complex social system as it has begun in our previous work (cf. Situngkir, 2004) - for
instance, the use of computational simulations to see the pattern as it is emerged in the
sense of micro-macro linkage. These regularities of social conflict can be observed from the
pattern of the tensions among the different collective identities interacting in the social
system (cf. Lustick, 2000), the dynamics of the mass mobility (cf. Srbljinovic, et. al., 2003) or
possibly the unmatched settlements among the conflicting sides (cf. Woods, 2002).
An interesting possible qualitative and theoretical analogy could be plausible to
describe the massive conflict as it has occurred in several places in the country - and in our
cases the social conflict in Ambon (1999-2004). Imagine we have a forest with some
percolations (Reynolds, et. al., 1977) among them which somewhat is also showing the size
of “population” distribution that also emerging the power law (Moura, 2006). A lightning
strikes and ignites a fire on one particular tree. Then the fire spreads in the adjacent trees
within certain percolations, and so on. This brings any possibilities of the ignited fire till
eventually there would be some places in the forest with the burning trees with apparently
different sizes. As it has been showed in Drossel & Schwabl (1992), the cumulative
distribution of the fired trees in some values of the computational simulations (see figure
[6]) would also yielding the power law. Turcotte (1999) used this as an exemplification of
the used analogy of the self-organized criticality to explain the Richardson’s (power) law.
The plausibility of the analogy can be brought from the power-law distributed of the initial
percolations (since there are empirical findings of the power law distributed population in
cities and municipalities) and the sizes of the fire could represent the sizes of the social
clashes as measured in the sizes of the death tolls. Since both macro properties in social
system and in forest fire model are organically settled, thus both could be interestingly
similar: showing the process of self-organized criticality. Properties of percolations, sudden
clashes lead to massive violence, and so on, have been depicted in figures [1], [2], and [3] at
the previous sections.
However, although there is no theoretical guarantee that the presence of the power
law distribution always represents the self-organized criticality as the underlying processes,
the computational frameworks as referred above along with our understanding on the self-
organized criticality could be useful to (at least) intuitively observing the percolations of
organic residences, populations, cities and municipalities to find a way in the practical effort
inhibiting the spreading of the conflict. The analogy of the forest fire model might not
plausible enough to explain sizes of wars as pointed out by Cederman’s (2003) caveats and
proposal of new model explaining the sizes of war. However, by distinguishing war among
countries as not merely and organically as the massive conflict among civilians in bounded
regions; the analogy of the self-organized criticality depicted in the forest fire model might
become more suitable explaining the statistical feature we presented in the previous
section. Yet, further endeavors on this becomes a challenging further works to build a more
plausible computational model in sociological analysis of massive conflict and violence
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