ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents
- URL: http://arxiv.org/abs/2410.20746v3
- Date: Wed, 06 Nov 2024 13:05:51 GMT
- Title: ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents
- Authors: Xinnong Zhang, Jiayu Lin, Libo Sun, Weihong Qi, Yihang Yang, Yue Chen, Hanjia Lyu, Xinyi Mou, Siming Chen, Jiebo Luo, Xuanjing Huang, Shiping Tang, Zhongyu Wei,
- Abstract summary: We introduce ElectionSim, an innovative election simulation framework based on large language models.
We present a million-level voter pool sampled from social media platforms to support accurate individual simulation.
We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
- Score: 70.17229548653852
- License:
- Abstract: The massive population election simulation aims to model the preferences of specific groups in particular election scenarios. It has garnered significant attention for its potential to forecast real-world social trends. Traditional agent-based modeling (ABM) methods are constrained by their ability to incorporate complex individual background information and provide interactive prediction results. In this paper, we introduce ElectionSim, an innovative election simulation framework based on large language models, designed to support accurate voter simulations and customized distributions, together with an interactive platform to dialogue with simulated voters. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario. Through extensive experiments and analyses, we demonstrate the effectiveness and robustness of our framework in U.S. presidential election simulations.
Related papers
- Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations [49.908708778200115]
We are the first to specialize large language models (LLMs) for simulating survey response distributions.
As a testbed, we use country-level results from two global cultural surveys.
We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions.
arXiv Detail & Related papers (2025-02-10T21:59:27Z) - A Large-scale Empirical Study on Large Language Models for Election Prediction [12.582222782098587]
We introduce a multi-step reasoning framework for election prediction, which integrates demographic, ideological, and time-sensitive factors.
We apply our approach to the 2024 U.S. presidential election, illustrating its ability to generalize beyond observed historical data.
We identify potential political biases embedded in pretrained corpora, examine how demographic patterns can become exaggerated, and suggest strategies for mitigating these issues.
arXiv Detail & Related papers (2024-12-19T07:10:51Z) - Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models [12.582222782098587]
Election prediction poses unique challenges, such as limited voter-level data, rapidly changing political landscapes, and the need to model complex human behavior.
We introduce a multi-step reasoning framework designed for political analysis.
Our approach is validated on real-world data from the American National Election Studies (ANES) 2016 and 2020.
arXiv Detail & Related papers (2024-10-21T06:18:53Z) - Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - How to Estimate Model Transferability of Pre-Trained Speech Models? [84.11085139766108]
"Score-based assessment" framework for estimating transferability of pre-trained speech models.
We leverage upon two representation theories, Bayesian likelihood estimation and optimal transport, to generate rank scores for the PSM candidates.
Our framework efficiently computes transferability scores without actual fine-tuning of candidate models or layers.
arXiv Detail & Related papers (2023-06-01T04:52:26Z) - Design and analysis of tweet-based election models for the 2021 Mexican
legislative election [55.41644538483948]
We use a dataset of 15 million election-related tweets in the six months preceding election day.
We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
arXiv Detail & Related papers (2023-01-02T12:40:05Z) - Agent-based Simulation of District-based Elections [0.5076419064097732]
In district-based elections, electors cast votes in their respective districts.
In each district, the party with maximum votes wins the corresponding seat in the governing body.
The election result is based on the number of seats won by different parties.
arXiv Detail & Related papers (2022-05-28T11:19:04Z) - Exploring Fairness in District-based Multi-party Elections under
different Voting Rules using Stochastic Simulations [0.5076419064097732]
Many democratic societies use district-based elections, where the region under consideration is geographically divided into districts and a representative is chosen for each district based on the preferences of the electors who reside there.
We show that this can lead to situations where many electors are dissatisfied with the election results, which is not desirable in a democracy.
Inspired by current literature on fairness of Machine Learning algorithms, we define measures of fairness to quantify the satisfaction of electors, irrespective of their political choices.
arXiv Detail & Related papers (2022-02-25T18:03:03Z) - Electoral Forecasting Using a Novel Temporal Attenuation Model:
Predicting the US Presidential Elections [91.3755431537592]
We develop a novel macro-scale temporal attenuation (TA) model, which uses pre-election poll data to improve forecasting accuracy.
Our hypothesis is that the timing of publicizing opinion polls plays a significant role in how opinion oscillates, especially right before elections.
We present two different implementations of the TA model, which accumulate an average forecasting error of 2.8-3.28 points over the 48-year period.
arXiv Detail & Related papers (2020-04-30T09:21:52Z)
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.