An agent-based model of indirect minority influence on social change and diversity

Figures & data

Figure 1. Four different agent states.

Figure 2. An example demonstrating the agent updating process.

Figure 3. A workflow diagram portraying out attitude updating algorithm including majority and minority influences and a cognitive rebalancing process.

Table 1. The model parameters used in our simulation, the values used in the main exposition, and the ranges of the parameters we swept.

Parameter	Base value	Range of values	Description
Direct majority influence	On	Off, On	(Refer to the attitude updating algorithm section and Figure 3)
Indirect minority influence	On	Off, On
Cognitive rebalancing	1.0	.0, .90, .95, .98, 1.00
Ingroup size	8	3, 4, 5, 6, 6–3D 7,7–3D, 8, 8–3D, 9–3D, 10–3D, 16–3D, 26–3D	The number and orientation of neighboring agents being connected to the self agent
Minority location	Random	Random, Community	Initial minorities are either randomly scattered or clustered forming a community
Initial minority size	1%	1–50%	Initial minority faction size
(Effective size)	(21%)	(21–50%)
Updating method	Synchronous	Synchronous, Asynchronous	Agents either simultaneously update their attitudes or sequentially in a random order
Probability of minority influence	1.0	.0, .25, .5, .75, .8, .85, .9, .95, 1.0	Probability that an agent will change via minority influence when the conditions are satisfied
Consistency check bias	.5	.0, .05, .1, .15, .2, .25, .5, .75, 1.0	Biased likelihood to become consistent with old attitude vs new attitude

Note:

The top three reflect features of CCLC theory, while the others are required to build an agent-based model capable of embodying that theory.

Figure 4. Results of base runs showing typical system patterns and characteristics at the individual and system level for the six combinations of majority and minority influence and cognitive rebalancing.

Note: These general summary results hold for model parameters sweeps as well.

Figure 5. Screenshots show the growth in popularity of the nascent attitude until the system becomes stochastically mixed at roughly equal proportions.

Notes: Though every run is distinct in its particular spatiotemporal arrangements, the dynamics are consistent. This plot of 100 runs is truncated to 500 iterations to highlight details of earlier periods.

Figure 6. Screenshots from one run that achieved social change demonstrate the high volatility of the attitude change dynamics.

Note: The time series from 100 runs highlights this long-lasting volatile behavior and the prevalence of extended partial social change.

Figure 7. The points represent the proportion of runs that end with social change or with a population of at least half yellow agents at the 10,000 halting time aggregated over 500 runs.

Figure 8. This plot shows the proportion of yellow agents reached social change at the end of (left: binary social change measure) and that existed on average (right: continuous social change measure) for 500 runs when all three rules are activated.

Note: There is a clearly increasing tendency to achieve social change with increasing initial percent holding the nascent idea.

Figure 9. The results of 500 runs with all three rules activated showing no difference in outcomes between random initial conditions vs. a community of adjacent initial yellow agents.

Figure 10. Using the same parameters, but altering the topology to lattices of varying numbers of neighbors, we can see that the results are robust against ingroups of differing sizes and orientations.

Note: The points represent the proportion of runs that end with social change or with a population of at least half yellow agents at the 10,000 halting time aggregated over 500 runs.

Figure 11. Biases in favor in retaining either previously held vs. newly acquired attitudes affect the indirect minority influence on social change.

Notes: A value of .5 mean no bias (default model) and a value of 0 means that the agents always become consistent by changing any newly acquired attribute to match any previously held attributes. The points represent the proportion of runs that end with social change or with a population of at least half yellow agents at the 10,000 halting time aggregated over 500 runs.

Figure 12. Decreasing the probability (and hence rate) of minority influence events reduces the incidence of social influence.

Note: The points represent the proportion of runs that end with social change or with a population of at least half yellow agents at the 10,000 halting time aggregated over 500 runs.