David Hales (1998)
Journal of Artificial Societies and Social Simulation vol. 1, no. 4, <https://www.jasss.org/1/4/2.html>
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Received: 27-Aug-98 Accepted: 24-Sep-98 Published: 15-Oct-98
|Figure 1. Dominant meme / Maximum density synthesis|
|Figure 2. Distribution of memes in the noosphere. Experiment 1a - Just enough food.|
|Equilibrium is reached at cycle 160. The single black line through the graph indicates this point (just after the elimination of the single "10" meme). At the start of the run the "9" meme quickly takes over the whole of a territory. The "5" meme manages to hold out for over 100 cycles before it is replaced by the "4" meme (dominant in its territory).|
|Figure 3. Dominant meme / Maximum density synthesis|
|Figure 4. Dominant meme / Maximum density synthesis|
The instinct for survival is important to culture because a meme, in order to be invented or acquired must pass a severe test: If it in any way endangers the lives of the animals concerned, it will automatically be rejected. (Bonner 1980, p197)
|Table 1. Meta-meme values and biases|
|Each meta-meme value is shown along with the bias it produces. This value is added to the change parameter of each meme held by the agent when an attempted infection is taking place. Note that meta-meme value "6" has a zero bias. This means that a grazer possessing a "6" meta-meme will behave in exactly the same way as a grazer with no meta-meme at all.|
|Figure 5. Dominant meme / Maximum density synthesis|
|Figure 6. Distribution of the population over the four territories. Experiment 2a - Just enough food.|
|Notice the redistribution of the population between cycles 140 and 160.|
|Figure 7. Distribution of memes in the noosphere. Experiment 2a - Just enough food.|
|Notice the self-catalytic process and when it breaks down.|
|Figure 8. Distribution of memes in the noosphere. Experiment 2a - Just enough food.|
|Notice the oscillations and then the minor stability between cycles 140 and 160.|
|Figure 9. Dominant meme / Maximum density synthesis|
|Figure 10. Dominant meme / Maximum density synthesis|
|Table 2. A summary of results|
|......Experiments without meta-memes|
|1a) Just enough food||49||76||97||100||100||296||18|
|1b) Too much food||92||100||100||100||100||22||9|
|1c) Too much food & predators||67||92||100||100||100||349||41|
|......Experiments with meta-memes|
|2a) Just enough food||26||76||99||99||100||173||2|
|2b) Too much food||62||97||100||100||100||32||2|
|2c) Too much food & predators||19||49||85||95||100||690||7|
|The numbered columns represent cycles (in thousands). The numbers in those columns represent the percentage of simulation runs that had reached an equilibrium by the given number of cycles. The CBE column shows the average Cumulative deaths Before Equilibrium. The CAE column shows the average Cumulative deaths After Equilibrium. After each set of three experiments the average of the columns is given.|
|Figure 11a. Distribution of memes averaged over all the simulation runs for scenario A: "Just enough food".|
|The darker bars show the results of the meta-meme experiments. After the introduction of meta-memes, meme "3" representing the actual carrying capacity is favoured. Consequently the average accuracy of the memes is improved.|
|Figure 11b. Distribution of memes averaged over all the simulation runs for scenario B: "Too much food".|
|The darker bars show the results of the meta-meme experiments. After the introduction of meta-memes, meme "4" representing the actual carrying capacity is favoured. Consequently the average accuracy of the memes is improved.|
|Figure 11c. Distribution of memes averaged over all the simulation runs for scenario C: "Too much food with predators".|
|The darker bars show the results of the meta-meme experiments. After the introduction of meta-memes, lower value memes are favoured. Consequently the average accuracy of the memes is improved.|
bias = (m - 6) * 0.2
This formula was used because it delivers uniform increments, a neutral bias (meta-meme "6" = zero bias) and a fully closed minded meta-meme (meta-meme "1" = -1 bias).
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