Cognitive Science and the New Testament A New Approach to Early Christian Research

(Axel Boer) #1

conquered (almost) entirely some centuries later. In terms of our results, a few
itinerants could make considerable difference in those early years, even without
attributing to them unusual speeds of travel or extraordinary charisma. Indeed,
our results can be transformed into an interesting hypothesis about the narrative
tradition about the itinerants of the earliest Christ movement. Whereas early
Christian tradition explained the great initial impact of the apostles on the
movement by attributing to them extraordinary charisma and the support of
supernatural powers, which generated rich narrative traditions about the apos-
tles and their religious veneration, the single factor that they traveled (even at
normal speeds) could account for their influence in historical terms.^15


9.5 PATTERNS OF CONVERSION

In addition to starting with highly optimistic conversion rates, our initial
experiments also made the assumption that cultural learning works similarly
to catching a disease, where contact with a single carrier can result in trans-
mission. For the“any”learning strategy, it does not matter if one orfive or
all eight neighbors of a household are followers. As we discussed above in


Table 9.2.Number of days needed until ten percent of the households is converted
at different conversion rates, with and without apostles


Apostles Days at conversion
rate = 0.5


Days at conversion
rate = 0.1

Days at conversion
rate = 0.01

0 4.33 27.34 292.74
1 4.25 23.89 192.38
2 4.35 21.9 157.04
3 4.17 20.92 147.55
4 4.19 20.04 134.91
5 4.23 19.16 134.26
6 4.21 18.6 123.71
7 4.19 18.37 123.2
8 4.15 17.79 116.84
9 4.15 17.77 117.49
10 4.23 17.38 112.22


(^15) In fact, our model contains a parameter to take into account the apostles’charismatic work.
By changing the prestige-bias parameter, we can examine the effect of the apostles’ability to
convert households more efficiently. As expected, prestige-bias further increases the influence of
the itinerants in the early stage of the movement. Setting this parameter to the maximum pushes
back the number of days needed for an initial ten percent growth from about 300 to less than
50 with the help of two to three itinerants, reducing the amount of time used by more than eighty
percent. Although this result seems spectacular, caution is needed when adding extraordinary
qualities to the itinerants in the model.
Social Networks and Computer Models 201

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