People in different parts of a country, and across countries, begin to want a change in government.
We approach this modeling scenario from the perspective that country-level factors play a role in shaping the development of revolution among individual citizens. We imagine that a country generally predisposed to opposition (from a history of oppression, perhaps) is more likely to suffer from revolution than one historically predisposed to loyalty. Revolution, we suggest, is dependent on a long history of preceding events and so we account for differences in such predispositions across countries.
Additionally, we imagine that the density of a country’s population can moderate the extent to which loyalty or opposition views spread. While a very sparsely populated country would likely rise in opposition only when individuals are generally predisposed to revolution, a massively dense population may be more susceptible to rapid increases in opposition as communication among neighbors is commonplace.
Lastly, we imagine the process by which a desire for a change in government develops over time at the individual-level - conditioned on the views of one’s neighbors. Given our focus on country-level predispositions, it follows that an individual would consider the views of his/her neighborhood relative to the country at large. A moderate individual living in a moderate neighborhood in Country A, might be a moderate living in very extremism neighborhood in Country B.
We envision revolutions developing across space - both nationally and internationally. Therefore, we start with four countries in a 2x2 grid. The countries are all equally-sized, with each able to populate up to 2,500 individuals. Since space is fixed within each country, changing the population size in effect determines the country’s population density.
Assumptions:
Parameters:
Rules for Interaction:
Although support is measured on a continuous scale (at both the individual and national levels), we introduce the binary construct of extreme vs. moderate individuals/neighborhoods. Individuals are considered moderate if they are within one standard deviation (defined in starting conditions) of their neighborhood mean; otherwise they are considered extremists, either supporting the revolution or opposing it (i.e., strongly supporting current government). Similarly, neighborhoods are considered moderate if they are within one standard deviation from the national mean level of support for the revolution; otherwise, they are considered extreme. With this formulation, individuals act based on their position relative to the neighborhood, which in term is defined in relation to the national mean level of support.
Between individuals (within country) rules:
If neighborhood is moderate relative to national mean:
If neighborhood is extreme relative to national mean:
Between countries rule:
Constants:
The model can result in one of two outcomes for each state: either a revolution ensues or the population (not necessarily individuals) converges to some mean national level of support anywhere below the threshold. The following insights follow:
One foreseeable addition to our model would be the inclusion of government actors as agents in the model, who would seek to punish/imprison/attack individuals or neighborhoods that have extreme opposition views. For example, it would be possible to program a class of ‘police’ agents to increase in number as the country mean approached higher levels of opposition, up to some threshold whereby the regime would ‘run out of’ police reserves and suffer from revolution. How many would be needed, and in what force, would regime-backed police be able to sequester a regime? What population densities and predispositions of opposition could overcome even a well-entrenched government?
A second, and visually appealing, addition would include the restructuring of the model’s spatial landscape to better approximate the real-world. For example, our four country grid might be transformed into a five or six country ‘continent’ wherein adjacency varied across the landscape. Would changing the spatial landscape (and potentially limiting, or increasing adjacencies) impact the spread of revolution across countries? What spatial configurations set the stage for a cascade of revolutions?
Our model and approach may be useful for studying social science questions involving the propagation of an opinion, view, or behavior (measured on a continuous scale) among individuals within groups of individuals and subsequently across spatial units. The spread of pro-war sentiment, desire to mitigate carbon emissions, or even feelings of happiness could (potentially) be modeled given the above.
As an illustrative example, if a researcher was interested in the culture of graduate student cohorts within a university program our model would be easily applicable. Say that each cohort has a predisposition toward vigorous competition or collective solidarity among students, and that cohorts differed in their sizes. In the model, the competitiveness or solidarity of individual students would naturally adjust over time compared to other students around them, relative to the predisposition/mean of each cohort. Under what conditions would solidarity among graduate prevail over entrenched competition?
Download the .nlogo file here to edit the model.
Note that individual agents are colored on a scale according to their level of support against the current government: (white=highest level of opposition, black=highest level of support for current government).