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COMPUTING SCIENCE

Rumours and Errours

Brian Hayes

Typos and Thinkos

Computer programming teaches humility, or at least that's my experience. In principle, the discrepancy I observed might have pointed to an error in the published result, but that wasn't my first hypothesis. I checked my own code, fully expecting to find some careless mistake—running through a loop one time too few or too many, failing to update a variable, miscalculating an array index. Nothing leapt out at me. The problem, I began to suspect, was not a typo but a thinko.

I did know of one soft spot in the program. The individuals X and Y were chosen in such a way that they could both turn out to be the same person, suggesting the strange spectacle of spreading a rumor to oneself. ("Pssst. Have I heard about...?") When I went to fix this oddity, I discovered another bug. A variable named spreader-count was incremented or decremented on each passage through the loop, according to the outcome of the encounter; when this variable reached zero, the program ended. After each spreader-spreader interaction, I decreased spreader-count by 2—with potentially disastrous results if X and Y were identical. This was a serious flaw, which needed to be repaired; however, the change had no discernible effect on the value of θ, which remained stuck at 0.285.

I had another thought. Belen and Pearce were careful to state that their result holds only when the population size tends to infinity. Perhaps my discrepancy would go away in a larger sample. I tried a range of populations, with these results:

population θ
10 0.354
100 0.296
1,000 0.286
10,000 0.285
100,000 0.285

The trend was in the right direction—a smaller proportion of residual ignorants as population increased—but the curve seemed to flatten out beyond 1,000, and θ looked unlikely ever to reach 0.203. Even so, it seemed worthwhile to test still larger populations, but for that I would need a faster program. I wrote a new and simpler version, dispensing with the array of individuals and merely keeping track of the number of persons in each of the three categories. With this strategy I was able to test populations up to 100 million. The value of θ remained steady at 0.285.

Figure 2. The dynamics of rumors...Click to Enlarge Image

Looking at the distribution of θ values from single runs of the program (rather than averages over many runs) suggested another idea. Most of the results were clustered between θ=0.25 and θ=0.35, but there were a few outliers—runs in which 99 percent of the population never heard the rumor. I could see what must be going on. Suppose on the very first interaction X spreads the rumor to Y, and then in the second round the random selection happens to settle on X and Y again. The rumor dies in infancy, having reached only two people. Could it be that excluding these outliers would bring the average value of θ down to 0.203? I gave it a try; the answer was no.





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