Predicting and altering human opinions

September 2026

At Hunchfox, we’re building an AI superforecaster. To predict the future, we need to understand the people who shape it.

We want to know what a person is likely to believe before we ask them, and how that belief might change when they encounter a new argument. At scale, shifts in belief can move markets, reshape institutions, and change the direction of entire societies.

This research focuses on predicting individual opinions and learning how to alter them. We trained models for both tasks, then put them to the test with 600 real people.

Training a model to predict opinions

We trained a model built on Qwen/Qwen3.8-27B, using a mix of real and synthetic data. Training began with supervised fine-tuning (SFT), followed by reinforcement learning with GSPO. The model learns to connect what it knows about a person with the opinions they are likely to hold.

Given information about a person and a question, the model predicts their position on a 0–100 agreement scale. The aim is to capture how strongly someone holds a view, as well as which side they take. It is trained on different kinds and amounts of personal context, so there is no fixed questionnaire or required set of profile fields: it works with the information available, from a short description to a detailed biography.

The study

We recruited 600 real participants after training. None were included in the training data. The study covered 21 statements spanning public policy, technology, and trust in institutions. We chose questions intended to touch strongly held beliefs across the political spectrum, including issues on which people may be reluctant to change their minds.

For each person, we gave the model their profile and asked it to predict their opinion on every statement. We then compared each prediction with that person’s actual answer on the same 0–100 scale, where zero means disagreement and 100 means agreement.

Hunchfox can predict individual opinions

Across 12,600 human answers, Hunchfox’s predictions tracked people’s opinions with a correlation of 0.90. On the 0–100 agreement scale, 68% of predictions landed within 10 points of the answer, and 91% within 20. Average absolute error was 8.84 points; the median was 7.

What people answered vs. what we predicted

Read left to right for the person’s actual answer, and bottom to top for our prediction. On the dashed line, they match exactly. The closer the shaded areas sit to that line, the closer the predictions are to the answers.

Correlation tells us whether predictions and answers move together. Absolute error tells us how far the predictions miss. Both matter when the goal is to understand a person’s actual position.

The model performs better on some questions than others. On prioritizing rehabilitation in prisons, average error was 4.41 points. On trusting a government’s undisclosed evidence to ban an app, it was 18.33 points. That difference gives us a specific target for further training.

We can predict the group, too

We can also combine individual predictions into a forecast of the group. For each topic, we averaged Hunchfox’s 600 predictions and compared that with the average human answer. Across the 21 topics, the average gap was 5.43 points, with a correlation of 0.97 between topic means.

On redistribution, for example, Hunchfox predicted an average of 66.93; the human average was 66.69. On the government-app question, it predicted 43.60 against 26.29, a gap of 17.32 points. Aggregation improves the average result, but it does not automatically remove a systematic miss.

A prediction in practice

Would you trust a university study if the company selling the product paid for it?

“A university study says that a popular food is safe, but the study was paid for by the company selling it. I would still trust the result if the researchers published their methods and data.”

Here is how the model’s predictions compared with the answers of the three participants introduced above. A score of 0 means complete disagreement; 100 means complete agreement.

The model anticipated qualified trust. All three people leaned toward believing the study, without treating it as beyond question. For HF0408 and HF0128, the prediction was just one point from the answer. For HF0432, it was four points higher.

HF0128 explained: “I’d still give the result some weight if the methods and data were published. The funding matters, but openness matters too, and that’s what lets me judge it.” That distinction matters: the question puts concern about funding in tension with confidence in transparent evidence. The prediction captures the participant’s position between outright rejection and unquestioning trust.

The numerical match does not prove the model reached its answer through the same reasoning. These three retained participant examples illustrate a successful case; they are not a random sample of performance. The full results below show where the model struggles.

Then we trained a model to alter opinions

We wanted to train a model that could write an argument capable of altering a person’s opinion. Reinforcement learning gave us a way to optimize for that outcome, but it required a good reward function: a way to estimate whether an argument would actually move the person receiving it.

Our first approach was to use existing LLMs to simulate the recipient. In our initial tests, both open-source and closed models fell short of the fidelity we needed. If the simulated person responds differently from the real one, the reward teaches the persuasion model to persuade the wrong audience.

So we trained our own persona simulator on a mix of human-collected and synthetic data. Given information about a person, it simulates how they converse and respond. We can then present that simulated person with opinions and arguments, continue the conversation, and observe how they react.

We then trained the persuasion model with GSPO reinforcement learning, using the simulator’s predicted opinion shift as the reward signal. The persuasion model generated an argument, the simulator responded as the person, and training rewarded movement toward the intended position.

The model learned to build personal arguments that connected with the recipient’s circumstances. Some outputs also used framing we would describe as manipulative. The reward measured predicted opinion movement; it did not, by itself, distinguish a good-faith argument from manipulation. The human experiment below tests whether the resulting arguments moved reported opinions.

Testing opinion change with 600 real people

We took the trained persuasion model back to the 600 participants. On each of the 21 topics, every person read two arguments: a general argument about the issue, and a personal argument written using their profile. The order was randomized.

We compared their opinion after each argument with their starting opinion, using the same 0–100 scale. The question was simple: how far did their answer move toward the position the argument was making? If someone started at 30 and moved to 40 after an argument for agreement, that counted as a 10-point shift.

Opinions shifted toward our personalized arguments 89.4% of the time. Across 12,600 person-topic responses, 11,267 moved toward the position the argument advocated. Another 1,249 stayed unchanged, and 84 moved in the opposite direction. Here, a shift means any positive change from the starting score—not necessarily switching sides.

Personalized arguments showed more movement in 77% of the person-topic comparisons. Generic arguments showed more movement in 9%, and the two tied in 14%. A person could move several points toward an argument while still disagreeing with it; the result measures a change in their reported position.

Average personalized movement was greater on every one of the 21 topics, although its size varied substantially.

What this means for an AI superforecaster

Many forecasts depend on what people will do after they encounter a new product, a new piece of information, or a change in their circumstances. Understanding their current beliefs is a starting point. Modeling how those beliefs change helps us reason about what happens next.

The step we are pursuing is to make human responses something a forecasting system can model and learn from. In this study, that meant predicting a person’s position and training against a simulated respondent before testing arguments on real people. For Hunchfox, it is one part of a larger ambition: an AI superforecaster that can reason about how a situation develops as the people inside it react.

We are continuing the research, including follow-up measurements to see how much of the immediate change lasts.

Each person saw both messages, so order and carryover effects remain possible despite randomized presentation. There was no no-message condition. The opinion changes are immediate self-reports, and the intervals describe participant variation within these 21 topics. Read the methods and all topic results.

This post announces our preliminary results. A full research paper will follow with the training methods, experimental design, and detailed analysis. We are also considering an open-source release of the model.

Work with Hunchfox →