Efficient Portfolio Selection through Preference Aggregation with Quicksort and the Bradley--Terry Model

• URL: http://arxiv.org/abs/2504.16093v1
• Date: Sun, 06 Apr 2025 23:16:30 GMT
• Title: Efficient Portfolio Selection through Preference Aggregation with Quicksort and the Bradley--Terry Model
• Authors: Yurun Ge, Lucas Böttcher, Tom Chou, Maria R. D'Orsogna,
• Abstract summary: How to allocate limited resources to projects that will yield the greatest long-term benefits is a problem that often arises in decision-making under uncertainty.<n>We propose comparison rules based on Quicksort and the Bradley--Terry model, which connects rankings to pairwise "win" probabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: How to allocate limited resources to projects that will yield the greatest long-term benefits is a problem that often arises in decision-making under uncertainty. For example, organizations may need to evaluate and select innovation projects with risky returns. Similarly, when allocating resources to research projects, funding agencies are tasked with identifying the most promising proposals based on idiosyncratic criteria. Finally, in participatory budgeting, a local community may need to select a subset of public projects to fund. Regardless of context, agents must estimate the uncertain values of a potentially large number of projects. Developing parsimonious methods to compare these projects, and aggregating agent evaluations so that the overall benefit is maximized, are critical in assembling the best project portfolio. Unlike in standard sorting algorithms, evaluating projects on the basis of uncertain long-term benefits introduces additional complexities. We propose comparison rules based on Quicksort and the Bradley--Terry model, which connects rankings to pairwise "win" probabilities. In our model, each agent determines win probabilities of a pair of projects based on his or her specific evaluation of the projects' long-term benefit. The win probabilities are then appropriately aggregated and used to rank projects. Several of the methods we propose perform better than the two most effective aggregation methods currently available. Additionally, our methods can be combined with sampling techniques to significantly reduce the number of pairwise comparisons. We also discuss how the Bradley--Terry portfolio selection approach can be implemented in practice.

Related papers

• TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding [76.23719557942917]
  TETRIS actively selects the most promising draft tokens (for every request in a batch) to be accepted when verified in parallel.<n>We show theoretically and empirically that TETRIS outperforms baseline speculative decoding and existing methods that dynamically select draft tokens.
  arXiv  Detail & Related papers  (2025-02-21T04:19:24Z)
• Design of mechanisms for ensuring the execution of tasks in project planning [0.0]
  The paper reports an analysis of aspects of the project planning stage.<n>It takes into account restrictions on financial costs and duration of project implementation.<n>Models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed.
  arXiv  Detail & Related papers  (2025-01-02T13:47:20Z)
• CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing [70.25689961697523]
  We propose a generalizable algorithm that enhances sequential reasoning by cross-task experience sharing and selection. Our work bridges the gap between existing sequential reasoning paradigms and validates the effectiveness of leveraging cross-task experiences.
  arXiv  Detail & Related papers  (2024-10-22T03:59:53Z)
• Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning [81.69044784288005]
  Iterative preference learning requires online annotated preference labels. We study strategies to select worth-annotating response pairs for cost-efficient annotation.
  arXiv  Detail & Related papers  (2024-06-25T06:49:16Z)
• Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
  We introduce a novel greedy-style subset selection algorithm for batch acquisition. Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
  arXiv  Detail & Related papers  (2024-06-21T05:57:08Z)
• MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation [80.47072100963017]
  We introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP) MAP efficiently identifies a set of scaling coefficients for merging multiple models, reflecting the trade-offs involved. We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
  arXiv  Detail & Related papers  (2024-06-11T17:55:25Z)
• Efficient Prompt Optimization Through the Lens of Best Arm Identification [50.56113809171805]
  This work provides a principled framework, TRIPLE, to efficiently perform prompt selection under an explicit budget constraint. It is built on a novel connection established between prompt optimization and fixed-budget best arm identification (BAI-FB) in multi-armed bandits (MAB)
  arXiv  Detail & Related papers  (2024-02-15T05:31:13Z)
• Fuzzy Mathematical Model For Optimizing Success Criteria Of Projects: A Project Management Application [0.0]
  It is well known that measuring the success of projects under the umbrella of project management is inextricably linked with the associated cost, time, and quality. Most of the previous researches in the field assigned a separate mathematical model for each criterion, then numerical methods or search techniques were applied to obtain the optimal trade-off between the three criteria.
  arXiv  Detail & Related papers  (2024-01-11T21:54:05Z)
• Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime [59.27851754647913]
  Predictive optimization is the precise modeling of many real-world applications, including energy cost-aware scheduling and budget allocation on advertising. We develop a modular framework to benchmark 11 existing PtO/PnO methods on 8 problems, including a new industrial dataset for advertising. Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• Participatory Budgeting With Multiple Degrees of Projects And Ranged Approval Votes [0.0]
  In an indivisible participatory budgeting (PB) framework, we have a limited budget that is to be distributed among a set of projects. Each voter approves a range of costs for each project, by giving an upper and lower bound on the cost that she thinks the project deserves. The outcome of a PB rule selects a subset of projects and also specifies their corresponding costs.
  arXiv  Detail & Related papers  (2023-05-18T13:39:56Z)
• What Should We Optimize in Participatory Budgeting? An Experimental Study [28.76045220764571]
  Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget. We show that some modern PB aggregation techniques greatly differ from users' expectations. We identify a few possible discrepancies between what non-experts consider saydesirable and how they perceive the notion of "fairness" in the PB context.
  arXiv  Detail & Related papers  (2021-11-14T10:46:03Z)
• A portfolio approach to massively parallel Bayesian optimization [0.0]
  One way to reduce the time of conducting optimization studies is to evaluate designs rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.
  arXiv  Detail & Related papers  (2021-10-18T14:02:21Z)
• Participatory Budgeting with Project Groups [27.39571821668551]
  We study a generalization of the standard approval-based model of participatory budgeting (PB) We show that the problem is generally intractable and describe efficient exact algorithms for several special cases. Our results could allow, e.g., municipalities to hold richer PB processes that are thematically and geographically inclusive.
  arXiv  Detail & Related papers  (2020-12-09T18:23:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.