The landscape of market and political research is about to undergo a significant transformation. Traditional methods like surveys, panels, and in-person focus groups, while long-standing, will increasingly be replaced by AI-driven alternatives. These methods, leveraging autonomous agents to simulate human behavior and attitudes, are proving to be faster, more cost-effective, and potentially more accurate. This shift represents a turning point in how we gather insights, and it is happening much sooner than many anticipated.
There are two papers that have recently come out that I want to use to illustrate what I believe is going to be possible:
- "Generative Agent Simulations of 1,000 People" is a paper that just came out of Stanford. The paper presents an architecture for simulating human behavior using generative agents informed by qualitative interviews. What's really interesting is that these agents are modeled after 1,052 real individuals. They replicate attitudes and behaviors across various social science tasks with high accuracy, performing comparably to human self-replication over time. The research demonstrates the agents’ utility in predicting responses to surveys, personality assessments, economic games, and experimental settings. By reducing demographic biases and enabling scalable simulations, the approach offers a powerful tool for understanding individual and collective behaviors in diverse contexts.
- "Scaling Synthetic Data Creation with 1,000,000,000 Personas" is a paper that introduces Persona Hub, a collection of one billion synthetic personas designed to enhance data diversity and scalability in large language models (LLMs). By associating each persona with unique perspectives and knowledge, the framework enables the generation of highly diverse synthetic data across multiple applications, including math problems, instructions, and knowledge-rich texts. The approach overcomes limitations of previous data synthesis methods by leveraging personas to guide LLMs, demonstrating significant potential for advancing AI research, development, and practical applications
Limitations of Traditional Methods
I started out in market research many years ago in a part-time job during college checking data quality in survey questionnaires. When I graduated, I worked as an analyst for a company called Sophisticated Data Research (SDR). I left SDR after a few years, dissatisfied with the current state of software at the time for research and went on to join another company to write some of the first statistical software for Windows and for the internet for market research. In the early 2000s, I left to join a start up to build agents to model marketing effectiveness, so I've been around agents for over 20 years. So I'm very aware of agents, but also of the traditionalism in the industry and its limitations.
Traditional approaches to market and political research have long faced challenges, but these issues have become more pronounced in recent years. Phone-based surveys, once a cornerstone of consumer and political research, have seen their accuracy steadily decline. The widespread use of mobile devices has fundamentally changed how people interact with calls - screening is common, response rates have plummeted, and the pool of reachable participants is increasingly skewed. This has led researchers to rely on heavy weighting of subpopulations to align with presumed demographic truths. However, this practice has become increasingly tenuous, bordering on speculative guesswork, as the assumptions underlying these adjustments often lack a solid foundation. As a result, the reliability of phone surveys is now widely questioned, making them an increasingly impractical method for gathering actionable data.
These challenges extend beyond phone surveys. In-person focus groups and panels also face issues with scalability, cost, and bias. Facilitating these sessions requires significant resources, and their relatively small sample sizes make it difficult to generalize findings. Biases - both from facilitators and participants - can further distort results. Focus groups are increasingly having a difficult time recruiting in some segments - doctors, researchers, engineers, etc. Together, these factors have created a pressing need for new methodologies that are more efficient and reliable.
The Role of AI and Agents in Simulated Research
Recent advancements in artificial intelligence, particularly in the creation and deployment of generative agents, are addressing many of these challenges. By using large libraries of personas, AI systems can simulate the attitudes, preferences, and behaviors of diverse populations - and difficult to reach populations like doctors. Studies have demonstrated that these agents can replicate human responses with a high degree of accuracy. For example, as mentioned earlier, research from Tencent’s Persona Hub highlights the ability to synthesize billions of personas, enabling nuanced and scalable simulations, while Stanford’s work on generative agents shows their effectiveness in predicting individual attitudes and behaviors.
These systems allow for the creation of virtual focus groups and the simulation of surveys in which each participant is an AI-driven persona. In virtual focus groups, the personas can interact dynamically, mimicking the complex interpersonal dynamics found in real-life settings. These approaches don't have to wait to recruit participants or field a study. They can be done immediately and not just done once but repeated hundreds or thousands of times. This approach enables the collection of insights that are not only faster to obtain but also potentially more comprehensive.
Benefits and Broader Implications
Simulated research offers several advantages that are increasingly difficult to ignore. First, it reduces the time and costs associated with traditional methods. Virtual focus groups can be conducted instantaneously and at a fraction of the cost, making it possible to run studies that were previously too expensive or logistically complex.
Second, the accuracy of these methods is rapidly improving. Generative agents have demonstrated their ability to align closely with human responses in studies, offering reliable insights that rival or exceed those obtained through traditional research. This capability challenges the reliance on demographic sampling by using more detailed persona-based approaches, which can reduce biases.
Third, the technology is evolving to overcome limitations such as knowledge cutoffs in large language models. Although, not too many people are talking about this, but in a paper titled "Mixture of a Million Experts" the authors talk about this idea of continuous learning. And this is really exciting - AI models are going to be able to continuously be updated. Continuous learning capabilities will enable AI agents to stay updated with real-time information without relying on web searches. This development will further enhance the utility of simulated research by providing more contextually relevant and up-to-date responses. This will enable the type of research based on recent current events - opening up a potentially effective tool to measure practically real-time economic and political attitudes.
Preparing for the Future
The rise of AI-driven research methods signals a need for companies in the market and political research sectors to rethink their approaches. Adapting to this new reality will require investing in AI capabilities and integrating them into existing workflows. Organizations will also need to reconsider their business models, as the cost structures of traditional methods are unlikely to remain competitive against the efficiency of synthetic research.
Some of the larger organizations will be unable or unwilling to adapt as they try to protect the ways they have been doing things going back decades. Most will try and add AI to their current offerings as a sincere but ultimately half-hearted attempt to remain relevant. They will talk about things like their agent based profiles in their new AI based consumer segments. First, if you are talking about your new "AI-based insights" as part of your new marketing, well everyone is saying that now and how is that any different than saying in the early 2000's that your new solution is using the World Wide Web. How does that excite a customer - when you are stating something obvious and what everyone else is saying? Second, don't just tack on AI onto your existing offerings. That's not going to fly in a time of exponential change. In order to adapt to exponential change, you need to think radically.
Because with the coming of agents, there will be some use cases where the cost of doing research will be driven to zero and the barrier to entry will be minimal.
While this transition may not render traditional methods entirely obsolete overnight, it is clear that the trajectory of research is changing. The industry must embrace these advancements to stay relevant in a world where insights will become increasingly instantaneous and accessible. If old value propositions are driven to zero, new value propositions and strategic advantages will need to be identified.
An Inevitable Shift
The adoption of AI-driven research is not a distant prospect; it is already happening. As the tools and techniques improve, they will become integral to understanding consumer and voter behavior. The question for organizations is not whether to adopt these methods but how quickly they can do so and how effectively they can integrate them into their operations.
The AI transformation of market and political research signals that innovation doesn't merely enhance - it redefines and disrupts industries entirely. AI agents are not just an alternative to traditional methods - they are a glimpse into the future of how we understand and engage with the world.