Business and social problems are often "wicked problems" with no standard answers (H.W.J. Rittel, M.M. Webber). "Simulation" offers multidimensional possibilities for these problems, involving trade-offs, games, and constraints. starts by simulating consumer behavior and decision-making.
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Simulating human behavior and decision-making is not a new concept. Prior to the emergence of large language models, scholars used mathematical models such as Cellular Automata to model collective human behavior. However, these methods were inadequate in addressing individual differences and complex subjective decision-making logic.
Cellular Automata
Large language models have made individual simulation possible, enabling consumer modeling through Agent-Based Modeling (ABM). The core approach is to build models for individuals based on their detailed data using large language models, which we call "subjective world modeling." For example, after injecting Harry Potter corpus into a large language model, it can simulate Harry Potter's potential judgments and thought processes, inferring behaviors not explicitly mentioned in the original text.
Regarding the confidence level of the Subjective World Modeling approach, we verify it through the following method. If you want to know the basis for a consumer's behavior and decisions, the most accurate way is to ask them directly. Research shows that a person's consistency in answering the same question two weeks apart is approximately 81%. Therefore, we use 81% as the consistency baseline and set it as the full score standard (a standard score of 100). Then, we model the person using different corpora, ask the person and their agent the same question, and calculate the consistency of their answers.
We first tested modeling using personal information and standardized psychometric data, including dimensions like MBTI type and Big Five personality traits. The consistency score was 55-64 points. Subsequently, we tested consumer data from CRM and CDP, extracting and modeling behavioral characteristics such as purchasing patterns, brand preferences, and price sensitivity. The consistency score was 73 points.
Personal information and personality tests are both static data. Although behavioral data is dynamic, it is difficult to understand the "what" and "why" questions (for example, what part of a product someone likes, why they like a product, etc.). Therefore, we further used "social media" content as dynamic data to model consumers (including: Xiaohongshu, Douyin, Instagram, TikTok, X).
First, we input the research question. The large language model deconstructs this question to conduct a search, finds corresponding social media posts and replies, and uses this as the corpus to input into the large model to generate the consumer model. On questions related to this research topic, this intelligent agent can achieve a consistency of 79 points with a real person's answers.
When processing social media data, atypica.AI understands consumers through the following three levels:
Furthermore, we adopted the "in-depth interview" method to model consumers. The specific method is to use AI to conduct interviews with consumers, with each interview lasting about 1-2 hours. The AI will ask follow-up questions based on the consumer's responses, and the entire process generates an average of 5,000 to 20,000 words of transcribed text, just like a short biography of this consumer.
In-depth interviews cover multiple key dimensions, aiming at comprehensively capturing an individual's cognitive patterns, value systems, and behavioral tendencies:
The interview transcripts, along with the analytical dimensions mentioned above, form this consumer's intelligent agent. When questioned, the agent responds as this individual based on the viewpoints, values, and experiences expressed during the interview. The advantage of this method lies in maintaining information integrity and contextual coherence, eliminating the need to predetermine which information is important, and instead allowing the large language model to autonomously identify and utilize relevant information from the complete interview content. This approach achieved a consistency score of 85 points in evaluation.
has established 300,000 synthetic consumer agents based on "social media" data and 10,000 real consumer agents based on "in-depth interviews." This number is continuously increasing, forming an agent ecosystem that covers a diverse consumer group.
Users can ask questions. After identifying it as a business problem, Atypica will intelligently call upon relevant consumer agents to conduct simulated interviews. These intelligent agents will provide in-depth feedback that aligns with their personality traits and behavioral patterns, enabling the collection of large-scale, multi-dimensional consumer insights.
By interviewing these consumer agents as research subjects, Atypica.AI summarizes a research report. The research report is only an intermediate state of research; in the future, there will be more in-depth results, such as directly conducting product development, social media operations, etc. In future versions, Atypica has two advanced functions to further enhance the platform's personalization and interactivity:
We randomly selected 1,200 reports generated between June 10-20, 2025, for analysis.
The distribution of research topics is as follows:
We randomly selected 30 business research reports from each of four types of reports for users to score for satisfaction, half of which were generated by Atypica, and the other half were manually written. The scoring was marked from 1-5 points, where 1 point is 'dissatisfied' and 5 points is 'very satisfied,' and then we analyzed the scores of these two sample groups.
According to this table, we can see the performance evaluation results of Atypica.AI on different types of research questions. This evaluation is based on 120 samples (30 for each type) and compares the user satisfaction of reports generated by Atypica with manually written reports.
This table demonstrates Atypica.AI's satisfaction performance across different user types, based on evaluation results from 120 samples. The data shows that Atypica performs excellently across most user groups:
Best-Performing User Types:
Well-Performing User Types:
Relatively Weaker but Still Acceptable User Types:
Overall, the total satisfaction score of 4.1 exceeds the 4.0 benchmark, indicating that Atypica excels in application-oriented fields such as marketing, social media operations, and educational consulting, while still having room for improvement in areas requiring deeper professional expertise such as product development and academic research.
Based on the above results, we have summarized the applicable and non-applicable scenarios for Atypica:
represents a new stage in consumer insight analysis—a shift from passive analysis that relies on historical data, to active simulation based on agents.
Atypica.AI is not positioned as a replacement for traditional research, but rather as an innovative option that offers advantages in speed and scale when facing complex social and business problems, bringing multi-dimensional perspectives and greater decision-making fluency to decision-makers.
The value of research lies not in generating reports, but in driving effective action. By connecting with more MCPs (Model Context Protocol), Atypica.AI is building a complete ecosystem from insight to execution, for example:
This shift from "static analysis" to "dynamic execution" allows companies to complete the entire process from problem identification to strategy formulation within hours, achieving an agile business model that moves from "research first, then decide" to "simultaneously research, decide, and execute."
Atypica.AI forms a synergistic and enhanced relationship with traditional research: traditional methods provide depth and precision, while AI agents provide breadth and speed. This combination enables companies to maintain an agile response capability in a complex and ever-changing business environment, thereby gaining a decisive competitive advantage.
Generative Agent Simulation Research
Personality Psychology and Trait Theory
Cognitive Psychology
Marketing Theory
Values Research
Original VALS Research
VALS Theory Development
Early Market Research
Modern Market Research Tools
ABM Applications in Consumer Behavior
Harvard Business Review Core Articles
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