# The Aesthetic Value of the World ![rw-book-cover](https://readwise-assets.s3.amazonaws.com/media/reader/parsed_document_assets/397954168/6Cvz5obbU3Mr9S8ahU7AfAnlVszUEWTIkv2XdlrEvyo-cove_7I2NTKk.jpg) ## Metadata - Author: [[Tom Cochrane;]] - Full Title: The Aesthetic Value of the World - Category: #books - Summary: insert summary - My notes: - Summary: Cochrane argues that Aestheticism sees aesthetic value as a vital way to live well and respond creatively to the world. Art and aesthetic sensitivity help us discern values and can sustain people even amid suffering. Aestheticism claims the world is broadly valuable and that this outlook guards against nihilism. - Source File: The Aesthetic Value of the World - Tom Cochrane;.txt ## LLM Chats ## NotebookLM ## LLM Audio ## Highlights > Like Kolmogorov complexity, measures of statistical complexity appeal to the length of the programme required to reproduce a description of the phenomenon. However such a programme is only tasked to reproduce the *statistical properties* of the phenomenon. Thus a purely random string of information requires only a minimal programme because there are no interesting statistical features to capture; all variables are equally probable at any given point. Meanwhile, a perfectly ordered or repetitive phenomenon can be described in terms of the 100 per cent probability that a certain state will repeatedly occur. Hence both random and totally ordered states lack statistical complexity. ([View Highlight](https://read.readwise.io/read/01kcpxcc4381j6jdj0zf5v737e)) > What ramps up the level of complexity is less repetitive details. For example, if we were to produce a statistical description of Mondrian’s grid paintings, we might say that their essential features—containing rectangles, bold primary colours, and so on—appear in 100 per cent of cases, while more particular features, the appearance of the colour blue for instance, only appear in a proportion of cases. Such descriptions may potentially become extremely fine-tuned because although statistical complexity is basically concerned with the overall distribution of states in a phenomenon, the way that states are distributed in complex structures are likely to display mutual dependencies. For example, for a piece of music in traditional sonata form, it is highly probable that if the main theme is in C major, then the secondary theme will be in G major. Or, for music based on motifs, repetitions and modifications of motifs are probable—and some modifications are more probable than others. Thus the programme may describe the probability of one string of information given the presence of another. We can see then that the measure of statistical complexity will increase with the amount of structured, interrelated features in a phenomenon—cohering well with our intuitive sense of complexity. ([View Highlight](https://read.readwise.io/read/01kcpxcm1a2nj3mc3ysvav01vj)) ## New highlights added January 22, 2026 at 9:23 AM > To grasp this approach requires a digression on complexity, so please bear with me. The early ‘Kolmogorov’ definition of complexity measures the complexity of an entity or phenomenon by the length of programme it would take to reproduce a complete description of it. Highly ordered phenomena (e.g. a row of identical squares) contain predictable regularities, and so can be algorithmically compressed (e.g. with the instruction—‘repeat square a hundred times’). This means they are relatively simple. Meanwhile, random phenomena are maximally complex because the only way to fully describe them is to list each of their details in turn. ([View Highlight](https://read.readwise.io/read/01kfjbg8mmmwv31rcdchreget1)) > Yet many theorists have wanted to place complexity somewhere in-between total order and total randomness. In ordinary language, complexity denotes an interesting degree of structure: of highly involved order or intricate relationships between entities. Thus various modifications of Kolmogorov complexity have been proposed (see e.g. Grassberger [1989](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-118) for a review). Here I appeal to one of the strongest candidates—statistical complexity (e.g. Crutchfield & Young [1989](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-60); Gell-Mann [1995](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-105)) which has recently been philosophically defended by James Ladyman and colleagues ([2013](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-162)) and which, most importantly, can be linked with models of neural processing that conceive the brain as following principles of statistical predictive processing (e.g. Hohwy [2013](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-132), Clark [2013](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-44); see also the Hick-Hyman law, Hick [1952](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-128); Hyman [1953](020_BM_bibliographyGroup.xhtml#oso-9780192848819-bibliography-1-bibItem-143)). ([View Highlight](https://read.readwise.io/read/01kfjbgbpc0c33spj1tpdj8fyw)) > Thus we are closer to understanding why it is that complexity engenders cognitive processing costs. These cognitive processing costs in turn have metabolic costs for an organism. Thus it is likely that an organism is disposed to minimize these costs. But at the same time, a creature endures metabolic costs only for the sake of acquiring some benefit. This is something that the processing fluency model seems to ignore. One point Reber et al. mention (quoted in section [2.3](#oso-9780192848819-chapter-3-div1-3)) is that fluency is ‘associated with progress toward successful recognition of the stimulus’. Successful recognition is a positive good: one that might be worth expending some effort to achieve. Moreover, by using the term ‘association’, Reber and colleagues are suggesting that the goodness of fluency is only due to a link with something else that is good, namely, successful recognition. But if it’s successful recognition rather than fluency that is itself good, why not focus the account of beauty on that instead? ([View Highlight](https://read.readwise.io/read/01kfjbzmhjka4311zqa3hk6r21)) > The basic idea is that the pleasure of things fitting together is a distal version of the reward we get from knowledge. Knowing things is plausibly an innate concern for creatures like us because on the one hand our knowledge of the world is vital for our practical engagements and, on the other hand, it is such an energy hungry process. As a consequence, it is plausible that creatures evolve mechanisms of pleasure or reward that track and reinforce this activity. That is, our brains reward us with pleasure when we successfully gain knowledge. So in this account knowledge is a kind of good, and knowledge-seeking or learning is a kind of attractant response. ([View Highlight](https://read.readwise.io/read/01kfjc4750yt1bvx3g7gscz7qn)) > Now we can bring in beauty as a distal version of this drive for knowledge. My claim is that beautiful objects appear to be cognitive bargains. We discern them as offering the opportunity to grasp lots of information relatively efficiently *because* they appear to harmoniously fit together. They appear to contain mutual probabilities that will allow us to predict or make sense of why a particular detail is the way it is. So, the more features or aspects of a thing harmoniously fit together, the more we can gain the benefit of knowing the object. In beautiful objects we detect the *high accessibility* of knowledge. ([View Highlight](https://read.readwise.io/read/01kfjccch6zpjbr7pgg94ntc85))