Equations are not theories; prediction is not explanation.

Do it while you can

Is media-enabled escapism the same as procrastination?

Specificity aversion may contribute to second-order procrastination

One of the most significant, recent publications about procrastination is a comprehensive meta-analysis conducted by Piers Steel (University of Calgary, Alberta) and published in Psychological Bulletin (2007). This paper is well written and a must read for my thesis students. Unfortunately, I find it misleading, because of its focus on an equation to explain procrastination. Economists and naïve realists need not read on.

Given that the paper is a recent review of much of the procrastination literature, I will summarize aspects of this paper in blog postings to come. For example, Dr. Steel clearly identifies a number of important variables that are strongly related to procrastination such as: self-efficacy, need for achievement, proneness to boredom, distractibility, impulsiveness, self-control and organization. Each of these deserves attention, and we'll get there.

My focus today is on the expression of Temporal Motivation Theory (TMT) as an equation that Dr. Steel proposes to explain procrastination. TMT attempts to synthesize well-established motivational formulations with a specific focus on time. In short, the theory integrates two ideas, expectancy theory and hyperbolic discounting, in 4 variables:

1. we are more likely to do things at which we expect to succeed (E) and we value (V), and

2. we typically discount future rewards in favor of immediate rewards (D) which is moderated by our tolerance for or sensitivity to delay (Γ).

Taken together, these variables in the expression, E x V/Γ x D, predict how desirable a task or choice is for an individual (defined as "utility" = U). This all makes sense, at least in principle, as an integration of a number of concepts related to our motivation over time.

As Dr. Steel explains through his literature review, many (but not all) of the variables that have been found to relate to procrastination can be linked to the variables in the equation. However, these links and the application of this equation to the understanding of human behavior, relies on many assumptions. I take exception to the assumptions in the application of this theory.

I'll take one assumption as an example. In the graph in the diagram above, the horizontal line represents the utility of "socializing," while the utility of "writing an essay" (a task typically associated with academic procrastination) is represented by the hyperbolic, curved line on the graph. The assumption is that socializing has a fixed utility. I believe, as do my students, that this doesn't reflect reality.

As presented in the graph, this assumption of a fixed utility for socializing makes the equation and theory fit our experience well. We see the desire (motivation) for essay writing overcome (intersect) the desire for socialization late on the time line, reflecting our own experience of end-of-term efforts.

More realistically, here's how socialization might change: a party for Friday night is put off to next week greatly reducing expectation of success and increasing delay, thus decreasing the utility of socializing overall. So on that particular Friday night, the utility of socializing could dip down below the level of the utility of essay writing. Without the assumption that socialization remains constant, the theory would now predict that the individual would begin work on the essay. However, we still may not see essay writing. In fact, my experience and my students tell me that we wouldn't. There are more things to consider here once we remove the assumption in the model.

The point is that complex human behaviors are not best understood by simple equations or formulae, although the theories that these formulae represent can be useful in our discussion of behavior. The reason this is important is that we sometimes mistake a mathematical equation (particularly one that reminds us of Newtonian physics) with real theory and understanding. We begin to believe that we could plug numbers into the equation and predict behavior, or in this case procrastination, much like we could predict the trajectory of a projectile. Unfortunately, this is not true except for overly constrained examples that depend on unwarranted assumptions as noted.

Predicting human behavior is analogous to something our kindergarten teachers told us years ago, "Each of us is like a snowflake." In this regard, the modern physics of snowflakes that incorporates chaos theory and understands a phenomenon in terms of non-linear, unstable, free-boundary conditions may serve us much better than an equation as presented in TMT.

Interestingly, another PT blogger, Jesse Bering, recently posted about the two types of psychologists (see the blog "Quirky Little Things"). As he puts it, there are those who get into psychology to help others, and those who work to explain others. I'm adding to his distinction by saying that even those of us who belong to the "explain it" camp (i.e., research psychologists), don't necessarily agree about the best approach to making these knowledge claims.

Does this mean that I believe people don't discount future rewards or that we are not motivated to do things we value and at which we expect success? Not at all. These are well substantiated theories about human behavior. In addition, temporal issues of our projects over time must be taken into account as we explore how our volitional action breaks down. The issue is that these theories and variables as expressed in the TMT equation fall short of explaining procrastination, even if the computational formula predicts general trends in the population.

As Dr. Steel notes in the concluding section of his important review paper, "extensive research is needed that will fully explore procrastination and its underpinnings." I couldn't agree more, and I would advocate that we not adopt an overly simplified approach to providing a unifying theory as we move forward. We have to ensure that we don't mistake the statistical partitioning of variance or the mathematical product of psychological measures as an understanding of the phenomenon or the individual. But, that's me, and as Dr. Bering noted in his distinction of the types of psychologists, it could be that we're just in different camps.