**Links to**: [[All things mirrored]], [[Gauge theory]], [[Emmy Noether]], [[Rotation]], [[Reflection]], [[Variance/invariance]], [[Preservation of energy]], [[Anticontinuous]], [[Linearity]], [[Representation]], [[Pattern recognition]], [[Schematism]], [[Equivalence]], [[Difference]], [[Dialove]], [[Principle of least action]], [[Path of least resistance]]. ### Main entry: _[[All things mirrored]]:_ an account of symmetry through active inference. See also: [[A mirror, not a picture]]. %% How is it that symmetry breaking occurs with respect to laterality in biological systems [[51](https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015#RSFS20230015C51)–[54](https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015#RSFS20230015C54)]? How is molecular chirality, or asymmetry with respect to _direction_ (present in almost all cells) amplified into body-wide asymmetry with respect to organ _position relative to the midline_ in metazoan development? What is the functional significance of asymmetries in the organization of nervous systems [[55](https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015#RSFS20230015C55)–[65](https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015#RSFS20230015C65)], and do they sometimes reflect a _lack_ of organismal fitness [[66](https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015#RSFS20230015C66),[67](https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015#RSFS20230015C67)]? ” Saffron, Levin et al., special issue on symmetry: https://royalsocietypublishing.org/doi/10.1098/rsfs.2023.0015 _______ Jaynes's Maximum Entropy Principle and its relationship to uncertainty and explanatory power: The principle essentially states that when making inferences from incomplete information, we should choose the probability distribution that has the highest entropy (greatest uncertainty) while still being consistent with what we know. Here's why this is profound: 1. Less Commitment to Specifics: When we choose the distribution with maximum entropy, we're making the fewest additional assumptions beyond our actual data. Think of it as being maximally honest about our ignorance - we're not pretending to know more than we do. 2. Connection to Degrees of Freedom: A highly detailed explanation with many parameters (high degrees of freedom) might seem more precise, but it's also more likely to be overfitting - making specific claims that go beyond what our evidence actually supports. By maximizing entropy, we're using the simplest model that explains our observations. 3. Practical Example: Imagine you only know the average height of a population. There are infinite possible distributions that could give that average, but the maximum entropy principle says we should choose a normal distribution (bell curve) because it: - Makes the fewest additional assumptions - Preserves the uncertainty we actually have - Doesn't commit to specific shapes or patterns we haven't observed 4. Philosophical Implications: This ties to the earlier discussion about patterns and symmetry - by choosing maximum entropy explanations, we're acknowledging that simpler, more general patterns (with higher uncertainty in the details) are often more fundamental than complex, highly specific ones. The principle essentially formalizes Occam's Razor in probabilistic terms - the simplest explanation (highest entropy given our constraints) is usually the best starting point. _____ "Furthermore, Latour foregrounds the concept of symmetry, by which he means a symmetry of humans and non-human entities, in which the latter have the same agency as humans do.""https://theparliamentofthings.org/parliament-parlement-van-de-dingen-noordzee-ambassade-bruno-latour/