There is an old saying  “necessity is the mother of invention” (in German “Not macht erfinderisch”) which means difficult situations inspire innovative solutions. It goes back to Plato who speaks in The Republic of necessity, who is the mother of invention: “let us begin and create in idea a State; and yet the true creator is necessity, who is the mother of our invention.” When people really need and want to do something, they will figure out a way to do it.  Are people on the edge of survival more innovative than others? We argued earlier that innovation is an adaptation to the permanent threat of extinction. Individuals which are critically endangered or threatened by extinction have a stronger incentive to be innovative than others.

One example are the people of two small valleys in the Italian dolomites, Val di Zoldo and the neighboring valley “il Cadore” (or “Val di Cadore”), with the towns Zoldo Alto, Forno di Zoldo and Zoppe di Cadore. In the second half of the 19th century an economic crisis hit the Val di Zoldo, a narrow, picturesque valley in the Dolomites. Today, the region is a wonderful skiing area, but at that time the economic crisis forced residents to reinvent their professions. Many emigrated to Austria and Germany, trying to succeed in different professions. The official site of the valley says:

The people of the Zoldo Valley, always forced to emigrate in search of work, did their best in every field: shipwrights in Venice, carpenters in the building of roads and dykes, woodsmen, mechanics, pastry makers, peddlers of cooked pears and candied fruit. From the mid-19th century the Zoldo Valley inhabitants started to produce and sell their own ice-cream.

Selling homemade ice cream (“gelato”) was apparently one of the more successful activities, and finally the famous Italian ice cream carts and parlors were born. Due to the excellent quality of the ice cream produced by the Italian ice ceam makers according to local Zoldo tradition, the Italian ice-cream immediately gained success establishing itself by far the best on the European market. Zoldo Alto became the valley of the ice cream makers: Val di Zoldo is now known as the “valle dei gelatieri”.

Today, allegedly 75% of the ice cream parlors in Germany come from two small valleys in the Italian dolomites, val di Zoldo and Val di Cadore. What can we learn from this? On the edge of survival, ice cream can save your life..

(The Flickr photo of the strawberry ice cream is from Flickr user Jessica Merz)

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Unmanned aerial vehicles (UAVs) are the latest and most advanced weapon technology in warefare, like rockets in WWII. Both can be used as deadly weapons. The V2 rocket developed by Wernher von Braun in Nazi Germany at the time of WWII was a weapon used to attack London, and it enabled intercontinental ballistic missiles, but it was also the prototype and foundation for the Saturn V moon rocket 20 years later. Rockets had a scientific and a civil purpose in the form of space exploration.

What is the value of UAVs, can they be used for civil and scientific purporses, too? Well, they can be used for exploration, for example for search and rescue missions. And they can be used as autonomous vehicles to explore foreign worlds. NASA’s Mars exploration rovers Pathfinder, Spirit, and Opportunity were very successful. They are controlled remotely like UAVs. The latest and largest NASA rover “Curiosity” is scheduled to land on the surface of Mars in August 2012.

Space exploration is exciting. But as new technology develops, old becomes obsolete, and astronauts seem to be among the obsolete stuff. The more distant the destinations, the easier it is to use robots instead of humans, and the more important autonomous behavior becomes. Astronauts are threatened by extinction. The heros of space exploration were once indispensable because the computers of the PC stone age were not developed enough. Today, astronauts are no longer necessary for many exploration missions, machines and robots can do a much better job: they don’t have to return to Earth.

Thus pilots and astronauts are more and more replaced by remote operators and in some advanced cases and in really remote sites, by intelligent autonomous agents. The ultimate UAV would be an AI-empowered machine, an UAV controlled by an AI, by an artificial agent who understands the world. Operating without a remote controller would be like leaving the earth orbit, and would require a very strong engine. In this sense, building the ultimate UAV or creating an AI is like reaching the moon in the 21st century: agents are the astronauts, UAVs are the new rockets, artificial curiosity is their fuel, adaptation their propulsion, and data centers their engine. Instead of space and cosmos, these agents would explore unknown foreign or virtual worlds.

I guess the secret of true artificial intelligence lies here, at the path to AI-empowered machines:

• create a device capable of travelling to the moon: an extremely adaptive system (with vast cognitive capacity and the right kind of internal architecture, let us say an UAV controlled by an own set of datacenters)
• place the system on the launch pad, i.e. in an artificial environment which is complex enough. Either you build robots (bring the computer in the world), or you create agents for virtual worlds (bring the world in the computer). In both cases you need a strong engine, a large number of really powerful data centers to let the device take off
• launch the device, let the system learn and grow

Some cosmologists and astrobiologists even claim that this is the ultimate path any intelligent civilization must follow if it wants to explore the universe. They argue if we ever encounter aliens, it will be in the form of intelligent machines, because biological intelligence would only be a transitory (!) phenomenon. Paul Davies for instance says in his new book “The Eerie Silence” that any aliens exploring the universe will be AI-empowered machines:

“I think it very likely – in fact inevitable – that biological intelligence is only a transitory phenomenon, a fleeting phase in the evolution of the universe. If we ever encounter extraterrestrial intelligence, I believe it is overwhelmingly likely to be post-biological in nature.”

Davies writes further

“Human intelligence is no more than a few hundred thousand years old, depending somewhat on definition. In a million years, if humanity isn’t wiped out before that, biological intelligence will be viewed as merely the midwife of ‘real’ intelligence – the powerful, scalable, adaptable, immortable sort that is characteristic of the machine realm. Thereafter, machin intelligence will accelerate in power and capability until it hits fundamental bounds imposed by the physical environment, whatever they might be.”

(The pictures of an unmanned aerial vehicle and the NASA rover curiosity are from Wikipedia)

 
If genes can be the blueprints of bodies, is there such a thing as a memetic blueprint, a blueprint made out of ideas, memes, or cultural genes for a cultural body, too? Is there such a thing as a cultural or corporate DNA for a memetic body? Owl city sings that “dreams don’t turn to dust”, but if there are memetic bodies, can there be memetic dust, too? Can “cultural genes” be expressed like biological ones?

A cultural or corporate DNA would be a set of ideas or memes which is sufficient to reproduce or recreate the corresponding culture or corporation. A party for example has a manifesto, program or charter which defines the identity of the party. The genes from this cultural DNA are expressed in a party conference: the information from the books, charter or program of the party is read and translated into the language of the ordinary people. It is a process where someone reads the “genetic” information, so that the members of the group can translate the information later into appropriate behavior.

We know that architects create blueprints for buildings, cooks create recipes for meals, and composers create compositions for pieces of music. But what about naturally evolving systems – complex adaptive systems like languages, cultures, countries, companies or even persons? Who creates their blueprint? For political parties and economic entities like companies and corporations, it is often the founders. A culture or corporation be coded in a set of instructions, rules, guidelines, recipes and policies. These policies can also be contained in a book which describes the history of the founder. Biographies of persons, histories of countries or companies, stories for a culture or civilization are maybe the thing which is the most similar to such a memetic blueprint. One difference to genetic blueprints is the temporal relationship: genetic blueprints exist before the life of the individual, whereas biographies of persons and histories of companies exist only by hindsight. Often the set of rules, guidelines and policies which define a nation, corporation, culture or organization emerge during the course of time: the identity emerges. The bureaucratic rules of Wikipedia and Stackoverflow are an example.

Social systems, organizations and cultural objects in general such as sports clubs, companies, corporations or even societies can be viewed as constructs of cultural DNA: companies for instance contain Corporate DNA, the basic information which defines the corporate culture and the corporate identity, and which is often related to the history of the company. Google for example has the two famous rules “Don’t be evil” and the “20% rule”, which says employees get 1/5th of their time to work on projects of their own choosing, to name only two. There are more policies, guidelines, rules and traditions, for instance the tradition to use inexpensive commodity hardware. And of course not all of them are public.

While the DNA of Google contains a preference for Linux, C++, and Python (and also some bits of Java), the DNA of Microsoft contains a preference for MS-DOS, Assembler, and Basic. They still offer a Visual Basic compiler. In the beginning Microsoft was a programming language company in Albuquerque building BASIC interpreters. At that time it was really innovative. The programmers at Microsoft – originally only Paul Allen and Bill Gates – did their early work in Assembler on PDP minicomputers from DEC. Things in the computer industry are moving fast. Does anyone know DEC today? DEC built minicomputers, and nobody needs minicomputers today. Digital Equipment Corporation (DEC) existed only from 1957-1998. Later Microsoft bought Q-DOS (Quick-and-Dirty Operating System) from Tim Paterson and adapted it as MS-DOS for the IBM PC. The rest is history.

All companies try to be innovative, creative, and most of all, successful. Every business has it’s own style, office environment and culture. Some companies are evil and digest people: they chew you up and spit you out. It is also called “burn and turn” philosphy: they use you up, then turn you out for some new hire who has new ideas and will work for half the money. Some companies are bold when it comes to innovations and yet anxious that somebody steals their ideas. These companies have often very strong security policies. Other companies are very political: it is important who you know, and not what you know, if you want to make a career. If we look at the major technology companies today, one could say roughly that Apple has a marketing culture, Google a technology culture, and Microsoft a political culture:

• Apple: marketing culture focused on design which leads to beautiful products and marketing driven decision making but also a totalitarian culture, high levels of secrecy and very strong security policies
• Google: technology culture focused on engineering which leads to awesome products based on state of the art technologies
• Microsoft: political culture focused on power. Originally it was a technology culture like Google in the beginning, now more and more an isolated and idiosyncratic culture focused on itself.

The corporate DNA is made of the company culture – a set of customs, rules, policies and guidelines -, corporate knowledge, and the business idea. It can be altered. This cultural DNA can be written down somewhere, in a book (for example the biography of the founder) or a website, but it also can exist in form of stories and myths, which may condense later into written form. Languages themselves are defined by a set of semantic (a vocabulary) and syntactic (a grammar) rules. Not only companies, but also social groups and societies in general are based on a common set of shared customs, laws, and policies. In this sense, both genes and memes are capable of having complex “bodies”. Corporations, organizations, languages, religions, complex societies and maybe even whole civilizations can be considered as memetic bodies, at least to some extent.

A cultural or corporate body can have multiple set of “cultural” genes, which can be merged and split apart again. Smaller companies swallowed successfully by larger ones often have their own DNA. The YouTube, DoubleClick, or Motorola Mobility departments form Google may have their own set of rules and guidelines, because they were originally independent companies before they were acquired by Google. Such “swallowed companies” sometimes remain their own character, i.e. their own set of corporate rules, just like Mitchondria and Chloroplasts which have their own DNA although they are a part of a larger cell which has a central core containing the DNA for the whole cell. Lynn Margulis proposed the theory of endosymbiosis, which says that basic organelles in eukaryotic cells – mitochondria in animals and chloroplasts in plants – originated in free-living bacteria, which were swallowed and integrated in the cell. There you have it Motorola and DoubleClick – you may generate your own energy (“revenue”), but you are nothing but a chloroplast 😉

Individual minds can at best be described as the place where multiple bodies, genetic and memetic bodies, meet. As cultural beings, we live in multiple worlds, are members of different organizations, cultures, countries and companies, and have multiple circle of friends. Often we have a special name, nickname or ID in each cultural world (in the film Matrix, the main character Neo lives in different worlds, in the Matrix as Tom Anderson, and in the real world as Neo). Each of these cultural worlds we live in has its own rules. The biological DNA is a blueprint for a self-constructing being. Biological organisms construct and build themselves, by being adaptive and curious.

To anwser the questions in the beginning: yes, yes, and yes. Yes, there is something like cultural genes or corporate DNA. Yes, there is something like memetic dust: isolated ideas, single slogans, party program snippets, individual guidelines. And yes, “cultural genes” can be expressed like biological ones, for example through party conferences, regular company meetings, etc.

(the DNA picture is from Wikipedia)

In the film Tron:Legacy, ISOs or isomorphic algorithms are digitally-evolved independent forms of AI, artificial lifeforms that have somehow spontaneously evolved and emerged from the artificial environment of the virtual world. Nice technobabble, isn’t it? Technobabble is meaningless chatter about technology, technobabble is for example if in Star Trek someone says to the captain “try to re-modulate the shields with a tachyon beam; that might slow them down” or “what if we modified the main deflector to emit an inverse tachyon pulse. That might be able to scan beyond the subspace barrier”. Although this makes of course no sense, in the fictional Star Trek universe, actions like “modifying the main deflector to emit an inverse tachyon pulse” always work.

ISO images are known as disc images. An ISO image is an archive file of an optical disc, for instance a CD. Here ISO means something different, it is the abbreviation for “isomorphic algorithms”. Ismorphic in mathematics means having the same form and structure. Objects which may be represented differently but which have the same essential structure are often said to be “identical up to an isomorphism”. An isomorphism is a bijective 1-to-1 mapping between two mathematical structures. Two structures (for example graphs or groups) are said to be isomorphic if an isomorphism between both exists. So what are isomorphic algorithms? I have no idea, it is only technobabble which does not have to make sense. It would be interesting if there would be a connection between isomorphisms and new forms of AI, though.

Recently there was indeed someone who talked about isomorphic javascript code. I think it makes sense to consider code as isomorphic to other code if it has the same function but looks different (different names, different formats, ..). Similarly, algorithms would be isomorphic to other algorithms if they have the same function but a slightly different implementation. However, if every isomorphic algorithm looks like Quorra (Olivia Wilde), the last known isomorphic algorithm in the film Tron Legacy, then I would like to get to know a few..

Besides “isomorphic algorithms” (which contain the interesting idea that the mind is a kind of algorithm or program), there are a few appealing ideas in the film, mainly the DNA disc and the complex virtual world.

The mind is a program. Is the mind an algorithm or a program? An algorithm is an effective method for solving a problem expressed as a finite sequence of steps. A program is a sequence of instructions created for the automatic performance of a particular task, usually to perform a specified task with a computer. Now, the DNA is certainly an algorithm, it is a biological algorithm to build a biological body in a finite number of years, whereas the mind is more a program than an algorithm, a program mainly created to replicate the “selfish” genes of the biological body it controls. It could be viewed as the result of a complex interplay of various algorithms: the biological algorithm in form of DNA and various cultural algorithms which specify how to build persistent cultural objects. It contains the patterns for the local evolution of the computational processes in the brain in form of various cognitive rules and procedures. According to Abelson and Sussman, “a procedure is a pattern for the local evolution of a computational process”.

The DNA disc. How will new forms of AI look like? Will they have a new form of DNA? AIs with DNA, an interesting idea. In the film, each program or artificial character carries a disc with the digital DNA of the person. It contains the program or the “blueprint” of that person. In the real world, the DNA is a kind of biological boostrap algorithm to build a body out of biochemical molecules. A blueprint to build a biological body. Our minds have been formed by various kinds of cultural DNA: the social behavior we learn from society, the customs and language we learn from our parents and our family, the knowledge we learn in school, college and university, the skills we learn in companies and corporations at work, etc. Therefore a single DNA disc would not be enough to specify a human, unless it contains many different blueprints. Each of us is built and formed by the influence of multiple discs: a biological, a social, a cultural, etc. Such discs can be easily exchanged in the film. In the real world, there is an inseparable mapping between body and mind, it is impossible to separate them. Each mind is intrinsically tied to a biological body. In a virtual world, it would be much easier for an AI to separate body and mind or to exchange bodies, just as we are able to copy software from one computer to another.

The virtual world. Virtual worlds a places of infinite possibilities. In the film the “grid” is a complex lifelike world which resembles the “matrix”. A complex lifelike artificial 3D world is certainly a place were an AI could emerge, although probably not spontaneously. Supervision is needed for supervised learning, and one can hardly learn a (new) language without supervised learning. In the film, the isomorphic algorithms were create in the “sea of simulation”. If AIs emerge at all, then a virtual world or a “sea of simulation” would be a perfect place. To create a true AI, we would either have to build a robot in the real world, which is controlled by a large number of advanced datacenters, or an agent in a complex lifelike virtual world. Like the robot in the virtual world, a number of datacenter would be needed just to control the agent. You need two kinds of datacenters, one group that renders the virtual world (the “real”, physical world for the agent), and one that executes the program of the agent (the “mental” world). The difficulty is to make meaningful connection. Once an AI has been made in a virtual world, it should be able to transfer it to the physical world by using a physical body, i.e. a robot. And if we manage to create a robot with a real AI, or to control a robot with an AI, we are able to create an AI in a virtual world by controlling a autonomous agent instead.

Many resarchers who work in “complexity science” or deal with “the sciences of complexity”, especially scientists associated with the Santa Fe Institute (SFI), complain a lack of a sound, rigorous mathematical theory. We have a few suggestive metaphors and disparate piece of knowledge, but no rigorous and useful mathematical framework. John Howard Miller, Scott E. Page, Melanie Mitchell, Stuart Kauffman, James Crutchfield and Steven Strogatz argue that we need a calculus of complexity, a mathematical theory that describes complexity in a general way, which would allow us to explain phenomena and make predictions across many different systems. It is an open question if such a theory exists.

Miller and Page ask in their book about “Complex adaptive systems” [1] if there is a mathematics of complex adaptive social systems (Appendix A.4, p. 234)

“While computational models of complex adaptive social systems are a valuable theoretical tool, there may be other complementary tools that can be developed. The calculus allowed us to take certain, difficult-to-solve, nonlinear equations and reform them into simple linear problems. Is there a mathematics of complex adaptive social systems that will provide a similar transformation? Any simulation can be written as an instantiation of a recursive function, suggesting that a given model run is nothing more than a sequence of interconnected algebraic equations. But can we say something more general here? Ultimately, we are seeking a simple explanation for complex behavior. While there are examples from cellular automata that suggest that the only way to predict the future behavior of the system is to let it fully run out, the obvious hope is that there are other opportunities to uncover more compact descriptions of complex behavior.” [1]

Steven Strogatz said in his book SYNC [2] that we need the conceptual equivalent of a calculus:

“We’re still waiting for a major breakthrough in understanding, and it could be a long time in coming. I think we may be missing the conceptual equivalent of calculus, a way of seeing the consequences of the myriad interactions that define a complex system. It could be that this ultracalculus, if it were handed to us, would be forever beyond our comprehension. We just don’t know.” [2]

He says that traditional calculus is based on the concept of change: “Calculus is the mathematics of change. [..] you’ll find two ideas shining through. All the rest, as Rabbi Hillel said of the Golden Rule, is just commentary. Those two ideas are the “derivative” and the “integral.” Each dominates its own half of the subject, named in their honor as differential and integral calculus. Roughly speaking, the derivative tells you how fast something is changing; the integral tells you how much it’s accumulating.”

Stuart Kauffman writes in his book “At Home in the Universe” [5] on page 299:

“We lack a theory of how the elements of our public lives link into webs of elements that act on one another and transform one another. We call these transformations ‘history’. Hence with all the accidents of history, one must engage in a renewed debate: Is there a place for law in the historical sciences? Can we find lawlike patterns, cultural, economic, and otherwise?”

This question is quite similar to the question of Leo Nikolayevich Tolstoy in his epic novel War and Peace (Book 11, Chapter 1): “Only by taking infinitesimally small units for observation (the differential of history, that is, the individual tendencies of men) and attaining to the art of integrating them (that is, finding the sum of these infinitesimals) can we hope to arrive at the laws of history.”

Both Kauffman and Tolstoy are great story tellers, and history is teld in stories, too. Is history just a collection of stories or more? It is unclear if we can establish universal “laws of history”, as they say, but it is clear that the most basic law which governs history is evolution and co-evolution. Besides evolution, what else can we say if we concentrate on agent based models? Are phenomena like the “Butterfly Effect”, “Path Dependence” or “Frozen Accidents” equivalent to laws? Is the micro or the macro level more important for the “laws of history”?

Melanie Mitchell thinks we need a calculus of complexity, too. She asks in her ook “Complexity – a guided toor” [3]:

“We are waiting for the right concepts and mathematics to be formulated to describe the many forms of complexity we see in nature […] Calculus provides a mathematical language to rigorously describe change and motion, in terms of such notions as infinitesimal, derivative, integral, and limit. These concepts already existed in mathematics but in a fragmented way. Newton was able to see how they are related and to construct a coherent edifice that unified them and made them completely general. This edifice is what allowed Newton to create the science of dynamics. Can we similarly invent the calculus of complexity that captures the origins and dynamics of self-organization, emergent behavior, and adaptation in complex systems?”

Let us summarize what Miller, Page, Kauffman, Mitchell, and Stogatz said so far.  We would like to to describe the many forms of complexity in nature,  and we are..

• ..waiting for the right concepts to be formulated
• ..seeking a simple explanation for complex behavior in general
• ..looking for the conceptual equivalent of calculus to explain complex systems
• ..trying to find universal “laws of history” to describe the change of complex systems

Wait – to seek explanations for complex behaviors and to find a calculus of complexity in general, is the task of science in general, isn’t it? If there is a general theory, a theory of everything, for all these vastly diffent kinds of complex systems we find on Earth it is simply evolution. Evolution is the only theory that can be applied to nearly all fields, subjects and systems, because everything has evolved and is still changing and evolving. Therefore the most universal theory of science is evolution, so if we can explain evolutionary and complex adaptive systems, we have made good progress. There is a lot we know about evolution already [6]. We can describe certain aspects of evolutionary systems by mathematical modelling.  And we can apply the concepts of evolution to other systems. What else can we do?

Let us go a step back. The reason why we can go beyond suggestive metaphors in complexity theory and why we can examine complex systems systematically in the first place is clearly the advance in computational power. Computing has pervaded nearly every science, but computing itself is not the science of nearly everything. Agent-based computational models are the scientific instruments to examine complex systems. They are the tool of choice to understand the complexity of economic, ecological, and social systems [7]. CA and ABM modeling allows us to explain the common patterns of some complex systems, and simple recursive algorithms allows us to produce complex fractals and fractal patterns. Simulation models are powerful tools to explain complexity in nature.

So we have evolution and evolutionary systems on one side, and we know it is the most basic theory. On the other side we have agent-based computational models, and we have found many of the underlying principles of insect colonies, economic systems, the brain, and other complex systems. The important ones are adaptation, emergence, path-dependence, and swarm-intelligence. Although these are appealing concepts, they do not always rest on well-defined notions and definitions. The challenge is to connect and reconcile both sides in a theory of complex adaptive systems.

This can only be successful if the concepts are well-defined, and if they are firmly grounded in computational science, since computational tools allow us to examine complex systems. Calculus provides a way to measure physical objects and their movements. We can hardly measure complexity if it is an ill-defined notion. However we can measure how “patterned” and diverse a system is. A pattern is something we can measure, for example in form of a fractal dimension. Likewise talk about emergence remains fuzzy unless we are focussing on concrete agent-based models.

What kind of models exist? Fractals are the right forms of mathmatics for self-similar forms. Using simple IFS rules, one can produce beautiful fractals and describe a lot of the beauty of nature. RBNs, CAs and ABMs are powerful simulation models.  How far can take us simulation models on the road to a “calculus of complexity”? Is it possible to combine a few smaller models to a larger model? Even simple agent-based models can exhibit complex behavior patterns, but most of them do not. They can not be added and subtracted like functions, although emergence is related to integration in calculus. ABMs can capture certain emergent phenomena, which result from the interactions of individual entities, as Eric Bonabeau says in his paper about agent-based modeling . Basically they tells us how a local interaction pattern translates into a global pattern. The rules of the ABM tells us like the derative how (how fast and how much) something changes locally. The name of the model (for example Conway’s Game of Life, Thomas Schelling’s Segregation model for ghetto formation, Craig Reynold’s Boids model for swarm formation, or Robert Axelrod’s Dissemination model for culture or cluster formation) tells us the overall effect of the rules, how much it is accumulating and how much it will change.

They are all “generative” models, since they generate patterns by repeated execution, just like an IFS. Basically all of them tells us how a local interaction pattern with the nearest neighbor (RBN: network, CA: grid, ABM: loosely coupled) translates into a global pattern. Besides these basic models (RBN, CA and ABM), we can simply use data to explain data. Mathematics is about symmetries and regularities among numbers and quantities. It is so successful and effective because we can explain the symmetries and regularities in Mathematics to explain the symmetries and regularities in the physical world. Complex systems are characterized by an overwhelming amount of data. We can use data to explain and understand data.

There is a Google Research Paper from Alon Halevy, Peter Norvig and Fernando Pereira named the Unreasonable Effectiveness of Data (a play on Eugene Wigner’s essay ‘the unreasonable effectiveness of mathematics in the natural sciences’). They argue the best approach is to embrace the complexity of the domain and address it by harnessing the power of data.

Is data the only way to explain data in some complex systems, too? After all, humans and the societies they live in are constantly changing complex adaptive systems with astonishing abilities and path-dependent behavior. If we know the complete history and biography of a person, company or society, we can explain and predict the behavior by this data to a certain degree. Since more and more data about persons is collected in social networks and elsewhere, this is indeed a possibility.

[1] John Howard Miller and Scott E. Page, “Complex adaptive systems: an introduction to computational models of social life”, Princeton University Press, 2007
[2] Steven H. Strogatz, “SYNC: The Emerging Science of Spontaneous Order”, Hyperion, 2003
[3] Melanie Mitchell, “Complexity – a guided toor”, Oxford University Press, 2009
[4] ACM ubiquity, April 2011: An interview with Melanie Mitchell On complexity
[5] Stuart Kauffman, “At Home in the Universe”, Oxford University Press, 1996
[6] Ruse, Travis, Wilson (Eds.), Evolution: The First Four Billion Years, Harvard University Press, 2009
[7] Joshua M. Epstein, Agent-based computational models and generative social science, Complexity, Vol. 4 (1999) 41-60

John R. Searle, an American Philosophy professor at the University of California, Berkeley, who is known for the Chinese room argument, writes in his book “The Rediscovery of the Mind” that subjectivity is the feature of consciousness that is the most puzzling to philosophical analysis. He writes “we find it difficult if not impossible to accept the idea that the real word, the world described by physics and chemistry and biology, contains an ineliminably subjective element. How could such a thing be?” (p.95 of [1]).  An interesting question. The answer is probably just path-dependence.

We have argued earlier in the blog post about the hard problem of consciousness and its solution, that each of us has taken a slightly different route during his life and is adapted to a slightly different world, i.e. everyone sees a different picture of the world on his trail, and has experienced a different “slice” of the same world along his path. Our subjective experiences arise from a long path-dependent learning process. The pure experience of the present depends on the whole series of previous experiences in the past, our subjective judgements depends on the whole series of previous judgements and personal evaluations (is it good or bad for me?).

Peter F. Strawson (1919-2006), English philosophy professor at the University of Oxford, writes in his book “The Bounds of Sense” [2] that a series of experiences has a double aspect. It contributes to our shared understanding of the common objective world, and at the same time it extends our individual subjective experience of that world. He says on page 105 (chapter “Unity and Objectivity”):

“On the one hand it (a series of experiences) cumulatively builds up a picture of the world in which objects and happenings (with their particular characteristics) are presented as possessing an objective order, an order which is logically independent of any particular experiential route through the world. On the other hand it possesses its own order as a series of experiences of objects. If we thought of such a series of experiences as continuously articulated in a series of detailed judgements, then, taking their order and content together, those judgements would be such as to yield, on the one hand, a (partial) description of an objective world and on the other a chart of the course of a single subjective experience of that world.”

All members of the same culture learn the same public language through supervised learning, but the results of unsupervised learning controlled by personal emotions is private and individual. The former is not path dependent, because supervised learning depends on an external teacher and does not depend on the own judgement. The latter is indeed path dependent, because it involves the self and is affected by positive feedback: small initial disturbances are reinforced and can cause large effects. In this sense subjective experience is self-referential: current positive/negative experiences depend on previous positive/negative experiences, etc.

This means adaptation occurs in two flavors: a shared, objective during common supervised learning (for example in schools, colleges or universities), and a personal, subjective during unsupervised learning. The former is responsible for our common understanding of the public world, the later is responsible for our subjective experience. Strawson continues to say that this double aspect of adaptation and experience can be found even in the smallest unit, the raw experience itself, and becomes visible if a judgement of an experience is altered by hindsight:

“Not only the series as a whole, but each member of the series, has a double aspect. This explicitly emerges when one objective judgement is corrected by another: what remains unaltered when the correction is made is the subjective experience.”

The history of a normal human being determines both, the objective and the subjective experience. Path dependent subjective experience arises from the individual, unique, and personal route one takes, one among many other possible routes. The objective experience, i.e. the common understanding of our shared knowledge, depends more on the goal and the milestones of the route, than on the actual path taken. We see the same world, but along different routes, and from different angles.

As Searle has noticed [1], every subjective experience is always someone’s subjective experience. It is associated with a certain path taken by a particular person. It always belongs to someone, whereas objective facts and knowledge belong to everyone, and can be checked by everyone.

Understanding an individual route which leads to a specific subject experience is of course difficult, but it is much less complicated if we also consider the environment. Herbert Simon has noticed in his book “The Sciences of the Artificial” [3] that “an ant, viewed as a behaving system, is quite simple. The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself.”

[1] John R. Searle, “The Rediscovery of the Mind”, The MIT Press, 1992
[2] Peter F. Strawson, “The Bounds of Sense”, Routledge, 1966
[3] Herbert Simon, “The Sciences of the Artificial”, The MIT Press, 1970

( The Flickr photo is from Flickr user bikehikedive )

There is now a Google+ page for the CAS group, and if you have not noticed it yet, there is a Twitter page, too. Maybe a way to bring more life into the group.. Originally the CAS Group started as a Yahoo discussion group about complex systems here. It was replaced by an own site, i.e. this page which includes a blog and a wiki. Now we have a G+ and a Twitter page, too. Here you can find announcements of blog posts, nice quotes and additional insights into complex systems. In short, everything about complex adaptive systems from gluons to galaxies.

You might also be interested in the G+ pages of the SFI. The Santa Fe Institute has a G+ page, and a Twitter page, too.

According to Robert McKee and his book Story: Substance, Structure, Style and The Principles of Screenwriting, a conflict is the essence of every story: nothing moves forward in a story except through conflict. A conflict propels the story forward, it causes the protagonists to do something, and it is the driving force behind every change.

Is this true for Agent-based models (ABMs) as well? Is every successful ABM based on some kind of conflict? Let us consider three basic models:

• Thomas Schelling’s Segregation Model for ghetto formation:
  neighbors have not the right color
• Craig Reynold’s Boids Model for swarm formation:
  neighbors have not the right place or position
• Robert Axelrod’s Dissemination Model Model for culture formation:
  neighbors have not the right traits

The driving force behind the change is in each case a small conflict. The agents act because their neighbors have not the right color, place, position, trait or attitude. Due to many cumulative interactions, already a small preference can lead to a large effect.

Thomas Schelling showed that a small preference for one’s neighbors to be of the same color could lead to total segregation. Many small conflicts about the right color in the neighborhood leads to large clusters of similar colors in form of ghettos.

Craig Reynolds showed that a small preference for one’s neighbors to be at the same place could lead to fascinating swarms. Many small conflicts about the right position in the neighborhood lead to large clusters of similar positions in form of swarms.

Robert Axelrod showed that a small preference for one’s neighbors traits could lead to separated cultures. Many small conflicts about the right trait in the neighborhood lead to large clusters of similar traits in form of cultures.

We do instinctively things that feel good, and avoid those which do not. This is no accident. As we have seen earlier, emotions are adaptations that serve a fundamental purpose to our survival. They inform us about what is good for us and what is not. Bad feelings are an adaptation to a complex environment with frequent bad conditions, good feelings an adaptation to good ones. A good condition is a favorable person-person or person-environment relationship, a bad condition an unfavorable one.

Bad feelings in particular tell us what is bad for us, what we should avoid, and what (or whom) we should steer off. They provide us information about unpleasant, dangerous and threatening situations. Disgust for example tells us that we should avoid eating something because it might make us ill. In this sense, disgust can be good for us. Disgust is a defense against precarious infections, fear is a defense against dangerous predators, and pain in general is a defense against invasive forces.

Emotions like fear, anxiety, and pain stem from mental adaptations to a dangerous world, they tell us what to avoid. Depression and addiction stem from mental adaptation to a hard world where beneficial resources are difficult to find. They tell us which goals we should to give up and which goals we should continue to pursue.

Thus if we listen to bad feelings, we can extract information that will help us improve our lives, as Nadja Geipert said in this Psychology Today article. In her article, she explains the roots of jealousy and envy. Like the other negative emotions, they tell us that the situation does not look good:

• sadness tells us we are going to lose an important resource or relationship
• disgust tells us that we should avoid something because it might make us ill
• fear tells us that we should avoid something because it is dangerous
• pain tells us that we need to protect our body because it has been damaged
• envy tells us what we need but are not getting, because somebody else has it
• jealousy tells us we are going to lose a loved one to the embrace of another
• depression tells us that we should stop doing what we do and try something different. It is an adaptation to desperate or hopeless situations

Therefore negative emotions are not completely bad, they serve a purpose. Unfortunately, if we identify them and pin them down, they are are not resolved yet. Even we listen to the message, it is sometimes difficult in modern times to cope with it. For example, there is nearly always someone in modern civilizations who has more wealth, beauty, success or fame than you. As a NYTimes article said “envy is a tax levied by civilization“. Or is it?

(The image is the illustration of the corresponding NYTimes.com article from Natalie Angier.)

What are the origins of innovation and creativity? What is the mystery behind the emergence of new companies, new theories, new species, new masterpieces of art? In the language of complex adaptive systems, major evolutionary jumps are equal to the passing of large fitness barriers. As I try to describe in my book and in the wiki, there are at least three different ways to cope with large fitness barriers in evolution, (1) bypass it (2) tunnel through it or (3) overcome it:

1. to bypass through exaptation: explore a different direction and make a side-leap
2. to tunnel right through the barrier by borrowing complexity
3. to wait for a catastrophe, until the barrier is reduced through catastrophic events

Two of them are illustrated in this picture from the book:

To be truly innovative, you must be an optimist, because you must surmont many unsurmountable obstacles: you must go beyond the existing limits of the field. To be innovative in the economy, you must go beyond the limits of traditional markets and discover a new one (or exploit an existing by creative destruction). To be innovative in science, you must go beyond the limits of knowledge. To be innovative in art, you must go beyond the limits of traditional art schools and styles.

To go beyond the existing limits, you must either be really lucky, or try very hard, or both. “Luck favors the prepared” as Louis Pasteur said. Very innovative people often are in an existential crisis, a situation with strong competition or an all-or-nothing situation where the own existence is threatened. For example

• a startup company which has to make a new innovation or to find a new market in order to survive in the economic world. A startup can lead to the emergence of a new company. If it runs out of money before it finds its product and market it will vanish.
• a PhD student which has to find a new thesis or a new scientific field in order to survive in the world of science. A PhD student – the scientific startup – can lead to the emergence of a new scientific theory or whole new scientific field, if he manages to publish the right material at the right time. If he fails, he will perish.
• an artist which has to create a masterpiece or a new style of art in order to survive in the world of entertainment. An art apprentice can lead to the emergence of masterpieces, new schools or styles of art, if he manages to create the right masterpiece at the right time. If he fails, he will hardly manage to live from his art.
• a fighter which has to find a new way to find fight the enemy in order to survive. It may lead to the emergence of new war forms, or if he fails to the complete annihilation.

In this sense, creation and innovation can be seen as a response to destruction and extinction. Innovation is an adaptation to the permanent threat of extinction . The startup in the innovate or vanish position, the student in the publish or perish stituation, or the artist in the create or cease to exist condition: they all share the desperate need to innovate. The drive to create comes often from the threat of extinction. This means it is also possible to create an atmosphere of competition and terror if you want to encourage innovation, a situation where everyone has constantly to fight for survival. Of course one would not want to do this deliberately. Yet in nature, an “eat or be eaten” situation is not uncommon. As Darwin noticed, there is a constant fight for the survival of the fittest in evolutionary systems.