Understanding Agent Complexity Through Affordances

The rapid development of autonomous systems, some of which emulate animal abilities, raises a question about how to understand and compare their complexity. Taking an “agent” to be any system that can be well-explained by attributing goals and intentional states to it, in what sense should we understand some agents as more “complex” than others? There is a sizable literature about complexity in biology, notably in comparative psychology, where an animal’s complexity informs predictions about its abilities and guides experimental design. In engineering and artificial intelligence, complexity is often assumed to be a sign of progress – we want to build more complex, capable machines. While the goals of researchers in these fields are different, they plausibly overlap in common conceptions of what a “more complex agent” entails. Existing work on “environmental complexity” and its relationship to “complex cognition” inquires into the origins of complexity, but not what it means to be a complex agent. Recognizing that complex behavior comes not just from complex cognition but from the interplay of body, environment, and cognition, we articulate an account of agent complexity in terms of Gibson’s (The Ecological Approach to Visual Perception (Classic), Houghton Mifflin, Boston, 1979) notion of affordances. We distinguish between cognitive and behavioral dimensions of affordances, and offer a framework for comparing agents based on the range of affordances they can exploit. Our approach ecologically grounds the sense in which some agents are more complex than others, while also clarifying why the answer to “which agent is more complex?” is sometimes indeterminate. It enables systematic comparison across diverse agent types, as we demonstrate through a detailed analysis of the nematode C. elegans and the hexapod robot RHex. We hope our account can advance discourse around the complexity of animals and guide the development of complex, agential machines.