{"id":3143,"date":"2014-11-16T14:07:46","date_gmt":"2014-11-16T19:07:46","guid":{"rendered":"http:\/\/synapse9.com\/signals\/?p=3143"},"modified":"2015-05-13T06:33:14","modified_gmt":"2015-05-13T11:33:14","slug":"can-science-learn-to-read-pattern-languages","status":"publish","type":"post","link":"https:\/\/synapse9.com\/signals\/can-science-learn-to-read-pattern-languages\/","title":{"rendered":"Can science learn to read “pattern language”…?"},"content":{"rendered":"

This post is a section of my report titled “Approaching 30 days from the 40th Anniversary<\/a><\/strong>” on attending the quite exciting 2012 40th Anniversary meeting on the Meadows and Randers authored Club of Rome “Limits to Growth” study. \u00a0 The excerpt is on the deep reasons why the science, as solid as it still seems to be, isn’t widely accepted. \u00a0 \u00a0Science is still struggling to find a comfortable way to discuss natural systems whose innovative systems are housed internally, and so largely hidden from view.<\/em><\/p><\/blockquote>\n

___________<\/p>\n

I think the real reason the public as well as most of the scientific community is largely ignoring the rather well established hard limits to growth, is that it presents the scientific community a new problem it hasn’t yet learn how to deal with. \u00a0\u00a0It has yet to\u00a0find a good way to make sense of self-designing and self-managing systems, like weather systems, cultures and economies,\u00a0that have working designs that are \u00a0hidden internally, displaying\u00a0organization\u00a0much too complex and localized to be determined\u00a0by\u00a0external forces.<\/p>\n

Science is built around identifying how one thing controls another,
\nnot how to study the \u00a0patterns of uncontrolled systems and how they became\u00a0designed to work by themselves.<\/h4>\n

So science is naturally somewhat lost in discussing how they work, having no model for what are better described as “opportunistic” than “deterministic” systems. \u00a0 Though both climate and economies display highly inventive systems, they do still necessarily operate within what traditional science can define as their natural bounds. \u00a0 \u00a0 Climate is still\u00a0fundamentally\u00a0a complex pressure-temperature behavior, of unchanging deterministic\u00a0processes following fixed laws of science.<\/p>\n

Economies though, are able to be far more creative, and move the boundaries of what is possible by innovative design, much further than the push and pull forces of the weather can. \u00a0 It has given traditional science very little to anchor reliable theory on, except as in the Limits to Growth study, fixing boundary conditions and experimenting with multiple options. \u00a0Still, because economies do display\u00a0deeply creative behavior, constantly inventing new ways to use\u00a0energy\u00a0as a normal rule, that natural science still lacks a widely accepted way to study them as\u00a0natural systems, adds uncertainty for others to\u00a0what anyone might say about them.<\/p>\n

Constantly inventing new organization is just what natural systems ‘do’. \u00a0 It lets economies as well as ecologies create new kinds of organization and uses for their energy resources, making formerly useless things highly profitable often enough. \u00a0 Using the profits as\u00a0returns on energy investment to grow by building more innovations. \u00a0 It’s complicated by not being a ‘numeric’ process, though we can see it through our measures. \u00a0 It’s an “organizational process”, of fitting complementary parts together, more amenable to study as a “pattern language” of “design elements” than equations.<\/p>\n

The rigid limits of any mode of productivity still do exist, of course, but as limits of the organizational processes science has yet to find a way to study. \u00a0 Those limits are still determined by the earth and the organization of the\u00a0internal and external systems that any innovation depends on, but with each new innovation there are new unknown limits. \u00a0It leaves a stubborn problem for traditional scientific prediction. \u00a0 What seems to work better is a language of observing such systems to see when their own organization is being stretched.<\/p>\n

\"\"<\/a>
Natural systems generally link individual units of organization in an open rather than deterministic environment, each with\u00a0its own internal organization that emerged during\u00a0its own development, creating a serious mis-match between\u00a0the natural design and the\u00a0information an\u00a0observer could collect,\u00a0and with the kinds of behaviors that can be emulated by equations.<\/figcaption><\/figure>\n

That big problem for science also makes a big and very fascinating subject of study, that science has quite generally not realized is there, having avoided the study of self-designing and managing systems in general. \u00a0 \u00a0Self-designing ans managing systems not only seem to develop by themselves, but to have their “works” hidden internally within the boundaries of their design, as an individual system maintaining internal organization for responding to external systems, like we see in living systems as a special case in point. \u00a0<\/p>\n

So their behaviors are not really determined externally, as if by the information outside observers can collect the way deterministic systems can be modeled. \u00a0 \u00a0They have to be studied as negotiating\u00a0their own behaviors between independently organized internal and external systems, quite an unusual posture for traditional science. \u00a0 I means outside observers normally\u00a0have no information on their critically important internal designs a behaviors. \u00a0 How that becomes a fascinating subject of study is recognizing their natural boundaries define enormous holes in our information<\/a> about how things work, a true gold mine for new science.<\/p>\n

How to begin studying the thermodynamics of\u00a0energy crossing boundaries of\u00a0self-organization is a very basic but important step, and the subject of my longer research paper last year, \u00a0System Energy Assessment (SEA)<\/a>.\u00a0 \u00a0By aggregating the data not by our arbitrary categories for how we collect it, but by how the system’s parts are connected to work together, produces a profoundly meaningful new result, the ability to study such systems as individual\u00a0wholes.<\/p>\n

There are many ways to identify the natural boundaries of self-managed systems, \u00a0as closely interconnected\u00a0parts distinct from an\u00a0otherwise passive environment, for example. \u00a0 When a concentration of energy uses is notably more complex than the external forces on it, you identify the system by its distinctive “miss-match in variety”. \u00a0 You can confirm such identifications with tracing how they developed, always displaying “S” curves of accumulative design over time, with recognizable processes for generating an energy surplus to be invested in expanding the system producing it. \u00a0 Those identifiers locate the heart of the working system, and how it is organized to work as a whole. \u00a0 You then understand what you are looking at, as you see them:<\/p>\n