Complexity

• Lowrys’s Principle (1965): the only true test of a model is when it is fitted for one situation and then is tested in a different situation.
• Occam's razor (14th-century): All other things being equal, the simplest solution is the best. lex parsimoniae.

The ability to reduce everything to simple fundamental laws does not imply the ability to start from those laws and reconstruct the universe. In fact, the more the elementary particle physics tell us about the nature of the fundamental laws, the less relevance they seem to have to the very real problems of the rest of science, much less to those of society.

Weisskopf: “Looking at the development of science in the Twentieth Century one can distinguish two trends, which I will call “intensive” and “extensive” research, lacking a better terminology. In short: intensive research goes for the fundamental laws, extensive research goes for the explanation of phenomena in terms of known fundamental laws. […] There is always much less intensive research going on than extensive. Once new fundamental laws are discovered, a large and ever increasing activity begins in order to apply the discoveries to hitherto (up to this time) unexplained phenomena.”

Surely there are more levels of organization between human ethology and DNA than there are between DNA and quantum electrodynamics, and each level can require a whole new conceptual structure.

Abstract: Creativity, one of the hallmarks of the human spirit, has yet to travel deep into the domain of artificial intelligence and computers. We argue that creativity intrinsically requires and exploits complexity. The dynamic multilevel properties of complex systems give us a natural way of scaling creative solutions, from the everyday to the paradigm shift. In particular, the complexity model implies that deep far-reaching creative solutions may emerge without the originator having any clear idea of what the outcomes might be. This has implications for fostering creativity in individuals and the resourcing of the creative class. Finally, it implies that the major paradigm shifts of the future may be created by computers rather than people, in analogy with how acts of genius can arise from an autistic mind.

at least some geniuses throughout history may have been high functioning autistic […] sufferers. Such people have a poor understanding of other people’s thoughts, and consequently, have difficulty in interacting with the world. Furthermore, they are less likely to have a big-picture conceptual outlook, tend to have a very literal perspective and little desire to communicate. It has even been suggested, though controversially, that certain scientific geniuses, such as Einstein and Newton may be so classified. Even more surprising, Literature Nobel Laureates such as W.B. Yeats and cabinet ministers such as Sir Keith Joseph, might have been HFAA. Surprisingly, so-called autistic genius apparently can arise from a person who builds uncharacteristically from the parts without appreciating the whole (Snyder, 2004). We suggest that the resolution of this paradox lies within understanding the relationship between creativity and complex systems.

To illustrate the complexity perspective it will suffice to consider just a few modus operandi:

• in the replacement model a component of a system is replaced by a similar component.
• the association model in which connections are made between distant concepts.
• the evolutionary model, in which incremental changes can become radically variations.

The paper shows a couple of examples of each case.

Abstract: This paper discusses the distribution of certainty around General Circulation Models (GCMs) – computer models used to project possible global climatic changes due to human emissions of greenhouse gases. It examines the trope of distance underpinning Donald MacKenzie’s concept of ‘certainty trough’, and calls for a more multi-dimensional and dynamic conceptualization of how uncertainty is distributed around technology. The certainty trough describes the level of certainty attached to particular technoscientific constructions as distance increases from the site of knowledge production, and proposes that producers of a given technology and its products are the best judges of their accuracy. Processes and dynamics associated with GCM modeling challenge the simplicity of the certainty trough diagram, mainly because of difficulties with distinguishing between knowledge producers and users, and because GCMs involve multiple sites of production. This case study also challenges the assumption that knowledge producers always are the best judges of the accuracy of their models. Drawing on participant observation and interviews with climate modelers and the atmospheric scientists with whom they interact, the study discusses how modelers, and to some extent knowledge producers in general, are sometimes less able than some users to identify shortcomings of their models.