I will give an introduction to the replicator equation and will apply it to study the dynamics of flower colours and preferences. This is based on a game we came up with in the last session, following a discussion on sexual selection.
We'll do journal club based on a review paper suggested by Alan Dorin, who will lead the discussion.
Aesthetic evolution by mate choice: Darwin's really dangerous idea
Richard O. Prum. Published 9 July 2012.DOI: 10.1098/rstb.2011.0285.
Link to paper.
Darwin proposed an explicitly aesthetic theory of sexual selection in which he described mate preferences as a 'taste for the beautiful', an 'aesthetic capacity', etc. These statements were not merely colourful Victorian mannerisms, but explicit expressions of Darwin's hypothesis that mate preferences can evolve for arbitrarily attractive traits that do not provide any additional benefits to mate choice. In his critique of Darwin, A. R. Wallace proposed an entirely modern mechanism of mate preference evolution through the correlation of display traits with male vigour or viability, but he called this mechanism natural selection. Wallace's honest advertisement proposal was stridently anti-Darwinian and anti-aesthetic. Most modern sexual selection research relies on essentially the same Neo-Wallacean theory renamed as sexual selection. I define the process of aesthetic evolution as the evolution of a communication signal through sensory/cognitive evaluation, which is most elaborated through coevolution of the signal and its evaluation. Sensory evaluation includes the possibility that display traits do not encode information that is being assessed, but are merely preferred. A genuinely Darwinian, aesthetic theory of sexual selection requires the incorporation of the Lande-Kirkpatrick null model into sexual selection research, but also encompasses the possibility of sensory bias, good genes and direct benefits mechanisms.
I study the behaviour of natural distributed systems, which are systems consisting of many individual units, each acting on its own with no centralised control of the collective. The thousands of tiny interactions between the individuals leads to sophisticated ‘emergent’ behaviour at the group level, such as solving mazes and trade-offs. I spend my time figuring out the mechanisms and simple behavioural rules that individuals use to gain the benefits of emergent collective behaviour, and working out ways we can implement those rules in human systems.
I am a lecturer at Macquarie University, and my main study organisms are ant colonies, as well as the slime mould Physarum polycephalum.
The New World Army ants in the genus Eciton link their bodies together to build complex, dynamic structures that perform a variety of functions for the colony. Such structures include fully functioning temporary nests called bivouacs, which house the workers, queen and brood inside tunnels and chambers made from the colony members themselves, and bridges, ramps, pot-hole plugs and scaffolding structures that allow foraging ants to move more easily through the environment. In tropical Australia, Weaver ants build bridges, hanging chains and pulling chains to explore the environment and build their rolled-leaf nests. I will talk about my recent work conducted on Army ants in Panama and Weaver ants in Townsville, to understand more about how living structures are built by these small animals, using local information and self-organised processes alone. These empirical data will allow me to build computer simulation models to; 1) test that the rules determined by experiment are sufficient for self-assembly in each environmental context; 2) analyse specific self-assembly behaviour properties (stability, convergence, etc.) which provide critical physical parameters for applications in the real world (swarm robotics etc.) and; 3) build a platform for examining the specific individual-level effects of information flow, feedback mechanisms, and other common aspects of collective systems, providing rules for collective function that can generalise to other systems.
Speaker: Prof. Arne Traulsen, Max Planck Institute for Evolutionary Biology
Cancer can be viewed as an evolutionary process, where the accumulation of mutations in a cell eventually causes cancer. The cells in a tissue are not only organized spatially, but typically hierarchically. This affects the dynamics in these tissues and inhibits the accumulation of mutations. Mutations arising in primitive cells can lead to long lived or even persistent clones, but mutations arising in further differentiated cells are short lived and do not affect the organism. Both the spatial structure and the hierarchical structure can be modeled mathematically. The effect of spatial structure on evolutionary dynamics is non-trivial and depends on the precise implementation of the model. Hierarchical structure can delay or suppress the dynamics of cancer. While these models can lead to important conceptual insights, fitting these models directly to data remains challenging. However, closely related models have the remarkable property that they can make a prediction with data obtained from a single measurement.
Arne Traulsen studied geophysics and physics in Kiel, Leipzig and Gothenburg and obtained a doctoral degree in Theoretical Physics at Kiel University in 2005. After a PostDoc with Martin Nowak at Harvard, he started his own group at the Max-Planck Institute for Evolutionary Biology in Plön, Northern Germany. Since 2012, he has been an honorary professor for Mathematical Biology at Lübeck University. In April 2014, he became Director of the Department Evolutionary Theory at the Max-Planck Institute for Evolutionary Biology.
Speaker: Dr. Jorge Peña - Geomar
In many natural systems, group formation processes lead to a dependence of the
group size distribution on the level of cooperation in the population. Examples include social microbes producing adhesive proteins, bark beetles attacking host trees, burying beetles preparing and burying carcasses, and social carnivores participating in collective hunting or confrontational scavenging. Here, we explore the evolutionary consequences of endogenous group formation in a prominent model of social evolution: the volunteer’s dilemma. In our variant of the model, individuals are sequentially recruited by focal
groups until the critical number of cooperators needed to provide a collective good is reached. This rule of group formation generates predictions that are in stark contrast to those resulting from the standard volunteer’s dilemma with constant group size. In particular, protected polymorphisms are impossible and the invasion barrier for cooperator mutants can be less severe at sufficiently low cost-to-benefit ratios. Our results highlight the importance of explicitly accounting for endogenous processes of group formation in models of social evolution.
Presenter: Dr. Christoph Kleineidam, University of Konstanz, Germany
The ability to flexibly allocate workers to different tasks at the same time (colony multitasking) underlies the vast ecological success of social insects. While we have models for a number of individual tasks, we are far from understanding how task allocation is organized at the colony level and how it is adjusted to changing conditions. The main focus in trying to decipher the underlying mechanism has been on internal processes among the workers of colony (response thresholds, genetics, polymorphism etc.). External processes, e.g. interactions with the environment and communication among colony members also play an important role. One of the most important communication mechanisms for adjusted task allocation in ants is not well understood: the impact of direct interactions among workers via antennation and trophallaxis for task allocation and organization of the collective. Direct interactions are crucial in modulating the individual’s decision to perform a task. Understanding how interactions and interaction networks modulate task allocation is an important missing piece in our understanding of social organization in societies. I will present our system, the investigation of nestmate recognition in ants, and will demonstrate how interactions with nestmates provide a social context for task allocation.
We will discuss the following papers:
- Santos, F. C., & Pacheco, J. M. (2011). Risk of collective failure provides an escape from the tragedy of the commons. Presented at the Proceedings of the National ….
- Pacheco, J. M., Vasconcelos, V. V., Santos, F. C., & Skyrms, B. (2015). Co-evolutionary Dynamics of Collective Action with Signaling for a Quorum. PLoS Computational Biology, 11(2), e1004101.
Adaptive Dynamics is an approach to studying evolutionary change when fitness is density or frequency dependent. Modern papers identifying themselves as using this approach first appeared in the 1990s, and have greatly increased up to the present. However, because of the rather technical nature of many of the papers, the approach is not widely known or understood by evolutionary biologists. In this review we aim to remedy this situation by outlining the methodology and then examining its strengths and weaknesses. We carry this out by posing and answering 20 key questions on Adaptive Dynamics. We conclude that Adaptive Dynamics provides a set of useful approximations for studying various evolutionary questions. However, as with any approximate method, conclusions based on Adaptive Dynamics are valid only under some restrictions that we discuss.
Presenter: Fatima Seeme
Title: Pluralistic Ignorance: Emergence and Hypotheses Testing in a Multi-agent System
Pluralistic ignorance (PI) is a common phenomenon, observed in many social settings. It occurs when the majority of a group become non-believer conformist, but mistakenly perceive others to be true conformist. PI takes many forms and leads to a wide variety of social problems, from binge drinking to repressive political regimes and ideologies. Although discussed extensively in the literature, the nature of the problem makes experimental study impractical and it has been virtually ignored in modelling and simulation studies. In this study, we present a simulation model of conditions that lead to PI in social networks. We use it to show that PI can emerge from interpersonal interactions and to confirm some well-known hypotheses about PI.
Presenter: Nader Chmait
Title: Factors of Collective Intelligence: How Smart Are Agent Collectives
The dynamics and characteristics behind intelligent cognitive systems lie at the heart of understanding, and devising, successful solutions to a variety of multiagent problems. Despite the extant literature on collective intelligence, important questions like “how does the effectiveness of a collective compare to its isolated members?” and “are there some general rules or properties shaping the spread of intelligence across various cognitive systems and environments?” remain somewhat of a mystery. We develop the idea of collective intelligence by giving some insight into a range of factors hindering and influencing the effectiveness of interactive cognitive systems. We measure the influence of each examined factor on intelligence independently, and empirically show that collective intelligence is a function of them all simultaneously. We further investigate how the organisational structure of equally sized groups shapes their effectiveness. The outcome is fundamental to the understanding and prediction of the collective performance of multiagent systems, and for quantifying the emergence of intelligence over different environmental settings.
Presenter: Rui Chen
Following the discussion on task allocation and game theory in a previous seminar, I would like to talk about our most recent work on this topic. We use game theory to investigate the factor of social interaction on task allocation in social insects. We analyse how different environmental conditions can influence the degree of specialisation in an insect colony by mathematical predictions and agent-based simulation. Particularly, we compare three mechanisms: emulation (which assumes there are non-elemental learning such as concept learning involved in this process), individual learning and reinforcement based on personal preference and recruitment.
Tom is introducing a model of the trail clearing behaviour in leaf-cutter ants. The model uses a Gillespie simulation to capture 4 different overlapping poisson processes. Built on one experimental data set, Tom aims to predict the removal behaviour under changed conditions.
Kao, A. B., Miller, N., Torney, C., Hartnett, A., & Couzin, I. D. (2014). Collective learning and optimal consensus decisions in social animal groups.PLOS Comput Biol, 10(8), e1003762.
Get a copy of the paper here.
Title: Numerical patterns in nature and the mathematical competency of honeybees
Presenter: Scarlett Howard