How AI can Model Complex Social Interactions
In a recent paper, the potential of multi-agent deep reinforcement learning as a tool for modeling complex social interactions, such as the formation of social norms, was explored. This new approach has the potential to create more detailed simulations of the world.
Humans are highly social beings, facing numerous challenges in cooperation, such as resource conflicts, poverty, and climate change. Overcoming these challenges is possible through the development of culture, norms, and institutions that organize our interactions.
However, when norms and institutions fail to resolve cooperation challenges, policy interventions may not always work as intended. This is due to the complexity of real-world social-ecological systems, which we may not fully understand.
Game theory has been widely used to study cultural evolution, but its major weakenss is that it requires a full understanding of individual choices and their payoffs, which can be difficult in complex social-ecological systems.
An alternative approach using multi-agent deep reinforcement learning incorporates aspects of artificial intelligence to create more realistic models of social-ecological systems. This approach allows for the development of rich, dynamic models of individual decision-making within complex environments.
One study using this approach sought to explain the existence of seemingly arbitrary social norms, demonstrating that enforcing and complying with social norms is a complex skill that requires training and practice.
The use of multi-agent deep reinforcement learning in modeling cultural evolution can improve our understanding of how to design interventions for social-ecological systems, such as policies to strengthen recycling norms. This approach provides researchers with the tools to develop detailed models of social phenomena.
By using this innovative approach, researchers hope to gain more insight into the dynamics of social-ecological systems and improve the design of interventions to address cooperation challenges.