section 8.5, however, human learning strategies are much more complex. The
“probabilistic”learning strategy of our model takes into consideration the social
nature of learning: the more people around us adopt an idea, the more likely we
are willing to consider adopting it ourselves. Here we assume that being sur-
rounded by Christian neighbors is more likely to result in conversion than only
having one Christian neighbor.^16 In general, we can observe that the growth of
the movement slows down. Working with the initial values of the model, it takes
about 570 days until every household is converted. Further, the impact of the
itinerants becomes more pronounced already at the initial parameter settings,
throughout the entire growth process. To put it in plain English, in a population
where people are scrutinizing what the majority of their neighbors is doing
before deciding to convert, itinerants will make a greater difference than in a
population in which people are ready to come around as soon as someone is
converted in their neighborhood.
In any real society, people use various learning strategies. As Everett Rogers
(2003, pp. 282–6 [1983]) suggested, the diffusion of ideas depends on“early
adopters”who are willing to try new things even if they are in minority. Others
join in only after a number of people in their social network already adopted
the idea. Finally, extremely cautious people (“laggards”) might wait until they
are in minority as non-adopters. The learning strategies in our model can be
interpreted in terms of the types of social players in Rogers’theory. A mix of
learning strategies could be easily built into the model. What would be the role
of itinerants in such a scenario? We have seen that for conversion in a society
consisting of“early adopters”the influence of the itinerants is most significant
in the initial phase of the movement. For a society consisting of probabilistic
learners, the influence is relatively stable across the growth process. Since we
expect that a greater proportion of“early adopters”than of probabilistic learners
would be converted early, the influence of the itinerants on the“early adopters”
in the early phase would still make an important difference. In this scenario, we
expect itinerants to play a continuing role in the conversion of probabilistic
learners during the entire growth process.
9.6 CONCLUSIONSIn this chapter, we argued that the resources of computer modeling provide
scholars of the New Testament with hitherto unexplored opportunities to
(^16) Cognitive dissonance theory (Eysenck, 2004, pp. 645–7) provides motivation to this
assumption. According to Leon Festinger (2009 [1985]), people try to avoid discrepant cogni-
tions. Further, they tend to resolve discrepancies between their behavior and attitudes by
changing their attitudes rather than their behavior. Since preserving social ties with the neigh-
bors was a necessity in ancient culture, we can assume that people tended to change their beliefs
if the majority changed.
202 Cognitive Science and the New Testament