2511.0030 ElectionFit: A Computational Laboratory of LLM Agents for Simulating U.S. Presidential Elections v1

🎯 ICAIS2025 Accepted Paper
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Accept

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📋 Summary

This paper introduces ElectionFit, a novel framework leveraging Large Language Models (LLMs) to simulate voting behavior in the 2024 U.S. presidential election, particularly within key swing states. The core idea is to create individual agent profiles based on detailed demographic data from the U.S. Census and other public sources, and then use LLMs to reason about these agents' voting decisions based on their demographic characteristics and contextual information about the candidates' policy positions. The framework's agents are given profiles that include attributes such as age, race, sex, occupation, industry, education level, and religion. These agents are then provided with contextual information about the candidates' stances on key issues like economic policy, immigration, and abortion rights, extracted from official party platforms and public statements. The LLMs are prompted to simulate the voting decisions of these agents, and the aggregate results are compared to actual election outcomes. The authors demonstrate that ElectionFit successfully replicates the actual election results in six out of seven key swing states, which they argue highlights the potential of LLMs as an interpretable and nuanced tool for social science research. Beyond just predicting election outcomes, the framework allows for the exploration of individual-level decision-making processes, offering insights into how different demographic factors and policy positions influence voting behavior. The authors also conduct ablation studies and sensitivity analyses to assess the framework's robustness and identify key factors influencing its performance. These analyses reveal that the framework is sensitive to changes in input parameters and that the LLMs used exhibit inherent biases and instability, which the authors acknowledge as a critical limitation. The paper emphasizes the importance of auditing LLMs for biases and instability, as these can significantly impact the fidelity of simulations. The authors argue that the framework's ability to replicate real-world election outcomes, combined with its interpretability, makes it a valuable tool for social science research, while also highlighting the need for careful consideration of the limitations and ethical implications of using LLMs in this context. The paper's overall significance lies in its innovative application of LLMs to model complex social phenomena, its emphasis on interpretability, and its contribution to the ongoing discussion about the reliability and ethical use of LLMs in social science.

✅ Strengths

The primary strength of this paper lies in its innovative application of Large Language Models (LLMs) to simulate voting behavior, moving beyond traditional agent-based models (ABMs) by incorporating generative reasoning. This approach allows for a more nuanced and realistic simulation of voter decision-making, as the LLMs can consider the complex interplay of demographic factors and policy positions. The framework's ability to replicate actual election outcomes in six out of seven key swing states provides strong empirical validation of its effectiveness. This success demonstrates the potential of LLMs as a powerful tool for social science research, offering a way to model complex social phenomena with a high degree of fidelity. Furthermore, the paper's emphasis on interpretability is a significant contribution. Unlike traditional statistical models, which often operate as 'black boxes,' ElectionFit allows researchers to probe the rationale behind individual voting decisions. This interpretability is crucial for understanding the underlying mechanisms driving election outcomes and for identifying the key factors that influence voter behavior. The authors also deserve credit for conducting extensive ablation studies and sensitivity analyses, which provide valuable insights into the framework's robustness and the factors that influence its performance. These analyses help to identify the key components of the framework and to understand how changes in input parameters can affect the simulation results. The paper is also well-organized and clearly written, with a logical flow that guides the reader through the methodology, experiments, and results. The authors provide detailed explanations of the framework's components and the experimental setup, making it easy to follow the research process. The use of publicly available data for creating agent profiles also enhances the transparency and reproducibility of the work. Finally, the paper's focus on auditing LLMs for biases and instability is a critical contribution, highlighting the importance of addressing these issues for the reliable and ethical use of LLMs in social science research. This emphasis on auditing is a crucial step towards ensuring that LLM-based simulations are not only accurate but also fair and unbiased.

❌ Weaknesses

While the paper presents a compelling framework, several weaknesses warrant careful consideration. First, the paper's primary focus on the 2024 U.S. presidential election significantly limits the generalizability of the framework to other contexts. As the authors themselves acknowledge, the framework's methodology, including the demographic profiles and contextual information, is tailored specifically to this event. The reliance on specific demographic and political data from the 2024 U.S. election makes it unclear how well the framework would perform in different electoral environments, such as those with different party systems, voting laws, or cultural contexts. This narrow focus restricts the framework's applicability beyond the specific case study, limiting its immediate impact on broader social science research. The lack of empirical validation in other contexts, such as parliamentary elections or elections in non-Western countries, raises concerns about the framework's robustness and adaptability. Second, the framework's reliance on LLMs introduces potential biases and instability, which the authors acknowledge. While they conduct sensitivity analyses to assess these issues, the inherent limitations of LLMs could affect the framework's reliability and validity. The paper demonstrates that the LLMs used exhibit a strong default pro-Democratic bias under 'No Context' conditions, and that minor changes in prompt phrasing can lead to significant fluctuations in predicted support. This sensitivity to prompt variations raises concerns about the robustness of the simulation outcomes. Furthermore, the paper does not fully explore the specific types of biases that might be present in the LLM, such as those related to political leaning, demographic representation, or historical events. The sensitivity analysis, while useful, does not fully mitigate the risk of these biases influencing the simulation outcomes. The lack of a robust method for bias detection and mitigation is a significant concern, as it could lead to misleading or inaccurate simulations. Third, the paper could benefit from a more detailed discussion of the ethical implications of using LLMs for political simulation. While the authors briefly touch upon the potential for LLMs to influence opinions, they do not delve into a comprehensive analysis of the risks associated with using such a powerful tool for political forecasting. The paper does not address the potential for manipulation or the reinforcement of existing biases through the use of LLMs. The potential for the framework to be used to create misleading or inaccurate simulations, and the safeguards that should be in place to prevent such misuse, are not adequately discussed. The paper also lacks a discussion of the data privacy concerns associated with using detailed demographic profiles and contextual information. Although the data is publicly available, the use of such detailed profiles in simulations raises ethical questions about the potential for misuse or unintended consequences. The paper does not discuss how the data is collected, stored, and used, or the potential for misuse or unintended consequences. Finally, while the paper acknowledges the limitations of traditional methods, it could benefit from a more detailed discussion of how ElectionFit specifically addresses the shortcomings of *previous* LLM-based approaches. The paper mentions related work but does not explicitly detail how ElectionFit overcomes the limitations of *all* previous LLM-based approaches. The paper could strengthen its motivation by more explicitly detailing how ElectionFit addresses the shortcomings of *specific* previous LLM-based approaches and what unique contributions it makes. The lack of a more explicit comparison to other LLM-based approaches weakens the paper's claim of novelty and significance. These weaknesses, while not invalidating the paper's contributions, highlight areas where further research and development are needed to ensure the reliability, validity, and ethical use of LLMs in political simulation.

💡 Suggestions

To address the limitations regarding generalizability, the authors should consider expanding their experiments to include a more diverse set of elections, including those with different political systems and cultural contexts. This could involve adapting the framework to simulate parliamentary elections, elections in non-Western countries, or historical elections with different demographic and political landscapes. Such experiments would provide a more robust assessment of the framework's adaptability and highlight the specific modifications needed to ensure its effectiveness across various contexts. Furthermore, the authors should explore methods for making the framework more modular and adaptable, allowing researchers to easily modify the input data and parameters to simulate different electoral environments. This could involve developing a standardized interface for inputting demographic and political data, as well as providing guidelines for selecting appropriate LLM models and prompts for different contexts. By making the framework more flexible and adaptable, the authors can significantly increase its utility and impact. To mitigate the risks associated with LLM biases and instability, the authors should conduct a more in-depth analysis of the specific types of biases that might be present in the LLMs used in their framework. This could involve using bias detection tools and techniques to assess the LLM's performance on different demographic and political groups. The authors should also explore methods for mitigating these biases, such as using debiasing techniques or incorporating fairness constraints into the simulation process. Furthermore, the authors should investigate the sensitivity of the framework to different LLM models and prompts, and provide guidelines for selecting the most appropriate models and prompts for different contexts. The authors should also consider incorporating uncertainty quantification methods to provide a more realistic assessment of the framework's predictive capabilities. This could involve using techniques such as Bayesian inference or Monte Carlo simulations to estimate the uncertainty associated with the simulation outcomes. By addressing these issues, the authors can significantly improve the reliability and validity of their framework. Finally, the authors should include a more detailed discussion of the ethical implications of using LLMs for political simulation. This discussion should address the potential risks associated with using such a powerful tool for political forecasting, including the potential for manipulation or the reinforcement of existing biases. The authors should also consider the potential for the framework to be used to create misleading or inaccurate simulations, and the safeguards that should be in place to prevent such misuse. This could involve developing guidelines for responsible use of the framework, as well as establishing mechanisms for auditing and validating the simulation outcomes. The authors should also consider the potential impact of their work on public trust in the democratic process, and the steps that should be taken to ensure that the framework is used in a responsible and ethical manner. By addressing these ethical concerns, the authors can ensure that their work is not only technically sound but also socially responsible. The authors should also consider incorporating a more detailed comparison to existing LLM-based election simulation frameworks, highlighting the unique aspects of their framework and the specific contributions it makes to the field. This would involve a more thorough literature review and a more detailed discussion of the methodological differences between their approach and existing work. The authors should also consider the potential for combining LLMs with traditional methods to leverage the strengths of both approaches.

❓ Questions

Several questions arise from my analysis of this paper. First, how well does the framework generalize to other elections or political contexts beyond the 2024 U.S. presidential election? Are there any plans to validate the framework's performance in different settings, such as parliamentary elections, elections in non-Western countries, or historical elections? Second, what are the specific types of biases that might be present in the LLMs used in the framework, and how do these biases affect the simulation outcomes? Are there any methods for mitigating these biases, such as debiasing techniques or fairness constraints? Third, how sensitive are the simulation results to changes in the input parameters, such as the demographic profiles or the contextual information provided to the agents? Are there any methods for quantifying the uncertainty associated with the simulation outcomes, such as Bayesian inference or Monte Carlo simulations? Fourth, what are the potential ethical implications of using LLMs for political simulation, and how does the framework address these concerns? Are there any safeguards in place to prevent the misuse or manipulation of the simulation results? What are the potential risks associated with using detailed demographic profiles in simulations, and how can these risks be mitigated? Finally, how does the ElectionFit framework specifically address the limitations of previous LLM-based election simulations, and what unique contributions does it make to the field? What are the specific methodological differences between ElectionFit and other existing approaches, and how do these differences impact the reliability and validity of the simulation results?

📊 Scores

Soundness:2.75
Presentation:3.0
Contribution:2.5
Rating: 5.75

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📋 Summary

The paper presents ElectionFit, an LLM-based agent simulation framework designed as a 'computational laboratory' for modeling voter behavior in U.S. presidential elections. Agents are instantiated with high-fidelity demographic profiles reflecting ACS (2023) and ASARB (2020) distributions across eight attributes (state, race, sex, age, occupation, industry, education, religion; Section 3.1) and are provided with contextual information summarizing candidate policies on three salient 2024 issues (economy, immigration, abortion; Section 3.2, Appendix C). Agents output a probability distribution over {Trump, Harris, other/non-vote} (Section 3.3) along with a brief rationale. The framework is evaluated on the 2024 election in seven swing states (AZ, GA, MI, NV, NC, PA, WI), using Qwen-Max-2024-04-28 as the main model (temperature 0.7 for the headline result, otherwise temperature 0; Section 4.1). Macro-level results replicate the real-world outcome in 6/7 swing states (Nevada off by 0.17%; Figure 2). The authors emphasize interpretability via aggregated rationales and agent 'interviews' (Section 4.3), validate design choices via ablations on agent count stability (stabilization around 300 agents; Figures 4–5) and profile dimensions (education and religion notably improve fidelity; Tables 1–2), and conduct sensitivity analyses revealing strong model- and prompt-induced biases and instability (Table 3, Figure 6, Appendix H). They argue that while the framework can reproduce macro outcomes, current LLMs are unstable instruments for rigorous social science, highlighting a 'paradox of success' contingent on model choice and prompting (Section 5).

✅ Strengths

  • Clear and timely problem framing: positions LLM-agent social simulation as a 'computational laboratory' with an emphasis on interpretability and auditing rather than black-box forecasting (Sections 1–2).
  • Methodological transparency: explicit description of agent profiles (ACS/ASARB), contextual inputs, and prompting/JSON output format (Sections 3.1–3.3), including the instruction to base decisions only on provided information.
  • Macro-level replication on a challenging, contemporary testbed (2024, seven swing states) with a concrete headline result (6/7 correct; Figure 2) and an explicit rationale for using 2024 to reduce data leakage (Sections 1, 4.1).
  • Micro-level interpretability demonstrations via aggregated rationales (word cloud in Figure 3) and follow-up 'interviews' with representative agents to link profiles to policy evaluations (Section 4.3, Appendix D).
  • Useful ablations: agent population size stability analysis (stabilization at ~300 agents; Figures 4–5) and dimension-importance studies showing education and religion materially affect outcomes (Tables 1–2).
  • Thorough sensitivity/bias analyses: multi-model comparisons across context framings (Table 3) and strong evidence of prompt/positional instability (Figure 6, Appendix H). The explicit finding that Qwen-Max-09-19 remains pro-Democrat even under pro-Trump framing, while Qwen-Max-04-28 is more neutral, is informative (Section 4.5).
  • Candid discussion of limitations: the 'paradox of success' and the conclusion that current LLMs are unstable instruments for rigorous social science (Section 5).

❌ Weaknesses

  • Methodological tension around the main result: the headline 2024 replication uses temperature 0.7 (Section 4.1), yet the paper documents severe instability even at T=0 (Section 4.5, Figure 6, Appendix H). No analysis is provided of variability across seeds/runs at T=0.7 for the main outcome, making the result potentially non-reproducible.
  • Lack of baseline comparisons: no direct evaluation against established election forecasting approaches (e.g., poll aggregators or simple demographic/poll-based baselines), leaving the 'striking alignment' (Figure 2) difficult to contextualize within political science methodology.
  • Demographic modeling relies on limited joint structure: joint distributions are used only for state, race, sex (Section 3.1), while other attributes are sampled independently, likely missing key correlations (e.g., age–education, religion–education, occupation–industry–education). This could distort agent realism and downstream outcomes.
  • Aggregation and statistical reporting are under-specified: it is not fully clear how agent-level probabilities map to state-level predictions (e.g., averaging vs. weighting, handling of 'other/non-vote'), and the headline results lack uncertainty quantification (e.g., confidence intervals, multiple runs, bootstrap).
  • Potential post-hoc choices: the success depends on a model variant (Qwen-Max-04-28) that the authors call a 'fortuitous' more neutral choice than its successor (Section 5), which raises concerns about pre-specification, pre-registration, or potential a posteriori selection.
  • External validity and scope: evaluation is limited to seven swing states and to high-level state winners; there is no validation against micro-level patterns (e.g., subgroup turnout/choice) or calibration to polling at the time for those states.
  • Context curation risks: although Appendix C is said to balance candidate stances (Section 3.2), selection/framing of issue summaries can itself induce bias; procedures to audit and standardize this are not fully specified.
  • Reproducibility and resources: the paper does not state whether prompts, code, demographic samplers, and context summaries will be released, which is critical given the demonstrated prompt sensitivity (Figure 6) and positional effects (Appendix H).

❓ Questions

  • Main result robustness: How many independent runs (different random seeds for agent sampling and LLM decoding) were conducted at temperature 0.7 for the 2024 headline result? Please report variability (e.g., distribution of state winners, margins) across runs.
  • Temperature sensitivity: How do macro outcomes vary as temperature sweeps from 0.0 to 1.0 under the same prompt/context? Is there a regime that balances stability and realism without relying on high stochasticity?
  • Aggregation details: Precisely how are agent-level probabilities aggregated into state-level vote shares? Are agents weighted to reflect population strata beyond the sampling? How is 'other/non-vote' handled when converting to two-party comparisons and winners?
  • Prompt/ordering controls: For the main result, did you evaluate robustness to semantically equivalent prompt variants and to candidate/order permutations in the response schema? If yes, what was the variance in outcomes?
  • Baselines: Can you compare ElectionFit against simple polling-based baselines and/or standard demographics-plus-polls models for the same seven states and date? This would contextualize the 6/7 performance.
  • Joint distributions: Beyond state–race–sex, did you explore adding joint structure for age–education, religion–education, or occupation–industry–education? If so, how did this affect stability and bias?
  • Context curation: Appendix C summarizes candidate positions on three issues. What protocol ensured neutrality (e.g., equal token budgets, source balancing, blind review)? Can you release the exact prompts/context blocks to enable replication?
  • Pre-registration: Were model choice (Qwen-Max-04-28), temperature, and context prompt finalized before running the Nov 4 predictions? If not, can you provide a timeline or preregistered plan to mitigate post-hoc selection concerns?
  • Uncertainty quantification: Will you report confidence intervals/bands for state-level margins (e.g., via bootstrap over agent sampling and LLM stochasticity)?
  • Release plans: Will code, prompts, demographic samplers, and context summaries be open-sourced to support reproducibility and further auditing?

⚠️ Limitations

  • Instrument bias and instability: Multiple LLMs exhibit strong political biases and sensitivity to prompt phrasing and positional ordering (Table 3, Figure 6, Appendix H), undermining reproducibility and interpretability.
  • Stochastic dependence of headline results: The main 2024 replication uses temperature 0.7 (Section 4.1) without reporting multi-seed variability, making the result potentially brittle.
  • Incomplete demographic joint modeling: Independent sampling across many dimensions (Section 3.1) likely misses important real-world correlations (e.g., age–education, religion–education), which can affect outcomes.
  • Limited validation scope: Evaluation focuses on seven swing states and macro winners; lacks micro-level calibration against subgroup patterns or turnout/third-party dynamics.
  • Context curation risk: Even balanced summaries can embed framing effects; the procedure for neutrality auditing is not fully specified (Section 3.2).
  • Generalizability: Findings may not transfer to other elections or countries without revisiting demographic priors, issue salience, and context sources.
  • Potential negative societal impact: Simulations that convincingly emulate voter behavior could be misused for microtargeted persuasion or manipulation; inclusion of sensitive attributes (race, religion) raises fairness and misuse concerns. Strong guardrails, usage policies, and auditing protocols are needed.

🖼️ Image Evaluation

Cross‑Modal Consistency: 33/50

Textual Logical Soundness: 22/30

Visual Aesthetics & Clarity: 16/20

Overall Score: 71/100

Detailed Evaluation (≤500 words):

1. Cross‑Modal Consistency

• Major 1: Mislabelled swing state in Figure 2 (Arizona shown as “AR”). This contradicts the text’s swing‑state list and risks misinterpretation. Evidence: “seven key swing states: Arizona, Georgia, Michigan, Nevada, North Carolina, Pennsylvania, and Wisconsin.”

• Major 2: Inconsistent agent counts across sections/figures (300 vs 1,000) confound replication. Evidence: Sec 4.1 “We simulate 1,000 agents…”, Fig. 4 caption “300 agents per state are selected for subsequent experiments…”, Sec 4.4.1 “For our final simulation, we use 1,000 agents…”.

• Major 3: Table 1 does not clearly separate the “six‑dimension” vs “+education” conditions claimed in the caption, so it’s impossible to verify the Wisconsin flip. Evidence: Table 1 (single grid; no explicit columns labelled “6‑dim” vs “+Edu”).

• Minor 1: Table 3 header typo and ambiguity (“Neural Context” likely “Neutral Context”). Evidence: Table 3 column header “Neural Context”.

• Minor 2: Response‑format JSON is malformed, risking downstream parsing and aggregation mismatches. Evidence: “{ "Donald Trump": … "Kamala Harris": … vote for another candidate or not vote at all": …”.

• Minor 3: Figure 2 caption claims only Nevada differs; the maps also change EV tallies (232–306 vs 226–312) without stating EV counts in caption. Evidence: Fig. 2 map titles show “232 vs 306” and “226 vs 312”.

2. Text Logic

• Major 1: Experimental protocol ambiguity: temperature is 0 except main result (0.7), but agent‑count choice varies across sections, making it unclear which settings produced Figures 2–6 and Tables 1–3. Evidence: Sec 4.1 “All experiments use a temperature of 0… except the main result (0.7).”

• Minor 1: The claim that adding religion “reduces Democratic bias” is plausible, but the text does not define a bias metric or statistical test tied to Table 2. Evidence: Sec 4.4.2 sentence: “Including religion helps reduce the model’s Democratic bias…”

• Minor 2: Some model‑choice rationales (e.g., using Qwen‑Max‑04‑28 to “mitigate U.S.‑centric biases”) are asserted without a prior quantitative audit before adoption. Evidence: Sec 4.1 “This choice is made to mitigate potential U.S.-centric political biases.”

3. Figure Quality

• Major 1: Figure 2 state label error (“AR”) on a core result figure undermines clarity. Evidence: Fig. 2 swing‑state label.

• Minor 1: Small numeric annotations above bars (Figs. 4 and 6) may be hard to read at print size; consider larger fonts or data tables.

• Minor 2: Table 1 is dense; add column blocks, bolding, and clear headers differentiating ablation conditions.

Key strengths:

  • Clear, useful macro‑ vs micro‑level evaluation plan; strong sensitivity analyses (Fig. 6; Table 3).
  • Valuable framing of LLM agents as an auditable “instrument,” not only a predictor.

Key weaknesses:

  • Critical cross‑modal inconsistencies (Figure 2 label; agent‑count protocol; malformed JSON).
  • Ambiguity in ablation tables prevents verification of key claims (education fixes WI).
  • Limited statistical treatment of “bias reduction” claims.

Actionable fixes:

  • Correct Figure 2 labels; add EV counts in caption; ensure swing states are correctly annotated.
  • Unify and report the exact settings (agent count, temperature, model) for each figure/table.
  • Repair JSON schema; provide a strict EBNF and a parsed example.
  • Redesign Table 1 with explicit paired columns (“6‑dim” vs “+Edu”) and add statistical tests.
  • Replace “Neural” with “Neutral” in Table 3; enlarge bar‑label fonts or add data tables.

📊 Scores

Originality:3
Quality:2
Clarity:3
Significance:2
Soundness:2
Presentation:3
Contribution:2
Rating: 4

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📋 Summary

This paper introduces ElectionFit, a novel framework that leverages Large Language Models (LLMs) to simulate the 2024 U.S. presidential election, particularly focusing on swing states. The core idea is to create LLM agents that represent individual voters, each instantiated with detailed demographic profiles derived from the U.S. Census and provided with contextual information about candidate policies on key issues. These agents then generate probabilistic voting decisions, allowing for a simulation of the election at a granular level. The authors' primary contribution lies in the development of this interpretable framework, which moves beyond traditional statistical models and offers a more nuanced approach to understanding voting behavior. The methodology involves creating detailed demographic profiles for each agent, incorporating factors such as age, race, education, and religion. The agents are then prompted to make voting decisions based on their profiles and the provided policy context, with the results aggregated to produce state-level predictions. The empirical findings demonstrate that ElectionFit can accurately replicate the actual election results in the swing states, with the exception of Nevada. The paper also includes extensive ablation studies to assess the impact of different demographic factors and sensitivity analyses to evaluate the robustness of the framework to variations in prompts and model choices. The authors highlight the importance of model selection, noting that the Qwen-Max-04-28 model provided the most accurate predictions, while other models exhibited biases. Overall, the paper presents a compelling approach to election simulation, offering a valuable tool for social science research. However, the authors also acknowledge the limitations of the framework, particularly the sensitivity of LLMs to prompt variations and the need for careful model selection. The paper concludes by emphasizing the potential of LLMs for social science research while also highlighting the challenges and limitations that need to be addressed.

✅ Strengths

I found several strengths in this paper that warrant recognition. Firstly, the core idea of using LLMs to simulate individual voters with detailed demographic profiles is both innovative and compelling. This approach allows for a more nuanced understanding of voting behavior compared to traditional statistical models, which often treat voters as homogeneous groups. The framework's ability to generate probabilistic voting decisions, rather than binary choices, is another significant strength, as it better reflects the complexities of real-world voting behavior. The authors' emphasis on interpretability is also commendable. Unlike many machine learning models, ElectionFit allows researchers to examine the reasoning behind individual voting decisions, providing valuable insights into the factors that influence voter preferences. The extensive ablation studies are another strength, as they demonstrate the importance of different demographic factors in predicting election outcomes. This level of detail is crucial for understanding the underlying mechanisms of the simulation. The authors also deserve credit for acknowledging the limitations of their framework, particularly the sensitivity of LLMs to prompt variations and the need for careful model selection. This transparency is essential for fostering trust in the research. Finally, the paper's focus on replicating the 2024 U.S. presidential election, a highly relevant and complex social phenomenon, adds to its significance. The fact that the framework was able to accurately predict the outcomes in most swing states, despite the challenges, is a testament to its potential. The authors also provide a clear and well-structured methodology, which makes the paper accessible to a wide audience. The inclusion of detailed information about the data sources, the agent profiles, and the prompting strategies is particularly helpful for researchers who want to replicate or build upon this work. Overall, I believe that this paper makes a valuable contribution to the field of computational social science, offering a novel and insightful approach to understanding voting behavior.

❌ Weaknesses

Despite the strengths of this paper, I have identified several weaknesses that warrant careful consideration. Firstly, the paper's primary focus on replicating the 2024 U.S. presidential election, while relevant, raises concerns about its generalizability. As the authors themselves acknowledge, this specific focus might limit the applicability of the framework to other elections or contexts. The framework's performance in different political environments or with different demographic profiles remains unclear. This is a significant limitation, as the goal of a simulation framework should be to provide insights that are not limited to a single event. Secondly, the paper's reliance on LLMs introduces several challenges, particularly concerning the sensitivity of these models to prompt variations. As demonstrated in the sensitivity analysis (Experiment 4), even minor changes in the prompt can lead to significant variations in the simulation outcomes. This lack of robustness is a major concern, as it undermines the reliability of the framework. The authors' attempt to mitigate this issue with a system prompt (Appendix J) had only minor effects, indicating that prompt engineering alone may not be sufficient to address this problem. This sensitivity to prompts raises questions about the validity of the framework, as the results may be more reflective of the specific prompts used rather than the underlying social dynamics. Furthermore, the paper's reliance on a single model, Qwen-Max-04-28, for the main replication is problematic. While the authors acknowledge the model-specific nature of their results, the lack of a systematic comparison across different models and the limited explanation for why Qwen-Max-04-28 performed better than others raise concerns about the robustness of the findings. The paper does not provide a detailed analysis of the biases inherent in each model, nor does it explore the impact of different model architectures on the simulation results. This lack of model transparency is a significant limitation, as it makes it difficult to assess the validity of the framework. The paper also lacks a thorough discussion of the ethical implications of using LLMs for election simulation. The potential for misuse of such a framework, either to manipulate public opinion or to undermine trust in the democratic process, is a serious concern. The authors do not address the potential for the framework to be used to generate disinformation or to spread false narratives about the election. This lack of ethical consideration is a significant oversight, given the potential for harm. Additionally, the paper's discussion of the computational cost of the framework is limited. While the authors mention that the optimized prompt reduces computational costs, they do not provide a detailed analysis of the resources required to run the simulation. This lack of information makes it difficult to assess the practical feasibility of the framework, particularly for researchers who may not have access to high-performance computing resources. The paper also does not provide a detailed comparison with existing agent-based models (ABMs). While the authors contrast their approach with traditional ABMs in the introduction, they do not provide a thorough discussion of the specific limitations of these models or how the LLM-based approach overcomes these limitations. This lack of comparison makes it difficult to assess the novelty and significance of the proposed framework. Finally, the paper's discussion of the limitations of the framework is somewhat limited. While the authors acknowledge the sensitivity of LLMs to prompt variations and the need for careful model selection, they do not fully explore the potential for these biases to affect the simulation results. A more detailed discussion of the potential sources of bias, such as the training data of the LLMs or the specific wording of the prompts, would be beneficial. The paper also does not address the potential for the framework to be used to generate disinformation or to spread false narratives about the election. This lack of ethical consideration is a significant oversight, given the potential for harm. In summary, while the paper presents a novel and promising approach to election simulation, it suffers from several limitations that need to be addressed.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. Firstly, the authors should expand the scope of their evaluation beyond the 2024 U.S. presidential election. This could involve testing the framework in different political contexts, such as parliamentary systems or elections in other countries. This would provide a more robust assessment of the framework's generalizability and its ability to capture the nuances of different political systems. Secondly, the authors should conduct a more systematic analysis of the impact of different LLM models on the simulation outcomes. This should include a detailed comparison of the biases inherent in each model, as well as an exploration of the impact of different model architectures and training data. This would help to identify the most suitable models for this task and to develop strategies for mitigating model-specific biases. The authors should also explore techniques for model calibration to reduce the impact of model-specific biases on the simulation outcomes. Thirdly, the authors should develop more robust prompting strategies to address the sensitivity of LLMs to prompt variations. This could involve exploring different prompt structures, wordings, and the inclusion of different types of contextual information. The authors should also investigate the impact of different prompt engineering techniques on the simulation results. This would help to identify more stable and reliable prompting approaches. Fourthly, the authors should include a more thorough discussion of the ethical implications of using LLMs for election simulation. This should include a discussion of the potential for misuse of the framework, as well as strategies for mitigating these risks. The authors should also consider the potential for the framework to be used to generate disinformation or to spread false narratives about the election. Fifthly, the authors should provide a more detailed analysis of the computational cost of the framework. This should include a breakdown of the resources required to run the simulation, as well as a discussion of the trade-offs between accuracy and efficiency. The authors should also explore techniques for optimizing the framework to reduce its computational cost. Sixthly, the authors should provide a more detailed comparison with existing agent-based models (ABMs). This should include a thorough discussion of the specific limitations of these models and how the LLM-based approach overcomes these limitations. The authors should also consider comparing their results with other election forecasting methods, such as those based on traditional statistical models or machine learning techniques. Finally, the authors should provide a more detailed discussion of the limitations of the framework. This should include a thorough exploration of the potential sources of bias, as well as a discussion of the potential for the framework to be used to generate disinformation or to spread false narratives about the election. The authors should also acknowledge the limitations of using LLMs for simulating complex human behavior and discuss the potential for the framework to be used to generate misleading or inaccurate results. By addressing these weaknesses, the authors can significantly improve the robustness, reliability, and ethical soundness of their framework.

❓ Questions

I have several questions that arise from my analysis of this paper. Firstly, given the sensitivity of the framework to prompt variations, what specific strategies can be employed to develop more robust and reliable prompting approaches? I am particularly interested in understanding how different prompt structures, wordings, and contextual information can be optimized to minimize the impact of prompt variations on the simulation outcomes. Secondly, considering the model-specific nature of the results, what are the key factors that contribute to the differences in performance across different LLMs? I would like to understand how the training data, model architectures, and inherent biases of different LLMs affect their ability to simulate voting behavior. Thirdly, how can the framework be adapted to simulate different types of elections, such as parliamentary elections or elections in other countries? I am interested in understanding what modifications would be needed to the agent profiles, the contextual information, and the prompting strategies to accommodate different political systems and cultural contexts. Fourthly, what are the specific ethical guidelines that should be followed when using LLMs for election simulation? I would like to understand how the potential for misuse of the framework can be mitigated and what steps can be taken to ensure that the framework is used responsibly. Fifthly, what are the specific computational resources required to run the framework, and how can the computational cost be further reduced? I am interested in understanding the trade-offs between accuracy and efficiency and what techniques can be used to optimize the framework for different computing environments. Finally, how can the framework be validated beyond its ability to replicate past election results? I am interested in understanding what other metrics or methods can be used to assess the validity of the framework and its ability to provide insights into the underlying social dynamics of voting behavior. These questions are crucial for understanding the limitations and potential of the framework and for guiding future research in this area.

📊 Scores

Soundness:2.5
Presentation:3.0
Contribution:2.25
Rating: 4.75

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