Principles of Systems Science

14.4 The Systems Engineering Process 723 Ideally the meta-model of the solution system will readily incorporate all sub- models at, however, many levels are required. We say “ideally” because this is usu- ally a very difficult problem. Perhaps someday there will exist the appropriate modeling tools that will expedite such integration and allow engineers to “zoom in” on lower level models or “zoom out” to see the big picture. 14.4.2.3.5 Specifications The output products of this stage are called “specifications” and essentially describe in great detail every aspect of the solution system in terms of function, performance, and how the system will be tested (see Fig. 14.4). These specification documents are organized in the same hierarchical fashion as the system. That is, there are whole system functional specifications as well as functional specifications for every sub- system all the way down the hierarchy to the lowest components. Oftentimes the numbering scheme used to identify these documents correspond with that we showed in Chap. 12, reflecting the system hierarchy. Functional specifications are just what the name implies. They detail the function(s) that the system performs. These are tied directly to the solution of the problem. For example, the function of the city traffic control problem is to maxi- mize the flow through subject to the arriving volume of traffic at either end of the corridor. The performance specification tells how well the functions are performed. A performance criterion for the city traffic might be something like “no automobile should be on this street for more than ten minutes.” This specifically puts require- ments into measurable units. This is important for later monitoring of the system to make sure it is performing as expected and required (see Sect. 14.4.2.8 below). The test specification details what testing is required for each subsystem (some- times called “unit tests”) from the bottom of the hierarchy up to the system as a whole. The kind of testing that can be used, just like the kind of modeling, depends on the nature of the subsystem. For example, one of the subsystems of the traffic flow system is the “intelligent traffic light” timing. It might be possible to construct a test bed somewhere out in the country where engineers can simulate several city blocks and test real light timing algorithms. That is probably a fairly costly approach (although it turns out there are such test beds already built that can be “rented” for doing this kind of testing!). Contrast that with what it takes to test the operational characteristics of the computer controls for the lights. For example, the performance specification may require the switching response time for changing the lights along with responses to the triggering events (called a real-time response requirement). This can easily be tested in an electronics laboratory so the test specification would call for the kinds of real-time response tests that would be required for the hardware once it had been determined. At the end of each solution analysis for each component, sub-subsystem, subsys- tem, and the system as a whole, the systems engineer’s job is to verify the complete- ness of all of these documents. This includes making sure that the inputs to a subsystem are accounted for as outputs from another subsystem or interface. This is where the completeness of the systems analysis in Chap. 12 is particularly impor- tant. If that work was done well and completely, the placeholders for all of the flows

724 14 Systems Engineering (inputs and outputs) are already in place and just need to be filled in as specifica- tions. Unfortunately this is perhaps the most tedious part of systems engineering; there is a lot of paperwork to deal with! However, tools for aiding this are in the works and a few have already been developed. The ideal tool would assist the engi- neer to do the analysis, produce the placeholder slots in the knowledgebase, gener- ate the models, test the integration, and produce forms the domain experts can fill out directly. Perhaps a systems engineer can get to work on producing that kind of tool—it is a system after all. 14.4.2.4 Solution Design Once a specification for a subsystem has been determined, it is time to get to work on designing the physical aspects of the solution. This is, again, where domain expert engineers are required to do the detail work. The specifications developed during the solution analysis now pass to those experts for their work (Fig. 14.5). This is where things get really messy! Up to this point, the work undertaken by the systems engineer has been very generic. Even the solution analysis process is essentially the same regardless of the nature of the system solution. But because the design stage involves much more specific work within domains of expertise, there is very little we can say about how this stage proceeds. The work of the systems engineer may become that of project management, keeping track of manpower resources, schedules, etc., the scope of which is beyond this book. The methods of project (or sometimes called program) management are covered in detail in many fine textbooks and trade books. So we will effectively wave our hands and proclaim “a miracle happens!” Out of the design stage, a set of design documents appropriate to the nature of the components/subsystems and the system as a whole emerges to guide the actual construction or implementation of the system. 14.4.2.5 Solution Construction At this point, we should mention the fact that sometimes systems will be built as prototypes versus the final product. Prototypes are often needed because there is still some uncertainty in how the design will work in practice. Some systems cannot be prototyped, for example, the city traffic flow system is just going to be con- structed in place. Models of such systems might be used instead of prototypes to test as much as possible. Regardless of whether the system to be constructed is a proto- type or the final system, the process is fundamentally the same. As with design, the work of construction is given to domain experts who know how to build them. The job of the systems engineer is, again, one of coordination and monitoring progress of all of the parts. And again this is largely a paperwork process. If all of the specifications and models were of high quality, the systems engineer has a lot of background documentation to guide their work and decisions.

14.4 The Systems Engineering Process 725 Solution Specification Analysis Package Test Specifications Functional Specifications Performance Specifications Models & Simulations Solution design ‘Design’ Documents Solution construction ‘As-Built’ Documents Fig. 14.5 The specification package is passed into the design stage where domain expert engi- neers practice their trade creating designs that will fulfill the specifications. They also have access to the models to assist in understanding the dynamics of the system. The output is a set of design documents like blueprints and schematics—whatever form is needed to communicate the design to those who will construct the system. The “as-built” documents provide information about how actual physical construction may have had to deviate from the design because of various factors One interesting aspect of actual system construction is the fact that all too often, there will be latent discrepancies still in the designs that the design engineers were unaware of. For example where an engineer may have called out a component part with which they were familiar and which, according to the catalog, had the functional and performance specifications needed, the company that manufactured that component may have a new model or even discontinued the part. The construction worker usually has some discretion in substituting another component in the latter case or using the upgraded component if the price isn’t too much greater. In all such cases, the construc- tors mark copies of the design documents with the altered specifications. These docu- ments are often called “as-built” because they reflect what was actually built. As a rule, when these documents are generated, the systems engineer is responsible for making

726 14 Systems Engineering sure they find their way back to the design engineers for confirmation or redesign. If they confirm that the as-built system is OK, they will sign off and the original design documents may be modified to reflect the new configuration. 14.4.2.6 Solution Testing At this point, we should probably mention that while what we have so far described as a linear, cascading process (as in Fig. 14.2), the reality is that there is a lot of up and down, back and forth between many of these stages. As we just described, where the design engineers had to verify an as-built design before the project pro- ceeds, throughout the various stages, discrepancies between what was determined in the prior stage and the current work turn up. That is why in Fig. 14.2 we showed some recurrent arrows back from lower stages back up to the previous stage. In fact it can be much messier than that. A problem discovered in a lower stage might end up propagating further up to a very early stage. The point of having check points along the way is to catch these discrepancies as they occur and get them fixed as soon as possible so that the whole project is not disrupted. Testing at every level is a good example. Remember the test specifications writ- ten in the solution analysis stage? Once a unit (component or subsystem, final, or prototype) is built, it is tested according to the specification. 14.4.2.7 Solution Delivery (Deployment) Eventually, and hopefully on time and budget, the solution is delivered to the client. The systems engineer is still involved in this process because for complex systems, the client’s employees (users) need to be trained. The systems engineer may not be directly involved in the training but is involved in making sure the training docu- mentation is properly prepared and may even manage the training process. In addition, deployment of the artifact system is not just a matter of turning on the equipment and running it. Most very complex systems require a “staged” start- up, meaning that parts are put into place and put into limited operations, checked for the validity of operations (users trained, etc.), and then certified by both clients and engineers. Systems engineers guide the system of installation and start-up just as they managed the systems of analysis and design. Their overall role is to monitor this process and double-check results against expectations to ensure the client is satisfied with the project results. 14.4.2.8 Monitor Performance A complete system life cycle process requires that the systems engineers continue to collect data on the artifact system’s on-going performance. They can identify problems that might develop and intervene when necessary. The kind of data they

14.4 The Systems Engineering Process 727 collect is based on the specifics of the system so there isn’t much to say about spe- cifics. But the general idea is to collect the data to minimize any performance deg- radation and have the data on hand for a later evaluation process that should take place before the next generation of the system, or its replacement, is started. Performance histories of existing systems feed back into the analysis and design of newer systems in the future. Unfortunately, aside from ongoing managerial analysis for operating costs, the monitoring of systems for purposes of future design is often considered too costly and therefore not followed through on. Detailed performance data are generally not collected and that will have a negative impact when it comes time to upgrade or replace the system (which is inevitable). We say unfortunate because it turns out that if the data on performance is collected and saved, it will cut costs of later analysis. 14.4.2.9 Evaluate Performance In Fig. 14.2, performance monitoring feeds into an evaluation process. As just men- tioned, the only performance analysis that usually takes place is the business case, i.e., cost of operations versus benefits (e.g., profits). Operations and financial man- agers look at the dollars and make decisions about the value of the system as it operates. Performance evaluation, however, has more to do with the criteria of per- formance of subsystems as well as the system as a whole. The data that we sug- gested above to be collected involves physical as well as financial performance measurements. If the former are collected and periodically analyzed, it is possible to identify problems before they hit the financial performance. But even more important is looking to the future. Assuming the client will still be in business (in the generic sense of that word) when it comes time to upgrade or replace the system, the ongoing performance data will prove valuable. Evaluated periodically, that data can help in determining whether or not the time is coming to start considering such upgrading or replacement. In the former case, the systems engineer is looking for performance issues that imply adaptation intervention; they may need to modify select parts of the system to bring it back into compliance with its long-term performance requirements. In the latter case, we may be talking about an evolutionary change. 14.4.2.10 Performance Discrepancy Analysis We treat this as somewhat different from performance evaluation in that the systems engineer is looking closely at the data and comparing the results with long-term expected performance. Recall that all systems exist in nonstationary environments. Changes in the environment may actually call for changes in the performance requirements of the system. The systems engineer has to look for discrepancies between environments over time and system performance over time.

728 14 Systems Engineering 14.4.2.11 Upgrade/Modification Decision Finally, the system is subject to modification (adaptation) or upgrade (evolution) when sufficient discrepancies between actual performance and desired performance (which can include those due to new environmental factors) reach a critical point. The client and the systems engineer consult over the results of ongoing monitoring and analysis to come to that decision. For the client, it comes down to the cost/ben- efit analysis as well as future projections for operations (e.g., would replacement cause major disruptions to other operations). The system may be reaching the end of its planned life cycle. More often than not, in this modern world of rapid change, it may simply be a case of the environment changing unpredictably and the larger meta-system, say a company, needing to change its ways of doing things to continue to operate in a relevant manner. Whatever the case, we find ourselves back to the beginning of the system devel- opment cycle. This is where that data collection and analysis we mentioned above can really be a saving. In Fig. 14.2, we show two arrows out of the decision box, one leading back to problem analysis and one, dashed, leading back up to problem iden- tification. The need to go back to problem identification is often the result of not doing a good job of monitoring the system during its operating life. In the second case, the problem is already well known and the jump to problem analysis will save time and money. Moreover, the actual amount of analysis that would need to be done may be foreshortened because most of the problem is understood from the performance analysis steps. Think Box The Brain as Tool Maker We come to the end of our thinking inside the boxes with a recognition of how our human brains, having mental models with which to ask “what-if” ques- tions about the world, are able to generate stories that combine different ele- ments together in new ways. We can try out combinations of things, actions, and relations and use our powerful analytical abilities to test the worthiness of the new story by comparing it with our current models of reality. As noted in Think Box 13, this is imagination. The current chapter is about the notion of systems improving through intentional design, what we call engineering. People are forever exploring their environments in search of resources. Using affordance (as described above), people can “see” possible ways in which those resources can be extracted or exploited. What they can see is opportunity but also they see a “problem.” In order to actualize the exploitation of a resource, they have to also have a “tool.” It is one thing to dig up a root vegetable with your hands or catch a rabbit by running it down. But to really get production going requires some kind of digging tool or a bow and arrow. In other words, it takes tools to obtain more useful work than can be done by hand alone. (continued)

14.4 The Systems Engineering Process 729 Think Box (continued) The brain commands our muscles to perform actions that are feasible for human bodies to do. But when the actions needed are not within the abilities of the body, there needs to be some instrument devised that can do the work. Tools are instruments that extend and amplify the capacity of the human body to do work. With the ability of the brain to build models of possible world states, to see how natural objects can be shaped or put together to achieve a purpose, and to grasp the rewards to be reaped by investing time and energy in the tool construction process, humans have become consummate tool mak- ers. In fact an argument could be made that that ability is the basis of our culture. Even art can be considered a tool for exploiting enjoyment! Engineering is actually the art of creating tools or making tools better. A craftsperson is one who makes the tools with skill. But the engineering mind conceives of the tool and how it can be used to do other work. This whole process is the epitome of the cybernetic hierarchy of the brain, with the strategic level actively planning the design and construction of the first tool for its purpose. 14.4.3 Process Summary Throughout the systems engineering process, as it is coupled with the system life cycle, we have seen a continuing role for the systems engineers to be involved in all stages. Primarily the systems engineers act as “big picture” overseers to make sure that all the parts of both the physical system and of the processes in each stage fit and are completed properly (and on time and within budget). Unlike say a computer engineer who might be involved only in developing, testing, and delivering one component in the system, the systems engineer lives with the system for its entire life, all the way through development, etc., and then through its ongoing operations. The latter is because evolution is invariably the dominant long-term force that shapes artifact systems. And those systems are evolvable, not just adaptable. Look at the modern automobile as an example of an artifact system. Not only has the system improved in performance criteria over the decades, it has had to evolve to meet changing demands from its environment. That environment includes the phys- ical infrastructure (the roads and fuel supply systems), customer preferences, manu- facturing processes, new technologies, and many more factors. The fundamental function of the automobile has not changed particularly (in spite of many predic- tions, it has not really morphed into a transformer-like private airplane or boat on demand). It is still a personal/family ground transportation unit. But how it performs its functions has been very influenced by the environment. Among many changes that have taken place in the automobile industry over the years is how cars are designed and engineered. Today new models are developed by teams of designers/engineers who, collectively, act as systems engineers. That is, when you look at the process through which new autos are created, you will find the elements of systems engineering even when no one person is necessarily designated

730 14 Systems Engineering with that title. Indeed, look at almost any industry that produces very complex sys- tems products or services, and you will find that they involve every element of the systems engineering process. Some industries, for example, the large passenger air- craft industry, has officially adopted the role of systems engineers because they recognize that what they are doing is systems engineering. Having a titled group of people not only recognizes that function but also effectively makes the organization “cognizant” of it. In the coming years, we expect to see more industries and also governmental and nongovernmental agencies that undertake very complex systems projects to turn to “official” systems engineering to improve their successes. 14.5 Systems Engineering in the Real World Systems engineering has been employed by many companies that build extremely complex artifact systems such as large airplanes (Boeing Company) and government- based organizations that have complex missions (NASA—putting men on the moon). Many other organizations, business, government, civil social, and others that have very complex missions and need to organize around artifact systems (both physical objects and operations procedures) are already practicing systems engi- neering, either explicitly or, more generally, implicitly. Complex missions require complex artifacts to solve problems, and these organizations have no choice but to use many of the tools of systems science and engineering to tackle these problems. As systems engineering becomes more recognized for what it can accomplish, it is likely that more organizations will adopt formal methods. Several organizations have formed to develop standards and methodologies for a formal approach. The International Council on Systems Engineering (INCOSE),10 formed in 1990, is ded- icated to advancing the practice of systems engineering in all arenas. The Institute of Electrical and Electronic Engineers (IEEE) has a standard setting process and has worked out a number of standards related to systems engineering. For example, “15288-2004—IEEE Standard for Systems engineering—System life cycle pro- cesses” provides guidance for just what its name implies. From the web page: …establishes a common framework for describing the life cycle of systems cre- ated by humans. It defines a set of processes and associated terminology. These processes can be applied at any level in the hierarchy of a system's structure.11 NASA (National Aeronautics and Space Administration) also has produced a set of standards for its organization and suppliers to use when working on complex missions.12 Not all applications of systems engineering are necessarily about 10 See http://en.wikipedia.org/wiki/INCOSE. 11 See http://standards.ieee.org/findstds/standard/15288-2004.html. 12 You can download one of the documents, NASA (2013): nodis3.gsfc.nasa.gov/npg_img/N_ PR_7123_001B_/N_PR_7123_001B_.doc.

Bibliography and Further Reading 731 complex products like airplanes, complex technical missions like putting people on the moon, or information systems to support a supply chain operation. All of us live in complex systems. And those systems are likely to show stresses if they are not well designed. More and more of the “problems” that our civilization face are inherently com- plex and, hence, systemic. Their solution, if they can be solved, will involve systems engineering. We will need the application of systems engineering for what we often take for granted—sustainable living systems in a world beset by extraordinarily complex problems. Our current society is based on the availability of fossil fuels (over 80 % of our energy comes from fossil fuels) and those fuels are a finite and diminishing resource. The burning of fossil fuels has dumped an amazing amount of carbon into the atmosphere and oceans (CO2) and has contributed to heating the planet and changing the basic climate system. The change in climate and human uses of freshwater is already changing the availability of water for drinking and agriculture. There are other resource threats that we have to consider. Bibliography and Further Reading Gibson JJ (1977) The theory of affordances. In: Shaw R, Bransford J (eds) Perceiving, acting, and knowing. Lawrence Erlbaum, New York McDonough W, Braungart M (2002) Cradle to cradle: remaking the way we make things. North Point Press, Berkeley, CA McKibben B (2007) Deep economy: the wealth of communities and the durable future. Henry Holt & Company, New York NASA (2013) NASA systems engineering processes and requirements NPR 7123.1B, April 2013 NASA procedural requirements. nodis3.gsfc.nasa.gov/npg_img/N_PR_7123_001B_/N_ PR_7123_001B_.doc. Accessed 9 May 2013 Rogers GFC (1983) The nature of engineering—a philosophy of knowledge. The MacMillan Press Ltd, London

Index A and interaction matrix, 666 Abstract networks as systems, 166 and judgment-based decision processes, Actuator 671–674 control of, 286 limitations as modeling approach, 677 description of, 286 and modeling emergent behavior, 667 and response delay, 382 modeling framework of, 666 Adaptation and modeling probabilistic decision to environment, 419 and expectation, 290 making, 670 and feed-forward information, 421 and rule-based decision modeling, 670 and mapping, 132 as simulation of auto-organization and mutual, 569 systems definition of, 468 emergence, 676 Adaptive control strengths of as modeling approach, 676 costs of, 399 Agents, 676 memory in, 394 Aging and modification of expectations, 394 and feedback loops, 207 Adaptive learning, 18 and system failure, 207 Adaptive systems Agriculture and shift of dependence, 579 life as, 394 Aim message flow in, 247 and function, 115 timing of messages in, 247 and metabolism, 115 Adaptivity, 222 Aleutian Islands, food web collapse, 161 and information, 223 Algorithm(s) Adaptrode and classes of problems, 197 model of changeable synaptic junctions, 687 genetic or evolutionary, 474 model of neuron memory-conditioned as measure of complexity, 197 multiple parameter, 391 response, 689 Algorithmic computation, 327 Adders, 319 Amplification of signals, 287 Agent-based modeling Analysis as decomposition, 595 and autonomous agents, 675 of network flows, 166 and decision processes, 668–676 of percepts, 131 and decision trees, 668 processing inputs to outputs, 144 example modeling of collective Anthropic principle, 104 Antibiotic, 44, 49, 50, 52, 58, 61, 65 intelligence, 686 © Springer Science+Business Media New York 2015 733 G.E. Mobus, M.C. Kalton, Principles of Systems Science, Understanding Complex Systems, DOI 10.1007/978-1-4939-1920-8

734 Index Anticipation new linkage formation, 495 and feed-forward control, 391 of new linkages, 494 and imagination, 304 and pulsed cycles, 487 and models, 304 role of chance in, 487 versus prediction, 303, 331 and selective pressure, 501–502 and self-assembly, 500 Anticipatory control in social dynamics, 502 costs and returns on, 400 structure formation, 493–502 diagram of, 401 and tilt from random process, 490 Auto-organization of structure, 493–502 Anticipatory response, relative costs of, 403 and minimal energy configurations, 494–496 Anticipatory systems, 303–307 Auto-organizing and path dependence, 492 Aparicio, M., 338 Autopoiesis, 40, 396, 477, 480, 513, 545, 621 Artificial intelligence, 330 B and robot foraging search, 672 Baars, B.J., 338, 339 Artificial neural networks, 131 Bacon, K., 149 Ashby, W.R., 34, 43 Bacon, R., 630 Associator, 401, 402 Bagley, R., 682 Atomism, 10–11, 82 Bak, P., 251 ATP bio-energy molecule, 515 Barabási, A.L., 23, 39, 147, 149, 150, 153 Attraction and repulsion, 91, 102–105, 482 Barrs, B.J., 334 Barto, A.G., 393 social relationships, 104 Bateson, G., 34, 35, 84, 268, 321, 531 Attributes Bayes, T., 292, 294, 329 Bayesian inference, 294 canonical, 120 Behavior(s) dynamical, 146 of networks, 144–146 of components, 224 Auto-catalysis mental, 120 emergence of, 498 of networks, 142 and emergence of life, 514 Behavior analysis and self-referential feedback, 510 of subsystem as white box, 225 in super-saturated solution, 498 whole systems as black box, 224 Auto-catalytic cycles Bifurcations, 37 and auto-organization, 497–500 Binary numbers, 319 precursors of life, 499 Biological computations types of, 347 Autogen and emergence of life, 514 Biology Auto-organization cellular networks, 158 and auto-catalytic cycles, 498 embryology, 147 in brain development, 522 emergence of from chemistry, 462 and catalysis, 498 Biophysical economics, 635–637 and catalytic processes, 489 Black box analysis, strengths and weaknesses components and energy forms in, 485 cooperation and competition in, 489 of, 237 and cycles, 487 Black boxes, 139 cycling dynamics in, 487 diffusion process in, 488 instrumented, 236 and dissipative systems, 479, 493 as outside perspective, 223 dynamics of, 484–493 Black box white box analysis, 225 and emergence, 504–525 Boole, G., 316 and energy Boolean logic, 269, 316–318 in digital computation, 316 flow, 493 Boundary gradient, 484 attributes, 134 partitioning, 485 conceptual, 94 transfer, 486 and forced moves, 491 and hyper-cycles, 499 and linkage dissociation, 489

Index 735 concrete, 90 C conditions, 96 Capitalism, 12, 59, 62, 439, 575, 582, 635, 636 and flows of input and output, 79 Carroll, S.B., 547, 557–559 fuzzy, 92, 164 Carter, R., 638 kinds of, 90–96 CAS. See Complex adaptive systems (CAS) logical, 140 Cascading, 14, 37, 172, 207, 715, 726 and object perception, 134 Catalysts and modification of probabilities, 489 physical, 140 Categories and conceptualization, 129 porous, 92 Causal processes, 13, 17, 370, 675 Boundary conditions Cause and effect, 13, 251, 304 external personalities, 99 Cell membrane and emergence interaction potentials, 97 Boundary-crossing, 48 of life, 514 Bourke, A.F.G., 9, 449, 564 Cells Brain(s) areas and language production and control hierarchy in, 438 and genetic control, 438 interpretation, 276 structure of, 631 computation in, 333, 346 subsystems of, 631 Cellular automata non-mathematical, 349 complex behaviors of, 200–201 and conceptualization, 120 simple rules, complex behavior of, 201 contain models, 119 Cellular life common ancestor of, 185 cybernetic structure in, 451 Change development and evolution, 534 measurement of, 214 emergent structure of, 439 measuring trajectories of, 531 and facial recognition, 121 and response, systemic propagation of, 246 and feature detection, 131 trajectory of, 530 and information overload, 441 Channel definition of, 273 and math, 354 Chaos and memory recall, 345 and complexity, 202 and mind, 637–643 and sensitivity to initial conditions, 252 and object conception, 342–345 theory, 9, 15, 36, 37, 170, 250–253 and pattern mapping, 129 Characteristics and pattern recognition, 128, 130 of channel, 273 perceptual processing in, 131 of chaotic systems, 251 processing information into connection potential, 100 emergent, 505 knowledge, 305 of evolvability, 536 and process of comparative as features, 127 of knowledge encoding, 546 analysis, 640 of life, 33 and senses, 126 of living systems, 393 size and complexity, 187 structural, 37 and strategic control, 439 of systems, 18 as a system, 638 Chemistry and thinking process, 253 path dependency in, 492 as tool-maker, 728 as pattern of structure formation, 177 Braungart, M., 714 Classification, 128–129 Brenner, J., 57 Climate, 79, 87, 199, 208, 236, 243, 332, 427, Brittle systems, 206 Buffers, 413 443, 467, 487, 529, 568, 622, 643, and coordination, 414 647, 649, 658–660, 696, 731 Business management and resistance to change, 4, 14, 223 Closed loop control sufficient analysis, 634 components of, 372 Business process structural analysis of, 633 and cybernetics, 372 Butterfly effect, 251 Byproducts of increasing organization, 496

736 Index Closed loop control (cont.) and hierarchical management and homeostasis, 374 architecture, 435 limits of, 384 and information flows, 266 Clustering, 152 and information for future, 290 Clusters size distribution, 153 internal control of, 359 Codes definition of, 274 and living networks, 147 Coevolution and need for coordination, 433 and need for regulation, 360 of culture, 572 resilience and stability of, 356 of ecosystems, 569–570 solar energy system example of, and fit with community, 568 and interdependence, 570 256–260 in social process, 572–575 and strategic management, 434 Cognition, 63 and unpredictability, 447 as guidance, 65 Complex chemistry conditions for, 483 Collapse Complexity. See also Progressive and brittle networks, 207 of brittle systems, 206 complexity complexity as factor in, 29, 205 as algorithmic, 197 of complex societies, 207–210 algorithmic information measure of, 200 and dynamical mismatches, 25 approaches to defining (see Complex of ecosystems, 569 of food web, 161 systems) in human and natural systems, 25 of behavior, 172 of networked systems, 207 behavioral measure of, 200–202 and positive feedback, 37 in behavior of cellular automata, 201 Color, 125 and biodiversity, 532 Communication(s) categories of, 111 channels, 286 and chaos, 202 and cooperation, 361–363 and chaos theory, 170 definition of, 271 combinatorial, 190 markets as protocols, 365 common views of, 172 protocols for, 364 and component personalities, 97 results of, 278 and control hierarchies, 405 between systems, 365 as depth of hierarchy, 193 Competition role in selection, 561 emergence of, 512 Complex adaptive systems (CAS) emergent, 511 and adaptive control, 394–399 four categories of, 174 agent-based modelling of, 149 and fractals, 201 and anticipation, 303–307 graph theory depiction, 174 anticipation and prediction in, 303 and hierarchical organization, 109–110 and anticipatory control, 399 of human organizations, 187–189 architecture of control hierarchy of, 436 increase of, 475 and budgeting, 411 increase through time, 475–477 and coevolution, 568 levels of, 177 computation in, 357 limits of, 204–212 as containing models, 412 of morphology, 532 as containing models of other multiplicity of approaches to, 169 and number of components, 172 systems, 646 organizational, 16 and control hierarchy, 435 potential, 111 and cooperation, 561 potential and realized, 203 and cybernetics, 454 from potential to realized, 112 and decision processes, 301 progressive increase of, 531–533 and environment, 360 realized, 112, 134, 462 function over time, 258 relational, 174 Science, 111, 200

Index 737 solution time measurement of, 197–200 Components systems perspective on, 170 of concept recognition, 130 systems science definition of, 173 connectivity of, 100 Theory, 19 coupling strength, 100 Complex question analysis effects of changes, 101 application of Principle 1, 45 failure of, 205 application of Principle 2, 46–48 heuristic, 448 application of Principle 3, 48–49 integration of, 192 application of Principle 4, 49–51 interaction potentials of, 176 application of Principle 5, 51–52 internal structure of, 97 application of Principle 6, 52–53 and linkage formation, 112 application of Principle 7, 54–56 linkage potential of, 486 application of Principle 8, 56–57 mapping of, 24 application of Principle 9, 58–60 personalities of, 97 application of Principle 10, 60–62 and self-assembly, 479 application of Principle 11, 62–65 stochastic, 447 application of Principle 12, 65 drug-resistant TB example, 44–69 Composition, 96–99 Complex systems Computation behavior summarized, 260 collapse of, 207 adaptive or fuzzy heuristic, 333 in complex environment, 425 basic description of, 312 and component failures, 205 in brains, 312, 333–346 composition of, 175 definition of, 314 connective functionality of, 139 delay in, 381 control schematic for, 424 heuristic, 329 coordinator in, 412 in intelligent life, 332 copying of, 542 mechanical, 313 decomposition of, 44, 176, 619 in neural network, 339–347 decomposition of levels of, 177 non-deterministic, 315 development and decline, 222 probabilistic heuristic, 328–331 and disruption of flows, 206 for problem solving, 347–352 dynamics, 219–223 and emergent functions, 507 (see also Problem solving) and energy flows, 236 process of, 311–316 features of, 175 purposes of, 359–361 hierarchical control structure in, 368 Computational complexity, 197 as hierarchical structure, 110 Computation process as informative, 212 and effective procedures, 323 intuitive characteristics of, 172 structure of, 315 life cycle of, 710 Computer(s) and nested hierarchy, 37 as adders, 319 resilient versus brittle, 205 and agent simulation, 677 and self-models, 60 algorithms, 325 and sources and sinks, 426 artificial intelligence, 330 and strategic management, 425 and binary difference, 321 surprising behavior of, 171 and combinatorial complexity, 190 theory as part of systems science, 18 CPU of, 319 and timing of flows, 40 and decision making, 442 and transfer functions, 624 electronic digital, 318–328 understanding of, 171 kinds of logic circuits, 321 and unpredictability, 303 logic circuits in, 189 unpredictability of, 5 logic circuits of, 319 ways to recognize, 171 memory registers, 323 Moore’s Law, 326 and network analysis, 166 numerical methods, 349

738 Index Computer(s) (cont.) Context and pattern recognition, 129, 131 in information and knowledge process, programming, 324 299–301 programming languages, 325 and memory, 299 software use, 323 Control. See also Cybernetics stored programs, 323 adaptive, 394–399 use of adders, 321 advanced methods of, 393–403 use of logic gates, 318 anticipatory, 399–403 value representation, 319 and computation delay, 381 of control processes, 417 Computing process coordination of processes, 404–423 digital, 316–318 costs of, 392–393 rules for, 326 and entropy, 446 types of, 316–347 factors in, 374–385 and feedback, 370–374 Concentration and diffusion in auto- feed-forward, 390 organization, 488 genetic, 121 hierarchy, 27 Concepts integrative, 388 activation of, 253 internal, 99 encoding of, 133 logistical, 368, 407–412 formation of, 194 models, 165 as models, 119 as mutual process, 372 as systems, 30, 121, 133 open loop, 370 and oscillations, 383 Conceptual proportional, 388 connections, 141 and reaction delay, 382 frameworks, 127–128 and sampling rates, 376 process, 248–249 schematic for complex systems, 423 structures, 120 structures of, 32 systems linking components in, 248 and synchronization, 383 tactical, 419–423 Conceptualization as core of systems science, 31 temporal factors in, 375–378 Connections, energetically favored/ theory of, 369–374 disfavored, 240 Control computations Connectivity, 100 and information feed-forward, 390 and multiple parameter algorithms, 391 of all things, 138 PID control, 386 small world, 149 Constraints Control hierarchies, 416, 435–440. See also budget, 490 Hierarchical management as characterization of problem, 680 collapse of space-time, 577 Controllers and feed-forward information, 421 in environment, 177, 360 Control systems, evolution of, 449 of flows, 117 Cooperation math expression of, 410 motivational, 673 and communications, 361–363 mutual, 250 versus coordination, 406 and optimality, 677 evolution of, 562 and optimization, 392 role in selection, 561 and path-finding, 251 Cooperation and competition pattern-creating, 250 at atomic level, 509 performance, 721 in auto-organization process, 490 progressive satisfaction of, 658 in catalytic environment, 510 self-imposed, 571 emergence of, 508–511 of space and time, 518, 571 and emergence of teleology, 509 space-time, 248, 577 and information, 509 Construction, 477, 547 of concepts, 120 Constructor, 396–399, 406, 542–547, 625

Index 739 Coordination Cycles control categories of, 405 autocatalytic, 497, 514, 539 controllers, 405, 421 boom and bust, 50 with environment, 369 carbon, 108 and feedback loops, 434 catalyzed, 498 multiple levels of, 416 of component life, 536 of operations, 423 convective, 486 processes coordination of, 416–418 disruption of, 489 of resource distribution, 411 dynamics of, 487 with stochastic environment, 425 food, 33 hyper, 499 Copying of interactions, 178 DNA as knowledge medium for, 546–549 linkage, 487 errors and evolvability, 536 material, 236 and evolution, 541–543 of molecules and atoms, 108 from representational model, 543 mutual catalytic, 541 role of constructor in, 542 mutually reinforcing, 524 ways of, 542 physical, 487 “Xerox” method, 542 pulsed, 487 pulsed energy, 487 Copying knowledge replication, 473 biologically, 549–551 reproductive, 25 in supra-biological organizations, 551 as temporal patterns, 123 of temporal transformation, 463 Corporate management and hierarchy, 437 Correlations versus causation, 656 D Cortical columns in neural net computation, 340 Daly, H.E., 636 Costanza, R., 636 Damasio, A.R., 673 Coupling strength, 100, 145 Darwin, C., 161, 464, 493, 511, 538, 540, 548, Creationism, 476 Cultural evolution and transmission 552, 559, 562, 564 Darwinian natural selection, 39, 472, 476 of models, 572 Data Cultural values, 10 Culture(s) capture and storage, 352 and information, 277 and coevolution, 570–572 interpretation of, 277 coevolutionary dynamics of, 574 Dawkins, R., 473, 538, 564, 573 emergence of, 516 Deacon, T., 39, 120, 514, 517 and emergence of agriculture, 579 and emergence of life, 514 and emergence of money, 579 Decision process. See also Agent-based evolutionary trajectory of, 571 and expectation, 571 modeling and language, 518 and game theory, 302 versus nature, 571 judgment in, 302 and role of memory, 573 types of, 668–676 Cybernetics Decision trees, 301, 669 adaptive control, 394–399 Decomposition advanced control methods, 393–403 analysis, 595–623 anticipatory control, 399–403 and an “atomic” level, 616 and control computations, 385–403 application of lexicon in, 600 coordination of processes, 404–423 and black box, 607 definition of, 367 from black box to white box, 603 feed-forward control, 390 boundaries issues in, 620 and hierarchical control, 366–369 and complexity of flows, 610 information and computation in, 453 depth of levels of, 617 and information feedback, 13, 370 and monitors, 372 Cybernetic structure in brains, 451

740 Index Decomposition (cont.) Digital computation, 318 and evolvable systems, 622 and Boolean logic, 316 first stage (level-0), 607 and functional map, 614 Digital computing, 316 of hierarchies, 604–606 Disciplinary assigning levels in, 605 and identifying processes and boundaries, 6, 44 stocks, 608 focus, 9 issue of adaptive behavior in, 621 Disorder, 26, 87, 111, 208, 251, of levels of complex system, 177 lexicon of symbols, 596 464, 511 and mapping flows, 608 Disorganization, 87, 240, 480 and modeling, 625 Dissipative systems, 15, 39, 240, 479, 579 products at level 1, 614 as progressive microscoping, 603 and environment, 116 recursive at level 2, 614 Distributed control, 367, 405 recursive method of, 614 Distributed operational control, 406 representing level 2, 615 Distribution coordination, 411 scale and precision of measurements in, 604 Disturbances stage 2 of, 609 of structure and function, 606 ordinary versus critical, 242 and system knowledge base, 611 systemic response to, 242–246 and systems analysis, 590 temporal profile of, 243 three products of, 623 DNA as top-down process, 603 copying process of, 549–551 unbalance in tree of, 619 as knowledge medium, 546–549 use of interfaces in, 612 DNA and RNA, emergence of, 547 when to stop, 616–619 Dobzhansky, T., 527 and white box analysis, 239 Dorigo, M., 686 Drug resistance, 49, 53, 54, 60 Decomposition lexicon Drug-resistant TB, case study in complex actuators, 600 energy flows, 598 question analysis, 44–69 interfaces, 599 Dynamics material flows, 596 message flows, 598 of adaptation, 223 sensors, 599 as change over time, 214 sources and sinks, 596 conceptual approach to, 214 stocks and buffers, 598 contradictory, 60 types of flows, 596 of cycles, 487 value slots, 599 of development and collapse, 25 development and decline, 221 Degrees of freedom, 99, 425, 677 growth or shrinkage, 220 DeMarco, T., 634 of hub formation, 153 Democritus, 82 internal, 134 Denaturing of proteins, 205 and internal structure, 99 Dennett, D.C., 472, 491, 538, 694 kinds of, 219–223 Descent with modification, 552–553 measurement of, 213–216 Design motion and interaction, 219 of networks, 149 and evolution, 476 of population growth, 215 by random search process, 538 probability, 488 space, 534, 538 relational, 28 Determinism, 13, 330 of a single weight variable, 691 Deterministic chaos, 251–253 DYNAMO programming language, 663 Diamond, J., 46 Diffusion and gradients, 118 E Earth organizing process of, 480 system as network relations, 160

Index 741 Ecology, 8, 9, 16, 33–35, 39, 115, 159, 215, and elastic interactions, 469 365, 515, 622, 630, 636 and emergence of Economic(s) autocatalytic cycles, 497 ecological, 636 internal structure, 493 growth, 47, 57, 220, 444 encoding of, 363 holistic models of, 636 and environmental economics, 636 short-comings of standard models, 635 in far-from-equilibrium dissipative Economy systems, 465 growth and limits, 221 and forming new structure, 241 measuring GDP, 220 geological, 483 and gradients, 236, 480 Ecosystem(s) and increasing complexity, 209, 503 coevolution of, 569–570 and increasing organization of disturbed balance in, 161 and expectation, 569 universe, 529 as food webs, 161–164, 569 and increasing structural complexity, 240 as network, 159–161 and information transmission, 356 stability of, 569 for maintenance and repair, 54 mapping of, 608 Edelman, G., 522 and molecule formation, 479 Efficiency money as, 485 pulsed, 487 and energy input, 229 and shaping World Wide Web, 496 limits of, 229 from stars, 482 Electro-magnetic force proportion to systemic effects of, 26 and thermodynamics, 227 gravity, 103 tidal, 483 Emergence and transduction, 284 and universal evolution, 535 of complexity, 511 and work, 284 of constraints, 117 Engineering, 20, 36, 65, 703, 704, 707, 714 of culture, 516 Entropy, 15, 26, 35, 107, 109, 111, 116, 177, of language, 516 of life, 515 202, 203, 205, 227, 228, 240, 265, 280, 314, 362, 444, 445, 447, 464, and enclosing membrane, 514 465, 479, 487, 493, 529 of new levels of complexity, 505 and energy input, 229 of new properties, 505–507 increase of, 229 of not working, 510 and probability, 369 of something new, 505 Environment(s) of tool-making, 519 interface with, 419–423, 420 Emergent functions, 507 meaning of, 116 Emergent properties non-stationary, 531 molecular, 506 as perceptual background, 134 in proteins, 506 as relational matrix, 117 Energy stochastic, 427 form partitioning of, 485 Environmental gradients, 228 flows, 116, 117 and resource depletion, 445 selection, use of in modeling sources and positive feedback in programs, 681 Enzymes role in DNA copying, 549 culture, 519 Epigenetics, 147, 556 and work, 226 Equilibrium, 240 Energy flows Eusocial insects and models of collective and amplification, 356 intelligence, 686–695 and auto-organization, 494 Eusociality, 566 and black box analysis, 618 in computer simulations, 511 and connectivity of components, 505 coordination of, 515 and economic theory, 635

742 Index Evolution. See also Coevolution; Emergence; F Selection Face recognition, 343 Far from equilibrium systems, and versus adaptation, 527 as algorithm, 472–475 thermodynamic equilibrium, 465 of antibiotic resistance, 58 Farmer, D.J., 682 biological and supra-biological, 539 Features of brains, 535 and constant change in environment, and categorization, 129 components of, 127 529–531 Feedback dynamics and social and copying errors, 554 cultural, 520 coevolution, 575 and cumulative change, 533 Feedback loops, 13, 25, 34, 40, 205–207, 252, Darwinian, 477 and descent with modification, 410, 434, 483, 496, 558, 573, 585, 662, 664, 715 552–553 and closed loop control, 371 and design, 476, 538 in control theory, 369–374 as design search, 538 of information and knowledge, 308 and epigenetics, 557 in systems thinking, 16 and genes, 547 Feed-forward and heritable variation, 552 control, 391 of human society, 575–578 information and adaptation, 421 and increasing complexity, 475 Findeisen, W., 368 and knowledge mixing, 556 Fit and mutations, 554 improvement of, 471 of networks, 142 over time, 469 and optimization, 475 and selective evolution, 472 and path dependency, 492 short versus long-term, 472 and positive feedback loop of change, 531 and sustainability, 471 pre-life, 472 systems definition of, 469–473 and progressive complexity of Fitness and intelligence, 583 life, 532 and multi-dimensional selection, 558 and redundant copies, 534 and reproduction, 469 and replication, 473 Flow graph, 23 role of auto-organization and emergence Flow network(s) algorithms for, 166 in, 539–541 ecosystem as, 159–161 and selection, 557–568 Flows and selective fit, 557 between components, 193 and self-copying, 546 and connectivity, 105–108 social, 52 and cycles, 108 as a universal principle, 528 inputs and outputs, 106 Evolvability, 528, 536–537, 541, 592 as interface processes, 419 contributing factors, 536 sources and sinks of, 235 Expectation(s), 63. See also Systemic fMRI. See Functional magnetic resonance imaging (fMRI) expectation Food webs, 161–164 and adaptive learning, 290 and reproductive rate, 162 adaptive modification of, 291 Forces, 102, 103, 106, 145, 558 field of, 510, 531 attraction and repulsion, 102 and moving into future, 291 Ford, A., 664, 686 Expectation change and Bayesian probability Forrester, J., 40, 661, 663 and systems dynamics, 661 formula, 291–294, 292 Four forces, 102, 145 Exploration versus exploitation, 432 External coordination and sources and sinks, 422

Index 743 Fractals H and complexity, 202 Hall, C., 260, 636 Halting problem, 328 Function Harel, D., 313 mathematical maps, 125 Hawken, P., 582, 583 measurable, 67 Hawking, S., 9 as process, 113 Heterogeneous networks, manufacturing and purpose, 113–115 company as, 164 Functional decomposition, 114 Heuristic computation Functional hierarchies, 191–193 and induction, 329 and flows among components, 191, 192 and instincts, 330 Functional magnetic resonance imaging Heylighen, F., 412 Hierarchical control (fMRI), 226, 604, 640 model of, 368–369 and mapping brain activity, 604 and point mutations, 446 Functional map in decomposition, 614 Hierarchical decomposition, levels in, 602 Fuzzy logic, 332 Hierarchical management in biological structure and process, G Gage, N.M., 334, 338, 339 437–439 Galileo, 15 and entropic decay, 445 Game theory, 302 environment, problems in, 440–445 Gardiner, G., 66 and force overload, 443 Geary, D.C., 639 and heuristic components, 448 General systems theory, 18, 33, 39 and imperfect components, 447 Genes as encoding knowledge, 547 and information overload, 441 Genetic algorithms and optimal and internally motivated agents, 449 internal problems in, 445–449 solutions, 474 of organizations, 439 Gibson, J.J., 701 problems with, 440–453 Gill, V., 53 and resource depletion, 444 Gilovich, T., 332, 635, 677 and stochastic components, 447 Gleick, J., 15, 252 Hierarchical organization, 109–110, 139 Globalization and sustainability, 582 components in, 175 Global warming, 79, 81, 220, 303, 658 of functions, 114 Gödel’s Incompleteness Theorem, 327 Hierarchical structure levels of, 176–179 Gollaher, D.L., 50 Hierarchies Governance, 19 in cell structure, 183–185 Government and hierarchical in computer structure and function, control, 439 189–190 Gradient control, 27 emergence of, 435 chemical, 118 of features, 127 energy flow, 486 formed by chemical bonds, 177 as signal, 118 functional, 22 Grand Unified Theory, 480 and management, 437 Graph theory, 75, 154–156, 157 of nearly decomposable modules, 37 as network math, 154 role of, 436 Gravity, 17, 93, 102, 104, 109, 137, 267, in tree of life, 185–187, 187 Higher organization, 26 467, 480, 482, 483, 486, 487, 491, HIPO modeling, 630 529, 663 Holism, 11–12 and fusion, 103 Holistic analysis of cigaret smoking, 11 as gradient for energy flow, 529 Holling, C.S., 25 Gridlock, 12, 13, 53, 574 Group selection, 564 Gunderson, L.H., 25

744 Index Homeostasis, 241 transmission of, 278 and adaptivity, 222 vernacular usage of term, 265 Inputs Homeostatic mechanism diagram of, 398 of energy, matter, information, 107 Homogeneity versus hererogeneity, 99 neural analysis of, 131 HTML, 148 transformation into outputs, 113 Hubs Instability, 29, 496, 578 Intelligent design, 476 carbon as, 152 Interaction potentials, 176 and power laws, 152 Interface processes, 419 Human brain levels of complexity of, 639 Interface with environment active versus Human fit with environment, 578–584 Humanities, 6, 9, 10, 51, 52 passive, 421 Hyper-cycles Internet as network, 141 emergence of, 500 and redundancy, 500 I J Immune system, 46, 54, 298, 347 Jablonka, E., 147, 559 Improvement social, 67 Johnson-Laird, P., 120 Improving systems, 65–68 Joslyn, C., 412 Induction and abduction in heuristic Judgment and models, 302 computation, 329 K Information Kahneman, D., 448, 635 Katsnelson, A.L., 46 about cause, 84 Kauffman, S., 15, 39, 488, 499, 514, 538 about world, 84 Kevin Bacon Game, 149 approaches of Bateson and Shannon, Keystone species, 115, 162 Kim, J.Y., 47 267–269 Klir, G., 18 Bateson’s definition, 268 Klitgaard, K., 260, 636 and change of expectation, 280 Knowledge and constructing knowledge, 305 for coordination, 363 construction of, 278–280 and difference, 84 feedback loop with information, 278 dynamics of, 278–280 functional definition of, 297 and entropy, 280 versus information, 299 exponential growth of, 576 as inverse of surprise, 298 flow dynamics of, 279 and learning, 298 flows, 117 nature of, 297–307 as guidance for survival, 266 as patterned expectation, 297–307 guidance function of, 515 and pattern recognition, 86 and interpretation, 85 scientific, 88 and knowledge, 268 soft and hard-wired, 85 as structure, 55, 63 growth feedback loop of, 308 systemic encoding of, 548 versus knowledge, 299 true, 4 measure of, 268 Koestler, A., 20 as news of difference, 54, 85 L that makes a difference, 268 Lamb, M., 147, 557, 559 process, 356 Langton, C.G., 201 quantification of, 289–294 Language quantified as surprise, 289 quantitative definition of, 281 distinctiveness of, 517 quantity of in message, 281 Shannon’s definition, 268 and surprise, 86 transduction and amplification of, 283–289

Index 745 emergence of, 516 as mental system, 248 natural, 21 and systems in the abstract, 21 symbolic character of, 517 ternary versus binary, 323 visual, 79 as tool of systems science, 30 Learning Logical network, 141 as adaptation, 214 Logic circuits, 189, 319 as encoding pattern mappings, 132 in computers, 269 LeDoux, J., 120, 639, 688 Logic gates, 315–318, 320–322 Levels of organization and scale of transistor based, 434 Logistical control, 407–412 components, 466 buffers in, 413 Levine, D.S., 338 and distribution of resources, 411 Life modeling of, 412–413 Logistic function, 216 artificial, 172, 511 Lorenz, E., 36, 252 conditions for rise, 104 Lovins, A., 582 conscious, 66 emergence of, 499, 512–516 M evolution of, 515 Maintenance coordination of, 415 and extinction events, 581 Mandelbrot, B., 37, 201, 202 extra-terrestrial, 179 Mandelbrot Set, 202 functional definition of, 513 Map functions of, 512 minimal system for, 513 of brain activity, 249, 640 origins of, 475, 499 of component connections, 75 processes, 46 conceptual, 141 ramping up to, 483, 515 decision tree as, 668 and self-maintenance, 514 of decomposed system, 595 tree of, 186 definition of, 124 Linear of dynamic structure, 14 causal analysis, 13 of flows, 609 causality, 12 of flows and processes, 623 causality and predictability, 17 of inner working of system, 24 cause and effect, 12, 14 of internal flows, 238 isotropic, 352 analysis, 11 of level 2 decomposition, 614 mathematics, 15 linking kinds of space, 132 math function, 197 mathematical, 125 programming, 678, 680 as model, 27 systems, 704 of network, 147 Linkage(s) and networks, 24 coupling strength of, 145 of outputs, 608 density of, 145 and pattern recognition, 128 dissociation of, 489 produced by decomposition, 623 in networks, 139 of social network, 149 organizational, 48 of streets, 124 physical and logical, 157 of tissue regions, 638 social, 59 as translation of positions, 124 and structure, 151 Mapping Living systems adaptive control in, 395 to neural clusters, 341 Logic and pattern recognition, 132 Boolean, 316 Margin of error, 21, 378 deduction and induction, 329 Margulis, L., 186, 540, 564 deterministic, 325 Markets as protocols, 365 fuzzy, 296, 332, 333 and heuristics, 329 of machine self-reproduction, 38

746 Index Mass of dynamic process, 214 attractive force of, 482 of dynamic systems, 216 as boundary condition, 480 of flow rates, 377 and growth measurement, 220 frequency and errors, 379 as information, 515 frequency and margin of error, 379 and measuring, 126 gaps in, 530 and onset of fusion, 103 GDP as, 221 and organization of atmosphere, 483 of growth and shrinkage, 221 and structuring, 54 and identifying properties, 289 and mechanical computation, 314 Massively parallel search, 538, 542 and sampling frequency, 378–380 Mathematics scale and precision of, 604 scales of, 277 and chaos theory, 78, 252 and scientific method, 64 and chaotic behavior, 202 and sensation, 381 and communicating abstract concepts, 702 senses as measuring devices, 381 and computation, 348 and sensors, 280 and computers, 348 and sensory transducers, 288 in engineering communication, 703 standards of, 88 and finding optimal solutions, 410 and state variables, 600 and Gödel’s Incompleteness Theorem, 327 of stocks and flows, 663 graph theory, 23 technology of, 225 and the logistic function, 216 time intervals, 377 and mapping, 125 and time intervals, 215 and measurement, 21 and useful quantification, 277 of networks, 139 Mechanistic causal thinking, 13 networks and graph theory, 154 Memory non-linear, 36, 252 as context, 299 and operations research, 678 traces, 338 and physics, 8 Mental models, 20, 60 of PID control, 386 Message(s) and precision, 704 definition of, 271 as systems tool, 30 as modulated energy flows, 288 Maturana, H., 40 Message receiver Maximize, 4, 17, 50, 57, 408, 449, 580, 581, definition of, 272 internal work in, 295 659, 675, 723 Message sender, definition of, 272 McClelland, D., 343, 691 Metabolic process, 116 McDonough, W., 714 Meta-science, 5, 17, 708 McKibben, B., 714 Microscopes as tools of decomposition, 603 Meadows, D., 40, 75, 683, 686 Milner, P.G., 50 Meaning Mitchell, M., 25, 33, 111, 173, 511 Mobus, G., 18, 250, 338, 350, 402, 636, 651, and message interpretation, 276 and protocols, 276 652, 672, 687 Measurement Model(s) advantage as method, 8 advantage of, 64 and anticipating the future, 353 and agreements, 80 and anticipation, 305 in an actuator, 377 computer intensive types, 657–658 approximating analog values, 376 and computer iteration, 657 as bridge, 21 contained in systems, 647 as comparing systems, 126 definition of, 648–650 of complexity, 173 and depth of understanding, 650 and control, 375 as embedded control systems, 660 and decomposition methods, 225 guide systemic interaction, 58 decomposition of, 604 devising new instruments of, 703

Index 747 incremental development of, 654 Modeling engines, 625 mathematical, 655 and system simulation, 625–626 as medium for copying, 544 mental, 66, 346 Modeling systems numerical, 216 at differing levels of decomposition, 629 physical, 655 and graphing results, 628 of population, 683 input data for, 628 of self, 60–62 and instrumenting output read, 628 and system representation, 627 and environment, 430 time steps in, 627 of sources and sinks, 422 statistical, 656 Modularity versus overlap, 98 stock and flow, 160 Modules, 23, 37, 75, 98, 200, 339, 349, 417, of strategic management, 431 of system life cycle, 710 535, 624, 639, 665 testing, 239 nearly decomposable, 37 types of, 649, 654–660 Money verification of, 653 and communication, 637 Modeling cultural emergence of, 579 adaptrode models of neurons, 687–691 emergence of, 508 adaptron as hybrid approach to, 687–691 as emergent function, 507, 508 agent-based approach to, 666–677 and energy expenditure, 393 agent based type of, 658 and environmental misfit, 581 approaches to, 661 as feedback message in market, 365 and artificial life, 682 as flow in social system, 485 collective intelligence, 686–695 flow of, 579 as completing understanding, 696 and global market system, 580 and computation, 353 history of, 507 of decision processes, 668–676 as independent of anything and degree of deviation, 652 and degree of precision, 652 physical, 636 of dynamics of social organization, 666 as informational form of energy, 493 and genetic algorithms, 681 as mediated way of making a living, 579 the ideal combined tool for, 696 as message, 636, 637 of motivated decision processes, 673 in neo-classical economics, 635 neuronal stimulus and response, 689 and positive feedback with technology, 580 neuron memory-conditioned response, 690 and profit motive, 67 operations research approach to, and resource allocation, 411 as stand-in for well-being, 579 677–681 as stock and flow, 662 operations research type of, 658 as system guidance, 582 of population dynamics, 682–686 as systemic flow, 166 to predict behavior, 658 and uncertain systemic guidance, 580 and resolution, 651 Monitor of model, 372 stochastic processes, 427 Montague, R., 312, 336 of stocks, flows and feedback, 662 Moore, J., 36, 177, 326 systems dynamics approach to, 661–665 Morowitz, H.J., 39, 185, 298, 465, 546, 631 systems dynamics type of, 658 Multi-causal, 12, 472 technical issues in, 651–654 social problems, 65 to test scenarios, 659 Multicellular organisms, 147 to test design, 660 and time steps, 653 N types of evolutionary models, 681–682 NASA, 730 uses of, 658–660 Natural selection. See Selection and verification, 653 Near decomposability, 175, 178, 191 to verify understanding, 660 Nested structural hierarchy, 37, 45, 243 Nested systems and control hierarchy, 438

748 Index Network(s) Nodes attributes of, 144–146 labeling, 165 basic idea of, 139–140 of network, 139 cellular, 158–159 clustering, 152 Noise and clusters, 142 definition of, 274 and complexity, 157 and sampling frequency, 379 connections and aggregation of power, 153 in signal, 288 ecosystem as, 159–161 fixed versus changing, 142 Non-linear flow, 143 behavior, 592 function and purpose of, 113–116 dynamics, 18 growth and evolution of, 147–149 equations, 15, 37 heterogeneous, 151 interactions, 78 and interdependent components, 162 iterated math process, 252 logical, 140–142 math, 250 manufacturing company as, 164–168 patterning process, 37 organization, 113 process and computers, 9 physical, 140–142 properties in deterministic chaos, 252 of relations, 18, 19, 45, 48, 156 subsystems, 659 size and composition of, 144 systems, 649 small world, 150 and system structure, 156 Norgaard, R., 572–574, 580, 582 theories, 23 Nowak, M.A., 562 Theory, 19 Nyquist, H., 378, 379 Networked relationships, 17 O Neural clusters and conceptual imaging, 344 Object conception in neural net computation, Neural computation 342–345 encoding association, 338 Objectivity, 88–89 encoding causal relations, 336–339 synapses as signal filters, 335 scientific, 88 synaptic potentiation in, 334–336 and subjectivity, 80 Neural net Observers cortical subsystems, 339 definition, 272 data storage as synapse excitability, 352 and objectivity, 81 Neural net computation presence of, 81 experience and reinforcement, 341 Odum, H.T., 365, 630, 636 feature recognition in, 339 Ogle, R., 149 and mental models, 346 Operational control and coordination of object conception in, 342–345 object identification, 341 processes, 404 perception, 341 Operations coordinators, 405 sensory cortices, 340 Operations research Neural networks, 121 artificial, 132 as approach to modeling, 677–681 Neurobiology, 19 limitations as modeling approach, 680 Neuronal networks modeling optimization with constraints, 677 hard-wired, 121 strengths as modeling and representation, 81 Neurons approach, 680 adaptrode models of, 687–691 Order and computation, 333–347 computational functions of, 337 as minimal energy state, 464 and fuzzy computation, 333 versus organization, 464 Organization(s) and control of work processes, 189 and disorganization as measurement of complexity, 203 emergence of new levels, 148 and entropic decay, 465

Index 749 external, 116 and recognition, 130 hierarchical structure in, 187–189 unpredictability of aggregates, 505 increase over time, 462 Pesticides, 12, 50, 246, 553, 581, 583 internal, 116 Physical flows, 8, 117, 145, 502 levels and scale of components, 466 Physics levels of, 465 and auto-organization, 16 of life and complex chemistry, 483 and chaos theory, 78, 252 versus order, 465 and definition of properties, 125 the process of, 479–484 and determinism, 675 as real, 119 as a discipline, 8 and structure, 99 and dissipative systems, 39 of universe, 482 and dynamics, 219 Organization process emergence of chemistry from, 39, 462 of the earth, 483 energy and work in, 226 and energy flows, 483 and the four forces, 102 and gravity, 482 Heisenberg Uncertainty Principle, 327 Organizing process and increase of complexity, 509 and boundaries, 480 and inferring black box internals, 78 and bounded components, 480 information content of, 515 of universe, 480 laws of, 117 Oscillations and zero-crossovers, 383 laws of and forced moves, 491 Output behaviors, 78 laws of as shared information, 509 Output parameters and time intervals, 377 and machine paradigm, 10 Overlap, 18, 31, 32, 98, 465, 504 Newtonian, 14 not paradigmatic, 8 P and organization of universe, 479 Parts and wholes, 82–84 reduction to, 33 Patel, S.J., 318 and structural knowledge, 27 Path-dependency, 492 PID control Patt, Y.N., 318 mathematics of, 387 Pattern(s) in social situations, 389 Pollack, J., 44 of attraction and repulsion, 104 Pollution, 360, 493, 580, 598, 637 definition of, 128 Population as maps, 125 dynamics of model, 682–686 spatial, 122 modeling dynamics of, 686 temporal, 123 Positive feedback loops and system failure, 206 Pattern recognition, 87, 122, 128–133, 199, Potential and realized complexity as 308, 339, 352 measurement of complexity, 203 by machines, 131 Potential complexity, 111–112, 134, 203 in neural net computation, 341 Power law, 152 Pavlov and neural encoding of causal Precision, 13, 21, 28, 64, 88, 349, 381, 389, relations, 336 604, 652, 653, 704 Personality Pre-life Urey–Miller experiment, 512 Prigogene, 15, 16, 39, 465 of atomic particles, 177 Prigogine, I., 15 of catalysts, 498 Primack, J.R., 103, 246 catalytic, 490 Primary flows relational transformative clashes of, 556 and combination potential, 495 network of, 161 and component connectivity, 101 Principle(s), 18, 246, 415 of components, 97 degradation of, 205 eight, 56–58 and interaction potentials, 100 eleven, 62–65 of organizations, 432 five, 51–52 four, 49–51

750 Index Principle(s) (cont.) of systems, 99–116 nine, 58–60, 646 wholeness, 89 seven, 54–56 Protocols six, 52–53 markets as, 365 of systems science and meaning, 276 (see Systems science) Psychology, 19 ten, 647 Public health, 57 three, 48–49 Puget Creek Restoration Society, 502 twelve, 65–68, 115 systems can be improved, 699 Q two, 46–48 Qualitative approaches, 32 Probability and uncertainty, 269 R Problem identification, flow chart of inputs Randomness, 37, 78, 87, 200, 251, 330, 487, for, 718 488, 677 Problems multi-causal, 6 Reaction delay, 382 Problem solving Realized complexity, 112–113 Redundancy mathematical problems, 348–349 path-finding, 349 and codes, 274 pattern matching, 351 in computer design, 206 translation, 350 and confirmation, 88 Problem-solving approaches copies and evolution, 534 abstract thinking, 702 and coupling strength, 145 affordance, 701 and evolvability, 536 invention, 702 in experimental process, 88 Process and information, 88 adaptive, 132 and pattern, 89 as black box, 234 and reducing effects of noise, 275 communication between, 362 and resilience, 447 in conceptual systems, 248–249 and room to explore, 536 coordination of, 404–423 Relational description of, 234–236 structure, 13, 22, 121, 478 diffusion, 118 webs, 11, 14, 16, 20, 48 of pattern recognition, 128 Relationships, 4 predictable and unpredictable, 54, 249–251 Remington, K., 44 transformations of, 239–256 Replication. See also Copying Profit, 50 errors in, 473 motive, 53, 62, 67, 582 Reproduction Programming, 31, 201, 318, 323, 324, 506, and adaptation, 468 asexual, 552 594, 634, 647, 648, 661, 665, cancerous, 446 678, 680 characteristic of life, 162 for pattern recognition, 131 and chromosome crossover, 556 Progress and coevolution, 161 and competition, 534 and copying, 473 and cooperation, 561 and copying errors, 536 and evolution, 476 and copy of system, 545 hopes for, 64 and cultural models, 572 systemic meaning of, 531 and DNA, 550 Progressive complexity in eukaryotic cells, 532 evolution of, 531–533 of the fittest, 537 mechanisms of, 533–536 in machines, 38 Properties of features, 128 measurement of, 125–126 of networks, 140

Index 751 and maintaining food web, 162 Scale, 501 microbial, 49 and features, 127 and mutations, 554 as prediction of environment, 569 Science(s) and probing possibilities, 304 fields of, 5 and propagation of fitness, 469 method of, 80 rate and evolving around problems, 49 as more than method, 7 rate of and species resilience, 246 social, 7 rates of, 569 social sciences, 7 rates of and system stability, 163 and specialization, 8 selecting out less fit, 559 what constitutes a science, 7–8 and selection for fit, 470 and selection of fit, 52 Scientific and selective fit, 438 method, 7, 38, 64 as self-copying, 545–546 process, 7, 593 and self-model, 60 sexual, 532 Second Law of Thermodynamics, 26, 34, 107, and species expansion, 224 118, 205, 207, 227, 228, 285, 308, time scales, 51 393, 486, 488, 494, 529 Resilience active, 244 Selection and changing conditions, 441 for group cooperation, 564 in changing environments, 266 in multiple dimensions, 557 in complex adaptive systems, 360 role of climate, 568 and complexity, 577 role of coevolution, 568 and cybernetic mechanisms, 31 role of competition in, 560–561 of cyclic systems, 497 role of cooperation in, 561 definition of, 204 role of environmental factors in, 567 and gridlock, 13 role of geological factors, 567 and hierarchical cybernetic systems, 592 and homeostasis, 245 Selective pressure, 501–502 and human communication, 246 and combinatorial potential, 502 and human strategic abilities, 582 and information feedback, 372 Self-assembly, 500 and information flows, 357 Self-copying, 545–546 and information, knowledge, and Self-image, 61 Self-organization. See Auto-organization computation, 360 Self-reference, 20, 304 of life, 581 Self-regulation, 28, 33, 34, 368, 370, 374 in living organisms, 245 Self-replication. See Self-copying in manufacturing system, 244 Senge, P.M., 621, 634 and optimizing internal operations, 450 Senses as measuring devices, 126 and strategic control, 450 Set points, 381, 390, 408, 412, 415, 416, 434 Resource depletion, 580 Response regulation of, 415 constructor, 398 Shannon, C., 34, 35, 268, 269, 274, 280, 281 mechanism, 398 Shared understanding, 63, 64, 721 Robot search strategies, random Signal definition of, 274 Simon, H., 170, 173, 175, 193, 199, 200, 212, versus stochastic, 250 Rumelhart, J., 343, 691 475, 604 and description of complexity, 193 S and description of structural hierarchy, 37 Sagan, D., 186, 564 Simulation of systems. See Modeling systems Sampling rates and time scales, 375–378 Six degrees of separation, 149 Small world, 149 Smith, A., 449 Smoking, 11–13, 16 Sober, E., 561, 564, 565 Social auto-organization and information flows, 494 Social complexity and collapse, 207

752 Index Social evolution and human flexibility, 571 exponential process of, 576 human versus non-human, 50 positive feedback loops in, 577 interpretive, 433 interweaving of, 570 Social networks, 148, 150 for maintaining life, 578 Social process in prisoners dilemma, 562 for rapid economic gain, 57 and coevolution, 572 rate of modifying, 49 and environment, 573 selection for, 161 Social sciences, 7, 8, 80 self-maximizing, 61 Society short-term, 472 and fit with the environment, 578–584 for structural analysis of complexity, 37 and sustainability issues, 581 symbiotic, 489 Sociology, 7, 8, 16, 35, 39, 54, 113, 149, 515 Striedter, GF, 187, 534, 639 Software, 323 Structural Solar energy system, 256–260 knowledge, 55, 56 analysis, policy consequences of, 259–260 organization, 109 black box white box analysis of, 256–260 Structural hierarchies Sources and sinks, 108, 235 as decomposed, 611 and environmental problems, 581 as measure of complexity, 174–175 Specialization, 6, 11, 16, 19, 37, 51 upward growth of, 405 Stability Structure, 97 and energetically favored connections, 240 dynamic, 14 and environmental conditions, 177 and minimal energy configurations, 494 in far from equilibrium system, 39 Subjectivity, 87 operational, 384 and objectivity, 87–89 passive versus active, 244 Subsystems resistance to disturbances, 244 hierarchy of, 108, 175 response to disturbance, 242 and processing inputs, 234 Stability and change as factors in within systems, 108 Subsystems of internal control and managing evolvability, 536 Statistical models and regression analysis, 656 complexity, 185 Stengers, I., 16 Supervenience, 516 Stimulus-response costs in, 396 Sustainability, 9, 10, 25, 28, 40, 51, 145, 366, Stochastic, 447 392, 429, 433, 440, 450, 471, 575, environments, 427 592, 621 model, 652 issues and social dynamics, 581 Stochastic process, 249–251 as systemic issue, 583 and statistical probability, 250 Sutton, R.S., 393 Stocks and flows Symbiosis role in selection, 564 ecosystem model of, 160 Symbiotic strategies and auto-organization, 489 modeling of, 661–663 Symbolic language, 20, 304, 307 Strategic challenge Synapse and memory traces, 335 solutions to, 429 Synergy, 11, 34, 47, 57, 62, 562, 580 and sources and sinks, 428 Synthesis Strategic control emergence of, 450 and analysis, 590 Strategic management of approaches to complexity, 170 and plans, 433 of ATP, 631 summary of, 434 autocatalysis of, 499 Strategies neo-Darwinian, 548 against bacteria, 58 System(s) complexity of, 475 in the abstract, 21–23, 28 cooperative, 439, 509 and atomism, 82 evolution of, 571 boundaries of, 73–75 exploration versus exploitation, 432 canonical, 121 genetic, 438 hard-wired, 450

Index 753 causal dynamics of, 12–14 properties of, 99–116 changing environments of, 529–531 ratcheting, 15 chaotic, 249–251 as receiving and sending information, communication between, 361–365 composition of, 96–99 54–56 conception of, 119–122 as relational organization, 6 conceptual, 248 resilience of, 244–246 conceptualization of, 30 resource reliance of, 444 as containing models, 646 response to disturbance, 242–246 and scales of space and time, 49–51 of other systems, 58–60 simulation of, 625–626 as containing self-models, 28, 646 social, 65 decay, 26 sources and sinks of, 235 decomposition, 595–623 as stabilized by regulatory subsystems, 27 definition of, 73 in steady state, 241 dissipative, 16 stress to, 243 as encoding knowledge, 26 subclasses of, 8 engineered, 15 three domains of, 22 engineering of, 708 as understandable, 62–65 and environment, 116–119 ways of viewing, 32 and environment change and response in the world, 21 Systemic expectation between, 246 becoming self-referential, 515 epistemology of, 84–89 and constraints of social system, 571 as evolving, 26 difference of autocatalysis, 515 and expectation, 291 differs with emergence of complexity, 515 formal definition of, 75 and disturbances, 242 function disturbances to, 242–244 and ecosystem collapse, 569 as improvable, 65–68 of ecosystems, 569 improvement of, 65–68 and evolution, 454 from the inside, 79–81 of market system, 582 interactions, 60 modification by information, 298 of interest, 20 modified by catalysts, 510 interface with environment, 419–423 new field of in by-products, 496 internal organization, 99–116 as progressively constrained by kinds of change in, 527 knowledge, 75 organization, 491 layered, 61 Systemic fit schematic diagram of, 469 (four) levels of, 20 Systemic levels differing aims, goods, 30 and levels of complexity, 25 System in software, 21 life cycle of, 709–714 System knowledgebase mathematic, 78 mechanical as model of system, 629 as product of decomposition, 623 and entropy, 228 System maintenance and entropy, 229 cybernetic, 371 Systemness heat engines, 228 Principle 1, 20 mechanistic model of, 9 Systems analysis. See also Decomposition in the mind, 21–23, 26, 28, 87, 89, 94, 120 constraints to understanding, 594 modeling of, 624–629 examples of, 630–643 as networks of components, 48–49 and life-cycle analysis, 623 open and semi-closed, 236 open issues in, 619–622 open to energy flow, 240 purpose of, 590 from the outside, 77 understanding parts and wholes, 82 personality, 97 of adaptability, 592 as processes organized in functional of complexity, 591 limited hierarchies, 22 limited: by choice of subsysten, 595

754 Index Systems analysis. See also Decomposition (cont.) monitoring performance, 727 meaning of, 590 needs assessment, 716–718 of organization, 591 problem analysis, 720 of persistence, 592 solution analysis, 721–724 as purpose of systems analysis, 590 solution construction and of system development and evolution, 593 documentation, 724 solution delivery, 726 understanding as limited solution design, 724 by brain capabilities, 594 solution testing, 726 by CAS change during analysis, 594 upgrade or modification by misapplication of analysis, 594 decisions, 728 understanding of behaviors, 592 Systems engineer, role of, 708 Systems analysis examples Systems in the world, 21–23, 28, 29, 81, 94, biophysical economics, 637 119, 120, 123, 333, 367, 647, 696 business process, 632–635 correlated with systems in the mind, 89 cells and organisms, 630–632 Systems life cycle human brain and mind, 637–643 decommissioning, 713 Systems dynamics, 19, 40 development and birth, 710 and adaptron model of neuron, 691 early development, 711 Systems dynamics modeling mature useful life, 711 and DYNAMO programming language, 661 obsolescence, 712 limitations as approach to modeling, 664 Systems science population dynamics example of, 682–686 and boundary-crossing, 6 stock, flows and feedback in, 661–663 as complement to disciplinary science, 5 strengths as modeling approach, 664 to connect knowledge, 3–5 Systems engineering description of, 8–10 and approaches to problem-solving, 700–703 and disciplinary boundaries, 16 and complex modern problems, 707 and integration of knowledge, 5 for complex problems, 704 interaction of with disciplinary and crafting techniques, 703 and definition of problem, 705 science, 6 IEEE standards for, 730 as mode of inquiry, 10–12 INCOSE standards for, 730 philosophical background of, 82–89 and mathematics, 704 Principle 1, 20–22 NASA standards, 730 Principle 2, 22, 46–48 the process of, 715–730 Principle 3, 23–24, 48–49 and real-life needs, 731 Principle 4, 24, 49–51 and science, 703 Principle 5, 51–52 standards for, 730 Principle 6, 26, 52–53 and system life-cycle, 709–714 Principle 7, 26, 54–56 what is a solution, 722 Principle 8, 27, 56–57 Systems engineering process Principle 9, 27, 58–60, 646 as adaptable or evolvable, 715 Principle 10, 28, 60–62, 646 analyzing components of solution, 722 Principle 11, 28, 62–65, 647 considering feasibility, 722 Principle 12, 29, 65–68, 115, 699 design documents, 725 Principles of, 17–30 documentation for stages, 719 and relating parts, 6 evaluating performance starting place, 73 table of contributing fields, 18 discrepancies, 727 topics, 31 functional specification for all levels of what is systems science, 8 Systems theory, many forms of, 9 system tree, 723 Systems thinking, 7, 9, 13, 19, 32, 81, 82, identification of problem, 718 model of, 716 114, 621

Index 755 T V Tactical control, 419–423 Validation of models, 653, 654 Varela, F., 40 in living organisms, 422 Variations Tactics versus strategy, 430 Tainter, J., 25, 208–210, 578 functional, 115 TB, drug resistant, 45 inheritable, 52 Technology and growth, 577 Vastag, B., 49 Teleology, emergence of, 510 Visual processing of patterns, 131 Temporal patterns in neural processing, 345 von Bertalanffy, L., 18, 33, 34, 39 Thermodynamics, 227–234 von Neumann, J., 34–36, 38, 323 and energy gradients, 227 W Thresholds, 14, 16, 37, 307, 571, 579 Warfield, J.N., 43 Time Weaver,W., 35, 202, 203, 268 Web of relationships, 9–12 delayed response, 382 Well-being, 30, 55, 57, 63–65, 67, 221, 305, evolution, 52 evolutionary, 129 449, 569, 570, 579, 580, 675, 706 intervals and measurement of dynamics, 214 White box analysis, 237–239 sampling frequency, 378–380 Wholeness, 89 scale, 49 Wholes Time scales and coordination, 416 concepts of, 119 and evolution, 464 and environmental flows, 116 and organization management, 188 and holism, 11 Tobe, R.G., 66 kinds of, 83 Tool-making emergence of, 519 more than sum of parts, 83 Trade-offs, 25, 65, 67 open systems as, 34 Transduction, 284 and organicism, 33 and second law of thermodynamics, 285 outside and inside perspectives on, 224 Transfer functions, 624 and parts, 82 Tree organization diagrams, 114 and parts levels of, 82 Turbulence, 15, 173, 649, 655 and pattern recognition, 339 Turing, A., 35, 322, 323, 325, 328 reality of, 82 Turing Machines, 322 relational, 33 systems as unified, 584 U Whole systems, 5, 6, 9, 44, 117, 219, 226, Unanticipated consequences, 4 Unintended consequences, 10, 48, 63, 208, 239, 359, 435, 629, 714 Whorf, B.L., 518 210–211, 581, 706 Wiener, N., 34, 373 Universal Turing Machine, 322 Wilson, D.S., 561, 565 Universe Wilson, E.O., 562 Wolfram, S., 38, 201, 511 connectedness of, 137 Work and energy input, 227 four forces of, 102 World system, 4, 5, 113, 360 organizing process of, 479 size of, 103 disconnect with specialized systemness of, 134 disciplines, 4 Unpredictability and chaos theory, 252 holistic understanding of, 5 and complexity, 17 World Wide Web (Web). See also Web of in stochastic processes, 174–175 Unpredictable change, 13 relationships Urey–Miller experiment, 512 as adaptive, 148 as physical and logical network, 142 and social networks, 148