%0 Generic %1 wolpert2014framework %A Wolpert, David H. %A Grochow, Joshua A. %A Libby, Eric %A DeDeo, Simon %D 2014 %K engineering framework optimal science %T A framework for optimal high-level descriptions in science and engineering---preliminary report %U http://arxiv.org/abs/1409.7403 %X Both science and engineering rely on the use of high-level descriptions. These go under various names, including "macrostates," "coarse-grainings," and "effective theories". The ideal gas is a high-level description of a large collection of point particles, just as a a set of interacting firms is a high-level description of individuals participating in an economy and just as a cell a high-level description of a set of biochemical interactions. Typically, these descriptions are constructed in an $ad~hoc$ manner, without an explicit understanding of their purpose. Here, we formalize and quantify that purpose as a combination of the need to accurately predict observables of interest, and to do so efficiently and with bounded computational resources. This State Space Compression framework makes it possible to solve for the optimal high-level description of a given dynamical system, rather than relying on human intuition alone. In this preliminary report, we present our framework, show its application to a diverse set of examples in Computer Science, Biology, Physics and Networks, and develop some of technical machinery for evaluating accuracy and computation costs in a variety of systems.

@misc{wolpert2014framework, abstract = {Both science and engineering rely on the use of high-level descriptions. These go under various names, including "macrostates," "coarse-grainings," and "effective theories". The ideal gas is a high-level description of a large collection of point particles, just as a a set of interacting firms is a high-level description of individuals participating in an economy and just as a cell a high-level description of a set of biochemical interactions. Typically, these descriptions are constructed in an $\mathit{ad~hoc}$ manner, without an explicit understanding of their purpose. Here, we formalize and quantify that purpose as a combination of the need to accurately predict observables of interest, and to do so efficiently and with bounded computational resources. This State Space Compression framework makes it possible to solve for the optimal high-level description of a given dynamical system, rather than relying on human intuition alone. In this preliminary report, we present our framework, show its application to a diverse set of examples in Computer Science, Biology, Physics and Networks, and develop some of technical machinery for evaluating accuracy and computation costs in a variety of systems.}, added-at = {2014-09-29T14:05:38.000+0200}, author = {Wolpert, David H. and Grochow, Joshua A. and Libby, Eric and DeDeo, Simon}, biburl = {https://www.bibsonomy.org/bibtex/24719025e7e5d7e0b351c8e72111b5624/marcogherardi}, description = {A framework for optimal high-level descriptions in science and engineering---preliminary report}, interhash = {7ed31b77d949f2ac213e57b12de63a71}, intrahash = {4719025e7e5d7e0b351c8e72111b5624}, keywords = {engineering framework optimal science}, note = {cite arxiv:1409.7403Comment: 71 pages, 9 figures}, timestamp = {2014-09-29T14:05:38.000+0200}, title = {A framework for optimal high-level descriptions in science and engineering---preliminary report}, url = {http://arxiv.org/abs/1409.7403}, year = 2014 }