I write deeply researched articles about simulations with virtual audiences
Showing 1-6 of 6 articles
At the London Agentic AI Meetup, Mike Taylor showcased how to train an **LLM-as-a-Judge** and use it to optimize creative generative tasks through the **Evaluator–Optimizer pattern in DSPy**. His live demo built a *comedian AI* that learns to tell better jokes using **GEPA evolutionary optimization**, illustrating how to systematically create, evaluate, and improve AI programs.
Listen to this behind the scenes impromptu training between Mike and the Every.to team. Mike gives an introduction to the DSPY framework.
Discover how researchers are using LLMs to create synthetic data simulating virtual audiences of role-playing agents that mimic real demographic behaviors and opinions. This article introduces the Virtual Audience Simulation Canvas—a practical framework for designing AI personas that avoid common pitfalls like demographic blind spots and sanitized responses. Discover how this approach is being applied across UX testing, marketing, and policy research to generate insights in days instead of months, while learning key techniques to improve simulation realism through belief anchoring and anti-memetic constraints that keep personas true to life.
As researchers increasingly employ large language models (LLMs) to role-play virtual survey respondents, significant demographic gaps have emerged in their accuracy and realism. Certain populations—such as older adults, racial minorities, women (particularly women of color), lower socioeconomic groups, and ideological centrists—are consistently underrepresented or misrepresented by these models. This post examines the underlying reasons for these demographic discrepancies, including biased training data, alignment-induced censorship, and oversimplified demographic interactions. It also presents practical strategies for mitigating these biases, outlining how thoughtful prompt engineering, targeted fine-tuning, and nuanced alignment adjustments can help create more authentic and inclusive LLM-based role-play simulations.
As researchers increasingly utilize large language models (LLMs) to simulate human behaviors and attitudes based on real-world demographic characteristics, important questions arise about how accurately these AI-generated agents replicate true demographic biases and preferences. While recent studies demonstrate promising alignment between simulated outputs and actual demographic trends—referred to as “algorithmic fidelity”—they also expose notable methodological challenges and limitations, including potential oversimplifications, exaggerated stereotypes, and inconsistent representations of marginalized groups. Understanding these nuances is essential for responsibly leveraging AI simulations as reliable proxies for real human populations in social science research.
Discover how LLM-powered agent-based simulations can revolutionize business strategy. Learn how AI-driven models predict market trends, optimize customer experiences, and enhance decision-making with realistic simulations.
