Collaborative Procrastination

Authors Aris Anagnostopoulos , Aristides Gionis , Nikos Parotsidis

Thumbnail PDF


  • Filesize: 0.66 MB
  • 20 pages

Document Identifiers

Author Details

Aris Anagnostopoulos
  • Sapienza University of Rome, Italy
Aristides Gionis
  • KTH Royal Institute of Technology, Stockholm, Sweden
Nikos Parotsidis
  • University of Copenhagen, Denmark

Cite AsGet BibTex

Aris Anagnostopoulos, Aristides Gionis, and Nikos Parotsidis. Collaborative Procrastination. In 10th International Conference on Fun with Algorithms (FUN 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 157, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


The problem of inconsistent planning in decision making, which leads to undesirable effects such as procrastination, has been studied in the behavioral-economics literature, and more recently in the context of computational behavioral models. Individuals, however, do not function in isolation, and successful projects most often rely on team work. Team performance does not depend only on the skills of the individual team members, but also on other collective factors, such as team spirit and cohesion. It is not an uncommon situation (for instance, experienced by the authors while working on this paper) that a hard-working individual has the capacity to give a good example to her team-mates and motivate them to work harder. In this paper we adopt the model of Kleinberg and Oren (EC'14) on time-inconsistent planning, and extend it to account for the influence of procrastination within the members of a team. Our first contribution is to model collaborative work so that the relative progress of the team members, with respect to their respective subtasks, motivates (or discourages) them to work harder. We compare the total cost of completing a team project when the team members communicate with each other about their progress, with the corresponding cost when they work in isolation. Our main result is a tight bound on the ratio of these two costs, under mild assumptions. We also show that communication can either increase or decrease the total cost. We also consider the problem of assigning subtasks to team members, with the objective of minimizing the negative effects of collaborative procrastination. We show that whereas a simple problem of forming teams of two members can be solved in polynomial time, the problem of assigning n tasks to n agents is NP-hard.

Subject Classification

ACM Subject Classification
  • Applied computing → Economics
  • Applied computing → Sociology
  • time-inconsistent planning
  • computational behavioral science
  • collaborative work
  • collaborative environments


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. George A Akerlof. Procrastination and obedience. The American Economic Review, 81(2):1-19, 1991. Google Scholar
  2. Susanne Albers and Dennis Kraft. Motivating time-inconsistent agents: A computational approach. In Web and Internet Economics, pages 309-323, 2016. Google Scholar
  3. Susanne Albers and Dennis Kraft. On the Value of Penalties in Time-Inconsistent Planning. In 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017), pages 10:1-10:12, 2017. Google Scholar
  4. Susanne Albers and Dennis Kraft. The price of uncertainty in present-biased planning. In Web and Internet Economics, pages 325-339, 2017. Google Scholar
  5. Shane Frederick, George Loewenstein, and Ted O'donoghue. Time discounting and time preference: A critical review. Journal of economic literature, 40(2):351-401, 2002. Google Scholar
  6. Joshua S Gans and Peter Landry. Procrastination in teams. Technical report, National Bureau of Economic Research, 2016. Google Scholar
  7. Nick Gravin, Nicole Immorlica, Brendan Lucier, and Emmanouil Pountourakis. Procrastination with variable present bias. In Proc. of the 2016 ACM Conference on Economics and Computation, EC '16. ACM, 2016. Google Scholar
  8. Jon Kleinberg and Sigal Oren. Time-inconsistent planning: a computational problem in behavioral economics. In Proc. of the fifteenth ACM conference on Economics and computation, EC '14, pages 547-564. ACM, 2014. Google Scholar
  9. Jon Kleinberg, Sigal Oren, and Manish Raghavan. Planning problems for sophisticated agents with present bias. In Proc. of the 2016 ACM Conference on Economics and Computation, EC '16, pages 343-360, New York, NY, USA, 2016. ACM. URL:
  10. Jon Kleinberg, Sigal Oren, and Manish Raghavan. Planning with multiple biases. arXiv preprint arXiv:1706.01062, 2017. Google Scholar
  11. Jon Kleinberg and Maithra Raghu. Team performance with test scores. In Proc. of the Sixteenth ACM Conference on Economics and Computation, pages 511-528. ACM, 2015. Google Scholar
  12. David Laibson. Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2):443-478, 1997. Google Scholar
  13. Robert A Pollak. Consistent planning. The Review of Economic Studies, 35(2):201-208, 1968. Google Scholar
  14. Robert Henry Strotz. Myopia and inconsistency in dynamic utility maximization. The Review of Economic Studies, 23(3):165-180, 1955. Google Scholar
  15. Stefan Wuchty, Benjamin F Jones, and Brian Uzzi. The increasing dominance of teams in production of knowledge. Science, 316(5827):1036-1039, 2007. Google Scholar
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail