Doctor of Philosophy (PhD)
Faculty of Science
Research examining the “planning fallacy” indicates that people frequently underestimate the time needed to complete tasks, and that this underestimation bias stems from a tendency to base predictions on plans that are idealized and oversimplified. The present research tested a potential debiasing strategy – known as backward planning – that involves beginning with the future target goal in mind, and working backwards toward the present by imagining all the steps needed to attain that goal in a reverse-chronological order. It was hypothesized that by altering the temporal direction of planning, this approach may lead people to have greater planning insights (i.e., clarify planning steps, think of new planning steps, break plans down into important steps), and plan less idealistically (i.e., consider potential problems and obstacles), which would in turn lead them to make more conservative predictions. Results from four experiments supported the prediction hypothesis. Participants assigned to the backward planning condition predicted to finish a variety of hypothetical tasks (Studies 1 & 2) and real, upcoming projects (Studies 3 & 4) later than participants in the other conditions. Further, in a follow-up study that tracked actual completion times (Study 4), backward planners were found to be less biased in their predictions than participants in the other conditions. Lastly, as predicted, backward planners reported more planning insights and potential problems and obstacles (Studies 1, 2, & 4) than those in the other conditions. Hypotheses concerning mediating processes received some support (Studies 2 & 4). These studies are the first to test the effects on prediction of a planning strategy commonly advocated in applied contexts, and provide some evidence that backward planning helps individuals generate more realistic predictions by influencing cognitive processes that normally lead to bias.
Wiese, Jessica, "Backward Planning: Examining Consequences of Planning Direction for Time Prediction" (2015). Theses and Dissertations (Comprehensive). 1755.