Programme

In our morning tutorial, we'll introduce participants to the basic methods used by modern machine learning researchers (supervised, self-supervised and unsupervised learning and reinforcement learning); the framework used to simulate and analyze multi-agent systems composed of artificial agents; the formal tests used by machine learning researchers to evaluate algorithmic bias and develop explanations; and the techniques used to construct the recommender systems that shape social media, online commerce, and more.

In our afternoon tutorial, we'll discuss research in machine learning that invokes institutions and organizations that are well known to the SIOE audience. We'll discuss the insights available for AI from incomplete contracting theory, how contracts might be deployed in multiagent settings, what we learn about the tragedy of the commons, norms and culture from multiagent simulations, how algorithmic bias and explainability challenges might impact organizational design and regulation, and how democratic processes might be devised to improve the alignment of algorithms and recommender systems with social welfare.

Location: Atrium