can either be
forgotten or not
Figure 8: The spread of gossip when individuals have
a 10 percent chance of transmitting the news and a 5
per cent chance of forgetting it setting the probability
of transmitting the gossip to 10 per cent and the
probability of forgetting the gossip to 5 per cent gives
the result shown in Figure 8. The small white holes
represent the cells that have ‘forgotten’ the gossip.
However, these white areas do not spread because a

cell that has forgotten the news is still surrounded by
other black cells, which have a high chance of
retransmitting the news to the newly white cell, thus
quickly turning it black again. In short, provided that
the probability of transmission from all the neighbour
cells is greater than the chance of forgetting, the
pattern of a growing roughly circular patch of cells
which have heard the news is stable in the face of
variations in the assumptions we make about
transmission and forgetting.
Other likely application areas:
i. Fashion: people tend to draw
closer to what they wear when they
see it.
ii. Success: when there is competition,
challenges exist among others
5. Conclusion
As we noted it is nearly impossible to predict the
form of these macro-level patterns just by
considering the rules operating at the micro-level of
individual cell.
In the above example, it is easy to think of the grid in
rather literal, geographical terms, with people
occupying each cell on an actual surface. However,
the analogy between the model and the target
population does not have to be, and usually will not
be, as direct as this. The grid can be mapped on to
many different kinds of social relationship. For
example, the interactions on which the gossip model
depends could be by telephone, over the Internet or in
any other way in which individuals communicate
with particular others.
References
[1] R. J. Gaylord, and L.J D’Andria, L. J. Simulating
Society: A Mathematical Toolkit for
Modelling Socioeconomic Behavior. (1998) TELOS
Springer-Verlag, Berlin.
[2] Hegselmann, R. Cellular automata in the
social sciences: perspectives, restrictions and
artefacts. In R. Hegselmann et al. (eds),
Modelling and Simulation in the Social
Sciences from the Philosophy of Science Point of
View, pp. 209–234. Kluwer, Dordrecht. 1996
[3] Hegselmann, R. Understanding social
dynamics: the cellular automata approach. In K.
G. Troitzsch et al. (eds), Social Science
Microsimulation, pp. 282-306. Springer-Verlag,
Berlin.1996
[4] Wolfram, S. (2002) A New Kind of Science.
Wolfram Media, Champaign, IL.
[5] A. Ilachinski, Cellular Automata. A
Discrete Universe. (2001) World Scientiﬁc, Singapore,
New Jersey, London, Hong Kong
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