My published papers are described in more detail on the Papers page. I have now worked on three study systems, which I hope to describe ad hoc in more depth over on my blog (the News page). Here I give a general overview of my study systems.
Swarm robotics is recognised as an increasingly important technology
Swarm robots: since the early 2000s, the idea of swarm robotics has become increasingly popular. The main idea is that rather than build one complicated robot to solve a task, many simple, cheap robots could work together to tackle the problem, using insights from social insects like ants, for example. I am working with the Kilobot platform, which was designed at Harvard and released to popular acclaim in 2012 (paper). I hope to translate insights from the two study systems below (social spiders, social insects) into relevant control strategies, to engineer useful emergent behaviours, like effective collective exploration.
Social spiders are a model system for the study of heterogeneous swarms
Social spiders: Nearly all species of spider (~43,000) are quite the opposite of social (solitary and even aggressive to other conspecifics), yet social spiders (~25 species) actually live together in a shared nest, or colony. Spiders of the genus Stegodyphus have become model organisms for the study of individual differences in groups, in particular animal ‘personality’ (consistent behaviours), whereby some spiders are bold and most are shy.
Social insects are among the most successful organisms existing today. What can we learn from them?
Social insects: some insects, notably ants, bees, termites and some wasp species, live together in colonies, and are tremendously successful in terms of the biomass they represent (i.e., if you gathered them up and weighed them, they would account for a very large proportion of all living things, perhaps 25% on land). Their extreme sociality (known as ‘eusociality’) is a key reason for their success. I studied Temnothorax ants with one of the great ant scientists, Nigel Franks, during my PhD at Bristol. These ants do not build their own nest but look for pre-formed rock cavities to house their colony (queen, workers, brood items). Their collective scouting, decision-making and emigration process has become a model for the study of collective animal behaviour.
As I see it my research has two levels: what you might call the ‘proximate’ and ‘ultimate’.
Understanding specific behaviours: what they are for and how they guide the group
At the first level, I’m interested in understanding particular social behaviours in complex societies. Specifically, how are collective decisions driven by the choices of individuals deciding what to do based on simple rules and locally available information? For example, at UCLA this aspect of my research is focused on the importance of variation among individuals – rather than assuming they are all identical, what happens if some are ‘bolder’ and others ‘shyer’, for instance?
What do complex biological systems have in common?
At the second, ultimate level, I want to gain insight into the general features of complex systems – systems that have many interacting parts, whose interactions result in the emergence of new, unanticipated properties. Do seemingly disparate systems – species in an ecological community, a colony of ants, neurons in the brain – actually have characteristics in common, perhaps in the way they process information? And can we then use such insights to engineer desirable emergent properties in manufactured systems?
Seeking general principles for engineering and life
Ultimately, in my work I seek to obtain an integrated understanding of these two levels, for practical application in swarm robotics, and also perhaps to touch on the ‘physics of life’ itself.
Finally, I have an ORCID profile here: