Measuring individual differences in the depth of planning

While making plans, people have to decide how far out into the future they want to plan: days, months, years, or even longer. Overly short-sighted planning can harm people's well-being in important life domains, such as health, finances, and academics. While self-report scales exist to measure people's planning, people's answers to such questions may be distorted by their desire to make a good impression and conform to norms and expectations. Here, we introduce a method for objectively quantifying people's propensity to plan into the future. Our method combines a process-tracing method with Bayesian inverse reinforcement learning to measure how prone an individual is to plan multiple steps ahead. To infer this from a person's process-tracing data, our method inverts a new resource-rational model of individual differences in planning. This model assumes that subjective planning costs are captured by a cost function with two parameters: a mental effort cost and a planning depth cost. Upon showing that our model of planning explains individual participants' planning behavior better than the best previous models, we validate our method on simulated data and real data from a large online experiment where the cost of planning was manipulated within participants. Our results show that our method can infer individual differences in the planning depth cost. Our model provides a mechanistic account for why some people plan too shortsightedly. The subjective planning costs inferred by our method can be used as an objective, non-self-report measure of individual differences in people's propensity to plan into the future.