Currently there exists no general theory for what life is. This makes it challenging to anticipate how a more fundamental understanding of life could inform the design (or evolution) of artificial life forms and/or artificial intelligences, or what the role of these will play in the future evolution of Earth and its biosphere. For artificial systems, designed in software, the role of information is clear, whereas for biological and other physical systems it is less so. Unifying the long history of biological evolution with what is happening currently on our planet, or with what might happen in the future due to the technological advances we are mediating, will require new paradigms for understanding what information is and does in natural systems. In this talk, I discuss quantitative approaches aimed at developing a new theory for understanding life based on the idea that life is fundamentally about information (life itself is an abstraction) and how that information interacts with the physical world. I discuss how this leads to new approaches to understand the abstraction that was the last universal common ancestor of known life on Earth, through the evolution of our biosphere to its current technologically mediated form and beyond.

Collective decision-making systems rely on many agents to gather, process and exchange information to arrive at a group decision. Critical to group success is the transfer of information among agents and between agents and their environment. Without information transfer, no consensus can be achieved. Yet, the role of individual rules in determining information transfer at the group level is poorly understood. With the aim to shed a light on how the decision mechanism of individuals affects information transfer in collectives, we analyze the information landscape of two decision-making strategies: one based on the majority rule and one based on the voter model. For each strategy, we consider a binary site-selection scenario and use transfer entropy to measure the flow of information in a spatial, multi-agent system. We find that information transferred among agents is dependent on the decision mechanism, increases with the time necessary to make a collective decision, and is loosely modulated by the uncertainty of the final outcome. This is the first study that compares collective decision making mechanisms through the lens of information dynamics. Although this approach is limited to simulated agents, similar approaches could in principle be used to study collective decisions in biological systems.

Earth has undergone a succession of stages driven by physical, chemical, geological, biological, and social processes. Among the most significant transitions in Earth’s planetary evolution are the emergence of life and subsequent biochemical innovations, the emergence of social behavior and cognition, and the emergence of technology. After life emerged, planetary processes became much more complex due to increased diversity in what is biogeochemically possible. With the evolutionary emergence of collective behaviors, social systems, and cognition, an increasing number of planetary processes became controlled by life. Since the emergence of technology, intentional steering of the environment became possible. In each stage, new mechanisms of control, mediated by new information processing architectures, are added to existing levels of control on the planetary environment. We can classify these evolutionary stages of planets into matter-dominated, life-dominated, and agency-dominated phases, where each is distinguished by the extent to which information processing systems control planetary processes. We aim to characterize how each phase shapes planetary environments.