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“Pull approaches differ significantly from push approaches in terms of how they organize and manage resources. Push approaches are typified by "programs" - tightly scripted specifications of activities designed to be invoked by known parties in pre-determined contexts. Of course, we don't mean that all push approaches are software programs - we are using this as a broader metaphor to describe one way of organizing activities and resources. Think of thick process manuals in most enterprises or standardized curricula in most primary and secondary educational institutions, not to mention the programming of network television, and you will see that institutions heavily rely on programs of many types to deliver resources in pre-determined contexts.

Pull approaches, in contrast, tend to be implemented on "platforms" designed to flexibly accommodate diverse providers and consumers of resources. These platforms are much more open-ended and designed to evolve based on the learning and changing needs of the participants. Once again, we do not mean to use platforms in the literal sense of a tangible foundation, but in a broader, metaphorical sense to describe frameworks for orchestrating a set of resources that can be configured quickly and easily to serve a broad range of needs. Think of Expedia's travel service or the emergency ward of a hospital and you will see the contrast with the hard-wired push programs.”
― John Hagel III

“Selection on one of two genetically correlated characters will lead to a change in the unselected character, a phenomenon called 'correlated selection response.' This means that selection on one character may lead to a loss of adaptation at a genetically correlated character. If these two characters often experience directional selection independently of each other, then a decrease in correlation will be beneficial. This seems to be a reasonably intuitive idea, although it turned out to be surprisingly difficult to model this process. One of the first successful attempts to simulate the evolution of variational modularity was the study by Kashtan and Alon (2005) in which they used logical circuits as model of the genotype.
A logical circuit consists of elements that take two or more inputs and transform them into one output according to some rule. The inputs and outputs are binary, either 0 or 1 as in a digital computer, and the rule can be a logical (Boolean) function. A genome then consists of a number of these logical elements and the connections among them. Mutations change the connections among the elements and selection among mutant genotypes proceeds according to a given goal. The goal for the network is to produce a certain output for each possible input configuration.
For example, their circuit had four inputs: x,y,z, and w. The network was selected to calculate the following logical function: G1 = ((x XOR y) AND (z XOR w)). When the authors selected for this goal, the network evolved many different possible solutions (i.e. networks that could calculate the function G1). In this experiment, the evolved networks were almost always non-modular.
In another experiment, the authors periodically changed the goal function from G1 to G2 = ((x XOR y) or (z XOR w)). In this case, the networks always evolved modularity, in the sense that there were sub-circuits dedicated to calculating the functions shared between G1 and G2, (x XOR y) and (z XOR w), and another part that represented the variable part if the function: either the AND or the OR function connecting (x XOR y) and (z XOR w). Hence, if the fitness function was modular, that is, if there were aspects that remained the same and others that changed, then the system evolved different parts that represented the constant and the variable parts of the environment.
This example was intriguing because it overcame some of the difficulties of earlier attempts to simulate the evolution of variational modularity, although it did use a fairly non-standard model of a genotype-phenotype map: logical circuits. In a second example, Kashtan and Alon (2005) used a neural network model with similar results. Hence, the questions arise, how generic are these results? And can one expect that similar processes occur in real life?”
― Günter Wagner, Homology, Genes, and Evolutionary Innovation

“So long as module improvement respects the protocols by which the module connects to other modules, module improvement can proceed independently of those other modules.
An extreme case of this is when the protocols are between different levels of the modular hierarchy and when there is richness on both sides of the protocol. When the upper side of the protocol is rich, the knowledge base on the lower side of the protocol is often referred to as a 'platform' on which knowledge modules above it can be based. In science, Newton's laws were a platform on which both celestial and terrestrial mechanics could be based. In technology, the personal computer software operating system is a platform on which a rich set of software application can be based. Moreover, when the lower side of the protocol is also rich, the shape of the knowledge network becomes hourglass-like. In the case of technological knowledge, the waist of the hourglass is a distinguished layer or protocol, with technologies underneath implementing the protocol and technologies above building on the protocol - with both sides 'screened' from each other by the protocol itself. As a result, the number of applications explodes independent of implementation details; similarly, the number of implementations explodes independent of application details. The number of software applications built on the Windows operating system is enormous; the number of hardware and software implementations of the Windows operating system is also enormous.
In other words, imagine two complex adaptive systems, one organized modularly and one not. At one moment, both might be able to exploit their environments equally and thus be equally 'adapted' to their environment. But they will evolve at vastly different rates, with the one organized modularly quickly outstripping the one not so organized. Modularity appears to be an evolved property in biology, one that is mimicked in the organization of human knowledge.”
― Venkatesh Narayanamurti, The Genesis of Technoscientific Revolutions: Rethinking the Nature and Nurture of Research

“By breaking our application into individual, independently deployable processes, we open up a host of mechanisms to improve the robustness of our applications. By using microservices, we are able to implement a more robust architecture, because functionality is decomposed, that is, an impact in one area of functionality may not bring down the whole system, we also can focus our time and energy on those parts of the application that most require robustness, ensuring critical parts of our system remain operational.”
― Sam Newman, Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith