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Beyond Biomarkers: Understanding Multiscale Causality — LessWrong

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In exercise science, we typically derive causality in a bottom-up manner. When we evaluate performance, we assess factors such as cardiovascular capacity, metabolic efficiency, or muscular contractile capacity. However, I’ve always grappled with a chicken-and-egg dilemma in exercise physiology. This dilemma highlights the challenge of understanding sequences of events where mutual dependencies exist — each outcome depends on a preceding event, and vice versa. Consider a simple example: biomechanical testing of an NBA basketball player might reveal that certain parameters (x, y, z) predispose them to excel at that competition level. However, we can also argue that these parameters likely developed in response to the competitive demands of the game. As players advance to higher leagues, they face greater technical demands, which drive their development and the evolution of their biomechanical parameters. This creates a paradoxical situation. If structure gives rise to behaviour, but stru

x Beyond Biomarkers: Understanding Multiscale Causality — LessWrong Aging Causality Data Science Exercise (Physical) Health / Medicine / Disease World Modeling World Optimization Frontpage 13 Beyond Biomarkers: Understanding Multiscale Causality by Matěj Nekoranec 7th Jul 2024 8 min read 0 13 Photo by Jigar Panchal on Unsplash In exercise science, we typically derive causality in a bottom-up manner. When we evaluate performance, we assess factors such as cardiovascular capacity, metabolic efficiency, or muscular contractile capacity. However, I’ve always grappled with a chicken-and-egg dilemma

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