The Future is Not Going to be Made Tomorrow: Semmelweis Reflex (Re-) Mastered

Rejection Area, Source: Own Elicitation, Background Office 365

Pardon? Hand Disinfection in a Clinic!? Are You Serious!? You Need to See the Shrink.

Whenever smart and well-intentioned people avoid confronting obstacles, they disempower employees and undermine change.
— John Kotter

The Saviour of Mothers:
Dr. Ignaz Philipp Semmelweis
(*1818 – †1865)
An engraved portrait by Jenő Doby, Source: Wikipedia IgSe

Reading the title, you might think: Washing hands in a clinic is absolutely common. Sure. But as with many common things, once it was not. And for the common there will always be a challenging uncommon that might emerge, unveil and shift reality, like in Ignaz’ case. But step by step.

Let’s imagine the following situation: You are a doctor, a researcher. You want to change a situation that is apparently not satisfactory: Desastrously high infant mortality rates due to puerperal fever (commonly known as “childbed fever”). Now, suppose you establish the theory that there could be something invisible like germs, which might be responsible at a high degree for the enormous amount of deaths of children. Perhaps accidentally, you disclose a new solution to help, reducing those mortality rates by 90%, because you observe that your solution worked best to remove some putrid smell of infected autopsy tissues. The solution seems too simple and obvious: Washing your hands using a chlorinated lime solution. Unfortunately, you cannot scientifically proof your procedure – it just works with an outstanding impact. However: Friends, colleagues, the medical community, even the public rejects your findings. Not only do they reject your solution, they subsequently discriminate against you – massively. Ending up in a psychiatric hospital, because of a nervous breakdown and a treacherous colleague, you are dying there mere 14 days later at the age of 47, somehow ironically, of a sepsis after an injury due to a fight with the staff. Just a few years later your procedure spreads out like an epidemy and is sanitarily the new state of the art. Impossible? Well, something like this happened to Hungarian physician Ignaz Philipp Semmelweis and his “Theory of Cadaverous Poisoning”. His observations conflicted with the established scientific and medical opinions of the time even though he was able to reduce mortality below 1% by applying his procedures. But he was not able to adequately explain his findings scientifically, despite achieving demonstrable and impressive reuslts. Even more paralyzing and bewildering: Some doctors were offended at the suggestion that they should wash their hands. The medical establishment lampooned, satarized and ridiculed him. Eventually, the true story might be a little bit more complicated, as there have been loads of political turmoils in the 19th century, too and for sure, he had to endure further confrontations in the scientific realm. Additionally, he also became a victim of his own obsession, fueled by external pressure to maintain reputation and, in some cases, the minimum necessary endeavors of self-assertion. Nevertheless, the rejection of a new approach that questions the established paradigm and world view should also go down in history using the name Semmelweis Relflex. Even though similar stories have already been happened in the past, just thinking about Galileo – but well, we are talking about hygiene procedures in hospitals not solar systems of universes. Anyways, equivalent happens again and again. Then, now and hereafter. Fortunately there is also the other side of the medal: Semmelweis is nowadays recognized as a pioneer of antiseptic policy. At least.

Thresholds, Sorting, Peer Effects? Some Explanatory Aspects

Essentially, all models are wrong, but some are useful.
— George E. P. Box

Empirical Model-Building and Response Surfaces

Besides the widely discussed topics in Change Management, for example on steps to accompany and design initiatives, in this article we are looking on some models from more sociological subjects, i.e. in this case agent-based models that focus on:

  • Individuals
  • Behaviors
  • Outcomes

In agent-based modeling, we model a system that is a collection of autonomous, decision-making individuals called agents. These agents make decisions on the basis of a particular set of rules. The decisions get aggregated to see what types of macro-level behaviors or patterns emerge. So the three things to look for in agent-based models are the agents, their decisions, and the aggregation of those decisions.

In the following sections, we are looking very briefly – as there exists already loads of detailed information and to test you on some aspects realted to the story of the Semmelweis Reflex – on the following models and especially on their conclusions that can be adopted to other change situations:

  • Schelling’s Segregation Model: Model on racial and income segregation.
  • Granovetter Model of Aggregation: Demonstrates for example the willingness of people to participate in some sort of collective behavior.
  • Standing Ovation Model: Extension of Granovetter Model by Scott Page and John Miller on how behavior changes based on peer effects to match people around you.
  • Identification Problem: People of a group look similar. Did they explicitly sort out with whom they meet or was there some sort of peer effect? Both?

What Schelling and Granovetter are Teaching us on Collective Action

Segregation Game, Source: Ma. Cro. Mind

Schelling’s Model on Segregation

Thomas C. Schelling (*1921 – †2016) was an American economist and professor of foreign policy, national security, nuclear strategy, and arms control at the School of Public Policy at University of Maryland, College Park. He was also co-faculty at the New England Complex Systems Institute. He was awarded the 2005 Nobel Memorial Prize in Economic Sciences (shared with Robert Aumann) for “having enhanced our understanding of conflict and cooperation through game-theory analysis.”
His model on segregation was originally developed to analyze racial and income segregation, but can be applied on many other settings, where agents decide based on an individual threshold, like tolerance for other people. The outcome of these independently deciding agents demonstrates then how segregated an amount of people is at the end of all decisions made, leading to an equilibrium if not any more influences occur. The model, as most others too, is of course reducing things to a minimum, leaving out other external influences as well. Anyways it helps to understand some processes that can also be used and influenced by pertinent actions.

Granovetter’s Model on Aggregation

Mark S. Granovetter (*1943) is an American sociologist and professor at Stanford University. Granovetter was recently recognized as a Citation Laureate by Thomson Reuters and added to that organization’s list of predicted Nobel Prize winners in economics for the year 2014. Data from the Web of Science show that Granovetter has written both the first and third most cited sociology articles. He is best known for his work in social network theory and in economic sociology, particularly his theory on the spread of information in social networks known as “The Strength of Weak Ties” (1973).
In his model on aggregation, rational agents take a binary decision based on their individual thresholds, which define how many other persons must make a choice for the agent to make a decision. All those individual decisions depend on aggregated decisions, i.e. action of one person is depend on which are the other people’s actions than can be identified and how does the first person decide to act based on this information and its threshold. Even though it is a simple and old model, it is still useful to understand the underlying mechanisms on how it works. It tells us less on why something happens, which is more bound to beliefes, value systems and more complex rational factors than just a pure cost/benefit evaluation.

The Pickable Fruits of These Two Models

For Schelling’s Segregation Model as well as Granovetter’s Model, the underlying concept is a rule-based approach, depending on thresholds. I.e. borders that must be passed that triggers an individual to act in a specific way. For example, if your threshold is 50 people that must incorporate the specific behavior, then you need to seek 50 people out there before you are joining them and before you start some sort of collective movement. Each person has a different threshold. More adequate a set of thresholds firing related to different aspects on different levels. The overall findings of these models are, that the probability of collective actions increases in relation to the following:

  • Lower Thresholds
  • More Variation in Thresholds

Intuitively you might have guessed that the lower the thresholds, the more likely a collective action to do something is. What you might not have thought of without modeling it, is the fact that the likelihood of collective action rises in relation to the variation of thresholds within a group of people. The more variation the higher the chances that collective action takes actually place. In reality, in order to actively gain advantages out of these insights, you need to know the average level as well as the distribution of variation, or negatively formulated, the discontent as well as the connection relationships of individuals.  This might be used to organize movements for example. If there is more and more people and diversity in the tails of the curve, it is more likely that you get collective action.

But: What you see at the macro level must not be the same as what is going on at the micro level – both spheres may not align! Micromotives  are not the same as macrobehavior! Or: When the Dog Walks the Dog Owner…


The rejection area, as mentioned in the initial picture of this post, can also be the inflammation zones of paradigm shifts. People at the ends of a distribution might drive what is happening, too. Extremists, to mention a pithy example. Reasonable tolerant people can lead to a macro level segregation, as wetness is a characteristic of a bunch of water molecules, not one single molecule or its atoms. All of this leads sometimes to tipping points, that can be distinguished into Exodus or Genesis Tips. At Exodus Tips on the one hand, somebody or something is leaving the system that makes you leaving the system as well. Wheras on Genesis Tips on the other hand, someone or something is entering system, that makes you leaving the system. This may analogically apply, when someone or something is making you enter a system. As when you leave one system you usually enter another at the same time or you enter a system because you follow someone who already entered. Eventually ending up in a chain reaction.
Additionally, one person might behave in context of people that have another opinion more adaptive, whereas when in company with more people that match their true system of opinions, the same person acts exactly the opposite way. So, the person is behaving on the micro level in different contexts differently, but their acting has impact on a macro level that tells another story. Or the person is adapting even though the threshold is passed, leading maybe to a new threshold level or even to a change of the set of thresholds and its asepcts. A change in behavior, following a change in opinion or adjustments in the whole value system.

Standing Ovations for the Identification Problem

There ist no point in using exact methods where there is no clarity in the concepts and issues to which they are to be applied.
— John von Neumann

Theory of Games and Economic Behavior

Standing Ovations Model by Page and Miller

Scott E. Page is an American social scientist and John Seely Brown Distinguished University Professor of Complexity, Social Science, and Management at the University of Michigan, Ann Arbor. 
John H. Miller is professor of economics and social sciences at Carnegie Mellon University. Facultiy member of Dietrich College of Humanities and Social Sciences.

As the name implies, Page and Miller explored how people behave in standing ovation situations after a show for example. Loads of people start to clap. But then might have to decide, if they stand up or not. Especially, after a standing ovation starts, you might feel some pressure to decide if you stand up or not. Whereas you rather want to decide for yourself, you might be influenced by the crowd and have to decide, if you follow the people that are standing or if you stay sitting. The observations, based on the model, lead to the finding that the following parameters influence probability of standing ovations:

  • Higher Quality: The quality of the show, that is individually meassured, e.g. according to individual experience and comparisons. Which may lead to the contrary as well, if audience is for example not understanding contents of the show.
  • Lower Thresholds: Thresholds are for example bound to expectations or individual enthusiasticism or proneness.
  • Larger Peer Effects: E.g. depending on connectedness, amount of people, group pressure and the individual resilience to such influences or fortitude.
  • More Variation: The more diversity among the audience, the higher the probability that the parameters above reinforce each other, which increases likelihood of having a standing ovation.

Without the model you probably would have found out, that higher show quality, lower threshold and larger peer effects are influencing standing ovations. What you probably would not have found out is, that more variation in participants’ individual sets and thresholds is increasing the chances of having standing ovations. Page and Miller have additonally shown, that these findings can be dramatically influenced when you use celebreties as role models or at least big groups – if strategically placed within an audience or group or network even more.

Identification Problem – Sorting or Peer Effects? Both?

The Identification Problem is telling us that it is – at the end – not identifyable or determinable just from looking at a result of any constellations, for instance with respect to how segregation or aggregation of groups of persons has emerged. We cannot distinguish from the result itself, exactly which process lead to the result. From the result alone, we cannot determine if people are together because of sorting, i.e. deliberately chosing people or groups according to own preferences or deliberately adapting to match. Equally, we cannot figure out if peer effects lead to changed behaviors, which are more unconcious. I am adapting behaviors automatically as I am exposed to my environment The same applies for the combination of those two.

Sorting & Peer Effects Schematically, Source: Own Elicitation, Background Office 365

Therefore, we must observe and meassure the processes. Regarding sorting and peer effects, this means: If sorting is underlying we can see moving people. If peer effects are underlying we have to historically analyze micro level data that people changed behavior and mindsets.

Mastering Semmelweis with System Dynamics and Change Curves? Some Arousing Awareness

Two opposite intelligent half measures always add up to a whole stupidity.
— Gunter Dueck

Lean Brain Management: More Success and Efficiency by Saving Intelligence
Step-by-step development of the self-similar Sierpinski-Triangle as example for systems. Made by Georg-Johann Lay, Gemeinfrei.

According to Emmanuel Levinas (*1906 – †1995), in the face of the other, the ego transcends into a state of responsibility towards the other in which unrestricted access is simultaneously impossible, but without having freely incorporated or aspired this state. You are just confronted. But it is precisely this responsibility that forms the foundation for deciding freely for or against something or someone. In any case, own motives and distortions should always be deliberately questioned. In addition, it should be examined on an exceptive basis whether the range of actions extends beyond a purely binary decision. In most cases it actually does. The world is more than opposite proverbs.


Physical elements do not have goals, objectives or believe structures and value systems. But people do, which makes it more complex than modeling the functionality and processes of a gas turbine than adequately modeling human behavior. Anyways, in memorial to George Box, even though models might have failures, as a decision takes more than a single threshold – maybe there are already some models and systems out there that image reality almost acurate – they help us in the following areas:

  1. They make us clearer, better thinkers, because they help sorting out logical inconsistencies.
  2. Models help to use and understand data by structuring it into information, and then turn that information into knowledge.
  3. Based on models we can better decide, strategize and design settings.

Change leads very often to uncertainty, to instability, to fragility. But change is always crisis and opportunity in one and the same suit. As Peter Kruse already mentioned in his book “next practice”: “Instability is a vital condition for rearrangement. But instability is always just the shift from one paradigm to the next. During instability a system’s capacity for action is reduced and its adaptability is increased. Of course, we destroy systems, if we keep them instable continiously. But if we create lasting stability, then we lose creativity.”

Nevertheless, you should always keep in mind that usually you are dealing with system dynamics, which do not necessarily run linear. The system might respond, react or act exponentially. Dynamics might be deferred and at one specific moment in time consequences of past actions are appearing as sudden phenomena, incalculatable or thelike. Exemplarially in Systems and Model Thinking i.a. different classes of outcomes of systems are distinguished, i.e. systems may “behave” equilibriating, compensating, in cycles, complex or completely random. The future is not going to be made tomorrow. It is made now, with relations to the past that might already have materialized the consequences of those actions or that are first going to materialize. Nevertheless, imposing the methods of yesterday on a changed today is neighter helpful nor an intelligent or – as currently en vogue – smart approach. We are walking through the kingdoms of dynamic systems, which is known from Self-Organization (incl. autopoiesis) and Chaos Theories.

Kübler-Ross Change Curve, Source: Own Elicitation, Curve Model from Kübler-Ross Foundation, Background Office 365

Digitization and globalization are already a lot more than mere concepts and terms. Both are experienceable realities that are significantly and incomparably (r)evolutonizing our known realms. Scaling, due to technological innovations, accelerates modern life. Neurological and psychological adaptabilities run the risk of not being able to keep the pace. Disruptive technologies influence impacts on socio-economic and political spheres crucially. All of this increases behaviors like the Semmelweis Reflex, or even worse ones, unfortunately. A huge paradigm shift is necessary that changes businesses, institutions and individuals likewise, their attitudes, behaviors and actions to conceive new adaptive and holistic mindsets as well as technological and intercultural co-creation strategies. All of this is sine qua non in order to fit the new realities and inevitable for sustainable future partaking. Boon and bane.
“Quite a few people feel threatened, imagine themselves in the role of the sorcerer’s apprentice, who is overrun by a self-reinforcing development” (Kruse 2013). Ladies and Gentlemen, please do not forget Kübler-Ross in this context: Mind the curve, its stages and implications – especially when you think about multiple individuals as well as multiple more or less simultaneous change initiatives. A person that is currently in state of frustration might not handle the next shock – multiplied by the amount of people: Paralysis is knocking at the door. For these reasons, management is responsible, obliged to do so, and needs the capability to ensure an appropriate balance between stability and instability, and between decentralised autonomy and central direction.

Typical Distribution in Change Settings, Source: Own Elicitation, Background Office 365

Usually, when change initiatives start, initial distribution of people that support or do not support innovations or new – even stronger – disruptive approaches and solutions is approximately as shown in the depiction above. First there are about 5-10% of supporters that abet the initiative. For ordinary, 30-40% of people are sceptics and actively or secretly doubting the initiative, with a modest commitment at most. 30-40% are brakemen that try to slow down the intitiative. They are usually not against the initiative, but they need more time for adoption or are still struggling with the changing aspects. Regarding the Change Curve, they might be in a state of denial or frustration, going down the depressive pathway. Danger threatens from the 5-10% in the area of the resistors. These people are opposed to change and work actively or passively in the opposite direction using a wide range of means and methods. Individual history and experience of each single involved person with change influence this distribution drastically. Therefore in most cases a more or less individual accompany is inevitable. Furthermore, you first should first focus on people from the group of sceptics and brakemen to accomodate with the initiative and winning them to become supporters.

In many cases, the success of change initiatives stand or fall with the willingness of the management level to change and how this is exemplified. Whether there is sufficient room for autonomy and creativity but also for direction, whether and how mistakes and conflicts are dealt with as well as how the flow of information is guaranteed and organized, how people are connected, communicating and collaborating. Stay away from the seductive sirens of change obsession, change is not an end in itself – but people are. Maybe, next to a better organization of collective action for example, all of this would have had helped Semmelweis not to die in an, so called, asylum at the age of 47.


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