developing analytical evidence of strong emergence. In 2009, Gu et al. presented a class of infinite physical systems that exhibits non-computable macroscopic properties. More precisely, if one could compute certain macroscopic properties of these systems from the microscopic description of these systems, then one would be able to solve computational problems known to be undecidable in computer science. These results concern infinite systems, finite systems being considered computable. However, macroscopic concepts which only apply in the limit of infinite systems, such as phase transitions and the renormalization group, are important for understanding and modeling real, finite physical systems. Gu et al. concluded that Emergence and interaction Emergent structures are patterns that emerge via the collective actions of many individual entities. To explain such patterns, one might conclude, per Aristotle, that emergent structures are more than the sum of their parts on the assumption that the emergent order will not arise if the various parts simply act independently of one another. However, there are those who disagree. According to this argument, the interaction of each part with its immediate surroundings causes a complex chain of processes that can lead to order in some form. In fact, some systems in nature are observed to exhibit emergence based upon the interactions of autonomous parts, and some others exhibit emergence that at least at present cannot be reduced in this way. In particular renormalization methods in theoretical physics enable scientists to study systems that are not tractable as the combination of their parts. Objective or subjective quality Crutchfield regards the properties of complexity and organization of any system as subjective qualities determined by the observer. Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analysed in terms of how model-building observers infer from measurements the computational capabilities embedded in non-linear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtly, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data. On the other hand, Peter Corning