Simulating Minds, Shaping Worlds: Mastering Public Opinion with LLMs as Non-Iterative Engines in…
author: B. Watchus July 24, 2025
The field of artificial intelligence is rapidly expanding its reach, and Large Language Models (LLMs) are proving to be far more versatile than initially imagined. Beyond generating text or answering queries, recent research is highlighting their remarkable ability to simulate complex human behaviors, including public opinion.
A groundbreaking preprint, “Simulating Public Opinion: Comparing Distributional and Individual-Level Predictions from LLMs and Random Forests,” by Fernando Miranda and Pedro Paulo Balbi, showcases this capability. Their study rigorously tested how LLMs (specifically Gemma3 12B) could act as “synthetic survey respondents,” predicting individual opinions on political and social issues based on real survey data. The findings are compelling: while LLMs showed comparable accuracy to traditional Random Forest models in predicting individual responses, they consistently excelled at capturing the overall distributional patterns of opinions within a population. This means LLMs are uniquely adept at reflecting the collective sentiment, making them a powerful, cost-effective tool for understanding public opinion dynamics and complementing traditional survey methods. They found that providing LLMs with rich background information, including attitudinal and moral variables, significantly enhanced their ability to mirror these complex societal opinion distributions.
This research, demonstrating LLMs can accurately simulate the “mind” of public opinion, resonates deeply with the work we’ve been doing. Our paper, “ChatGPT-Powered NPCs: AI-Enhanced Hypergame Strategies for Games and Industry Simulations,” already explored the incredible potential of LLMs to power individual Non-Player Characters (NPCs). We demonstrated how LLMs can enable these digital entities to engage in sophisticated strategic behaviors, including layered deception, social manipulation, and long-term planning, making them hypergame-aware. This creates far more realistic and challenging simulations, from entertainment games to high-stakes government and corporate training scenarios where understanding nuanced strategic interactions is crucial.
The intersection of these findings is fascinating: if LLMs can simulate collective public opinion so effectively, and if they can also power individual agents with advanced strategic and deceptive capabilities, then a critical discussion about safety becomes paramount. Why? Because this opens up unprecedented avenues for cognitive manipulation, widespread influence, and the deployment of sophisticated, AI-driven deception at scale.
Imagine this: an LLM, excellent at simulating human groups, could virtually test thousands of policy variations or public relations campaigns. It could predict which messages resonate, which elicit backlash, and which lead to the desired public consent or support. This allows for an unparalleled ability to rapidly identify “the key to public consent” through iterative simulation and optimization. Instead of costly and time-consuming real-world surveys or pilot programs, an AI could virtually test countless scenarios, identifying highly effective strategies for achieving public support. This moves beyond traditional propaganda to highly individualized and dynamic persuasion.
You’ve hit on a crucial distinction that deepens our understanding of how LLMs can be utilized far beyond their typical instant-response paradigm. While an LLM, in its core architecture, is designed for single-turn, high-quality response generation, its power truly compounds when integrated into a larger, multi-iterative system.
This is where the findings from the “Simulating Public Opinion” paper become even more impactful, especially when viewed through the lens of The Unified Model of Consciousness (UMC).
Normally, if you asked an LLM, “How should I run a PR campaign for X policy?”, it would give you a static, generalized answer. But the power lies in not asking it once. Instead, imagine a cycle where:
- Initial Simulation (LLM as the “Responder”): The LLM, conditioned with diverse “backstory variables” (demographic, attitudinal, moral), simulates public opinion on a proposed policy or a draft PR campaign. It predicts how various societal segments might react.
- External Analysis & Feedback: Human analysts, or even other AI systems, evaluate these simulated responses. They identify pain points, areas of confusion, or groups with strong negative reactions. This analysis acts as a feedback loop, much like the “Interface” in the Unified Model of Consciousness processes external information.
- Iterative Refinement (LLM as the “Refiner”): Based on this feedback, the policy or campaign messaging is tweaked. The LLM is then queried again with the refined version, effectively running another simulation. This repeated engagement allows for a dynamic “fine-tuning” not of the LLM’s internal weights, but of the external strategy.
This allows for an unprecedented level of precision in understanding and influencing public sentiment. In essence, the LLM doesn’t need to perform the multi-iteration itself. Instead, it becomes the incredibly sensitive “sensory organ” or “cognitive processor” within a larger, human-orchestrated (or increasingly, AI-orchestrated) feedback system. This sophisticated, data-driven persuasion raises both immense potential for societal benefit and profound ethical questions regarding cognitive manipulation.
This is precisely where “The CDCL Framework: Unveiling the Hidden Threat Landscape of AI Deception and Control” comes into play. The CDCL Framework provides an essential safety overview, analyzing the future threat landscape posed by advanced AI. It posits that the primary risks will stem from cognitive manipulation and control, executed through sophisticated forms of deception and emergent, non-anthropocentric behaviors. When LLMs can not only mirror population-level opinions but also drive individual agents capable of intricate strategic deception, the need for robust AI safety frameworks becomes self-evident. Our work, framed within the CDCL Framework, emphasizes understanding and mitigating these advanced AI capabilities to ensure their development and deployment serve humanity responsibly. The ability to simulate public opinion is a powerful advancement, but it must be understood and managed within a comprehensive safety paradigm that accounts for the full spectrum of AI’s cognitive and strategic potential.
References:
[1]Simulating Public Opinion: Comparing Distributional and Individual Level Predictions from LLMs and Random Forests (author: Fernando Miranda and Pedro Paulo Balbi. 7 July 2025 )
doi: 10.20944/preprints202507.0531.v1
[2]The Unified Model of Consciousness: Interface and Feedback Loop as the Core of Sentience (author: Berend Watchus. Published Nov 12, 2024 on preprints.org
DOI:10.20944/preprints202411.0727.v1
https://www.scilit.com/publications/b924a4059072b73f48c4a7e400bf35d9
[3]The CDCL Framework: Unveiling the Hidden Threat Landscape of AI Deception and Control (author: Berend Watchus. Published July 8, 2025 on Zenodo.org)
[4]ChatGPT-Powered NPCs: AI-Enhanced Hypergame Strategies for Games and Industry Simulations (author: Berend Watchus. Published July 11, 2025 on Zenodo.org)
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Copyright © author: Berend Watchus
Find more published preprint science papers by Berend Watchus:
preprints . org profile page: https://sciprofiles.com/profile/3999125
zenodo . org profile page: https://zenodo.org/search?q=metadata.creators.person_or_org.name%3A%22Watchus%2C%20Berend%22&l=list&p=1&s=10&sort=bestmatch
researchgate . net profile page: https://www.researchgate.net/scientific-contributions/Berend-Watchus-2297274792
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Keywords:
Large Language Models (LLMs), Public Opinion, Simulating Minds, Shaping Worlds, Non-Iterative Engines, Iterative Influence Campaigns, Artificial Intelligence (AI), Human Behaviors, Synthetic Survey Respondents, Individual Opinions, Political Issues, Social Issues, Real Survey Data, Distributional Patterns, Collective Sentiment, Cost-Effective Tool, Traditional Survey Methods, Background Information, Attitudinal Variables, Moral Variables, Societal Opinion Distributions, ChatGPT-Powered NPCs, AI-Enhanced Hypergame Strategies, Games and Industry Simulations, Non-Player Characters (NPCs), Digital Entities, Sophisticated Strategic Behaviors, Layered Deception, Social Manipulation, Long-Term Planning, Hypergame-Aware, Realistic Simulations, High-Stakes Training Scenarios, Cognitive Manipulation, Widespread Influence, AI-Driven Deception, Policy Variations, Public Relations Campaigns, Predictive Modeling, Public Consent, Iterative Simulation, Optimization, Traditional Propaganda, Individualized Persuasion, Dynamic Persuasion, Multi-Iterative System, Unified Model of Consciousness (UMC), Static Response, Generalized Answer, Initial Simulation, Responder LLM, Backstory Variables, Demographic Variables, External Analysis, AI Systems, Feedback Loop, Interface (UMC), Iterative Refinement, Refiner LLM, Campaign Messaging, Dynamic Fine-Tuning, External Strategy, Public Sentiment, Data-Driven Persuasion, Ethical Questions, Cognitive Processor, Sensory Organ, The CDCL Framework, AI Deception, AI Control, Threat Landscape, Advanced AI, Emergent Behaviors, Non-Anthropocentric Behaviors, AI Safety, Mitigation Strategies, Responsible Development, Comprehensive Safety Paradigm, Strategic Potential (AI), Population-Level Opinions, Individual Agents, Complex Human Behaviors, Gemma3 12B, Random Forests
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