Human cultural progress is powered by collective innovation - the process of gradual exploration and refinement of innovations across individuals and generations. However, the exploration and social learning strategies fueling this collective innovation process remain poorly understood, hindering efforts to enhance its efficiency.
In this project, our goal is twofold: (i) to understand the exploration and social learning strategies humans use for collective innovation, and (ii) to enhance collective innovation by partnering human participants with Artificial Intelligence (AI) algorithms. We will use computational modeling and interactive collective innovation experiments to address the first goal. The second goal will be met by harnessing AI algorithms' unique abilities to explore and learn from others in ways distinct from humans. We aim to design and test algorithms that complement human collective innovation, enhancing its efficiency while mitigating human biases.
Our results will both provide new insights into the strategies humans use for innovation and social learning while uncovering how AI can facilitate these processes to improve collective innovation. In an era where AI is poised to shape the course of human existence, studying the interplay between human and AI capabilities is essential. Through this project, we aim to shed light on what makes us human while harnessing the collaborative potential of humans and AI to drive collective innovation forward.