Emergence in artificial life
Carlos Gershenson
TL;DR
The paper addresses the lack of a single definition for emergence and proposes an information-theoretic framework to define and measure emergence across scales. It analyzes artificial life as a testbed, distinguishing soft, hard, and wet ALife, to study how local interactions generate global properties. Key contributions include a formal measure of emergence $E = -K \sum_{i=1}^{n} p_i \log p_i$ with normalization $K = 1/\log_2 n$, a relation $S = 1 - E$, and a complexity measure $C = 4 \cdot E \cdot S$, and a discussion of downward causation in an information-centric view. This approach facilitates cross-scale understanding of life and cognition and supports using information as a bridge between physics, biology, and social systems.
Abstract
Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement ``life is complex''. Thus, understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understand living systems? Artificial life (ALife) has been developed in recent decades to study life using a synthetic approach: build it to understand it. ALife systems are not so complex, be them soft (simulations), hard (robots), or wet (protocells). Then, we can aim at first understanding emergence in ALife, for then using this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but is present at another scale. This perspective avoids problems of studying emergence from a materialist framework, and can be also useful in the study of self-organization and complexity.