(Published in my newsletter and profile in Linkedin. English Version after the Spanish text)

No es casualidad que el primer psicólogo en obtener un premio Nobel, Daniel Kahneman, haya hecho una contribución muy apreciada por los tecnólogos de la información, especialmente los dedicados a la inteligencia artificial.

Pocas cosas más atractivas para un tecnólogo que la idea de que existen sesgos inherentes al procesamiento humano de información y que estos sesgos se producen por la necesidad de conseguir resultados rápidos, aunque contengan errores. Por añadidura, tales sesgos se deben a carencias para procesar grandes cantidades de información rápidamente. Dicho de otra forma, las personas procesan información incorrectamente porque no tienen la capacidad de un ordenador para hacerlo. Saltar desde ahí a “Si tengo la suficiente capacidad de procesamiento de información, eso no ocurrirá con una máquina” es muy tentador -aunque recuerda los viejos tiempos de Minsky y la llamada GOFAI (Good Old-Fashioned Artificial Intelligence)- y, de ahí, que la corriente de Behavioral Economics haya tenido gran aceptación en el mundo de la tecnología. Sin embargo, las cosas no son tan claras.

Kahneman y Tversky, principales autores de referencia dentro de esta línea, descubrieron un conjunto de sesgos y, además, los nombraron de formas bastante ingeniosas y los recopilaron en “Pensar rápido, pensar despacio”. Tras ello, fueron aplaudidos y criticados aunque tal vez no lo hayan sido por los motivos adecuados o, para precisar más, tal vez no hayan sido totalmente entendidos ni por quienes los aplauden ni por quienes los critican.

Si comenzamos por la parte crítica, se suele aducir que los sesgos se han probado en condiciones de laboratorio y que, en la vida normal, esas situaciones no se dan o simplemente los sesgos no aparecen. Es verdad; las condiciones de laboratorio fuerzan la aparición del sesgo y en el mundo real, en el que habitualmente se opera dentro de un contexto, no aparecen o lo hacen de forma muy reducida. Tomemos como ejemplo, una de las historias más populares de “Pensar rápido, pensar despacio”, la historia de “Linda, la cajera”:

Linda es una mujer de 31 años de edad, soltera, extrovertida y muy brillante. Estudió filosofía en la universidad. En su época de estudiante mostró preocupación por temas de discriminación y justicia social y participó en manifestaciones en contra del uso de la energía nuclear. De acuerdo con la descripción dada, ordene las siguientes ocho afirmaciones en orden de probabilidad de ser ciertas. Asigne el número 1 a la frase que considere con más probabilidad de ser cierta, dos a la siguiente y así sucesivamente hasta asignar 8 a la frase menos probable de ser cierta.

o Linda es una trabajadora social en el área psiquiátrica.

 o Linda es agente vendedora de seguros.

o Linda es maestra en una escuela primaria.

o Linda es cajera de un banco y participa en movimientos feministas.

o Linda es miembro de una asociación que promueve la participación política de la mujer.

o Linda es cajera de un banco.

o Linda participa en movimientos feministas. o Linda trabaja en una librería y toma clases de yoga.

Obsérvense las opciones cuarta y sexta: Obviamente, la cuarta es un subconjunto de la sexta y, sin embargo, la cuarta aparecía de forma generalizada como más probable que la sexta. Un claro error de lógica pero, al mismo tiempo, ignora un hecho elemental en las relaciones humanas: Entendemos que, cuando una persona nos aporta un dato -en este caso, el perfil que dibuja de Linda como activista social- lo hace porque entiende que dicho dato es relevante, no con el exclusivo propósito de engañar. Más aún: A los sesgos de Kahneman y Tversky les ocurre algo parecido a la ley de la gravitación universal de Newton: Se trata de formas de procesamiento de la información que funcionan bien en su contexto y sólo fallan fuera de éste y, por tanto, los modelos de procesamiento subyacentes no pueden ser rechazados sino, en todo caso, asumir que tienen una validez limitada a un contexto.

Veamos qué ocurre en el extremo que aplaude la Behavioral Economics: Aun admitiendo que hay un forzamiento hacia el extremo por las condiciones experimentales, se demuestra la existencia del sesgo y, con ello, que el modelo de procesamiento humano no es universalmente aplicable, sino que sólo funciona dentro de un contexto. Retomando el ejemplo de Newton, los tecnólogos creen disponer del equivalente a una teoría de la relatividad, con un ámbito de aplicación más amplio que el modelo de Newton y con capacidad para sustituir a éste. La corriente de Behavioral Economics y sus derivaciones suministró todos los argumentos requeridos para llegar a esta conclusión.

Sin embargo, tanto críticos como partidarios parecen aceptar que el razonamiento humano es “quick-and-dirty” y el elogio o la crítica obedece a la importancia o falta de ella que se concede a ese hecho.

La pregunta no respondida ni por los que aceptan el modelo ni por los que lo rechazan es si ese modelo de procesamiento sujeto a sesgos es el único modelo posible de razonamiento humano o si hay otros distintos y que no estén recogidos. Los propios Kahneman y Tversky trataron de responder a esta pregunta aduciendo la existencia de un sistema uno, automático, y un sistema dos, deliberativo. El primero estaría sujeto a los sesgos identificados y el segundo procesaría información en una forma parecida a cómo lo hace un ordenador, es decir, siguiendo las reglas estrictas de la lógica formal aunque -matizan- siempre es susceptible de ser engañado por el sistema uno. Al proclamar la existencia de un sistema lento, racional y seguro, volvieron a dar argumentos a los tecnólogos para promover la inteligencia artificial como alternativa a la humana. No sólo es una cuestión de tecnólogos de la información; si se revisan los textos desclasificados por la CIA sobre técnica de análisis de información puede encontrarse una oda al procesamiento racional que evalúa múltiples opciones y asigna pesos a las variables mientras ignora la intuición y la experiencia del analista basándose en que puede introducir sesgos. El procesamiento de información siguiendo los cauces estrictamente racionales está de moda pero…

¿Y si existiera un “sistema tres” e incluso un “sistema cuatro”, ajenos a los sesgos de la Behavioral Economics pero, al mismo tiempo, no empeñados en reproducir un modelo de procesamiento racional canónico? ¿Y si esos sistemas fueran capaces de conseguir resultados tal vez inalcanzables para un modelo lógico formal, especialmente si tales resultados se requieren con restricciones de tiempo y ante situaciones no previstas?

Esos sistemas existen y, tal vez, uno de los autores que más claramente los ha identificado es Gary Klein: No se trata de procesos “quick-and-dirty” motivados por la falta de capacidad de procesamiento o de memoria ni de un proceso reproducible por una máquina sino de algo distinto, que introduce en la ecuación elementos de experiencia pasada, extrapolaciones de situaciones tomadas de otros ámbitos y experiencias sensoriales no accesibles a la máquina.

Resulta paradójico que uno de los pioneros de la inteligencia artificial, Alan Turing, fuese la prueba viviente de ese modelo de procesamiento no accesible a la máquina: Turing redujo espectacularmente el número de variaciones teóricamente posibles en la máquina de claves Enigma introduciendo una variable en la que nadie había pensado -que el idioma original en que estaba emitido el mensaje era el alemán- y ello permitió trabajar con las estructuras del idioma y reducir opciones..

Cuando un piloto de velero -o del US1549- concluye que no puede llegar a la pista, no lo hace después de calcular velocidad y dirección del viento, y el coeficiente de planeo del avión corregido por el peso y la configuración sino observando la cabecera de la pista y viendo si asciende o desciende sobre un punto de referencia en el parabrisas: Si la cabecera asciende sobre el punto de referencia, no llega. Si desciende, sí puede llegar. Así de fácil.

Cuando un jugador de baseball intenta llegar al punto donde va a llegar la pelota y al mismo tiempo que ésta, no calcula trayectorias angulares ni velocidades, sino que ajusta la velocidad de su carrera para mantener un ángulo constante con la pelota.

Cuando un avión ve afectados sus sistemas por la explosión de un motor -QF32- y lanza centenares de mensajes de error, los pilotos tratan de atenderlos hasta que descubren uno muy probablemente falso: Fallo de un motor en el extremo del ala contraria. Los pilotos concluyen que, si la metralla del motor que explotó hubiera llegado al otro extremo del avión, tendría que haber pasado a través del fuselaje y, a partir de ahí, deciden volar el avión a la antigua usanza, ignorando los mensajes de error del sistema.

Cuando, en plena II Guerra Mundial, a pesar de haber descifrado el sistema de claves japonés, los norteamericanos no saben de qué punto están hablando, aunque tienen la sospecha de que podría tratarse de Midway, lanzan un mensaje indicando que tienen problemas de agua en Midway. Cuando, tras recibirlo, los japoneses se refieren a las dificultades con el agua en el punto al que se referían sus mensajes previos, quedó claro a qué se referían. La preparación de la batalla de Midway tiene origen en ese simple hecho difícil de imitar por una máquina.

Incluso en el cine disponemos de un caso, no sabemos si real o apócrifo, en la película “Una mente maravillosa” sobre la vida de John Nash: En un momento especialmente dramático, Nash concluye que los personajes que está viendo son imaginarios porque observa que una niña, que formaba parte de las alucinaciones, no ha crecido a lo largo de varios años y permanece exactamente igual que las primeras veces que la vio.

Todos ellos son casos muy conocidos a los que se podría añadir muchos más; incidentalmente, uno de los ámbitos donde más ejemplos pueden encontrarse es en la actividad de los hackers. En cualquier caso, todas las situaciones tienen algo en común: El procesamiento de información que no sigue el modelo racional canónico no siempre es un modelo a rechazar por estar sujeto a sesgos sino que también introduce elementos propios no accesibles a un sistema de información, bien porque le falta el aparato sensorial capaz de detectarlo o bien porque le falta la experiencia que permite extrapolar situaciones pasadas y llegar a conclusiones distintas y, por supuesto, mucho más rápidas de lo accesible a un avanzado sistema de información.

Los tecnólogos han visto en la Behavioral Economics un reconocimiento casi explícito de la superioridad de la máquina y, por tanto, la han acogido con entusiasmo. Sin embargo, para los psicólogos, la Behavioral Economics tiene un punto ya conocido: Adolece del mismo problema que otras corrientes anteriores en psicología: Así, el psicoanálisis puede alardear de descubrir fenómenos inconscientes…pero hacer de éstos el núcleo del psiquismo humano tiene poco recorrido; la psicología del aprendizaje ha trabajado intensivamente con los mecanismos de condicionamiento clásico y operante…pero tratar de convertir el aprendizaje en el único motor del psiquismo no tiene mucho sentido. La Gestalt descubrió un conjunto de leyes relativas a la percepción…pero tratar de extrapolarlas al conjunto del funcionamiento humano parece excesivo. Ahora la toca a la Behavioral Economics. ¿Existen los sesgos cognitivos? Por supuesto que existen. ¿Representa ese modelo de procesamiento quick-and-dirty lleno de errores la única alternativa a un supuestamente siempre deseable procesamiento racional canónico, susceptible de enlatar en una máquina? No.

No olvidemos que incluso el procesamiento “quick-and-dirty” funciona correctamente en la mayoría de los casos aunque no lo haga en el vacío; necesita hacerlo en un contexto conocido por el sujeto. En segundo lugar, hay otro modelo de procesamiento que hace uso de una experiencia cristalizada en conocimiento tácito que, como tal, es difícil de expresar y más aún de “enlatar” en algoritmos, y de un aparato sensorial que es específicamente humano. Reducir el procesamiento humano de información a los estrechos cauces marcados por la Behavioral Economics no deja de ser una forma de caricaturizar a un adversario, en este caso el humano,  al que se pretende contraponer la potencia de la máquina. Eso sí; no dejaremos de recordar a Edgar Morin y su idea de que la máquina-máquina siempre es superior al hombre-máquina. Si insistimos en despojar al humano de sus capacidades específicas o en despreciarlas, mejor quedémonos con la máquina.

BEHAVIORAL ECONOMICS: WHEN TECHNOLOGISTS FELL IN LOVE WITH PSYCHOLOGY

It is no coincidence that the first psychologist to win a Nobel Prize, Daniel Kahneman, has contributed something much appreciated by information technologists, especially those dedicated to artificial intelligence.

Few things are more attractive to a technologist than the idea that there are inherent biases in human information processing and that these biases are produced by the need to achieve fast results, even if they contain errors. Moreover, such biases are due to shortcomings in processing large amounts of information quickly. Put another way, people process information incorrectly because they do not have the capacity of a computer to do so. Jumping from there to «If I have sufficient information processing capacity, that won’t happen with a machine» is very tempting -although it reminds of the old days of Minsky and the so-called GOFAI (Good Old-Fashioned Artificial Intelligence)– and, hence, the Behavioral Economics current has had great acceptance in the world of technology. However, things are not so clear-cut.

Kahneman and Tversky, the main authors of reference in this line, discovered a set of biases and, moreover, named them in rather ingenious ways and compiled them in «Thinking fast and slow». After that, they were applauded and criticized, although perhaps not for the right reasons or, to be more precise, perhaps they were not fully understood by those who applaud them nor by those who criticize them.

If we start with the critical part, it is often argued that the biases have been tested under laboratory conditions and that, in normal life, such situations do not occur, or the biases simply do not appear. It is true; laboratory conditions force the appearance of the bias and in the real world, in which we usually operate within a context, they do not appear or do so in a very reduced form. Take, for example, one of the most popular «Thinking fast and slow» stories, the story of «Linda, the cashier»:

Linda is a 31-year-old woman, single, outgoing, and very bright. She studied philosophy at university. As a student, she was concerned about discrimination and social justice issues and participated in demonstrations against the use of nuclear energy. According to the description given, rank the following eight statements in order of probability of being true. Assign the number 1 to the statement you think is most likely to be true, two to the next one, and so on until you assign 8 to the statement least likely to be true.

o Linda is a social worker in the psychiatric field.

 o Linda is an insurance sales agent.

o Linda is an elementary school teacher.

o Linda is a bank teller and participates in feminist movements.

o Linda is a member of an association that promotes women’s political participation.

o Linda is a bank teller.

o Linda participates in feminist movements. o Linda works in a bookstore and takes yoga classes.

Note the fourth and sixth options: Obviously, the fourth is a subset of the sixth and, nevertheless, the fourth appeared in a generalized way as more probable than the sixth. A clear error of logic but, at the same time, it ignores an elementary fact in human relations: We understand that, when a person gives us a piece of information – in this case, the profile of Linda as a social activist – it is because that person understands that such information is relevant, not with the exclusive purpose of deceiving us. Moreover: Kahneman’s and Tversky’s biases are like Newton’s law of universal gravitation: They are showing information processing strategies that work well in context and only fail out of context. Therefore, the underlying processing models cannot be rejected but, in any case, assumed to have a validity limited to a context.

Let us see what happens at the extreme that applauds Behavioral Economics: Even admitting that there is not a natural setting, and they are forcing the conditions towards the extreme through the experimental design, the existence of the bias is demonstrated and, with it, it seems that the human processing model is not universally applicable, but limited to a context. Taking Newton’s example, technologists believe that they have the equivalent of a theory of relativity, with a broader scope of application than Newton’s model and with the capacity to replace it. Behavioral Economics and its derivations provided all the arguments required to reach this conclusion.

However, both critics and supporters seem to accept that human reasoning is «quick-and-dirty» and the praise or criticism is due to the importance or lack of it that is given to that fact. The unanswered question, neither by those who accept the model nor by those who reject it, is whether this model of processing subject to biases is the only possible model of human reasoning or whether there are others that are different and not captured. Kahneman and Tversky themselves tried to answer this question by adducing the existence of a system one, automatic, and a system two, deliberative. The first would be subject to the identified biases and the second would process information in a manner like that of a computer, i.e., following the strict rules of formal logic, although -they argue- it is always susceptible to being fooled by system one.

By proclaiming the existence of a slow, rational, and safe system, they once again gave technologists arguments to promote artificial intelligence as an alternative to human intelligence. It is not only a matter of information technologists; if one reviews the declassified CIA texts on information analysis technique one can find an ode to rational processing that evaluates multiple options and assigns weights to variables while ignoring the analyst’s intuition and experience on the grounds that it may introduce bias. Processing information along strictly rational lines is all the rage but… what if there was a different and valid way?

What if there were a «system three» and even a «system four», unbiased by the proclaimed Behavioral Economics biases but, at the same time, not trying to reproduce a canonical rational processing model? What if such systems could achieve results perhaps unattainable for a formal logical model, especially if such results are required under time constraints and in the face of unforeseen situations?

Such systems do exist, and perhaps one of the authors who has identified them most clearly is Gary Klein: They are not «quick-and-dirty» processes motivated by a lack of processing capacity or memory, nor are they a machine-reproducible process, but rather something different, which introduces into the equation elements of past experience, extrapolations of situations taken from other fields and sensory experiences not accessible to the machine.

It is paradoxical that one of the pioneers of artificial intelligence, Alan Turing, was the living proof of this model of processing not accessible to the machine: Turing spectacularly reduced the number of theoretically possible variations in the Enigma key machine by introducing a variable that no one had thought of – that the original language in which the message was issued was German – and this allowed working with the language structures and reducing options.

When a glider -or US1549- pilot concludes that he cannot reach the runway, he does so not after calculating wind speed and direction, and the glide ratio of the aircraft corrected for weight and configuration, but by looking at the runway threshold and seeing if it ascends or descends over a reference point on the windshield: If the threshold ascends over the reference point, he will not reach it. If it descends, he will arrive. It’s that easy.

When a baseball player tries to get to the point where the ball is going to arrive and at the same time as the ball, he does not calculate angular trajectories and velocities but adjusts his running speed to maintain a constant angle to the ball.

When an aircraft’s systems are affected by an engine explosion -QF32- and it sends hundreds of error messages, pilots try to deal with them until they discover a most probably false one: Failure of an engine on the opposite wing tip. The pilots conclude that if the shrapnel from the exploding engine had reached the other end of the plane, it would have passed through the fuselage, and so they decide to fly the plane the old-fashioned way, ignoring the system error messages.

When, in the middle of World War II, despite having deciphered the Japanese key system, the Americans do not know what point they are talking about, although they suspect it might be Midway, they send a message indicating that they have water problems at Midway. When, after receiving it, the Japanese refer to the water difficulties at the point to which their previous messages referred, it became clear what they were referring to. The preparation for the battle of Midway has its origin in that simple fact which is difficult for a machine to imitate.

Even in the cinema, we have a case, we do not know if it is real or apocryphal, in the movie «A Beautiful Mind» about the life of John Nash: In a particularly dramatic moment, Nash concludes that the characters he is seeing are imaginary because he observes that a little girl, who was part of the hallucinations, has not grown over several years and remains exactly the same as the first times he saw her.

These are all well-known cases to which many more could be added; incidentally, one of the areas where most examples can be found is in the activity of hackers. In any case, all situations have something in common: Information processing that does not follow the canonical rational model is not always a model to be rejected because it is subject to biases. Actually, it also introduces elements that are not accessible to an information system, either because it lacks the sensory apparatus capable of detecting it or because it lacks the experience that allows extrapolating past situations and reaching conclusions that are different and, of course, much faster than what is accessible to an advanced information system.

Technologists have seen in Behavioral Economics an almost explicit recognition of the superiority of the machine and have therefore welcomed it with enthusiasm. However, for psychologists, Behavioral Economics has a well-known point: it suffers from the same problem as other previous currents in psychology: Thus, Psychoanalysis may boast of discovering unconscious phenomena…but making these the core of the human psyche is a long shot; the Psychology of Learning has worked intensively with the mechanisms of classical and operant conditioning…but trying to make learning the only engine of the psyche does not make much sense. Gestalt discovered a set of laws related to perception…but trying to extrapolate them to the whole of human functioning seems excessive. Now it is the turn of Behavioral Economics. Do cognitive biases exist? Of course, they exist. Does that error-ridden quick-and-dirty processing model represent the only alternative to a supposedly always desirable canonical rational processing, amenable to canning in a machine? No.

Let us not forget that even quick-and-dirty processing works correctly in most cases even if it does not do so in a vacuum; it needs to do so in a context known to the subject. Secondly, there is another processing model that uses an experience crystallized in tacit knowledge that, as such, is difficult to express and even more difficult to be «canned» in algorithms, and of a sensory apparatus that is specifically human. Reducing the human processing of information to the narrow channels set by Behavioral Economics is nothing more than a way of caricaturing an adversary, in this case, the human, against which the power of the machine is intended to be set. Of course, we will not fail to remember Edgar Morin and his idea that the machine-machine is always superior to the man-machine. If we insist on stripping humans of their specific capacities or despising them, we should stick with the machine.

Today, when a new year still at its beginning, we are witnessing in Spain the old rivalry of left and right, driven by the most extremist factions of both sides in a dynamic from which, if it remains, nothing good can be expected.

History taught us that both sides have much to regret or hide about the past; on the right hand, the pursuit of the interests of the most privileged, supported by force; on the left hand, the savagery of revolutions which, in the end, placed at their tops a new and equally privileged class, whose interests are not more legitimate than those of their predecessors.

However, nowadays we see that both sides did not assume their history in the same way; the left clearly won what has been called «the story». While there is a right wing repentant for the sins of its predecessors, the left wing places its own ones on pedestals, as the supposed forerunners of democracy, even in those cases where the only thing they brought was dictatorship, ruin and death.

 Recent episodes in Spain of removal of statues while maintaining others with equal or greater merits for such removal, changes of street names and even the transfer of Franco’s remains speak by themselves of a hyper-legitimacy from the most extremist leftists hardly supported by facts.

Well into the 21st century, the ideological debate should not consist of the most extreme right and left wings competing to pull up the rugs of the past of their opponents, nor should anyone have to apologize for existing. Each side has an ideological legitimacy that certainly does not reside in the past. Perhaps it is time for both sides to show their real legitimacy while they send to the garbage can of history the principles and practices of their ideological great-great-grandfathers and those present leaders who, nowadays, pretend to behave like them.

A modern liberal right wing is legitimate because, as a guiding principle, it seeks the common good and not the maintenance of situations of privilege; it trusts that an individual initiative with few restrictions will bring a better life for the great majority and will put its best efforts into it.

A left wing is legitimate when, likewise, it seeks the common good and not a mere quest to subvert the existing order in order to place its own members at the head of a new and equally undesirable one; legitimate left and right wings coincide in ends, though not in means. Thus, while the right wing trust in the individual, the left wing is prone to social engineering and direct action to alleviate the situation of the less fortunate, preventing them from being abandoned to their fate.

Naturally, any non-sectarian reader, whatever his orientation, will see that both options are not only legitimate but compatible, and that alternation in power has a crucial role to play: to correct undesirable drifts which, in one case, could drive to the abandonment of layers of the population while in the other could produce elephantine States and ever larger groups willing to get their living from the resources that these States take from the productive part of the population.

At the same time, there are also illegitimate political practices ; those led by politicians who get their living from the confrontation and see the common good as an empty abstraction, preferring to opt for their own. That is a situation, very present right now, that transforms the political chessboard into modern Augean stables where every dirt has its seat and that, despite it, its cleaning would not require any river or any Hercules but simpler, punctual and modest actions.

Pointing to individuals or gangs of opportunistic people as the culprits, even though they clearly exist and are guilty, does not put us on the road to solve the problem. Such individuals are not the key, which does not mean that they should not be stopped, but they are mere anecdotes stemming from basic errors in a process that, in Spain, started in 1977: The lauded political transition in Spain introduced undesirable elements that constituted the germ of the current situation, granting legitimacy to anyone who claimed to be against the dictatorship, no matter how questionable their own actions could be:

 The design of the transition not only led to an unwieldy territorial situation, but -it is worth remembering- ETA prisoners with blood crimes were even pardoned, many of whom returned to their former activity. Situations of privilege were consecrated, both economic and electoral, which contradicted the supposed equality of all Spaniards proclaimed in the subsequent Constitution.

Once those principles were in place, the political pressures towards greater power of the successive executive powers and the concessions to groups favored by the electoral system to form blocking minorities started the process. In addition to a bad start, the Constitution, with the good offices of the Constitutional Court, has been twisted until it has become virtually unrecognizable. Today, we can find laws that require an advanced exercise in sophistry to be accepted within the constitutional framework.

Those have been the basic lines of a process that led to the present moment where, in addition, there are political actors -some of them inside the Government- who explicitly seek to liquidate the regime born in 1977 in order to replace it with adventures whose outcome cannot even be described as uncertain.

 Relying on the old parliamentary arithmetic and on the old parties -or the new ones with old principles and actions- on both sides of the political spectrum- may be a mistake. Today an «apolitical» party may be required, without a government program, willing to lend its support to the party with the most votes if it commits itself to making changes leading to a real democracy, with the design errors of 1977 corrected.

It would be unrealistic expecting a solution from someone who is benefiting, at the expense of society as a whole, from the current morass, as the present Government and its supporters; nor does it seem that a solution can be expected from someone, now in the opposition, who had his chance in 2012. They got more central and autonomic power than anyone in the recent past and, however, behaved as temporary tenants without daring to proclaim his principles -if they existed- and limited himself to achieve some economic respite. Finally, neither should we turn back the clock nor resort to the «we lived better with Franco».

Perhaps the most hopeful experiments in this long period have been the initial Ciudadanos and UPyD parties; it is true that UPyD was born almost as a PSOE in exile, but it progressively focused on nuclear issues and, perhaps that’s why it was made disappear. In the case of Ciudadanos, its origin is impeccable, but the ambition of its leaders turned the party into something unrecognizable today.

There are figures who could lead the rebirth of a principled political activity, but none of them occupy right now prominent positions within their parties (some of them did), if they even remain in them. Inviting these figures to action is not about bringing in more people to fight for power on the current political chessboard but, precisely, to change the rules of a game whose original design is wrong. The subsequent evolution have turned it into perfect terrain for the «gamblers of the Mississippi» -to use the former vice president Alfonso Guerra’s expression- to prosper at the expense of a society whose welfare they supposedly manage.

It is a matter of changing the rules of access and permanence in political activity, ensuring equality among all Spaniards, and that both the rules and the zeal in their application are the same for all.

Undoubtedly, the design of 1977 is coming apart at the seams in many places and its subsequent evolution was already foreseeable for some; a change is necessary, but this does not consist of returning to states prior to that date but of reinforcing what was already done well then and eliminating what was done badly or very badly.

There is a very extended mistake among Aviation professionals: The idea that regulations set a minimum level. Hence, accepting a plane, a procedure or an organization means barely being at the minimum acceptable level.

The facts are very different: A single plane able to pass, beyond any kind of reasonable doubt, all the requirements without further questions would not be an “acceptable” plane. It would be an almost perfect product.

Then, where is the trick and why things are not so easy as they seem to be?

Basically, because rules are not so clear as pretended, giving in some cases wide room for interpretation and because, in their crafting, they are mirroring the systems they speak about and, hence, integration is lost in the way.

The acceptance of a new plane is a very long and complex process till the point that some manufacturers give up. For instance, the Chinese COMAC built a first plane certified by Chinese authorities to fly only in China or the French Dassault decided to stop everything jumping directly to the second generation of a plane. It be judged a failure, but it is always better than dragging design problems until they prove that they should have been managed. We cannot avoid to remind cases like the cargo door of DC10 and the consequences of a problem already known during the design phase.

The process is so long and expensive that some manufacturers keep attached to very old models with incremental improvements. Boeing 737, that started to fly in 1968, is a good example. Its brother B777, flying since 1995, keeps a very old Intel-80486 processor inside but changing it would be a major change, despite Intel stopped its production in 1997.

The process is not linear, and many different tests and negotiations are required in the way. Statements of similarity with other planes are frequent and the use of standards from Engineering or Military fields is common place when something is not fully clear in the main regulation.

Of course, some of the guidelines can contradict others since they are addressed to different uses. For instance, a good military regulation very used in Human Factors (MIL-STD-1472) includes a statement about required training, indicating that it should be as short as possible to keep full operating state. That can be justified if we think in environments where lack of resources -including knowledge- or even physical destruction could happen. It should be harder to justify as a rule in passenger’s transportation.

Another standard can include a statement about the worst possible scenario for a specific parameter, but the parameter can be more elusive than that. The idea itself of worst possible scenario could be nonsense and, if the manufacturer accepts this and the regulator buys it, a plane could by flying legally but with serious design flaws.

Regulations about Human Factors were simply absent a few years ago and HF mentions were added to the technical blocks. That was partially changed when a new rule for planes design appeared addressing precisely Human Factors as a block on its own. However, the first attempts were not much further than collecting all the scattered HF mentions in a single place.

Since then, it has been partially corrected in the Acceptable Means of Compliance, but the technical approach still prevails. Very often, manufacturers assemble HF teams with technical specialists in specific systems instead of trying a global and transversal approach.

The regulators take their own cautions and repeat mantras like avoiding fatigue levels beyond acceptability or planes that could not require special alertness or special skill levels to manage a situation.

These conditions are, of course, good but they should not be enough. Compliance with a general condition like this one, in EASA CS25  “Each pilot compartment and its equipment must allow the minimum flight crew (established under CS 25.1523) to perform their duties without unreasonable concentration or fatigue” is quite difficult to demonstrate. If there is not a visible mistake in the design, trying to meet this condition is more a matter of imagining potential situations than a matter of analysis and, as it should be expected, the whole process is driven by analysis, not by imagination.

Very often, designs and the rules governing them try to prevent the accident that happened yesterday, but a strictly analytic approach makes hard to anticipate the next one. Who could anticipate the importance of controls feedback (present in every single old plane) until a related accident happened? Who could anticipate before AA191 that, perhaps, removing the mechanical blockage of flaps/slats could not be so sound idea? Who could think that different presentations of artificial horizon could drive to an accident? What about different automation policies and pilots disregarding a fact that could be disregarded in other planes but not in that one?…

Now, it is still in the news the fact that a Boeing factory had been attacked by the WannyCry virus and the big question was if it had affected the systems of the B777s that were manufactured there. B787 is said to have 6,5 million of code lines. Even though B777 is far below that number, checking it should not be easy and it should be still harder if computers calculating parameters for the manufacturing must be also checked.

That complexity in the product drives not only to invisible faults but to unexpected interactions between theoretically independent events. In some cases, the dependence is clear. Everyone is conscious that an engine stopped can mean hydraulic, electric, pressure and oxygen problems and manufacturers try to design systems pointing to the root problem instead of pointing to every single failure. That’s fine but…what if the interaction is unexpected? What if a secondary problem -like oxygen scarcity, for instance- is more important than the root problem that drove to this? How are we going to define the right training level for operators where there is not a single person who understands the full design?

In the technical parts, the complexity is already a problem. When we add the human element, its features and what kind of things are demanded from operators, the answer is everything but easy. Claiming “lack of training” every time that something serious happens and adding a patch to the present training is not enough.

A full approach more integrated and less shy to speak about using imagination in the whole process is advisable long ago but now it is a must. Operators do not manage systems. They manage situations and, at doing so, they can use several systems at the same time. Even if there is not an unexpected technical interaction among them, there is a place where this interaction happens: The operator who is working with all of them and the concept of consistency is not enough to deal with it.