Prediction in natural environments is a challenging task, and there is a lack of clarity around how a myopic organism can make short-term predictions given limited data availability and cognitive resources. In this context, we may ask what kind of resources are available to the organism to help it address the challenge of short-term prediction within its own cognitive limits. We point to one potentially important resource: ordinal patterns, which are extensively used in physics but not in the study of cognitive processes. We explain the potential importance of ordinal patterns for short-term prediction, and how natural constraints imposed through (i) ordinal pattern types, (ii) their transition probabilities and (iii) their irreversibility signature may support short-term prediction. Having tested these ideas on a massive dataset of Bitcoin prices representing a highly fluctuating environment, we provide preliminary empirical support showing how organisms characterized by bounded rationality may generate short-term predictions by relying on ordinal patterns.

自然环境中的预测是一项具有挑战性的任务，并且在数据可用性和认知资源有限的情况下，短视生物如何做出短期预测尚不清楚。在这种情况下，我们可能会问有机体可以使用什么样的资源来帮助它在自己的认知范围内应对短期预测的挑战。我们指出了一种潜在的重要资源：序数模式，它广泛用于物理学，但不用于认知过程的研究。我们解释了序数模式对于短期预测的潜在重要性，以及通过（i）序数模式类型、（ii）它们的转换概率和（iii）它们的不可逆签名施加的自然约束如何支持短期预测。在代表高度波动环境的大量比特币价格数据集上测试了这些想法后，我们提供了初步的实证支持，展示了以有限理性为特征的有机体如何通过依赖序数模式来生成短期预测。