The Chaos Theory of Careers: a user's guide

Career Development Quarterly, June, 2005 by Jim E.H. Bright, Robert G.L. Pryor

Ping-Pong Balls and Puppies: An Abductive Illustration of the Chaos Theory of Careers

Imagine you are in a room alone with a Ping-Pong ball. If you repeatedly drop the ball from waist height, you can be fairly confident of correctly predicting that it will fall to the ground somewhere near your feet. We call this Scenario 1.

However, suppose now that an eager ball-chasing puppy is in the room with you and also that a strong electric fan is brought into the room, placed near you, and switched on. Now, when you drop the Ping-Pong ball, how certain can you be that the ball will land near your feet? Presumably much less certain, because the puppy might catch it or the fan might blow it off course. We call this Scenario 2.

Now suppose there is a pack of eager puppies in the room and a series of electric fans; someone has opened the window and a howling gale is blowing; and, furthermore, you are now obliged to stand on an electric treadmill programmed to randomly vary its speed! Now when you drop the ball, how confident are you that it will land near your feet? Indeed, how confident are you in making any prediction about where the ball might end up? We call this Scenario 3.

In Scenario 1, the system is very predictable for two reasons. First, the person and the environment are fairly static and unchanging. Second, there are no unplanned events intruding. This is essentially the world as characterized in traditional person-environment models of career development, such as J. L. Holland's (1997).

In Scenario 2, there is a broader range of variables with the addition of the puppy and the electric fan, but we are probably still confident of working through most of the possible outcomes. In career development terms, this is not dissimilar to frameworks such as Gottfredson's (1981), in which gender, prestige, and interests are characterized as the key influences on career choice. With the "zone of acceptable alternatives," there is a constrained influence of happenstance (Chen, 2002).

In Scenario 3, there are many different variables to consider: Each puppy has a mind of its own, the treadmill is randomly programmed, the airflow is confused by the various fans blowing, and the gale force winds outside will all combine to confound our attempts at predicting where the ball will go. Such a scenario is closer in spirit to the wide range of influences identified by Krumboltz (1998) and Lent et al. (1996). This scenario also resembles the type of complex dynamic system that can be well accounted for in chaos theory (Pryor & Bright, 2003a). Here is an example where broad predictions can be made about the future behavior of the room. For instance, the ball will end up somewhere. It is highly likely the ball will remain in the room, because the gale force winds blowing in are more powerful than the fans in the room. So in the short term, we can make broad predictions, but we are unlikely to be able to make specific and accurate predictions. In the longer term, due to the characteristics of the system, things could alter dramatically, thereby making prediction impossible. For instance, if the gale abated, it is possible the ball would be blown out of the room, or a dog with the ball in its mouth could escape through the open window. If either of these happened, the dynamics of the system would be radically altered. Either one would have to find something other than the ball to drop or go after the ball (and the puppy).