Anthony Dekker (2007)
Studying Organisational Topology with Simple Computational Models
Journal of Artificial Societies and Social Simulation
vol. 10, no. 4 6
<https://www.jasss.org/10/4/6.html>
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Received: 28-Feb-2007 Accepted: 25-Jun-2007 Published: 31-Oct-2007
Figure 1. Characteristic of the three simple simulation models discussed in this paper, compared to real organisations |
Figure 2. Illustration of the Kawachi process, starting with a 60-node antiprism (top right) |
D = 2.04 + 1.07 p^{-0.32} | (1) |
Mathematically, the Kawachi process defines a continuous mapping from the interval [0,5] to the set of probability distributions on networks, and hence induces continuous mappings from the interval [0,5] to various network metrics. Figure 2 can be viewed as illustrating the mappings from the interval [0,5] to the metrics C and D.
Q = 12.5 + 85.8/D^{2} | (2) |
This regression equation, illustrated in Figure 3, explained 57% of the variance in the data (a correlation of 0.76, statistically significant at better than the 10^{-100} level, by analysis of variance). Since the average network distances D ranged from 2.6 to 7.9, this regression equation corresponds to the average quality percentages ranging from 14% to 25%. Extrapolating, it suggests that if every agent were directly connected to every other (i.e. D = 1), then the quality percentage would average about 98%.
Figure 3. Results for Assignment Problem simulation, showing fit of data to the regression equation |
Figure 4. Six Kuramoto oscillators connected in a ring network, showing the position (phase) of each oscillator |
(3) |
(4) |
Here the summation is over all agents j directly connected to agent i, and the multiplier k is 0.001 (substantially greater values than this result in chaotic behaviour when discretized). This differential equation can be approximated as a difference equation, specifying the changing phase over one timestep. We iterated this for 100,000 time steps to see if self-synchronization would occur, with the average frequency f_{i} being sufficiently low (around 0.02) to ensure that the timestep behaviour would be a good approximation to the continuous behaviour specified by the differential equation.
Figure 5. Average values of the correlation r between oscillator phases, for different values of the Kawachi parameter used to generate networks and for different frequency distribution widths |
(5) |
This equation explained 74% of the variance in the data (a correlation of 0.86, with all components statistically significant at better than the 10^{-100} level). Using a square root rather than a cube root explained only 73% of the variance, and other network characteristics (such as the clustering coefficient, or even polynomials in the Kawachi parameter) did not provide additional explanatory power.
Figure 6. Mean adjusted completion time (T) for the Lagrange Model, for different values of the Kawachi parameter p and the irrationality factor I. Networks with p = 0.5 performed best |
T = 22,200 I - 58.9 D^{3} - 15,700 K + 2,600 K D + 30,500 | (6) |
This predicts 76% of the variance in the data (a correlation of 0.87). All components of this polynomial are statistically significant at the 10^{-30} level or better, with the choice of the cubic power for D significant at the 10^{-5} level, and no other cubic or quartic terms significant.
Figure 7. Change in average network distance (D) and average connectivity (K) for Kawachi process, overlaid on contour lines for polynomial predictor of the adjusted completion time (T) |
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