An Algorithmic Introduction to Savings Circles
Authors: Rediet Abebe, Adam Eck, Christian Ikeokwu, Sam Taggart4744-4751
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |