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首页> 外文期刊>Journal of marine systems: journal of the European Association of Marine Sciences and Techniques >Progress in marine ecosystem modelling and the 'unreasonable effectiveness of mathematics'
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Progress in marine ecosystem modelling and the 'unreasonable effectiveness of mathematics'

机译：海洋生态系统建模和“数学的不合理有效性”方面的进展

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摘要

Modelling methodology, it is argued, is primarily about providing explanations of data which, if sufficiently convincing, provide a basis for prediction and forecasting. Models allow us to synthesise our knowledge and explore its ramifications, leading to insight and discovery. As such, modelling is invaluable to the progress of marine science, the development and implementation of ever more complex models moving in tandem with our expanding knowledge base. It is possible to argue, however, that mathematics can be "unreasonably effective" at describing phenomena, particularly for complex models where there are often many free parameters to tune against limited data. Errors become difficult to pinpoint and correct, and creativity may be stifled as models become entrenched within the prevailing paradigm. Indiscriminately adding layer upon layer of complexity in models may therefore be counter productive, particularly if prediction of future scenarios such as changing climate is the ultimate goal. The inclusion of additional complexity in models is nevertheless desirable, where relevant and practicable. New modelling approaches that are coming to the fore likely hold the key to future progress such as targeting complexity in key species and trophic levels, adaptive parameterisations and the representation of physiological trade-offs, providing the potential to simulate emergent community structure.
机译：有人认为，建模方法主要是提供数据解释，如果有足够的说服力，则可以为预测和预测提供基础。模型使我们能够综合我们的知识并探索其后果，从而导致洞察力和发现。因此，建模对于海洋科学的进步，随着我们不断扩展的知识库而不断发展的更复杂模型的开发和实施都是无价的。但是，有可能会争辩说，数学在描述现象时可能“过分有效”，特别是对于复杂模型，其中通常有许多自由参数可以针对有限的数据进行调整。错误变得难以查明和纠正，并且随着模型在主流范式中根深蒂固，创造性可能会受到抑制。因此，不加选择地在模型中逐层增加复杂性可能会适得其反，特别是如果预测未来情况（例如气候变化）是最终目标。然而，在相关且可行的情况下，仍希望在模型中包含其他复杂性。即将出现的新建模方法可能掌握了未来进展的关键，例如针对关键物种和营养水平的复杂性，自适应参数设置和生理折衷的表示，提供了模拟新兴群落结构的潜力。

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