Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
An Algorithmic Introduction to Savings Circles
Authors: Rediet Abebe, Adam Eck, Christian Ikeokwu, Sam Taggart4744-4751
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,567 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,532 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we take an algorithmic perspective on the study of roscas. Building on techniques from the price of anarchy literature, we present worst-case welfare approximation guarantees. We further experimentally compare the welfare of outcomes as key features of the environment vary. These cardinal welfare analyses further rationalize the prevalence of roscas. |
| Researcher Affiliation | Academia | Rediet Abebe1, Adam Eck2, Christian Ikeokwu1, Samuel Taggart2 1 University of California, Berkeley 2 Oberlin College |
| Pseudocode | Yes | Algorithm 1: Rosca Multi-Round Allocation |
| Open Source Code | No | The paper does not provide any links to open-source code or make an explicit statement about code availability. |
| Open Datasets | No | The paper mentions fixing a profile of participant values and gives details about them in the supplement, but it does not use or provide concrete access to a publicly available dataset in the traditional sense (e.g., via a link or DOI). |
| Dataset Splits | No | The paper describes simulation runs ("Welfare values are averaged over 10,000 simulation runs") but does not provide specific train/validation/test dataset splits needed for reproduction, as it simulates data rather than using predefined splits of a static dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Our first experiment fixes a profile of participant values and studies the performance of swap roscas as the convexity parameter a and starting wealth W vary. ... We consider values of a ranging from 0 (quasilinear) to 2 (very convex)... We take W in the range {1, . . . , 5}... Welfare values are averaged over 10,000 simulation runs, each starting with a random initial allocation... We give all value profiles explicitly in the supplement. |