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

XV Міжнародна конференція з математичної, природничо-наукової та технологічної освіти (ICon-MaSTEd 2022) 18-20 травня 2022 року, м. Кривий Ріг, Україна

Матеріали XV Міжнародної конференції з математичної, природничо-наукової та технологічної освіти (ICon-MaSTEd 2022) 18-20 травня 2022 року, м. Кривий Ріг, Україна.Proceedings of the XV International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2022) 18-20 May 2022, Kryvyi Rih, Ukraine

Playful Materialities

Game culture and material culture have always been closely linked. Analog forms of rule-based play (ludus) would hardly be conceivable without dice, cards, and game boards. In the act of free play (paidia), children as well as adults transform simple objects into multifaceted toys in an almost magical way. Even digital play is suffused with material culture: Games are not only mediated by technical interfaces, which we access via hardware and tangible peripherals. They are also subject to material hybridization, paratextual framing, and processes of de-, and re-materialization

Playful Materialities: The Stuff That Games Are Made Of

Game culture and material culture have always been closely linked. Analog forms of rule-based play (ludus) would hardly be conceivable without dice, cards, and game boards. In the act of free play (paidia), children as well as adults transform simple objects into multifaceted toys in an almost magical way. Even digital play is suffused with material culture: Games are not only mediated by technical interfaces, which we access via hardware and tangible peripherals. They are also subject to material hybridization, paratextual framing, and processes of de-, and re-materialization

Playful Materialities

Game culture and material culture have always been closely linked. Analog forms of rule-based play (ludus) would hardly be conceivable without dice, cards, and game boards. In the act of free play (paidia), children as well as adults transform simple objects into multifaceted toys in an almost magical way. Even digital play is suffused with material culture: Games are not only mediated by technical interfaces, which we access via hardware and tangible peripherals. They are also subject to material hybridization, paratextual framing, and processes of de-, and re-materialization

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run