The emergence of modular information processing in animal societies
Life depends on interaction. All organisms interact with their environment to acquire the information they need to adjust their behaviour in order to survive and reproduce. As soon as collective living starts, organisms interact with each other to distribute and process information. From bacteria to humans, life is replete with elaborate communication systems that organize its functions. Communication is essential not only between organisms but also within them: from cells that communicate during ontogenesis to the immune system and ultimately the brain’s billions of interacting neurons.
The living world consists of interacting networked systems that communicate to organise their function. Unravelling the fundamental properties of these communication systems will contribute vastly to our understanding of life.
The most fundamental property of a communication network is its structure. Simple biological communication networks exhibit a flat structure where information is homogeneously shared, but more advanced biological systems often exhibit a modular structure. The communication networks of collectively living organisms are particularly interesting because information is transported by free-moving individuals rather than fixed connections and hence they can be flexibly reconfigured. We posit that the transition to structured communication networks is a major transition in the evolution of collective life, because it has likely facilitated the emergence of modular information processing. This enables a separation of concerns that reduces the cost of communication, increases its robustness, and supports more efficient decision-making, ultimately yielding significant fitness advantages.
Our proposed research
We hypothesise that interaction and communication networks of individuals acquire structure as the size of collectives and the number of tasks to be accomplished grows, with modular information processing as a consequent emergent property. Social insects are an ideal and already well-established model system to investigate this transition. The sizes of their collectives naturally span several orders of magnitudes and can be manipulated, their individual behavior can be quantified with relative ease. Many aspects of information processing for specific tasks in social insects are already well understood. Yet, although the ability of social insects to integrate information processing at the colony level is often credited as the key to their enormous ecological success, we are far from understanding the mechanisms behind it.
Five core questions will guide our research:
Q1: How is communication structured, in large collectives? We analyse topological and temporal communication network structures in social insect colonies, as they simultaneously forage, defend, nurture, and build.
Q2: Does complexity in communication structures and information processing correlate with a collective’s size? We manipulate collective size, and analyse how the structures of communication networks grow in complexity.
Q3: How does structured communication support modular information processing? We identify possible pathways for colony-wide integration of information using mathematical and computational models of information sharing in multi-task communication networks – adapted to findings under Q1 and Q2.
Q4: What are the advantages and consequences of modular information processing? Starting from existing computational models of single task decision-making we build multi-task decision models by linking different instances with communication networks whose communication flow corresponds to the findings under Q1-Q3. Using these models, we quantify the efficiency and accuracy of collective multi-task decisions and compare flat and modular versions across small, medium, and large collectives.
Q5: What evolutionary steps lead from flat to modular information processing? We use computer simulations to model effects of individual communication behaviours on network structure and emergent modularity. Applying evolutionary game theory, we find possible evolutionary trajectories for such behavioural traits.
Our approach is integrative, combining behavioural experiments with mathematical and computational modelling based on network theory and information theory. We draw on recent advances in data capture of individual behaviour (automated video analysis, QR codes, and RFID techniques), enabling us to investigate even very large collectives. We aim to identify structures in a biological system, then transfer the observed properties to computational models – to test for functional significance and explore hypothetical scenarios. Finally, we will test empirically the conclusions we draw from models, by specific manipulations in behavioural experiments.