The Ladder of Inference – Raimo Hämäläinen

Hello. Today, my topic is the ladder of inference. What is ‘ladder’? And what is ‘inference’? It’s a way of describing our thinking. So, very often when we’re dealing with complex problems, - like the climate change, climate issues, - we think that the complexity is only related to the subject matter, - how climate is to be understood. But we are as complex as the climate, - so, the way we think can cause as much problems - as environmental impacts onto the climate. So the theme relates to systems thinking and systems intelligence. Systems thinking is a natural approach to understand inter- relationships, - which are the key drivers of climate change, climate issues, - environmental issues.

01:07 - But there are also interactions in human systems, - so we, as social animals, so to speak, - create a system when we interract with each other, - and this creates another kind of system. And Peter Senge, from MIT, wrote a book 30 years ago, - called ‘The Fifth Discipline’. It was describing human organisational systems, - and, he had a term, the ‘learning organisation’, - how organisations could learn - to take into account peoples’ behavioural patterns. And the fifth discipline in his book is systems thinking. And then there’s another concept, - ‘systems intelligence’, which I and Professor Esa Saarinen, - developed 15 years later, and our point is that, - we cannot look into the systems from the outside only, - we have to see that we are always part of the system, - and the way that we see the system, - can be different than how another person sees the system So these are the two key concepts that I am trying to illustrate - with the ladder of inference today.

02:21 - The ladder of inference, you can see on this slide, - starts from the fact that we have a situation, - we have lots of data we can observe, we can read, we can see, - and then we typically start selecting things, - so we select the data from the pool of available data. It’s shown on the slide with this little blue ground. Then we start thinking, “well, what is this data all about?”, - we start to add meaning to the data. This means this, and this means that, then we create assumptions, - then we make assumptions and start to think, - “what is the consequence of this assumption?” And then we make conclusions. And then we start to believe in our conclusions.

03:13 - And suddenly what happens, we believe in our conclusions, - and we go back and look for the same share of data, - to confirm that our conclusions are correct. So, we actually, in this vicious loop, we think that we are - improving our thinking by just looking into the same selection of data. And, once we are trapped in this loop, - we can make very dangerous, difficult decisions, and be wrong. So the learning thing is that, while climbing the ladder, - you should always, when you’re on the top level, - start thinking, “what did I really think?” “Do I challenge my thinking? Did I see the big picture?” “Did we only select one part of the data and made my assumptions based on that?” And the risks of this phenomenon of deceiving yourself - is very strong in particular in settings where you have a good goal, - you have an agenda that, “I want to save the environment” - “I want to save the organisation.” So, I am looking into the environmental issues only, and in the organisation, - I’m looking into how other people behave, so how they behave, - and I forget that I’m actually part of the same system myself, - and I forget that my own impact could be as crucial as the others.

04:43 - So, I will demonstrate this way of thinking - with an example from my last summer’s fishing trip. I was in Northern Finland, flying into the wilderness with this tiny plane, - and I was looking down from the plane and I saw barren land, - very exciting, nobody there. We landed, then we made a camp, and started fishing. The observations which I made, I was, environmentally, sort of triggered, - in this beautiful environment, I pay attention to what the environment is like, - started fishing… so I see nice flowers on the riverbank, - clear water, crystal-clear water… and no catch. No catch… flowers… and barren land…

• 05:37 - and then I start giving meaning to these phenomena. So, if there’s lots of flowers on the riverbank, - it may be that there’s lots of nutrients in the river, - so the flowers can get what they need and they’re flourishing. So there are nutrients in the river, but the water is very clear. I’ve heard that acid rain produces crystal-clear waters, - so perhaps acid rain. And there was no trees, so perhaps acid rain has also killed the trees.

06:11 - So, I came up with these assumptions and start to make conclusions, - that this must be an environmental problem. I can’t catch fish, this phenomena relates to environmental problems, - and then I start to believe, that maybe yes, - this is an environmental problem with the river. And the next day, I was still fishing, seeing the same phenomena, - no catch… and then I come to the confirming conclusion that, - I’ve seen this day after day, and there must be - something wrong with the environmental system. And then I come to the conclusion that I have to take an action, - to suggest that this river would be subject to a restoration project.

06:59 - Would this be the right conclusion? Or would there be another explanation? I had only looked into the river, the environmental issues. I had not considered myself, as a fisherman. My flies… were the flies the right ones to be used there? So, you can have two alternative outcomes, - you can think, sort of, systems-wise, - that, this is a system and - the environmental conditions have changed the river, - so the problem is in the environmental system. But, if you take a systems intelligence perspective, - you embed yourself into the system, - and see that, “oh Raimo, you’re such a lousy fisherman”, - “It’s not the environment which is the problem, but it’s your fishing skills.” So, with that perspective, you perhaps come closer to the real solution of the problem.

07:59 - So, you can try yourself, testing the way you think - with some of your own situations, and see how easy it is to - be trapped into this data selection and then coming to narrow conclusions - about your solutions. If you want to learn more about systems thinking or systems intelligence, - I encourage you to watch some videos made in Aalto University, - one by Peter Senge, he was visiting us, - he’s talking about systems thinking, - and Professor Esa Saarinen talks about systems intelligence in his presentation. .