The Peter Principle: Advancing to maximal incompetence

A new eprint (arXiv:0907.0455) reports a computational study of the Peter Principle. Many who, like me, have never heard of the Peter Principle before will be amused to learn that the it asserts that employees climb the career ladder until they reach a maximal level of incompetence with respect to their work tasks. I’m sure many workers will find support for this in their own experiences.

The underlying assumption of the principle is that an employees competence at one level of the career ladder is uncorrelated (and statistically independent) with the next level. Thus, promoting the best does no good, as their competence at the next rung of the ladder is random. The abstract explains the issues very clearly:

The Peter Principle Revisited: A Computational Study

Alessandro Pluchino, Andrea Rapisarda, Cesare Garofalo

In the late sixties the Canadian psychologist Laurence J. Peter advanced the apparently paradoxical principle, named since then after him, which can be summarized as follows: “Every new member in a hierarchical organization climbs the hierarchy until he/she reaches his/her level of maximum incompetence”. Despite its apparent unreasonableness, such a principle would realistically act in any organization where the way of promotion rewards the best members and where the competence at their new level in the hierarchical structure does not depend on the competence they had at the previous level, usually because the tasks of the levels are very different between each other. Here we show, by means of agent based simulations, that if the latter two features actually hold in a given model of an organization with a hierarchical structure, then not only the “Peter principle” is unavoidable, but it yields in turn a significant reduction of the global efficiency of the organization. Within a game theory-like approach, we explore different promotion strategies and we find, counter intuitively, that in order to avoid such an effect the best ways for improving the efficiency of a given organization are either to promote each time an agent at random or to promote randomly the best and the worst members in terms of competence.

Agents in the simulation study are distinguished based on numbers representing their rung on the career ladder, age and competence (for their given rung). At each time step agents below a critical level of competence are removed from the organization, as are agents who have reached retirement age. Agents from lower levels are promoted to fill their place. Two different statistical hypotheses for how competence changes along the career ladder are explored. The first hypothesis is just that the competence at one rung is the same as the competence at the rung below it plus a small random term. The second hypothesis corresponds to the Peter Principle with statistically independent competence at different rungs. Because the most incompetent agents are removed from the organization, promotion policies can make a difference even under the second hypothesis. In fact, under the second hypothesis, it is best to keep the most competent agents where they are and instead promote the worst agents, since their new competence will be random. This promote-the-worst policy results in an over all “efficiency” gain of 12% with respect to the initial random organization, compared with a 10% loss when the promote-the-best policy is followed. (“Efficiency” is defined by the authors as the sum of all agents’ competence weighted by their rung on the career ladder.)

It would be interesting to know to what extent the Peter Principle holds in actual organizations. I suppose administrative tasks typically take over as one climbs the career ladder, so employees initially hired because of their technical skills may find their competence to perform administrative tasks at higher rungs more or less uncorrelated with their initial competence for technical tasks. Still, it seems to me there are some underlying, more or less rung-independent, factors too, such as ambition, motivation to learn a new topic, etc. Future studies will hopefully estimate the strength of the correlation between rungs, if it hasn’t been done already.

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