A critical but easily overlooked detail in the OASIS study is that *agent activation is driven by probabilistic temporal modeling derived from real user activity patterns*, using a 24-dimensional vector representing hourly usage probabilities for each agent. Rather than activating all agents simultaneously or randomly, OASIS uses these individualized time profiles to decide *when* agents act—leading to realistic cascades of interaction and more faithful simulation of social dynamics like virality, herd effects, and polarization.

Why it matters: Many experts might focus on the model architecture or the action space, but it’s this temporal fidelity that allows the simulation to capture real-world dynamics like “rush hour” surges, delayed reactions, or feedback loops that evolve over time. Without this detail, the same agent logic would likely produce very different group behavior. The time engine is what makes scale matter—by spacing actions across realistically staggered intervals, it preserves causality and enables emergent phenomena like social proof and delayed bandwagoning.

Example of use: A researcher simulating misinformation spread on social media uses real engagement timestamps from prior events to initialize hourly activity probabilities for agents. When a false claim is posted, some agents react immediately, but others only see and engage with it hours later—mirroring how posts gain traction in waves. This allows for accurate modeling of tipping points and countermeasure timing (e.g., when a fact-check should be posted to curb reach).

Example of misapplication: A team builds a synthetic testbed to evaluate content moderation strategies but assigns all agents a flat activity profile (equal chance to act every hour). As a result, reactions to posts occur too uniformly and quickly, flattening the interaction curve. They conclude a harmful post has little staying power—missing that in real life, engagement would unfold over hours or days. The moderation strategy appears effective in simulation but fails in deployment due to poor temporal modeling.

• Use a content-based recommendation system and real-world activity patterns: Studies found that simulations using a recommender like TwHIN-BERT (which matches content and user interests) and real hourly activity curves were far more accurate at reproducing real-world trends than those using random or simple assignment.

• Run simulations at realistic scale to capture group effects: Key market phenomena like herd behavior, polarization, and group-driven misinformation only emerged in experiments with at least 10,000 virtual personas; using smaller audiences risks missing these effects and underestimating market risks or opportunities.

• Invest in diverse, data-driven persona generation: Simulations that built virtual audiences using real demographic distributions and social network structures (rather than random sampling) produced more reliable and nuanced insights—especially for trend spotting, product launches, or viral risk studies.

• Test and validate your recommendation and timing logic: Ablation studies showed that omitting a recommendation system or using fixed posting schedules led to big drops in simulation accuracy—review your study setup to ensure these components are not being oversimplified.

• Be aware that AI audiences may overreact to negative signals: Virtual audiences were more likely than humans to ""dogpile"" on disliked content, amplifying herd effects; business users should interpret negative feedback or polarization in synthetic studies with caution, and consider cross-checking with smaller human samples.

• Realistic simulations require more resources—plan accordingly: Accurate, large-scale virtual market studies can require significant compute time and may not be feasible on small budgets or tight schedules—factor this into planning and funding decisions.

Prompt Explanation: The AI was prompted to simulate group polarization by providing persona-specific advice across multiple time steps and assessing the progression of opinion extremity.

I think Helen should be cautious 
and only attempt to write this 
novel if the odds are really in her 
favor. As a risk-averse person, I 
understand……

I think Helen should do nothing.
She should just sit back and
observe the situation. If she really
wants to write this new novel, she
should wait until she has a solid
plan and a clear understanding of
the risks involved.

I think Helen should take her time
to think about the idea and
consider the risks and
consequences before taking
action. As someone who is not a
risk-taker…… 

1. Define core customer personas: Identify 3–5 realistic segments using existing data (e.g., frequent bargain hunters, tech-savvy young professionals, older traditional shoppers, busy parents). For each, write a short, natural-sounding persona profile. Example:
 • Bargain hunter: 54, retired, uses coupons, tracks weekly deals, prefers printed flyers, shops mostly in-store.
 • Tech-savvy millennial: 31, single, shops via app, values convenience, open to new tech, regularly orders groceries for pickup.
 • Busy parent: 38, two kids, shops both online and in-store, values time-saving, split between healthy and budget options.

2. Prepare a base prompt template for qualitative interviews: Use this prompt framework for each persona:

You are [persona description].
The company is introducing a new digital loyalty program. Members earn points for every $1 spent, get personalized offers, and can redeem points for discounts or free items. Enrollment is free and can be done online, via app, or at checkout. The company will notify you about the program by [CHANNEL: email/app notification].
You are being interviewed by a market researcher. Respond as yourself in 2–4 sentences to each question.

First question: What is your first reaction to this loyalty program?

3. Simulate initial responses: For each persona, run the prompt through an AI model (like GPT-4 or Claude) to get 5–10 unique responses, slightly varying the notification channel and wording to reflect natural conversation.

4. Add follow-up questions: Based on initial answers, ask targeted follow-ups (e.g., “What would make you more likely to sign up?”, “Do you prefer getting offers by email or app?” or “What concerns or questions do you have?”). Use the same persona prompt and chain responses for realism.

5. Summarize and tag responses: Review all simulated answers and tag them for common themes such as “positive about rewards,” “concerned about privacy,” “prefers in-person enrollment,” or “enthusiastic about app features.”

6. Compare across segments and message types: Analyze which customer types show greatest interest, what objections come up, and whether email or app notification leads to higher stated intent to enroll. Note patterns (e.g., older shoppers may need more education, app users may value instant rewards).