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The Origins of Communication Revisited

Quinn (2001) sought to demonstrate that communication between simulated agents could be evolved without pre-defined communication channels. Quinn’s work was exciting because it showed the potential for ALife models to look at the real origin of communication; however, the work has never been replicated. In order to test the generality of Quinn’s result we use a similar task but a completely different agent archi- tecture. We find that qualitatively similar behaviours emerge, but it is not clear whether they are genuinely communicative. We extend Quinn’s work by adding perceptual noise and internal state to the agents in order to promote ritualization of the nascent signal. Results were inconclusive; philosophical implications are discussed.

The Origins of Communication Revisted

Bicameralidad y la evolución de la mente humana

En esencia la teoría de la mente bicameral es un intento para explicar la consciencia humana partiendo de un estudio comparativo y multidisciplinar basado en historiografía, arqueología, psicología, sociología, antropología, lingüística y neurociencia. Nos aporta una visión radicalmente distinta de nuestro pasado como seres humanos. Solemos imaginar a nuestros ancestros como versiones cuantitativamente sencillas de nosotros mismos. Sin embargo lo que nos aporta Jaynes es una visión donde la diferencia mas importante no es solamente cuantitativa sino especialmente cualitativa. Los humanos de hace 6000 años eran, a todas luces, seres completamente distintos a nosotros, hasta el punto que es probable que si hipotéticamente fuéramos capaces de trasladarnos a esa época cualquier tipo de comunicación seria imposible. Los motivos para dicha incapacidad no tendrían nada que ver con la adquisición de un lenguaje que nos permitiera comunicarnos sino, como veremos mas adelante, por motivos mucho mas profundos. Jaynes rompe el tópico y nos descubre que nuestros antepasados vivían en un estado de consciencia distinto al del hombre moderno, lo que el llama mente bicameral. Este estado mental o tipo de psique seria el equivalente actual de la esquizofrenia. Sin embargo, 6.000 años atrás ese era el modus operandi “natural” del ser humano y el paso intermedio entre los primates mas evolucionados y el hombre moderno.

Necesitamos remontarnos mucho mas atrás en la línea temporal de la evolución de la mente humana para tomar algo de perspectiva. Tanto en los proto humanos (humanos no lingüísticos) así como en las mayoría de mamíferos el control de su comportamiento viene dado por una serie de instintos adquiridos a lo largo de muchísimos años de evolución así como cierta capacidad de aprendizaje que les otorga adaptabilidad en una escala temporal corta. A su vez los instintos tienen una función primordial en la supervivencia del individuo y es precisamente por esta presión que han pasado a ser rasgos hereditarios sin necesidad de ningún aprendizaje. Tenemos entonces que desde el punto de vista etológico, existe un centro de control interno que dicta al individuo que ha de hacer en cada momento de su vida. En algunos casos el centro de control es capaz de adaptarse a condiciones cambiantes del entorno mediante el aprendizaje y en otros es mucho mas estático y reactivo.

En algún punto de la evolución, el ser humano necesitó estrechar sus lazos en la comunidad. No vamos a tratar aquí si el ser humano siempre ha sido un animal gregario o no. La cuestión es que por distintos motivos relacionados con el ritmo de cambio de su entorno tuvo como única alternativa desarrollar una serie de adaptaciones con el fin de mejorar sus probabilidades de supervivencia. La necesidad de cazar en grupo (paleolítico) probablemente fue uno de los precursores del lenguaje. Por

otro lado, la especialización del trabajo (neolítico) implica el desarrollo de unas relaciones sociales mucho mas complejas lo cual implica una mayor riqueza lingüística para sostenerlas. Algunos investigadores también opinan que el tamaño del grupo tuvo un papel crucial en esta época de grandes cambios (REF). La idea principal es un aumento de la cooperación entre los antiguos humanos. La misma idea de cooperación esta íntimamente ligada con la perdida de poder en los individuos. Pertenecer a un grupo implica la cesión de un cierto grado de poder debido a que los beneficios de pertenecer al clan son mayores. Es por tanto que a medida que el ser humano iba estrechando sus lazos con los otros individuos de su tribu como adaptación a entornos difíciles, desarrollaba por un lado mayores habilidades lingüísticas y por otro cedía poder como individuo (no en beneficio del grupo, sino de el mismo o de sus genes [Kin Selection, Hamilton, Dawkins, R.]). Evidentemente la cesión de soberanía (Contrato Social de Rousseau) puede estructurarse en múltiples formas pero lo que subyace es que el comportamiento de un individuo que ha “firmado” el contrato ya no esta totalmente dirigido por su centro de control interno (instintos, etc.) sino que en ocasiones estará dictado externamente en forma de reglas, leyes. Es decir, los individuos grupales se comportaran como individuos en tanto en cuanto su comportamiento no entre en conflicto con un determinado conjunto de preceptos externos compartido por todos los individuos que pertenecen al mismo grupo. Es justamente ese conjunto de leyes lo que el grupo es en si mismo. Sin eso, no hay grupo. Es fundamental hacer hincapié en que la misma esencia del grupo es de naturaleza informacional y esta íntimamente ligada al lenguaje. Las condiciones de pertenencia están estructuradas como imperativos lingüísticos (Hammurabi, Moisés).

Aquí surge un problema clásico, llega un momento en que aparecerán individuos que intentaran aprovecharse de los beneficios del grupo sin ellos ceder soberanía. Si existe esta posibilidad, es decir, si es posible que algún individuo del grupo se aproveche de sus beneficios sin ceder parte de su soberanía individual nos encontramos que la estrategia de la mayoría (la cooperación) no es una ESS (Evolutionarly Stable Strategy) y por tanto es susceptible de ser invadida. A la larga, el grupo desaparecerá porque los tramposos obtendrán el beneficio de pertenecer al grupo sin ceder soberanía lo cual les llevara a valores mas altos de “fitness”, ergo mas probabilidades de descendencia. Como resultado en pocas generaciones la mayoría de los individuos serán tramposos y el grupo como entidad desaparecerá. Dicho de otra forma, es fundamental que una vez desarrollado algún tipo de cooperación entre individuos (a cualquier escala, no solo humana) se desarrolle en paralelo uno o mas mecanismos de protección (castigo). Solo así, la cooperación es una estrategia

evolutivamente estable no susceptible a invasión por parte de tramposos. Teniendo en cuenta que la cooperación no es un hecho asilado en la historia de la humanidad en particular y en el naturaleza en general, el desarrollo de estos mecanismos de defensa también ha de ser forzosamente común.

Recapitulando, tenemos unos individuos primitivos con capacidades lingüísticas muy limitadas en un entorno cambiante al cual necesitan adaptarse para sobrevivir. La adaptación que les ofrece mayores probabilidades es la cooperación. Cooperar les permite mejores oportunidades de conseguir comida, buscar refugio y protegerse de posibles amenazas (depredadores, otros grupos, clima, etc.). Esto les lleva a desarrollar capacidades lingüísticas (y cognitivas) mucho mas sofisticadas y como consecuencia aparecen pautas de relaciones sociales mucho mas complejas lo que les lleva a estructuras sociales mas avanzadas. La cantidad de soberanía que deben ceder es cada vez mayor porque a medida que el grupo se vuelve mas complejo el grado de interrelación y cooperación aumenta. La cultura grupal cuyo núcleo es el set de reglas-leyes-preceptos que da sentido al grupo también se vuelve mas compleja y tiene es cada vez mas determinante en la toma de decisiones a nivel individual. En este punto estamos hablando de un estado de pre-bicameralidad cercano a la bicameralidad. Lo que esta ocurriendo es que el control exterior (social) tiene cada vez mas protagonismo sobre el control interno (instintos). El control interno es puramente reactivo y adimensional, no tiene forma. Sin embargo, el control externo es de tipo lingüístico, narrativo. En si mismo el lenguaje (en ese punto) es una interfaz con lo social, con lo externo sin ningún tipo de internalización porque no existe una estructura interna que lo soporte. Aquí Jaynes nos ofrece la primera idea original: debido a que no existe ningún tipo de Self y a que lo social tiene estructura lingüista, los preceptos y reglas son percibidos como alucinaciones auditivas, es decir, son percibidos como algo externo, que es lo que justamente son. Para Jaynes el autentico promotor del Self no es interno, sino justamente lo contrario, lo externo, lo social, la identidad grupal estructurada en forma lingüística. Es el control externo lo que el individuo alucina en forma de voces que le dicen lo que ha de hacer. Jaynes encuentra numerosas pruebas de que son justamente estas voces lo que los antiguos llamaban dioses. Para ellos los dioses eran totalmente reales. Este es el estado de consciencia bicameral, donde el hemisferio lingüístico del cerebro le dice al otro lo que tiene que hacer de forma lingüística ya que no existe otra forma de comunicar ambas partes, es decir, las alucinaciones auditivas eran el mecanismo de comunicación entre los dos hemisferios cerebrales. Jaynes propuso que si su teoría era cierta las “voces” deberían proceder

de las zonas homologas a los centros del lenguaje pero en el hemisferio derecho, específicamente las zonas homologas a las áreas de Wernicke y Broca. Estas regiones son muy poco activas en el cerebro moderno pero estudios hechos recientemente muestran que durante las alucinaciones auditivas se da un aumento de la actividad en dichas áreas (REF).

Este modus operandi donde una parte (cámara) del cerebro habla (literalmente) a la otra es lo que Jaynes denomina mente bicameral.

El desarrollo del lenguaje metafórico unido a un continuo incremento de la complejidad social en las culturas antiguas desemboca finalmente en el desarrollo de una interfaz interna que maneja de forma mucho mas eficiente los dos mundos. Esa estructura lingüística que combina y hace posible el dialogo interno sin necesidad de alucinaciones es lo que denominamos Self. Para Jaynes, el Self y el lenguaje tal como lo conocemos hoy en día están íntimamente relacionados. Sostiene que cuando se desarrolla el lenguaje metafórico, las metáforas primarias son las relacionadas con sensaciones corporales y son justamente estas metáforas sobre las que se construye la identidad individual en contraposición a lo externo y es finalmente este constructo lingüístico el encargado de mediar entre los dos mundos.

De las tesis de Jaynes se infiere directamente que la esquizofrenia es una regresión a un estado primitivo de la mente y es quizás por eso que en muchas culturas al esquizofrénico se le ha tratado como un ser tocado por los dioses, lo cual no es sorprendente teniendo en cuenta que esa era precisamente la función de las alucinaciones: conectar el contrato social (Dios) con lo interno. Por otro lado, se deduce que existe una estrechísima relación entre esquizofrenia y lenguaje pero no esta claro cuales son los factores que impiden la correcta comunicación de lo externo a lo interno en individuos esquizofrénicos modernos

Uno de los pilares fundamentales en los que se asienta la teoría de la evolución es el gradualismo. Es decir, deben ser pequeños cambios los que impulsen la selección natural. Darwin llego a decir que si se demostraba que dichos cambios no existían o eran imposibles toda su teoría se vendría abajo. Hoy en día conocemos con bastante detalle como y cuando se producen esos cambios y gracias a la nueva síntesis del Darwinismo por parte de Fisher, et al. en los anos 20, disponemos de herramientas matemáticas muy potentes que nos han permitido profundizar en estos aspectos. Sin embargo sabemos que la evolución en la tierra a pasado por una serie de saltos bruscos y es justamente en estos momentos donde el aumento de la complejidad de los organismos

aparece. Maynard-Smith y Szathmáry nos hablan de cuales son esos saltos evolutivos que han aumentado la complejidad de los organismos vivos de forma brusca y no gradual como era de esperar. Se podría decir que la adaptación de los seres vivos sigue un patrón similar al de un condensador electrónico. Los condensadores se cargan de forma gradual hasta que llegan a un determinado nivel de carga y disparan de repente toda la energía que tenían acumulada. Los organismos vivos se adaptan de forma gradual, con ínfimos cambios. La escala temporal de esta adaptación es inmensa, sin embargo repentinamente se produce un salto abrupto. El resulto de estos saltos evolutivos es por un lado un aumento importante en la complejidad de los organismos involucrados y por otro la aparición de niveles superiores de organización. Desde un punto de vista cibernético (Turchin) debemos hablar también de la emergencia de un sistema de control que hace posible la cooperación entre distintas unidades del nuevo super organismo (componentes de una célula, animales pluricelulares, sociedad-cultura). Dicho sistema no tiene porque ser tangible. Es frecuente que la nueva meta capa que estabiliza el sistema sea de tipo informacional (reglas, mecanismos de castigo, etc. ...). Por supuesto, la especialización del trabajo es otra de las consecuencias importante de estos saltos evolutivos. Tanto para Maynard-Smith, Szathmáry y Turchin el lenguaje y la cultura es el ultimo de los grandes saltos evolutivos en la historia de la vida en la tierra.

Desde el punto de vista de las ciencias de la complejidad, un sistema adaptativo compuesto por elementos relativamente simples, ya sean replicadores, células o humanos, es susceptible de sufrir una alteración brusca en su naturaleza que da lugar a un nivel superior de organización. Aquí existen dos problemas, el primero es que desconocemos las condiciones generales que provocan el salto y finalmente que es imposible inferir las características del nuevo nivel estudiando los elementos que lo componen. Esto ultimo es crucial porque tradicionalmente el método científico utiliza la estrategia del divide y vencerás (de abajo a arriba) y en el caso de un sistema complejo muy poca información útil puede ser extraída estudiando en detalle los elementos que componen el sistema. Es por esto que nuevos enfoques sistémicos son fundamentales donde se ponga énfasis en el estudio del sistema como un todo.

Por todo lo anterior esta claro que la aparición del lenguaje es uno de los hitos mas importantes en la historia no del hombre sino de la vida. El papel del mismo en lo que somos nosotros es crucial hasta el punto que sin el simplemente no seriamos. Para los que estamos interesados en la inteligencia artificial y vida artificial nos abre nuevos caminos de

investigación. Un enfoque sistémico es indispensable, no se puede intentar estudiar (y reproducir) la mente humana sin tener en cuenta su lugar en el mundo. Que la consciencia humana haya evolucionado en un contexto social y que el lenguaje sea su substrato, barre de un plumazo decenas de líneas de investigación que no tienen en cuenta los factores socio-culturales ligados a la mente moderna. Si queremos construir algo que se aproxime a nosotros no podemos obviar nuestro pasado evolutivo porque es la única explicación que tenemos para donde hemos llegado. Por el mismo motivo la utilización de métodos evolutivos para conseguir el objetivo es probablemente la única forma de tener éxito.

Debido a que la escala temporal evolutiva y los grados de libertad son astronómicos es fundamental determinar con detalle cuales son les escenarios sociales que realmente provocaron la explosión socio-cultural y poder reproducirlos en simulaciones computacionales con el fin de obtener resultados interesantes. Así mismo, es muy importante determinar cuales son los requisitos mínimos para obtener resultados interesantes: 100, 1K, 100K, etc. neuronas? Que tipos de conectividad? Que parte de nuestras capacidades cognitivas están relacionadas con el lenguaje y cuales no? Porque hemos sido los únicos en llegar a donde hemos llegado?

Communication without dedicated signalling channels. A general finding?

The central finding of Quinn (2001) was that communication can evolve in an evolutionary robotics context without the use of dedicated signalling channels. Quinn simulated fairly realistic robotic agents controlled by neural networks and equipped with proximity sensors and wheels for locomotion.  The agents were set a coordinated movement task (i.e., to move their combined centre of mass as far as possible in a limited time frame).  Non-coordinated strategies do very poorly at this task, but coordination was not trivial to achieve, as the agents had no pre-given way of signalling to each other. Evolutionary runs revealed that coordinated overall behaviour could in fact emerge from a dance-like movement pattern that allowed the two agents to spontaneously establish “leader” and “follower” roles. Quinn’s result is very exciting because it shows the potential for ALife models to look at the origin of communication from genuinely non-communicative contexts. Other models that look at the conditions for the stability of a signalling system over a pre-defined signalling channel can only really refer to the evolutionary maintenance of communication rather than its beginnings. Although other evolutionary robotics researchers have referenced Quinn’s result, this has typically been in the context of interpreting some evolved behaviour in their own experiments. The importance of Quinn’s result for cognitive theorists interested in the evolution of language and social intelligence was acknowledged by Kirby et al. (2002) but this side of the work has not been pursued. Our project involves asking whether Quinn’s findings are general. In other words, we have successfully replicated Quinn’s central result but without using the particular simulation framework that he employed. Most of the results of our experiment match Quinn’s results and therefore it is suggested that the emergence of communication from non-communicative origins is likely to be a common evolutionary adaptation to niches that involve the coordination of cooperative behaviour.

DisertationFull

Emergence of Intelligence and Mind in an Artificial Living System

In 2000, a group of influential Artificial Life (AL) researchers compiled a list of problems, trying to reunite and synthesize the most important current open questions in AL (Bedau et al. 2000). Due to the high interdisciplinary nature of AL, many of the challenges proposed have a tight relationship with many different disciplines (computer science, mathematics, cognitive science, biology, ecology, chemistry, physics, etc.). Answering most of them would have important repercussions in many fields of knowledge. Among the fourteen questions proposed, the eleventh is particularly relevant due to its wide range of implications, including philosophical and ethical. The challenge states “Demonstrate the emergence of intelligence and mind in an artificial living system”. This is not a new challenge, it’s very old actually, and it’s shared between two main disciplines, AL and Artificial Intelligence (AI). When the AI research area was born in the 1950‘s, its motivations were around the same question and challenges: the building of machines with a general intelligence, hence being able capable to act like humans. As the AI field grew up, it became more and more divided into very specific subareas and, in many cases, with very little communication among them. At present, the AI research mainstream field, is much more concerned about engineering and optimization problems and its applications than with the initial problem. In the other hand current AL research interests feet very well with the challenge as well as Strong AI.

While the challenge is intuitively clear, the terms of mind and intelligence are not so. The question of mind, and by extension, consciousness, had been a recurrent topic in the history of human Though. The problem had been treated by many disciplines and by many different approaches among them. A definition of consciousness is not easy to achieve due to its intangible nature. David Chalmers divided the problem of consciousness in two different problems: the easy problem and the hard problem (Chalmers 1995). The easy problem is solvable and relates to the cognitive-computational aspects of the mind such image recognition, information integration, etc. The hard problem is related to why do we feel something associated with our perceptions of the world (Qualia). This clear dualism has been widely argued (Dennett et all 1997). Among them, Daniel Dennett denies the hard problem arguing the whole consciousness is the impact of experiences on behavior. A more pragmatic approach is given by Julian Jaynes (Jaynes 1976) and and more recently by Merlin Donald (Donald 2001), stating consciousness is in fact language, and no consciousness could exist without a high level of language complexity.In any case, consciousness and mind[1] are very difficult terms to define and measure, hence there is an inherent problem with this challenge: how to know if achieved or not.

Assuming complexity of living systems is a result of the adaptation to their environment, the development of minded individuals (and the mind itself), should be an adaptation result too. In the natural selection framework, the fittest individuals to a particular environment tend to prevail. Therefore, a particular environment would lead to a particular individual: it’s not probable to find eyed individuals in light absent environments. Following this reasoning line, a clear question emerges: what kind of problem (environment) did primitive humans (and other mammals) found, that drove them to develop a mind? Taking into account that very few species had developed something similar, the problem they faced should very uncommon. Not all environments produce the same amount of complexity as adaptation. In AL models, no complexity emerges applying therules of evolution by natural selection (at least, nothing comparable to natural complexity). The simulations tend to evolve and stay (forever?) in a stable configuration (Life, Terra, and others). These observations lead to two non-mutually exclusive conclusions. The first one, something is missing in evolution by natural selection theory (Bedau 2006). The second obvious conclusion is we are trying to reproduce results that took millions of years in a extremely complex environment with not enough resources.

Despite the large number of different technological and theoretical approaches that had been used to try to achieve a synthetic mind (Artificial Neural Networks (ANN’s), Recurrent Neural Networks (ARNN’s), Intelligent Agents, Rule Systems, etc.), we are going to discuss only two of them: models based on intelligent agents and a possible cellular automata model.

In 1994, Karl Sims developed a fully evolved realistic physical agent (Sims 1994). The aim of its work was to demonstrate that motor capacities could be evolved in a 3D realistic physical simulation. Using a Genetic Algorithm (GA) and a developmental representation, the individuals evolved on 3D physical simulation. In each generation, the population of creatures was selected maximizing the development of physical movement like walking, swimming and flying. Worst scored creatures of each generation were “killed” and the fittest were combined to give rise a new creatures. One of the key points in Sim’s model was how each of the individuals was represented. The genotypes of the creatures encoded both brain, in the form of a neural network, and body, making the whole creature highly evolvable: each of its components was susceptible to change in form of mutations and crossover.

Mind and language had probably very similar origins. The study of the origins of language and communication are fundamental to understand the origins of mind and maybe the mind itself (Jaynes 1976). In this sense, models that mimic the origins of language could be very useful to understand the evolution of consciousness and to study the role of language in the development of consciousness. From an evolutionary point of view, the origin of language is problematic because it contains a paradox: the existence of a signal has no sense if nobody can understand it (Maynard Smith 1997). No signal could exist without a response and no response could exist without a signal. Before 2001, all the models aimed to study the origins of language were designed with explicit communication channels. In 2001, Mark Quinn showed that communication between agents could be evolved without specific communication channels (Quinn 2001). In Quinn’s model, instead of using fully developmental representations of the individuals, only the brain, in form of a neural network, was evolved. The model was based in a population of simulated Khepera robots equipped with local sensors (short range IR). The individuals of the population are evaluated in pairs, maximizing their cooperation to solve a particular problem, so the fitness of each individual was highly coupled with its pair. The simulation showed that basic communication evolved from functional but non-communicative behavior. Another interesting observation is when communication evolved, always showed a hierarchy: one of the robots leads and the other follows, but none is intrinsically biased to adopt a role.

Intuitively, Cellular Automata (CA’s) do not seem to be an ideal framework for this goal, and at present, no cognitive model exists based in a strictly CA approach. CA’s are so simple that any sophisticated model using a CA would require enormous computing resources. Some specialized hardware architectures, like TM87, were designed improve the speed of the CA being simulated (Toffoli, Margolus 1987) but they were very far of the theoretical resources that big complex simulation would need (Poundstone 1985).

Imagine we’ve got an amazing complex simulation running in a very powerful cluster computing system, evolving with a state of the art genetic algorithm or even alone in a self-sustained universe. How can we know our model is conscious? The detection problem is clearly critical and it’s certainly non trivial. Since computational models are information based, a straightforward way of measuring its complexity would be through the Shannon’s Entropy. The problem with Shannon’s measure is that a totally random system would have maximum entropy, thus maximum complexity. In 1960’s, Andrei Kolmogorov proposed an algorithmic measure of complexity consisting on the measure of the size of the minimal program that can build the system being studied. Despite that Kolmogorov Complexity is uncomputable, it’s also maximized by randomness. Neither Shannon’s Entropy nor Kolmogorov Complexity measures capture structure or correlation from the system they are measuring. The intuitive relation between entropy and complexity has been proved false, no universal relationship between them exists (Feldman et al. 1998). Other measures based on similar approaches (algorithmically or thermodynamically) have been proposed but appear to be hardly computable or practical. The computational mechanics framework tries to combine the previous attempts into a new method to assess the complexity of a system. The Epsilon Machine (E-Machine) is the computational mechanics attempt to measure the effective complexity of a system (Crutchfield 1989). Another interesting complexity measures are based in the mutual information (a.k.a. transinformation) concept. In this context the complexity is a measure of the mutual dependence of two variables. A detailed study and evaluation of the different complexity measures is beyond the aim of this document, enough to say no complete measure exists yet to measure the sophistication in terms of information processing and hierarchy of a complex system.

Regardless of all, CA’s had fascinated researchers for the past 30 years because they show very interesting properties that make them ideal candidates for pure formal simulations. Due to they formal nature, a CA constitutes a universe by itself, with it’s own rules, universal computation capabilities (Berlekamp et al. 1982) and self-referencing abilities (Wolfram 2002). The entire research field of modern AL was founded around CA’s as the substrate for artificial life, starting with the Game of Life (Conway 1970) that was one of the first in-sillico successful models of artificial life.

An hypothetical large scale simulation based on a CA has potential to show all steps of evolution, from “bits” to “multi cellular organisms equivalent” and so on, providing an unified framework for modeling all the aspects of life (physical, biological and psychological) (Mandik 2008).

The evolution of a synthetic minded organism and therefore, the evolution of complexity comparable to biological systems is not a trivial task. Even with some assumptions regarding evolution issues, there are still too many critical open questions. The lack of a good measure of the complexity of a system is big drawback, it makes very difficult to accurate asses the model. The epistemological problems around the mind and consciousness concepts make difficult to have a well-defined target. In the other hand, the study of what kind of environments could lead to the evolution of mind would probably give valuable information. Language seems to have a strong paper in mind formation, their origins seem to be close (Jaynes 1976).

A hypothetical experiment is proposed. In the case we had a good method for measuring the complexity of a system (CA), what would happen if the system’s rules would be evolved through a GA in which the fitness function would maximize the amount of complexity? Even using the existing measures of complexity that take into account structure and hierarchy, the experiment would be interesting.

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[1] In this review, mind and consciousness are going to be used as a synonyms

The Decentralized Mindset

The archetypical example, ask someone: Who leads a flock of birds? (Resnick, 1994). You will have 99% chance to get the wrong answer. This is becoming a leitmotif on Complexity Science, and, by hence, on one way of thinking: the centralized mindset. It’s a simple question, then, why do everyone answers wrong? From children to adults, from officers to scientists. No matter who you ask, everyone will answer the same: “the first bird“. It’s been known for some years that no bird leads in a flock, the flock itself is a result of the interaction of the birds, it’s self organized (Potts, 1984). For someone belonging to the western culture, the correct answer is unintuitive. There’s a natural trend to look for an answer within the elements, while the next level of description is often omitted. In this case, the correct answer is related to the next level of description, the flock. In this text we are going to find the roots of such kind of thinking and study how it was maintained for such long period of time.

The centralized mindset has not always been the paradigm employed by the human being to explain his world and himself. More than 2500 years ago, even before the great philosophers Aristotle and Plato, the called pre-Socratic group, Heraclitus of Ephesus in particular, tried to find answers employing a systemic paradigm, focused in interrelationships and dynamic processes,  rather than a systematic one, centered in the concepts of classification and static order. In his doctrines of universal flux1 and the unity of opposites2, Heraclitus shows the unicity of opposites in a specific moment, time is the only factor of change.

Despite of pre-Socratics are the first sign of systemic though in the western culture, there are even more antiques examples. As far as 3200 years ago, the I Ching3, one of the chinese classic texts, is probably the oldest example of such kind of thinking. Its title itself “I Ching” means “Classic of Changes” and it’s a reference to its aim to model the dynamics between different elements as a whole4. The resemblances between Heraclitus thesis and I Ching principles show clearly that both cultures, in different moments of history, showed some degree of parallelism (Hammond, 2003).
This monistic understanding of the world can be found in most eastern religions-philosophies5. The collection is seen as a whole and not as the sum of its multiple parts. There is a natural systemic understanding of the world that flows from those philosophical frameworks.
Why two cultures starting from similar points, in terms of though, diverged so much? which was the cause of western culture became more and more dualistic, meanwhile eastern cultures stayed in a more holistic comprehension of the world?
One of the probables causes could be the incredible impact of the works of both Plato and Aristotle had in the western culture. Plato and Aristotle were more or less dualists, in the sense they argued to destroy the pre-Socratic monistic arguments. Unfortunately, in terms of historical success, no comparable figure to Plato and Aristotle existed who defended a systemic approach. That’s probably because all of the works of the pre-Socratics were lost, including Heraclitus6.
Plato is widely recognized to be the first to systemize the mind-body dualism in his Theory of Forms (Plato, Phaedo and other dialogs) in which he separated the perceived objects from their ideas or essences. Aristotle was contrary to the pre-Socratic monism. In Physics, Aristotle discusses against the natural pre-Socratic vision of change, in particular from Parmenides and Heraclitus, arguing that essences persisted through the change and opposites were different, separate thing (Aristotle, Physics, 184 b1).
After the classical period, the arrival and later imposition of the christian doctrine in Europe, constrained any philosophical position contrary to the christian catholic establishment. During this period, society was religion centered and most of the knowledge was controlled by the church, so philosophy were also god centered. In need of a more formal philosophical framework, christian philosophers belonging to the scholastic movement like Thomas Aquinas, incorporated Aristotelian ideas to the christian doctrine (McInerny, 2009), fixing a non-monistic approach in a religion that would rule Europe for 1000 years.
Starting in the seventeenth century, numerous attempts to re-introduce a systemic point of view were done by different scientists and philosophers. We can find monistic approached in authors like Spinoza, Berkley, Leibnitz and Hume. Despite most of them were very successful, in the seventeenth century the systematic approach was strongly rooted in Europe's society. The tremendous success of the systematic approach in science, achieved by Descartes7 and Newton8 among others, didn’t help.

It wasn’t until the twentieth century, when the limitations of the systematic approach became obvious. Scientists from all the areas, beginning from philosophy and social sciences, started to realize that a systemic approach, not only could help to understand, but maybe was the only method to gain knowledge in some disciplines. The centralized mindset, that had lot of success in the past, started to show it’s weaknesses: it was unable to give satisfying answers to important scientific problems9. The result of that need was the Systems Theory, an interdisciplinary framework whose main goal was to investigate all kind of disciplines from a systemic point of view.
In the first half of the twentieth century, the systemic approach was introduced in philosophy, sociology and economics by authors like Pareto, Spencer, Durkheim, Hartmann and others. Most of them explored the twentieth century advances, not from the classical Aristotelian-Platonic-Newtonian point of view, but from a systemic-monistic perspective, where the system gains relevance over the parts that is composed by. Around the 1950s, the tendency was extended to mathematics, physics and biology. Concepts like self-organization, complexity, connectionism, adaptive systems were explored by Wiener, Ashby, Neumann, Foerester, Lyapunov, Pointcare, and others. As the twentieth century advanced,  different flavors appeared: Cybernetics, Catastrophe Theory, Chaos Theory, Context Theory and Complexity Science. All of them were focused in studying what kind of patterns, behaviors and properties the systems show. They focused more and more on systems that showed complex behaviors, that’s behaviors that couldn’t be predicted observing the different elements of the system.

We’ve seen the origins of the centralized mindset are rooted in the more than 3000 years ago, when the western culture born. A bunch of circumstances and the big benefits it has reported to science helped to maintain it over more 2000 years.
The new born Complex Systems Science, seems to be the crystallization of the systemic approach.
Despite nowadays the systems approach is widely respected in many disciplines, the centralized mindset is still much more extended. Sometimes because it’s irrelevant and sometimes by ignorance. 2000 years of history of systematic thinking are not easy to fight with, and in the other side, it’s not an adequate framework to very important problems. Despite the systemic approach had been very successful in many disciplines, a breakthrough comparable to Einstein’s Relativity or Newton’s Laws hasn’t arrived yet. Over the twentieth century the systemic approach had been gaining critical mass, it’s just a matter of time that we will see an critical breakthrough, promoted by systems science.

References:
Graham, W. D. (2007), “Heraclitus, The Stanford Encyclopedia of Philosophy” (Winter 2003 Edition). Edward N. Zalta (ed.).
Hammond, D. (2003). “The Science of Synthesis”. University of Colorado Press (ed.). p. 23
Kahn, C (1979). “The Art and Thought of Heraclitus: Fragments with Translation and Commentary”. Cambridge University Press (ed.). pp. 1–23.
McInerny R. (2009), “Saint Thomas Aquinas, The Stanford Encyclopedia of Philosophy” (Winter 2003 Edition). Edward N. Zalta (ed.).
McKeon. R. (1941). “Basic Works of Aristotle”. Random House (ed.). pp. 34-56
Plato (390s-347 BC) Platonis Opera, vol. 1, Euthyphro, Apologia Socratis, Crito, Phaedo, Cratylus, Theaetetus, Sophistes, Politicus, ed. E.A. Duke, W.F. Hicken, W.S.M. Nicoll, D.B. Robinson and J.C.G. Strachan. Clarendon Press (ed.), 1995.
Potts, W. K. (1984). “The chorus-line hypothesis of coordination in avian flocks”. Nature 24: 344-345.

Resnick, M. (1994). “Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Micro worlds”. MITPress (ed.).