Budget-proposal aggregation

Budget-proposal aggregation (BPA) is a problem in social choice theory.[1][2][3] A group has to decide on how to distribute its budget among several issues. Each group-member has a different idea about what the ideal budget-distribution should be. The problem is how to aggregate the different opinions into a single budget-distribution program.

BPA is a special case of participatory budgeting, with the following characteristics:

  1. The issues are divisible and unbounded – each issue can be allocated any amount, as long as the sum of allocations equals the total budget.
  2. Agents' preferences are given by single-peaked preferences over an ideal budget.[citation needed]

It is also a special case of fractional social choice (portioning), in which agents express their preferences by stating their ideal distribution, rather than by a ranking of the issues.[4][5][clarification needed]

Another sense in which aggregation in budgeting has been studied is as follows. Suppose a manager asks his worker to submit a budget-proposal for a project. The worker can over-report the project cost, in order to get the slack to himself. Knowing that, the manager might reject the worker's proposal when it is too high, even though the high cost might be real. To mitigate this effect, it is possible to ask the worker for aggregate budget-proposals (for several projects at once). The experiment shows that this approach can indeed improve the efficiency of the process.[6]

The same problem has been studied in the context of aggregating probability distributions.[7] Suppose each citizen in society has a certain probability-distribution over candidates, representing the probability that the citizen prefers each candidate. The goal is to aggregate all distributions to a single probability-distribution, representing the probability that society should choose each candidate.

  1. ^ Freeman, Rupert; Pennock, David M.; Peters, Dominik; Wortman Vaughan, Jennifer (2019-06-17). "Truthful Aggregation of Budget Proposals". Proceedings of the 2019 ACM Conference on Economics and Computation. EC '19. New York: Association for Computing Machinery. pp. 751–752. arXiv:1905.00457. doi:10.1145/3328526.3329557. ISBN 978-1-4503-6792-9.
  2. ^ Caragiannis, Ioannis; Christodoulou, George; Protopapas, Nicos (2022-06-28). "Truthful Aggregation of Budget Proposals with Proportionality Guarantees". Proceedings of the AAAI Conference on Artificial Intelligence. 36 (5): 4917–4924. arXiv:2203.09971. doi:10.1609/aaai.v36i5.20421. ISSN 2374-3468.
  3. ^ Freeman, Rupert; Schmidt-Kraepelin, Ulrike (2023). "Project-Fair and Truthful Mechanisms for Budget Aggregation". arXiv:2309.02613 [cs.GT].
  4. ^ Airiau, Stéphane; Aziz, Haris; Caragiannis, Ioannis; Kruger, Justin; Lang, Jérôme; Peters, Dominik (2023-01-01). "Portioning using ordinal preferences: Fairness and efficiency". Artificial Intelligence. 314: 103809. doi:10.1016/j.artint.2022.103809. ISSN 0004-3702.
  5. ^ Elkind, Edith; Suksompong, Warut; Teh, Nicholas (2023), "Settling the Score: Portioning with Cardinal Preferences", ECAI 2023, Frontiers in Artificial Intelligence and Applications, IOS Press, pp. 621–628, arXiv:2307.15586, doi:10.3233/FAIA230324, ISBN 9781643684369
  6. ^ "Aggregation in Budgeting: An Experiment". publications.aaahq.org. Retrieved 2023-10-16.
  7. ^ Intriligator, M. D. (1973-10-01). "A Probabilistic Model of Social Choice". The Review of Economic Studies. 40 (4): 553–560. doi:10.2307/2296588. ISSN 0034-6527. JSTOR 2296588.