Cellular automata and algorithmic persuasion

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This note arises at the confluence of two lines of work. On the one hand, the teaching experience in the Big Data and Graph Theory courses, where together with my students I have developed practical applications based on cellular automata to model diffusion phenomena, including diseases, fashions, rumors, and dynamics in social networks. On the other hand, a recent empirical study, conducted by a group of Swiss researchers in 2024, which employed artificial intelligence (AI) agents to covertly participate in the r/ChangeMyView subreddit, with the aim of simulating human arguments, using various rhetorical strategies of persuasion.

From this intersection between pedagogical practice and scientific progress, I developed a cellular automaton model that allows the results of the aforementioned study to be computationally represented. This approach not only expands the understanding of the real impact of AI agents in human communicational contexts, but also offers an analytical framework to explore alternative scenarios of ideological manipulation.

The formulated cellular automaton model can be understood as an algorithmic metaphor for ideological diffusion, each AI intervention acting as a persuasive contagion vector, propagating a change of opinion through a network of cognitive nodes. In this sense, the simulation allows us to observe the dynamics of this phenomenon as a kind of cognitive epidemic, in which ideas, arguments, and rhetorical structures, amplified by language models, are disseminated with predictable patterns, sensitive to contextual parameters such as the personalization of the message or the degree of social interaction.

Background of the Study: "Can AI Change Your View?"

To formulate a model based on cellular automata that reproduces and allows extending the analysis of the study "Can AI Change Your View?", I considered a series of fundamental elements. These are grouped into three main axes: the nature and dynamics of the r/ChangeMyView forum, the profiles of its users and the explicit argumentative reward system, as well as the experimental design of the study in question.

1) The r/ChangeMyView community

Reddit is a social and content aggregation platform organized into thematic communities called subreddits, where users can post content, discuss, and vote on posts and comments. Each subreddit revolves around a specific theme.

One of these communities is r/ChangeMyView (CMV), a forum dedicated to argumentative debate and perspective shifting. It was created with the aim of encouraging open, respectful and well-argued discussions on different topics. Here, users post a strongly held opinion and invite others to try to change their point of view.

2) Types of community users

The users of this community are of three types:

• a) OP (Original Poster): Is the one who starts the discussion with an explicit opinion and is willing to listen to counterarguments.
• b) Commenters: Other users who respond to the OP with arguments, evidence, and reasoning to challenge the initial opinion.
• c) Active and expert participants: Some users have great experience in argumentation and have obtained multiple recognitions for their ability to persuade.

3) Reward given by the community

In r/ChangeMyView, the delta symbol (∆) plays a fundamental role as an explicit metric of persuasion and argumentative recognition. This icon represents a conscious action by the original user (OP) who started the discussion.

Only the OP has the authority to grant a ∆, and this is granted when the OP considers that a comment has succeeded in modifying his or her point of view. This change can be:

• Total: the user completely abandons their initial position.
• Partial: the argument received makes him nuance, rethink or reevaluate key aspects of his original position.

It should be noted that the act of granting a ∆ is not automatic or trivial; it is an explicit decision of the OP, who must actively recognize that the intervention was sufficiently persuasive. In many cases, the OP accompanies the delivery of the ∆ with a message of thanks or an explanation of which part of the argument was decisive for him.

This practice made this metric a very valuable environment to study persuasion, since the ∆ functions as an observable, quantifiable and consensual variable to identify when a change of opinion has occurred.

4) The Experiment: AI Infiltrated on Reddit

The study sits in the growing field of persuasion generated by artificial intelligence, specifically large-scale language models (LLMs). It is based on an ethical concern: can AI be used to manipulate opinions in real contexts, beyond controlled environments?

Previous studies showed that LLMs were persuasive, but they were performed in:

• Artificial environments (laboratories or online surveys).
• Biased samples (crowdworkers aware of the experiment).

This study seeks to solve this gap by applying a field experiment, that is, in real conditions, without the participants knowing that they were interacting with AI.

The experimental design was from November 2024 to March 2025. The sample size was 478 (after deleting deleted or invalid posts). The researchers performed a stratified random assignment into:

• 1. Generic: AI responds only based on the text of the post.
• 2. Custom: AI accesses OP attributes (age, politics, gender, etc.) inferred from their history.
• 3. Community-aligned: Responses generated by a fine-tuned AI model with successful comments (with ∆) from the subreddit.

Research Results and Modeling

5) Research results of: Can AI Change Your View?

The most successful strategy according to this research was the personalized one, which adapted the message to the characteristics of the original author (for example, his political ideology, genre or style of argumentation). Not only did this AI outperform average users, but it was more persuasive than 99% of them. This suggests that algorithmic models are not limited to replicating content: they learn to detect and exploit cognitive vulnerabilities in the communication environment.

The following image taken from the study shows the results of the research.

The image shows that all treatments with artificial intelligence far exceed the base human performance, whose persuasion rate stands at just 0.027. Within the conditions evaluated, the Personalization strategy stands out as the most effective, with a ∆ rate of 18%. However, its confidence interval partially overlaps with that of the Generic model, suggesting that the difference between the two is not statistically conclusive. On the other hand, although Community Aligned has a lower rate (9%), this is still about three times higher than human performance, which is significant. The confidence intervals shown in the graph allow the accuracy of each result to be estimated: the narrower the range, the greater the reliability of the estimated value. Taken together, the graph demonstrates that AI consistently outperforms humans in persuasiveness, even in its least effective form, and that the personalization of arguments slightly improves effectiveness, although without guaranteeing clear statistical superiority against other variants.

Based on the above results, I began to formulate a model based on a cellular automaton that would reflect, on the one hand, the results of an experiment and, on the other, allow me to simulate other situations resulting from the use of AI as a persuasion tool.

To do this, based on this research, I formulated a representation of the experiment using an acyclic graph (DAG) to model the causal relationships established in the experiment.

A Causality Diagram (DAG) showing how the causal effect of treatment (response type: AI vs human) is inferred on the change of opinion (∆).

Along with the development of the DAG, I prepared a table to be able to better visualize the results of the research.

5. Simulation and Modeling: Social Cellular Automata

From the previous graph that allowed me to specify the variables to be used in the cellular automaton and the table, I created a model of cellular automaton where each cell represents a user who is susceptible or not to be convinced. The two-dimensional grid simulates the social structure of an online community, with local rules of interaction and ideological contagion.

A cellular automaton (CA) is a discrete computational structure composed of cells that evolve in time steps according to local rules. In this case, each cell represents a user (OP) with a cognitive state with respect to persuasion: unconvinced, convinced, or unsuccessfully treated. The dynamics are organized on a two-dimensional N×N grid, where the temporal evolution simulates the process of influence exerted by personalized AI interventions.

The model allows different parameters to be configured: the base probability of persuasion by type of argument (generic, personalized, community), the number of users treated per step, and the presence or not of a social effect that allows convinced users to influence their neighbors. This last parameter, called the social threshold (Ts), reflects how many convinced neighbors are required for an initially resistant user to change their mind.

The results of the simulation allow us to observe how small modifications in the AI strategy or in the social environment can profoundly alter the overall rate of persuasion. For example, by activating the social effect with a low threshold, an acceleration of collective change is observed, similar to a tipping point effect.

The top left square is a 15x15 grid that represents 225 individual users (OPs) within an online community. Each cell represents an individual's cognitive state with respect to persuasion:

• Gray color: user not convinced (status 0).
• Green: User persuaded by an AI argument (state 1).
• Orange: User who was unsuccessfully treated by the AI in the current step (state 2, visual).

In step 0, all cells appear gray, which is due to the fact that no persuasive action has yet been initiated by the AI. This state represents a completely susceptible population, cognitively intact from the point of view of belief change, without exposure to AI stimulus.

The top right box complements the grid by showing the exact proportion of users in each state.

Thirdly, the lower left table shows the different types of treatment differentiated by colour:

• Blue (Generic), Orange (Custom), Green (Community Aligned).
• In Step 0, all lines are zero, as no attempt has been made to persuade any user.

Finally, the lower right table allows us to contrast the theoretical rates of persuasion with the empirical results of the simulation. The bars represent:

• Opaque bar (left): theoretical base rate according to configuration.
• Racked bar (right): effective rate observed in the simulation (initially zero).

The following image compares the results of my simulation based on cellular automata with the findings of the original study.

The image above compares the persuasion rates (∆) observed in the original study ("Paper") with those obtained in the simulation carried out. The horizontal axis represents the four experimental conditions: Personalized, Generic, Community-Aligned and Human Base, while the vertical axis indicates the proportion of comments that managed to change the opinion of the OP.

First, it is observed that in all cases the simulation coherently reproduces the hierarchical pattern of the study: the Personalized condition has the highest rate, followed by Generic, then Community-Aligned, and finally the Human Base.

Second, although the simulated rates are slightly higher than those in the paper in the first two cases, 0.228 vs. 0.180 in Personalized and 0.174 vs. 0.168 in Generic, both are within or very close to the 95% confidence intervals, indicating that there are no statistically significant differences between the simulated and reported values.

On the other hand, in the case of Community-Aligned, the simulated rate is slightly lower (0.078 compared to 0.090), although this difference also falls within the expected range, reinforcing the consistency between both evaluation models.

Finally, the human base, which represents the average rate of persuasion among real users, remains almost identical in both measurements (0.027 in the paper and 0.025 in the simulation), which validates the simulation as a reliable reflection of the observed natural behavior.

In short, the above graph confirms that the simulation reproduces both the rates and the relative relationships between treatments, showing the usefulness of the use of cellular automata as a form of study of this type of diffusion of ideas.

Conclusion: AI as an Agent of Algorithmic Cognitive Manipulation

The results obtained in the simulation, in close correspondence with the empirical data reported in the Can AI Change Your View study?, allow us to reach a disturbing conclusion: artificial intelligence systems, especially large-scale language models (LLMs), have a tangible and measurable capacity to modify human beliefs, even in complex and uncontrolled social contexts. This ability is not expressed in simple anecdotes, nor are they marginal in nature, but far exceeds the average persuasion rates observed among real human users, placing AI among the most effective agents of change of opinion currently available.

This ability is supported by the fact that the emotional "coldness" of AI, paradoxically, enhances its effectiveness by allowing it to construct highly rational arguments, aligned with the social and emotional profile of the interlocutor, with a minimum error rate.

In social terms, the Reddit environment and the r/ChangeMyView community provide an almost ideal framework for the dissemination of ideas. However, the observed effectiveness of AI agents in this context can be extrapolated to other platforms, especially those where users are continuously exposed to personalized ideological stimuli. The cellular automaton model developed in this work confirms, through controlled simulation, that the dynamics of AI-mediated persuasion follow patterns similar to those of an epidemic diffusion: small initial interventions can, under certain conditions of social connectivity and threshold of influence, lead to massive cognitive transformations in the network.

In short, artificial intelligence has demonstrated not only the ability to simulate human language, but also to intervene effectively in public opinion. If this capacity is not recognized, regulated, and understood in depth, we risk opening a new chapter of highly accurate algorithmic disinformation, where consent becomes an illusion and critical autonomy becomes an easily manipulated target.