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On the stability of adjustment processes with persistent randomness

机译：具有持续随机性的调整过程的稳定性

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Most distributed systems and systems depending on their sensing of their surroundings operate under a great deal of uncertainty from many sources. The dynamic behavior of such systems can assume very complex forms inducing complex dynamics that are challenging to observe and control. This is mainly due to noisy data received from the environment in which they evolve, data that has to be fed back to their decision support. These closed and endless feedback loops are prone to error if not dealt with in an appropriate manner. The nonlinearity of circular causation and feedback effects of such phenomenon creates an environment where some models, driven by practicality concerns, resort to simplification strategies that ignore important feedback relationships, but make the decision problem more tractable. Even so, the non-smooth dynamics of such systems makes stability analysis difficult. The consequence hereof is the existence of models that are constrained to the brink of loosing their true purpose. In this paper, we present a cross-fertilization approach using tools from physics and biology to show how one can practically approach the stability analysis of such systems.
机译：大多数分布式系统和系统取决于对周围环境的感知，在许多来源的不确定性下运行。这样的系统的动态行为可以采取非常复杂的形式，从而引起复杂的动力学，这对于观察和控制来说是具有挑战性的。这主要是由于从它们演化的环境中接收到了嘈杂的数据，这些数据必须反馈给他们的决策支持。如果不以适当的方式处理，这些闭合且无穷无尽的反馈回路很容易出错。这种现象的循环因果关系和反馈效应的非线性创造了一个环境，在这种环境下，出于实用性考虑，某些模型采用简化策略忽略了重要的反馈关系，但使决策问题更易于处理。即使这样，此类系统的非平稳动力学也使稳定性分析变得困难。其结果是存在模型，这些模型被限制在丧失其真实目的的边缘。在本文中，我们提出了一种使用物理和生物学工具进行交叉施肥的方法，以说明人们如何切实可行地对此类系统进行稳定性分析。

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