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Christopher Columbus. Photo Credit: Public Domain

World has been always dominated by two different ideas about organization of groups of people: Either a strong leadership that drives the actions of different people or a leadership based on coordination where people provide opinions and the leader only extract the common wishes of people. In social governance the sides of these ideas are dictatorship and direct democracy. In the former one, only the opinion of a single person is taken into account. In the latter one, there is actually no leader that is substituted by a decision making methodology based on voting. North America is considered a place of democracy; however, a democratic organization in Columbus’ expedition never discovered it. Sailors, probably, would have voted to go back to Spain before reaching the coast of America.

Without considering moral affairs, both solutions could run a small society in a prosper way. Prosperity does not proceed from the decision making process but the decisions themselves. Methodology or leadership is not required in order to provide good decisions but in order to cope with increasing complexity. As societies grow both accepting the decisions of a leader and agreeing about many matters make themselves more difficult tasks. Men have developed mixed solutions where leadership is seen in different forms, and leadership and management methodologies must cohabit in an effective organization.

Governance evolves very slowly, however, business organizations evolves much faster. The number of formal management methodologies and theories about leadership is huge, although market finally selects some of them that are commonly accepted as good practices and they got to be extensively spread.

However, management consultancy is a business like another one. The success of its products depends not on their quality only but on the marketing activity too. Standards ‘de facto’ can exists due to a good marketing activity instead of the added value of the consultancy product, in a similar way as VHS video recorders overcame Betamax ones. Management consultancy can be many times driven by fashion instead of added value, and unfortunately what is fashionable is not usually driven by management professionals but communication experts.

In spite of these issues, the traditional two management visions are preserved although nowadays they have evolved in different management methodologies. A professional consultant usually knows several of these methodologies and he applies them depending on the preferences of the client organization. Clients demand consultancy products that many times are fashionable because they are not experts in methodologies and their general knowledge about it is based on the specialist press and management forums. Mass media drive fashions but we should not forget that they are private companies with their own interests and value chain.

The implementation of a certain management methodology must be considered for the long term, and it will not be changed in several years. That is the reason why managers should avoid fashions and center their decisions on the added value for their organizations. Most of them usually demand the implementation of ‘de facto’ standards to reduce the risk of their decisions.

An example of this discussion can be seen in software development. Traditional engineering projects are fully predefined and driven by an engineer that leads the project. This scheme fits well the construction of a building, a road or a bridge. Construction knowledge is in the hands of the engineer than makes decisions about the task to be made by the operators. When the complexity of the project increases the required knowledge implies the involvement of several engineers expert in different areas. The figure of project manager arises in order to lead the activity of engineering. In a large engineering project the knowledge can be very specialized making ineffective a scheme of decision making by voting, and a clear leader can help to make the process more effective. Direct democracy works well when the information about the problem is common and all people understand it equally, however, it does not fit well highly specialized groups because the success of the project may not be distributed equally among the knowledge of different experts. In this case, the project manager values how different tasks affect to the whole progress.

Software projects are different. In a software project, programmers have similar tasks related to programming, and the team can take advantage of sharing knowledge among them in order to improve their capabilities to do their own tasks. The scheme of full leadership is not efficient. Software engineering has developed different management methodologies, for instance, those ones known as agile. Agile methodologies preserve certain leadership for decision making; however, a lot of decisions are made by the team.

In Scrum, an agile framework that can be considered as a ‘de facto’ standard in software development management, the scrum masters are activators of the agile process. They are more facilitators and coaches than leaders in the traditional sense of this word. There is another role in this methodology related to management: the product owner. This role has some responsibilities related to important decisions about the progress of the project but is not a classic project manager too.

Different management methodologies exist because there are different kind of organizations with different aims and different characteristics. Successful organizations are those ones that choose a methodology that let them to cope with higher complexity, or in other words, those ones that reduces their working complexity in a more complex environment, making their processes more effective and more efficient.

Aquila Audax. Photo Credit: Wikimedia Commons

Aquila Audax. Photo Credit: Wikimedia Commons

In Spain we have an old proverb: “God creates them and they come together”. In English the most similar proverb is: “Birds of a feather flock together”. The English version has got a reference to the race; however, the Spanish one has not. It is more generic, and it can be applied to any human characteristic.

The meaning of the proverb tries to show that individuals with a certain characteristic finally will be together because they will have finally similar preferences. I like the Spanish version because it is not related to the DNA, and it is more useful to analyze classic problems of people classification with computer algorithms.

We are living in a world where the classification of individuals is commoner than most people think, and computers are usually following that popular principle.

While in occidental societies any classification from race would be not considered politically correct, many IT systems could be following that directive automatically.

One of the most common and simple classification algorithms is known as the algorithm of the nearest neighbor. Many computers try to classify any new object searching for the nearest object in the space of characteristics, and then it is automatically classified in the same group as the nearest object.

The validity of this method depends on how many characteristics are involved and defining the space of characteristics, and how they are measured in order to provide a mathematical distance.

Some years ago, I was working in a company as innovation manager. Human resources department hired a new girl to work with them. She was living in same street I was living and I grew. One day we meet in the bus stop. As she was a work colleague, I said her hello. She asked me why she never saw me before. My answer was that I always studied in private schools far from that street and later I went to a far city in order to work after the university. It seems that the nearest neighbor algorithm does not fit well this situation.

Social networks are getting information like location every day about people and then they classify people from those properties in order to provide advertising; however, I was a living example that this is not a good way of classification.

If DNA is not politically correct, and neighboring location is not good enough, how can we make a good classification?

First of all, the nearest neighbor algorithm cannot be taken in a literal sense. A good classification is searching for neighbors in the space of characteristics where physical location can be only one of them as most. The same sentence in a mathematical context can be very far from the meaning of that sentence in a social or political context. A problem arises when you are using mathematics to analyze social situations. Something that is very common with social networks. We can see that the solution can be very different if the project is driven by a mathematical scientist or by a politician due to the different use of the language.

On the other hand, we need to improve the algorithm. There is another algorithm more complex that can be used instead. It is known as k-nearest neighbor. The algorithm is similar, but now we are searching for the group that has k elements nearby. Although it is better than the simple nearest neighbor, it is prone to the same errors.

A good classification depends on how the space of characteristics is defined and how the information is gathered and distributed. This can be more important than the algorithm itself.

Automatic IT systems for classification are not only a matter of IT algorithms implementation but a matter of system design mainly. Artificial intelligence provides techniques to cope with more complex situations; however, it cannot be good enough if the system is not properly designed in terms of selection of characteristics and the required information.

Computer scientists have spent several years analyzing classification problems with mathematical optimization algorithms and the introduction of AI techniques as neural networks, however, these techniques will not provide a good result if the system never was properly designed selecting the proper characteristics required to solve the classification problem. A good system would be got from a good IT engineering instead of only good programming. System architecture is at least as important as computer algorithms.

Planboard Planning. Photo Credit: Wikimedia Commons

Planboard Planning. Photo Credit: Wikimedia Commons

Some years ago I defined an innovation project as the proper basement to support ideas and to manage the development of innovation and its internal and external dissemination in order to get the essential characteristic of any innovation system that it is to make easy what is difficult. In more modern words, a project is an organized way to reduce the complexity of any activity.

I advanced that the project is the main way to control the risk of the activity. In other words, we can say now that reducing complexity we are limiting the risk of the activity. Going farther, I mentioned that innovation project is a special kind of investment project where there is more than a bounded risk, there is an inherent uncertainty. This inherent uncertainty must be balanced through a more rigorous definition of tasks. In other words we need to reduce the internal uncertainty of the defined activity to balance the total addition of internal and external uncertainty, because we can do nothing on the external one.

Complexity management is not new, however, in the recent years, new methodologies and approaches have appeared in order to make this activity more effective from the use of science.

Complexity management as science let us to understand better how we can improve in our investment decisions but this is not anything against the reduction of complexity organizing activities through projects.

Any activity to improve the future must be planned in a project or a set of them because the future is uncertain and our modern societies are driven through very complex activities. Project management continues being the best way to reduce complexity. We only need to add complexity management techniques to the definition of projects in order to make our work simpler instead of more complex.

A project is only a planned route to turn an idea into something real. Projects make our life simpler and they let that we can live far from uncertainty and risk. We cannot imagine an engineer working without an engineering project, a business man working without a business project, or a political party working without a social project. If there is no project, there is no work, and then there is no value for the society where they live.

Once we have understood this, we will be able to analyze which project is better to comply with its aim, or in other words, which project is less complex and which project produces a less complex result. This can be done through complexity measurement techniques.

The complexity of the project will define its capability to reach the result. The more complex the project is, the more difficult its management is and the more difficult to reach the aim is. On the other hand, different projects can drive to different results producing different amount of complexity and uncertainty.

John Maynard Keynes. Photo Credit: Public Domain

John Maynard Keynes. Photo Credit: Public Domain

Keynes is used many times to justify the growth of the volume of activity of states in economy; however, it is not based in his thinking. He wrote in “The End of laissez-faire” the following thinking:

“The most important Agenda of the State relate not to those activities which private individuals are already fulfilling, but to those functions which fall outside the sphere of the individual, to those decisions which are made by no one if the State does not make them. The important thing for government is not to do things which individuals are doing already, ad to do them a little better or a little worse; but to do those things which at present are not done at all”.

For Keynes the activity of states would be defined by technical reasons instead political ones:

“We must aim at separating those services which are technically social from those which are technically individual”.

Many current economists defend the increase of the intervention of states in economy as a way to preserve the economic growth; however, they usually forget these important Keynesian sentences.

Keynes would not accept states looking for activities in the private field in order to be replicated by public institutions, however, the opposite thing could be reasonable. He would not consider this one as a way to improve the efficiency of economy. His critique to capitalism is not related to technical questions, but moral ones if it produces an increase of poverty:

“For my part I think that capitalism, wisely managed, can probably be made more efficient for attaining economic ends than any alternative system yet in sight, but that in itself it is in many ways extremely objectionable”.

A private economic activity that does not increase substantially the differences between people in an unfair way would not be objectionable from any viewpoint, both economic and political.

Related to risk management, Keynes understands economic risk as one of the main economic troubles of our societies:

“Many of the greatest economic evils of our time are the fruits of risk, uncertainty, and ignorance”.

As other classical economist, Keynes makes a distinction between risk and uncertainty. You must remember that risk is something that can have an assigned probability, and uncertainty is related to events where we cannot assign a probability. It is very illustrative that he put ignorance at the same level of risk and uncertainty. Ignorance of people is a huge source of complexity in a society, and it lets that a few people can perceive a great amount of money taking it from many other people.

Although he has not a theory of complexity management, he can see the problem of complexity produced by nationalization of industries. Related to complexity, he understands nationalization as something driving to a very complex society to be managed:

“We must probably prefer semi-autonomous corporations to organs of the central government for which ministers of State are directly responsible”.

Keynes is aware that ignorance in the political field drives to odds arguments to justify certain policies in every political party:

“Confusion of thought and feeling leads to confusion of speech. Many people, who are really objecting to capitalism as a way of life, argue as though they were objecting to it on the ground of its inefficiency in attaining its own objects. Contrariwise, devotees of capitalism are often unduly conservative, and reject reforms in its technique, which might really strengthen and preserve it, for fear that they may prove to be the first steps away from capitalism itself”.

For Keynes, capitalism cannot be critiqued from an economic viewpoint, however, he think that political aims can drive the economic policies. This thinking was shared by more liberal economists like John Stuart Mill. For both of them, moral reasons can be over technical ones, but this is the role of politics instead of economics.

Mixing both fields only produces confusion and complexity. Moral aims, if there exist, must be justified from moral reasons instead of trying to do it from economic ones.

A drone is launched from the amphibious dock landing ship USS Tortuga (LSD 46) . U.S. Navy photo by Cmdr. James Ridgway (Released)

A drone is launched from the amphibious dock landing ship USS Tortuga (LSD 46) . U.S. Navy photo by Cmdr. James Ridgway (Released)

Artificial Intelligence is the natural advance to control theory. One of the main figures that developed the control theory was Norbert Wiener, a German mathematician who worked with Bertrand Russell before. He and his colleagues modelled a regulatory mechanism trying to minimize the error, the difference between the goal state and the current state. More modern approaches try to design regulators as devices that maximize an objective function over time. Artificial Intelligence was considered later as a way to overcome the limits of the mathematics of control theory to face some problems as language, vision and planning.

Nowadays, artificial intelligence has produced an industry and it is being used successfully in several fields of working. It is used for autonomous planning, game playing, autonomous control, medical diagnosis, logistics, robotics, language understanding, and computer vision. On the other hand, it is considered a new science trying to use mathematics again and the scientific method to produce a better comprehension of the process of intelligence.

An agent is something that can gather information about its environment through sensors and can produce an active response through actuators. Any human being can be considered an intelligent agent following this definition. Artificial agents will be defined by their sensor, their actuators and their control rules that produce a certain output from the inputs. Artificial intelligent agents will be able to produce outputs from more complex rules than those ones based on control theory.

Intelligent agents work under a lot of uncertainty. They work trying to maximize the expected performance because the actual performance is always unknown. An intelligent agent learns from the information gathered in order to improve its performance.

Control lets to reduce the complexity of a system because control devices and algorithms increase the probability to preserve the system in a desired state although the number of possible states of the system increases.

A control agent working through control theory rules will not improve its performance to reduce the complexity of the system; however, an intelligent control agent can improve its performance to reduce it through learning.

The Project Management Institute points out three sources of organizational complexity: based on human behavior, based on system behavior and based on ambiguity. The use of artificial agents in the processes of an organization reduces the complexity based on human behavior, however, it can increase the complexity based on the system behavior, because the dynamics of the system changes and it can become unknown, and they do not fit well the complexity from ambiguity.

Ambiguity means: “not knowing what to expect or how to comprehend a situation”. This kind of complexity is very undesirable. Control agents without intelligence cannot cope well with ambiguity, and intelligent agents require a way to measure the expected performance. An intelligent agent can adjust the dynamics of the system through learning; however, it must have an expected performance. Intelligent agents are not omniscient because they cannot know the actual performance but they require a way to measure the expected performance anyway.