Intelligence and stupidity

 I used to naively think that intelligence and stupidity are opposites, but have come to understand that stupidity is in some sense orthogonal to intelligence. Stupidity was defined by Carlo Cipolla as a personal trait, causing loss for others without gains to themselves. Stupidity is independent of any other trait or characteristics, and underestimated in many different ways. Stupidity and intelligence can according to this definition be placed in opposite corners of a two dimensional graph, where you have benefit to self on the x axis and benefit to others on the y axis. Intelligent people are hence defined as producing benefit both to self and others, where stupid people cause negative benefit both to self and others. To complete the definition you can also be classified as helpless (benefit to others, but not self) or as a bandit (benefit to self, but not others).

In an organisational setting stupidity takes on additional dimensions as described by Mats Alvesson at Lund University. He defines functional stupidity as people doing exactly what is expected of them without consideration or thought. The person does everything according to what is expected without affecting any real progress. When likeminded people who conform to current practice are more commonly promoted we end up with stupidity gathering at the higher levels of organisations.

More intelligent people are not immune to stupidity. Some traits of intelligent people have even been shown to cause stupid behaviour. More intelligent people seems to be more prone to explain away facts that are against their own beliefs. Schools and educational institutions give better opportunities to people with high intelligence. As intelligence seems to have cultural and environmental causes there exist some form of inequality that perpetuates in this system.

The level of intelligence, at least as measured by IQ tests, have increased steadily over the years in the population, and is expected to continue to increase over time. This is what is called the Flynn effect after the researcher James Flynn. For some populations in highly educated countries the level of intelligence has been found to decline now lately. According to Flynn himself this is caused by the level of intelligence reaching a limit. The increase in intelligence seems likely due to cultural and environmental effects. The decline could presumably also be due to change in culture or environment. Spreading misinformation and closing libraries are examples of things that can have an effect. Intelligent people promoting activities like this is most disturbing but not entirely unexpected. As competition for prestigious positions increase it is easy to see how privileged groups could opt for interventions that reduce competition. There can however also exist less visible effects lowering the level of intelligence in a population that is built in due to a lack of knowledge. The school system with an excessive focus on learning toward a specific objective and a loss of motivation of students may hence also contribute to this effect.

We urgently need to counteract all kinds of stupidity. This can be done by encouraging curiosity, learning and questioning. Striving for more intelligence in itself is probably not the solution. The question is what we as a society wants. Im not certain that striving for increased intelligence is wise. There should be more discussion and debate on targets and effects of our school system and what is promoted in our society.

For those of you who know Swedish i would once again recommend listening to P3 Dystopia, especially the episode named "Dumhet" that can be found at https://sverigesradio.se/avsnitt/dumhet-p3-dystopia. The material they used for research can be found at https://sverigesradio.se/ with references partly in English.

Introducing bufferts and slack in organisational processes

Typically when estimating how long a task will take for a project you see managers multiplying the estimate with some factor. This leads to estimates inflating like a balloon. Estimation is however typically done based on a deficient understanding of the problem. The effort put into estimation is further limited, typically to just something that can be measured best in minutes for tasks taking days.  Surprises and new circumstances arise as work is done. I would  however argue that these additions to the time a task will take is not necessarily linear in nature, as the multiplication of effort estimates would assume.

Perhaps an alternative way of inflating estimates would be to apply an exponent to the initial effort, but this will mean even bigger estimates that will not be easy to sell. This would be better if the distribution of delay as compared to initial effort estimations are exponential in nature. This may still not reflect the true nature of problems with estimates. Could applying a random unlimited exponent to estimates produce a value better reflecting the true nature? Perhaps the conclusion is that random values would be the best, as you save the effort of estimating over all... Work however exhibit the behaviour that it takes as long or longer than estimated. People tend to use the time allocated to the full extent. Padding processes and estimations slows down everything and waste time as a result.

Another alternative is to not inflate the estimated duration of single tasks, but add a buffer for the entire whole of all tasks to  be performed as a part of what is done. Each task overrunning its initial estimate can then use a fraction of this buffer. The remaining buffer would then give a good estimate of what the state of the over all project. When your buffer is going to zero, you know that your project is in trouble.

When organisations mandate that the utilisation of people processing work is kept very high any delay will have a severe impact. Full utilisation in a non deterministic system will mean that everything grinds to a halt. For an entirely deterministic system full utilisation can be seen as efficient, but entirely deterministic systems are rare and if the demand exceed the capacity also a deterministic system will cause delays. According to the Universal Scalability Law adding a node (person or machine) to process some work can degrade the overall performance of the system as a whole, due to coordination costs between nodes.  

Failure demand is also seldom included into the model used for estimating work. Depending of your circumstances some things will need to be done to correct mistakes, misunderstandings, communication gaps, variation related issues and systematic errors that may occur with the outcome of the work. When failure demand is high all predictability will anyway be close to none. Addressing failure demand can be a first step to reach some form of predictability, but failure demand will never go away completely and time must always be allocated to addressing issues. Reducing failure demand, especially in terms of common causes arising from the attributes of the system can in the long run free up some time, but require an investment in terms of work, further increasing the workload momentarily.

Time buffers are clearly needed whenever you have tasks performed by people. So what if the individual person has time to take a pause, as long as the system over all run smoothly?  Efficiency of the individual steps in a process or of the individual doing the work does not mean that the process overall is running efficiently. Efficiency of the overall process is dependent on that each node has the bandwidth to address needs as they arise. At high individual utilisation this is impossible.

Curing organisational incompetence

Organisational incompetence was defined in the 1990:s by J. Steven Ott and Jay M. Shafritz  an inability of an organisation to learn from its success, failures, or its environment (see https://www.jstor.org/stable/977385). Predating that in the 1980:s Chris Argyris used the term skilled incompetence to describe something related, where the repeated use of a behaviour results in unintended consequences as described in an HBR article (https://hbr.org/1986/09/skilled-incompetence).

Today we still see a lack of critical thinking when applying best practices, rules and procedures. Although best practices has a valid application when dealing with clear or obvious problems there is an inability of organisation to limit the applicability of the practice. In dealing with complicated problems the use of a best practice can be questionable. With what certainty can anybody say that the practice is the best possible? In the complex domain, where the cause and effect can only be seen in retrospect, the use of anything like a best practice is going to produce a suboptimal result. The same goes for problems in the complex and disordered domain as the result of an intervention may be all over the place. 

Most organisations fail to recognize this limitation to best practises. In stead organisations reward and promote individuals following the rules, applying the best practices and who does not criticise the entrenched behaviour. Individuals also them self opt for this behaviour, as it is risk free, comfortable and does not need any extra effort. As a result organisations are likely to end up with managers having limited skills in critical thinking. What is worse is that this can be a negative spiral and the situation become worse over time.

Mostly the result of any organisational incompetence is just waste of time, money and effort that can be considered as normal. On a society level this waste amounts to astronomical amounts, but to the individual organisations it is just the cost of doing business. Some times things do blow up, as exemplified by huge overruns in terms of cost or schedule. The outcome may also be dysfunction in what the organisation produce. There is also risk for organisations being affected by organisational incompetence that is worth noting. Organisations may end up living in a dream world where all is well according to the norms, but in reality they are headed for disaster that they fail to see. Required business transformation may also fail as a result of the inertia existing norms.

To solve the problem a first step  should be to recognize there is a problem. Many people are highlighting that there is new thinking required but few listen. Organisations also must ensure that any issues can be brought forward without risk of repercussion. Psychological safety is a crucial a precondition. Once the problem is recognised and safety is ensured, a sense of curiosity towards all existing practises should be encouraged. According to my understanding this is what Toyota does with its philosophy around the use of the andon cord. The leadership of an organisation finally need to make sure to enable change. More diversity in leadership is one thing that may help. The diversity should also include diversity of thought in addition to what is traditionally considered in diversity. Traditional MBA and other management training does not seem to produce any favourable outcome in this respect.

As people slowly are starting to recognize the problem also the media is starting to apply pressure towards organisations affected by organisational incompetence. The recent episode of Dystopia from Swedish Radio (https://sr.se/avsnitt/1742863 in Swedish) highlights this in an excellent way that i would recommend anyone with a basic understanding of Swedish to listen to...

Is Organisational DNA a broken metaphor?

In the current business jargon the term "organisational DNA" is more and more used to describe the culture an organisation embodies. It is not a new term, as it was used already in the 1990:s. I consider the term somewhat misleading. I have no idea why this term was adopted as a suitable metaphor, but perhaps it was chosen because DeoxyriboNucleic Acid (DNA) is seen as the ultimate source code for all living things and that was what people would have liked culture to be for organisations. 

Perhaps the vision is to communicate that there is a role for central control and order, like in eucaryotic cells, where the DNA is stored in the cell nucleus. All DNA is however not stored in the nucleus. Even part of our euaryotic cell DNA is located in mitocondria. Bacteria will not even have any nucleus. Bacteria also has DNA in plasmids. DNA can even be found outside cells as a result of DNA break down.

Is order something that is associated with DNA? Could the vision hence be that DNA is seen as ordered but complex, like culture? I would argue that as culture has more diverse manifestation than DNA, as DNA is only built out of 4 bases repeating in a certain pattern holding a message. Although computationally combinatorially complex the basic structure is simple. DNA naturally is a double helix consisting of two strands corresponding to each other in an antiparallel sense. Adenine (A) pars with Thymine (T) across on the other strand. Cytosine (C) pairs with Guanine (G). This base pair sequence has meaning. Comparing to culture, I wonder if there is either simple building blocks or pairing.

The processes surrounding DNA are highly complex and subject to tight feedback restricting and promoting the processes. Replication is one thing, producing an exact copy of the DNA in order to split the cell in two. Transcription is the process by which the DNA sequence is copied into RNA with Uracil (U) in stead of Thymine (T). Expression is a subsequent step which results in translation of RNA to proteins. Proteins in turn constitutes part of the cells. Only messenger RNA is used to produce proteins. Non encoding RNA, ie t-RNA and r-RNA is used for translation, kind of adding one amino acid at a time to the protein chain as the m-RNA base pattern defines. Other DNA has regulatory effects on these processes.

Proteins can be structural or mediate a chemical reaction, for example breaking down chemicals for metabolism or transporting signals. In this respect I also don't see any corresponding mechanism in how organisations embody a culture. 

With normal living beings some aspects are inherited from our DNA while other attributes are learned. Learned attributes does not become part of the DNA sequence, except for methylation. Culture would in this view perhaps belong more to what is learned, at least in terms of higher beings, while structure is more encoded in DNA. For viruses and less complex organisms the role of learning would perhaps be of less significance, but then again they do not exhibit any higher order of culture.

Although organisational DNA can be seen to propagate, inheritance is not something organisations exhibit. Culture typically spread from one part to the next in an organisation, from one person to the next, so this should perhaps be seen as more as infection than reproduction... Taking this idea further would give a problematic view of organisations as being primitive, simplistic and perhaps a bit sinister on the side infecting other cells... 

Well, anyway, some viruses have DNA, except RNA viruses of course where the situation is even more complex. For a virus to spread it requires also what cells ultimately produce from DNA: Proteins. Messenger RNA is only a step on the way. The replication requires a machinery that cells provide which is subject to tight feed back loops. I don't see the same level of tight feedback in regards to organisational culture.

DNA can be read, or sequenced, to identify the constituting base pair pattern in a sequence of DNA. DNA can be analysed and used to identify species, relationship between samples and identities. I do not know how to elucidate the constituents of Organisational DNA, although Dr. Westrum describes three types of organisations. The constituents i would however say are very unlikely to be sequential or produce a pattern.

DNA does provide a well known acronym that perhaps lend credibility to the organisational ideas in relation to culture. Perhaps nothing more than this is required as an explanation. Nothing wrong with metaphors, even bad ones can be useful, but it is unrealistic to think that everyone will be on board with your metaphor. 

Over all, knowing a bit about DNA makes the organisational DNA metaphor seem superficial and fluffy, perhaps just like culture generally is viewed. For more on organisational culture i would in stead suggest you go to the podcast interview with Dr. Ron Westrum by Gene Kim at ITRevolution.

Models for thinking, evaluation, discussion and affecting people

Models can greatly aid in thinking about systems: A model can be a stake in the ground to disagree with. A model can be something to help evaluate the properties it exhibits. A model can be used for ensuring that the thinking around the model is consistent with reality,  for visualizing of just for general communicating an idea among numerous other uses. Some use cases however takes the potential impact of the models much further.

A model can be formal, in between or just ad hoc, depending on the need. Formal models can be expressed in mathematical notation and even queueing models in some cases, not to mention UML. Based on the model various calculations can be performed to draw new conclusions apart from communicating the idea behind the system depicted by the model. The model can live on the back of a napkin, on paper, on a whiteboard, in mathematics, in computer programs or in an AI implementation generated by machine learning, or just as an abstract concept in ones mind.

With a well equipped mental model you can more easily spot counterfactual misinformation or other issues. Models can however host various kind of bias. When the underlying assumptions are flawed, also the model will be flawed. The model can be too simple to capture what is seen as crucial, like for example when missing feedback loops.

In the obvious domain it is simple to create models of well behaved systems. As a system grows more complex, the difficulties grow. Modelling a chaotic system, or a system in disorder should be seen as virtually impossible. 

Constructing a model used to be a human activity, and the impact somewhat limited, but with the spread of AI / ML the origins of models and use of models will be breaking more new ground. There has been a lot of discussion about the importance of model explainability, fairness and transparency, and i support the idea, although i'm skeptic that this will have the desired result of reducing harm. The outcomes of the use of models need to be tested, evaluated and verified from a very diverse set of points of view, including those affected by the model. The currently proposed regulation and limitations seems prone to circumvention. Perhaps part of the solution is that models with impact to humans need to be evolved to become better over time.

Any model will always be imperfect in some aspect. There is no such thing as a perfect model, except for the system itself in a very limited sense. Some models will even cause harm. Modelling a complex system can newer capture the full detail of the system. Some models are however still useful. 

Language matters

 Using acronyms, internal established slang, management speak and special terms can make writing simpler. This however comes at a cost to the reader having to understand the written content. When your  reader is not initiated to the terms the text may even act as a barrier to exclude people from understanding what you are writing (or talking) about.

There is however a certain trend to exhibit expertise trough obscurity that is especially troubling. The definition of an expert should first and foremostly contain the criteria to communicate clearly and understandably. People may not have the sufficient experience to challenge the expert, don't bother, or are busy doing more important stuff. It requires energy to point out the problem. As an expert the most important thing to accomplish is however to communicate with others, and doing so clearly.

When you write it takes time, but only once. Using an acronym can save a few keystrokes, but is this a significant optimisation? If the text is read by many people you should in stead optimise for reader efficiency. Forcing the reader to look up terms is time consuming and even error prone. Making the reader wonder what something means also costs time.

There are also a lot of terms to avoid that has a problematic past history, even if you do not intend any offence or you intend to give a new meaning. You can however not know what the reader understands and experiences when encountering such a word. It can be hard to know upfront all problematic words, so be ready to apologise and change when someone reacts to what you communicate. 

Extensive use of acronyms, slang, and specialised terms are all ways to set yourself up to fail with what you try to communicate. At least define the terms you are using and assume that the reader does not know the meaning of the special terms. The reader who already know the term can always easily skip the definition at a very low cost.

Knowing what is true and spotting lies

There is lots of research being done on various themes like Artificial Intelligence among other popular fields. We see constant daily stream of publication with new findings. The volume is overwhelming.

All publications are however not that great. It seems that anyone can publish almost anything without putting in the required effort of verifying that the findings are based upon a real effects. There also seems to be a disregard for existing research, either by authors not knowing enough about the subject or authors selecting to reference only the works that fit together with the desired findings. This seems to be especially a problem when the subject of the study is multidisciplinary. Using the language of one field can hide issues with the findings behind difficult terminology. 

Reviewing multidisciplinary research must be quite hard, as none can be expected to be an expert in all areas of many subjects. Recognising bad research publications is also not easy, but there are a few things you can do. First and most importantly everyone needs to practice skepticism when reading. Learn to know what bias looks like. If things look too clean and too good to be true the warning should sound that there may be a problem. When serious money or similar high value stakes are in question we need to be on the lookout for conflicting interests. 

The problem is made worse by the tendency of people to uncritically repeat findings with a catchy point, regardless of the truthiness of the findings. Once a problematic finding is out in this way it takes a lot to refute it.

The book "Calling Bullshit: The Art of Skepticism in a Data-Driven World" by Carl T. Bergstrom, Jevin West gives a very thorough overview of the problem that i highly recommend anyone to read.

Fooling oneself is the easiest thing to do, and by doing so we easily fool others.