Economic Perspectives IV
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Optimal Incentives Depend on Connection Density
Note: in order to run the simulation referred to in this slide, go here, to the Java Applet version. You will be directed to download the latest version of the Java plug-in.
As promised above, this slide will expose the dependence of the optimal value of α on an organization's connectivity. In other words, we are trying to determine how the optimal mixture of absolute and relative incentives is affected by the average number of contacts maintained by each agent.
Every time you press "Go" in the panel below this text, several things happen. Firstly, the simulator clears all the connections in the graphic to your left, and randomly introduces a number of new ones, specified by the slider below the graphic. Then, the simulation runs for 100 steps, as α varies linearly between 0 and 1, just like in the previous slide.
A couple of interesting features of the simulation soon become apparent. First of all, for a constant number of connections, the curve maintains its basic shape no matter how many times the "Go" button is pressed. Evidently, the agent-level differences in channel count cancel out to produce the same macroscopic measurement.
On the other hand, if the number of connections is reduced, the curve's peak moves over to the right, implying that the optimal incentive strategy is increasingly absolute. Intuitively, this means that in an organization where no one knows anybody else, a manager is well-advised to boost information exchange as much as possible along the contacts that do exist. Hypothesis 4 implies that she may do so by reducing relative, and increasing absolute agent incentives.
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