John Salvatier's blog post argues that reality is far more detailed than we typically assume or perceive. We create simplified mental models to navigate the world, filtering out the vast majority of information. This isn't a flaw, but a necessary function of our limited cognitive resources. However, these simplified models can lead us astray when dealing with complex systems, causing us to miss crucial details and make inaccurate predictions. The post encourages cultivating an appreciation for the richness of reality and actively seeking out the nuances we tend to ignore, suggesting this can lead to better understanding and decision-making.
John Salvatier's 2017 blog post, "Reality has a surprising amount of detail," delves into the profound implications of the vastness and intricacy of the real world, particularly as it pertains to our attempts to model and understand it. Salvatier begins by establishing the sheer scale of reality's detail, highlighting the immense quantity of information required to perfectly describe even seemingly simple objects or systems. He posits that a complete description of reality, down to the quantum level, would be astronomically large, far exceeding the capacity of any current or foreseeable computational system.
The author then explores the ramifications of this complexity for our models of reality. He argues that all models, by necessity, are simplifications. They abstract away from the full detail of the real world, focusing only on specific aspects relevant to the model's purpose. This act of simplification introduces a fundamental trade-off: while models become more tractable and computationally feasible, they also become less accurate representations of the underlying reality. Salvatier illustrates this concept with the example of a map, which can never perfectly capture the full complexity of the territory it represents. Different maps, designed for different purposes, will emphasize different aspects of the territory, further highlighting the inherent subjectivity and limitations of models.
Furthermore, the post emphasizes the dynamic nature of reality, constantly evolving and changing over time. This dynamic complexity adds another layer of difficulty to the task of modeling. Not only must a model capture the immense detail at a single point in time, but it must also account for the intricate interplay of factors that drive change and evolution. This dynamic nature contributes to the "surprise" element mentioned in the title, as unexpected emergent behavior can arise from the complex interactions of numerous individual components within a system.
Salvatier then touches upon the implications of this complexity for our understanding of cause and effect. He suggests that the traditional notion of simple, linear causality is often inadequate in the face of such intricate systems. Instead, he advocates for a more nuanced understanding of causality, acknowledging the complex web of interacting factors that contribute to any given outcome. This perspective acknowledges that seemingly small changes in initial conditions can lead to dramatically different outcomes, a hallmark of chaotic systems.
Finally, the post concludes with a reflection on the implications of this understanding for how we approach learning and problem-solving. Salvatier suggests that the inherent complexity of reality necessitates a more humble and adaptable approach. We must acknowledge the limitations of our models and be prepared to revise them in the face of new information. This requires a shift away from rigid, deterministic thinking towards a more probabilistic and Bayesian approach, embracing uncertainty and acknowledging the possibility of surprise. Ultimately, Salvatier argues that appreciating the surprising amount of detail in reality can lead to a deeper and more nuanced understanding of the world around us.
Summary of Comments ( 60 )
https://news.ycombinator.com/item?id=43087779
Hacker News users discussed the implications of Salvatier's post, with several agreeing on the surprising richness of reality and our limited capacity to perceive it. Some commenters explored the idea that our simplified models, while useful, inherently miss a vast amount of detail. Others highlighted the computational cost of simulating reality, arguing that even with advanced technology, perfect replication remains far off. A few pointed out the relevance to AI and machine learning, suggesting that understanding this complexity is crucial for developing truly intelligent systems. One compelling comment connected the idea to "bandwidth," arguing that our senses and cognitive abilities limit the amount of reality we can process, similar to a limited internet connection. Another interesting observation was that our understanding of reality is constantly evolving, and what we consider "detailed" today might seem simplistic in the future.
The Hacker News post titled "Reality has a surprising amount of detail (2017)" linking to John Salvatier's blog post has generated a moderate number of comments, exploring various facets of the main article's theme.
Several commenters delve into the implications of the core idea – that reality is far more detailed than our perceptions or models. One commenter highlights the vastness of information contained within a single cell, contrasting it with our limited understanding and computational capacity to fully grasp such complexity. This echoes the article's point about the surprising depth of reality.
Another commenter discusses the "bandwidth" limitations of our senses and cognitive processes, suggesting that our experience is a highly filtered version of reality. They use the analogy of a low-resolution image failing to capture the intricacies of the original scene. This resonates with the article's premise about the limitations of our perception.
A different thread emerges around the nature of scientific models and their relationship with reality. One commenter argues that the article's title is somewhat misleading, suggesting "reality has a surprising amount of relevant detail" might be more accurate. They contend that while reality is undoubtedly complex, not all details are equally relevant for our understanding or for building useful models.
The discussion also touches upon the practical implications of this concept in fields like physics and machine learning. One commenter mentions the challenge of creating simulations that capture the full complexity of physical systems, highlighting the computational demands and limitations of current approaches. Another comment connects this to the limitations of machine learning models, emphasizing that their performance is often constrained by the level of detail they can capture from the training data.
Finally, some comments explore the philosophical implications of the idea. One commenter ponders the nature of consciousness and its role in filtering and interpreting the overwhelming detail of reality. Another discusses the implications for our understanding of the universe and our place within it, suggesting that the vastness of unknown details can be both humbling and inspiring.
While the overall number of comments is not exceptionally high, the discussion provides valuable perspectives on the implications of the article's central thesis, exploring the limitations of our perception, the nature of scientific models, and the philosophical questions raised by the sheer complexity of reality.