Chunking Tasks for Present-Biased Agents

TL;DR

This paper investigates optimal task chunking strategies for present-biased agents using a graph-theoretic model, demonstrating how effective chunking can significantly reduce procrastination costs and extending solutions to multiple agent types.

Contribution

It introduces efficient algorithms for optimal chunking of task graphs to mitigate present bias, including for multiple agent types, advancing understanding of procrastination interventions.

Findings

Optimal chunking on shortest paths makes early chunks easier.

Efficient algorithm for optimal chunking of non-shortest path edges.

Chunking can exponentially reduce the cost for biased agents.

Abstract

Everyone puts things off sometimes. How can we combat this tendency to procrastinate? A well-known technique used by instructors is to break up a large project into more manageable chunks. But how should this be done best? Here we study the process of chunking using the graph-theoretic model of present bias introduced by Kleinberg and Oren (2014). We first analyze how to optimally chunk single edges within a task graph, given a limited number of chunks. We show that for edges on the shortest path, the optimal chunking makes initial chunks easy and later chunks progressively harder. For edges not on the shortest path, optimal chunking is significantly more complex, but we provide an efficient algorithm that chunks the edge optimally. We then use our optimal edge-chunking algorithm to optimally chunk task graphs. We show that with a linear number of chunks on each edge, the biased agent's cost can be exponentially lowered, to within a constant factor of the true cheapest path. Finally, we extend our model to the case where a task designer must chunk a graph for multiple types of agents simultaneously. The problem grows significantly more complex with even two types of agents, but we provide optimal graph chunking algorithms for two types. Our work highlights the efficacy of chunking as a means to combat present bias.