Task Release Control for Decision Making Queues

TL;DR

This paper studies optimal duration allocation in human decision-making queues, balancing decision accuracy and task value decay, and proposes strategies including task dropping and receding horizon optimization.

Contribution

It introduces a novel model for decision queue management with sigmoidal accuracy functions and develops optimal policies that include task dropping and horizon-based strategies.

Findings

Optimal policies involve dropping some tasks to maximize overall value.

Receding horizon optimization outperforms greedy strategies in simulations.

The decision accuracy improves with tailored duration allocation strategies.

Abstract

We consider the optimal duration allocation in a decision making queue. Decision making tasks arrive at a given rate to a human operator. The correctness of the decision made by human evolves as a sigmoidal function of the duration allocated to the task. Each task in the queue loses its value continuously. We elucidate on this trade-off and determine optimal policies for the human operator. We show the optimal policy requires the human to drop some tasks. We present a receding horizon optimization strategy, and compare it with the greedy policy.