Abstract
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.
Highlights
In natural environments, prediction is a cognitive activity that is functionally coupled with a decision-making process, entailing potential risk and gain
We used several machine learning (ML) classifiers to classify each target permutation using three main features corresponding to the three above-mentioned constraints: (i) the permutation preceding the target (PRE), (ii) the transition probability scores (TPSs) learned from the epoch and (iii) the two irreversibility scores (IRR and IRR_SYM)
Our paper examines the ability of a hypothetical organism to perform short-term prediction using limited resources
Summary
Prediction is a cognitive activity that is functionally coupled with a decision-making process, entailing potential risk and gain. Excluding seasonal events, prediction deteriorates as a function of the time distance from the future event, making it difficult to generate longterm predictions This limit may be attributed to the chaotic dynamic of the observed system and suggests that organisms may have natural adaptive preference for short-term predictions. Tested on financial data relating to Bitcoin prices, which represent a highly fluctuating environment, the results provide preliminary support showing how organisms with limited cognitive computational resources may naturally and rationally use ordinal patterns for short-term prediction
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