Information Flows And Networks I

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Manager Interviewing a Pool of Candidates

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.

In the simulation to your left, the blue agent represents a manager faced with the prospect of a job hire. The rest are candidates for the one available position - potential interviewees for the manager to choose from. The catch is that in order to know the value of each candidate, the manager has to expend resources in the form of interview time and effort. If you start pressing the "Go" button, the manager will spring into action, connecting to previously unexplored interviewees, and assessing their worth. She also maintains a permanent link to the best candidate she has found so far, the one indicated by the green-colored circle.

In this scenario, each agent's value is drawn from the same probability distribution function: it is equally likely to be any number between 0 and 100. The cost of interviewing each of them is, however, a constant - the manager spends the same amount of time with every one. This fact is, incidentally, reflected by the linearity of the Total Cost graph (shown as a red line below the simulation display).

What, then, is the manager's reward-to-date? It is the maximum value of any given agent interviewed thus far, i.e. the size of the green circle in our graphic. Its progress is marked by the green line in the aforementioned graph, which asymptotes to 100% as the manager keeps looking for more qualified candidates.

Finally, the most important measure is the manager's Total Payoff - the difference between her reward and the total interviewing costs she has incurred. After going through a few runs, you should note that its blue line peaks somewhere between 5 and 15 time periods - the point after which the expected marginal reward of interviewing each candidate becomes smaller than the corresponding cost.

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