How to measure group selection in real-world populations

Multilevel selection and the evolution of cooperation are fundamental to the formation of higher-level organisation and the evolution of biocomplexity, but such notions are controversial and poorly understood in natural populations. The theoretic principles of group selection are well developed in idealised models where a population is neatly divided into multiple semi-isolated sub-populations. But since such models can be explained by individual selection given the localised frequency-dependent effects involved, some argue that the group selection concepts offered are, even in the idealised case, redundant and that in natural conditions where groups are not well-defined that a group selection framework is entirely inapplicable. This does not necessarily mean, however, that a natural population is not subject to some interesting localised frequency-dependent effects -- but how could we formally quantify this under realistic conditions? Here we focus on the presence of a Simpson's Paradox where, although the local proportion of cooperators decreases at all locations, the global proportion of cooperators increases. We illustrate this principle in a simple individual-based model of bacterial biofilm growth and discuss various complicating factors in moving from theory to practice of measuring group selection.Comment: pp. 672-679 in Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (Advances in Artificial Life, ECAL 2011). Edited by Tom Lenaerts, Mario Giacobini, Hugues Bersini, Paul Bourgine, Marco Dorigo and Ren\'e Doursat. MIT Press (2011). http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760. 8 pages, 5 figure

Analysis of the influence of altruism on life expectancy in an artificial world

Competition is such an obvious and every day part of our lives that we probably do not even notice it most of the time. It is in fact so ingrained in our society, that many consider it a part of our human nature. However most people do not realize that capitalism in its insatiable appetite for more and higher profits, forces us into constant competition among ourselves, which in turn, forces us into bad habits and behavior. Different fields of science, such as neurobiology, psychology (namely behaviorism) and sociology, have however shown that we human beings are mostly a product of the environment in which we live in and are far from being ``bad by nature'' as some would like us to believe. So the main purpose of this thesis is to simulate different environments with a simple model and see how the entities (which can represent any living beings in nature) behave in these environments. Our hypothesis is that cooperation, at least in the long run, is more beneficial to survival than competition. Our model consists of entities that are constantly moving and collecting sources of energy for their survival in a closed environment. If at any time during the simulation, an entity is running low on energy, it can ask other members of it's group for a portion of their energy. The other entities then decide, based on their level of altruism, if they are going to share any of their energy with the entity in need. This is where we see how different levels of altruism influence the behavior of entities and their groups. Results from the test group, where we have, among others, very selfish and very altruistic entities in the same group, are close to what we would expect. The most successful entities in such a group are also the most selfish ones, living at the expense of the more altruistic ones. However, when we ran our simulations with four different groups, where entities with similar levels of altruism were in the same group, so we did not have selfish entities living off at the expense of altruistic ones, the results were much more interesting. As it turns out, selfish entities or their groups survive longer in an environment where energy sources are scarce, but do not do as well in an environment where sources of energy are abundant

Parallel simulation methods for large-scale agent-based predator-prey systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand

The Animat is an agent-based artificial-life model that is suitable for gaining insight into the interactions of autonomous individuals in complex predator-prey systems and the emergent phenomena they may exhibit. Certain dynamics of the model may only be present in large systems, and a large number of agents may be required to compare with macroscopic models. Large systems can be infeasible to simulate on single-core machines due to processing time required. The model can be parallelised to improve the performance; however, reproducing the original model behaviour and retaining the performance gain is not straightforward. Parallel update strategies and data structures for multi-core CPU and graphical processing units (GPUs) are developed to simulate a typical predator-prey Animat model with improved perfor- mance while reproducing the behaviour of the original model. An analysis is presented of the model to identify dependencies and conditions the parallel update strategy must satisfy to retain original model behaviour. The parallel update strategy for multi-core CPUs is constructed using a spatial domain decomposition approach and supporting data structure. The GPU implementation is developed with a new update strategy that consists of an iterative conflict resolution method and priority number system to simultaneously update many agents with thousands of GPU cores. This update method is supported by a compressed sparse data structure developed to allow for efficient memory transactions. The performance of the Animat simulation is improved with parallelism and without a change in model behaviour. The simulation usability is considered, and an internal agent definition system using a CUDA device Lambda feature is developed to improve the ease of configuring agents without significant changes to the program and loss of performance

Simulations and Modelling for Biological Invasions

Biological invasions are characterized by the movement of organisms from their native geographic region to new, distinct regions in which they may have significant impacts. Biological invasions pose one of the most serious threats to global biodiversity, and hence significant resources are invested in predicting, preventing, and managing them. Biological systems and processes are typically large, complex, and inherently difficult to study naturally because of their immense scale and complexity. Hence, computational modelling and simulation approaches can be taken to study them. In this dissertation, I applied computer simulations to address two important problems in invasion biology. First, in invasion biology, the impact of genetic diversity of introduced populations on their establishment success is unknown. We took an individual-based modelling approach to explore this, leveraging an ecosystem simulation called EcoSim to simulate biological invasions. We conducted reciprocal transplants of prey individuals across two simulated environments, over a gradient of genetic diversity. Our simulation results demonstrated that a harsh environment with low and spatially-varying resource abundance mediated a relationship between genetic diversity and short-term establishment success of introduced populations rather than the degree of difference between native and introduced ranges. We also found that reducing Allee effects by maintaining compactness, a measure of spatial density, was key to the establishment success of prey individuals in EcoSim, which were sexually reproducing. Further, we found evidence of a more complex relationship between genetic diversity and long-term establishment success, assuming multiple introductions were occurring. Low-diversity populations seemed to benefit more strongly from multiple introductions than high-diversity populations. Our results also corroborated the evolutionary imbalance hypothesis: the environment that yielded greater diversity produced better invaders and itself was less invasible. Finally, our study corroborated a mechanical explanation for the evolutionary imbalance hypothesis – the populations evolved in a more intense competitive environment produced better invaders. Secondly, an important advancement in invasion biology is the use of genetic barcoding or metabarcoding, in conjunction with next-generation sequencing, as a potential means of early detection of aquatic introduced species. Barcoding and metabarcoding invariably requires some amount of computational DNA sequence processing. Unfortunately, optimal processing parameters are not known in advance and the consequences of suboptimal parameter selection are poorly understood. We aimed to determine the optimal parameterization of a common sequence processing pipeline for both early detection of aquatic nonindigenous species and conducting species richness assessments. We then aimed to determine the performance of optimized pipelines in a simulated inoculation of sequences into community samples. We found that early detection requires relatively lenient processing parameters. Further, optimality depended on the research goal – what was optimal for early detection was suboptimal for estimating species richness and vice-versa. Finally, with optimal parameter selection, fewer than 11 target sequences were required in order to detect 90% of nonindigenous species

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Computational mechanisms for action selection

Imagine a zebra in the African savannah. At each moment in time this zebra has to weigh up alternative courses of action before deciding which will be most beneficial to it. For instance, it may want to graze because it is short of food, or it may want to head towards a water hole because it is short of water, or it may want to remain motionless in order to avoid detection by the predator it can see lurking nearby. This is an example of the problem of action selection: how to choose, at each moment in time, the most appropriate out of a repertoire of possible actions. This thesis investigates action selection in a novel way and makes three main contribu¬ tions. Firstly, a description is given of a simulated environment which is an extensive and detailed simulation of the problem of action selection for animals. Secondly, this simulated environment is used to investigate the adequacy of several theories of ac¬ tion selection such as the drive model, Lorenz's hydraulic model and Maes' spreading activation network. Thirdly, a new approach to action selection is developed which determines the most appropriate action in a principled way, and which does not suffer from the inherent shortcomings found in other methods

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance