牛津大学赛德商学院

Large Language Models (LLMs) enable unprecedented social science experimentation by creating controlled hybrid human-AI environments. We introduce Epitome (www.epitome-ai.com), an open experimental platform that operationalizes this paradigm through Matrix-like social worlds where researchers can study isolated human subjects and groups interacting with LLM agents. This maintains ecological validity while enabling precise manipulation of social dynamics. Epitome approaches three frontiers: (1) methodological innovation using LLM confederates to reduce complexity while scaling interactions; (2) empirical investigation of human behavior in AI-saturated environments; and (3) exploration of emergent properties in hybrid collectives. Drawing on interdisciplinary foundations from management, communication, sociology, psychology, and ethics, the platform's modular architecture spans foundation model deployment through data collection. We validate Epitome through replication of three seminal experiments, demonstrating capacity to generate robust findings while reducing experimental complexity. This tool provides crucial insights for understanding how humans navigate AI-mediated social realities, knowledge essential for policy, education, and human-centered AI design.

To delineate the criteria that characterize healthcare as a complex adaptive system (CAS), including nonlinearity, self-organization, and emergence. Thereafter, we reframe health disparity as an emergent phenomenon, and show how this alternative perspective can be used to identify novel strategies and opportunities to close the health equity gap. It is a moral imperative to ensure that every individual is afforded a minimal level of medical care, regardless of background or means. Unfortunately, healthcare systems around the world are failing to deliver on this commitment. One of the greatest frustrations is the growing inequity in health outcomes between the ‘haves’ and ‘have nots.’ Traditional models of understanding and addressing health disparity have fallen short and the health inequity gap continues to increase. We need to think differently. Healthcare is a CAS—the interactions between patients, care providers, and suppliers lead to outcomes that are more than the sum of the contributions of the individual agents. In this context, health disparity is an emergent phenomenon, the result of complex interactions among multiple interdependent variables that are unevenly distributed across people of different backgrounds. Using infant mortality rate as our outcome of interest, we define a novel 3-stage framework to understanding health disparity. Healthcare is a CAS and health disparity is an emergent phenomenon—it is independent of the motivation of individuals, and is neither intended nor desired. This alternative framework can be used to identify novel strategies and opportunities to close the health equity gap.