We propose that externally induced time limits on a task overly affect predictions of other people’s completion times for that task, due to an over-generalized association between the time available and inferred task scope. We find higher estimates of the time needed to complete a given task by another person when the time limit is longer. While such predictions could be normative when time limits are informative, the effect persists even when the decision-maker knows that the limit is arbitrary and is unknown to the other person, and therefore, cannot affect behavior. Perception of task scope mediates the relationship between time limits and completion time estimates, and weakening the association between time limits and task scope attenuates the effect. The over-learned cognitive bias persists even among experienced decision-makers making estimates in a familiar setting. Our findings have implications for people who make decisions that use judgments of others’ task completion time as an input.

Many families, despite need and eligibility, struggle to meet programme deadlines to retain critical benefits. When families fail to complete programme recertification on time, they lose needed support. While scholars have tested behavioural theories like chunking, implementation intention, and loss framing to promote programme uptake, less is known about how well-designed communications can promote continuity through successful recertification, especially where recertification entails a significant administrative burden. Further, scant evidence guides how best to frame recertification deadlines. In a randomised trial with government partners (n = 3,539), we find that sending a reminder letter informed by these behavioural theories increased the number of families maintaining participation by 14 per cent. Further, anchoring people to a deadline month may suffice to thread the motivational needle: overcoming procrastination without lowering self-efficacy by anchoring them to a specific day. Adopting the most effective letter in Washington, DC, would lead 766 more families to participate uninterrupted each year.

In this paper, we consider a single-server queue with stationary input, where each job joining the queue has an associated deadline. The deadline is a time constraint on job sojourn time and may be finite or infinite. If the job does not complete service before its deadline expires, it abandons the queue and the partial service it may have received up to that point is wasted. When the queue operates under a first-come-first served discipline, we establish conditions under which the actual workload process—that is, the work the server eventually processes—is unstable, weakly stable, and strongly stable. An interesting phenomenon observed is that in a nontrivial portion of the parameter space, the queue is weakly stable, but not strongly stable. We also indicate how our results apply to other nonidling service disciplines. We finally extend the results for a single node to acyclic (feed-forward) networks of queues with either per-queue or network-wide deadlines.

This paper is devoted to the following version of the single machine preemptive scheduling problem of minimizing the weighted number of late jobs. A processing time, a release date, a due date and a weight of each job are given. Certain jobs are specified to be completed in time, i.e., their due dates are assigned to be deadlines, while the other jobs are allowed to be completed after their due dates. The release/due date intervals are nested, i.e., no two of them overlap (either they have at most one common point or one covers the other). Necessary and sufficient conditions for the completion of all jobs in time are considered, and an O(nlogn) algorithm (where n is the number of jobs) is proposed for solving the problem of minimizing the weighted number of late jobs in case of oppositely ordered processing times and weights.