The Minimal Complexity of Adapting Agents Increases with Fitness

What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario

Integrated information as a metric for group interaction

Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration

Integrated information as a metric for group interaction

Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration

Love thy neighbour? Social and sexual accommodation in fruit flies

Many animals plastically adjust their reproductive phenotype in response to their social and sexual environment. A common example of this type of plasticity occurs when males tailor their reproductive effort to the risk and intensity of sperm competition. In this thesis, I studied reproductive plasticity in male Drosophila melanogaster in response to cues of sexual competition. I found some evidence that cues signalling the likelihood of male-male competition affected the morphology of male reproductive structures and wings differently at two developmental stages, suggesting a high degree of environmental sensitivity in these traits. However, these findings were not fully consistent, highlighting the limitations of proxies when measuring complex, multi-faceted traits. I also showed that reproductively plastic behaviours can evolve in response to the prevailing social/sexual environment. Male D. melanogaster that evolved under a high degree of male-male competition expressed longer overall mating duration, reduced courtship delivery and altered courtship repertoire, in comparison to males evolved under less intense competition. I investigated the role of redundancy in cues signalling male-male competition and showed that occluding one sensory modality did not reduce the ability of male D. melanogaster to detect rivals and express behavioural responses. However, responding to rivals by extending mating duration did not confer any clear fitness benefits under the conditions tested. Finally, I tested the hypothesis that responses to redundant environmental cues can be underpinned by redundancy at the gene expression level. I found preliminary evidence that quasi-equivalent behavioural responses to rivals by male D. melanogaster can be reached by alternative transcriptomic pathways. Overall, this thesis demonstrates the important and varied effects that the social and sexual environment can have on individual development, behaviour, fitness, and the evolution of populations. My findings highlight the important context-dependence of many key reproductive traits and suggest several important avenues for future research