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How Leaders Jump To Conclusions and What To Do About It

Written by Ron May and Kaylee Somerville Our experience at work involves day-to-day interactions and solving problems, among many things. Yet, our mindset often gets in the way of correctly interpreting our conversations and interactions with others. How we separate facts in our interactions with others might positively influence our decision-making and interactions, thus helping us become better employees, leaders, and friends.

The context: To better understand this phenomenon, consider the following brain teaser first sent to Albert Einstein from his friend Max Wertheimer who was a founder in the psychology discipline of Gestalt: An amoeba propagates by simple division: each split takes three minutes to complete. When an amoeba is put into a glass container with a nutrient fluid, the container is full of amoebas in one hour. How long would it take for the container to be filled if we start with not one amoeba but two? Without any other insight we could use to solve the problem, we might simply indicate that if one amoeba takes an hour to fill the container, then two will take half as long or 30 minutes. Easy. But wait, maybe our intuition is wrong. Perhaps, the whole construct is a false perception and assumption. If we examine the details, there might be a different and correct result.

The solution: 1) The container is full of amoebas in one hour when starting with one amoeba. 2) The splitting process takes three minutes to complete. 3) So, in 60 minutes or one hour, the amoeba splits 20 times. Determine by dividing 60 minutes by three minutes. 4) Therefore, after 20 Splits: 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1,024, 2,048, 4,096, 8,192, 16,384, 32,768, 65,536, 131,072, 262,144, 524,288, the container is full at 1,048,576 amoebas. How does this result change when starting with two amoebas? Well, starting with two amoebas the splits reveal: 2, 4, 8, 16, 32, 64, 128, 256, 512, 1,024, 2,048, 4,096, 8,192, 16,384, 32,768, 65,536, 131,072, 262,144, 524,288, the container is full at 1,048,576 amoebas. The container will be full after 19 splits. In reality, only one less split is necessary to fill the container, therefore saving a paltry three minutes. Surprisingly, the container is full after the long-awaited fifty-seven minutes and not the thirty-minute assumption. Using our intuition, we incorrectly assume information and jump to conclusions without thoroughly considering the problem. We use our beliefs to choose the data, thinking that doubling the amoebas would equal half the time.

Jumping the Ladder of Inference This thinking frequently occurs in everyday life. Peter Senge and Chris Argyris explain this well, showing how we climb the “Ladder of Inference” to solve problems. They suggest an accurate method of problem-solving is to: 1) Experience the data. 2) Select important information. 3) Interpret the information. 4) Add assumptions if necessary. 5) Make conclusions. 6) Develop beliefs based on the information. 7) Take action. But often, this process is not what we use. We tend to prioritize our beliefs, and these beliefs shape how we choose the data we use to solve problems. We refer to this as the reflexive loop. Our beliefs shape what data we select. We then jump to conclusions and cause problems in accurately measuring and solving the problem.

Theory in practice We apply this reasoning to other business processes routinely. Consider an outcome of productivity improvement. Similarly to how we think adding one amoeba halves the time, we might think that we can dramatically improve productivity by simply adding workers. When the result is not what we initially expected, we make assumptions: that people involved are not performing productively or are not motivated to succeed. A solution might be to change the people through training, closer supervision, or to even get new people. Additionally, in this effort, we begin to ask pointed and directive questions. We might ask which workers are slow, how productivity is measured, and what additional factors might be driving the rate of work. Relating this errant thinking to the amoeba problem, would it make sense to ask why the amoebas are growing at the rate they are or why the split takes three minutes to complete? We might even question why we chose amoebas versus any other single-celled animal. None of these questions are aligned with the solution. The critical information was simply the time it takes to split the amoebas and the length it takes to fill a container. Yet, in many work decisions, we gather extraneous data and build lofty expectations without focusing on the specific data to solve the problem. These inquiries can feel like blame as to why the problem exists to the person answering the questions. To demonstrate this process, I offer an opportunity of a leader I know who related his experience. The leader came into a room of employees where most people were engaged in a lively conversation about a current project they were involved in. A person he was aware had has some difficulty in work performance was simply sitting in the room on the side and was not participating in the conversation. The leader walked over to the individual and started a one-way directive about being more involved. The employee looked at the leader with tears in his eyes and said he needed to go home as he was just informed that his dog was hit by a car. He told the leader he was waiting for him to let him know. He was devastated. Then he looked around and realized that no one had observed their colleague in his time of need. The situation was untenable. He had jumped to an answer as to why the employee was disengaged. This moment was a tough reflection for him. He wished he had asked a question as opposed to stating his position. He also wished that his team would have been more inclusive in their behavior regardless of the individual’s history.

Takeaways: In life, we typically only get a portion of the info we need to make a judgment. As we climb the ladder of inference, we often fill the blanks in a negative way and use our personal beliefs to select the data we use to solve the problem. 1) Check your assumptions. Always question your intuition and separate facts from assumptions before you prioritize your beliefs. 2) Ask open-ended questions.Open-ended questions that are not leading are essential to this learning. The questions should be thoughtful and aligned with problem solutions. If you require context setting, declare this intent but do not make it part of the solution process. 3) Stop filling in the blanks in a negative way. Start problem-solving with compassionfor those involved. Bring people into the process and do not have the process work for some and not the whole. Doing so will help strengthen the connections you have and the ones that are being newly created. Want more insights on culture transformations? Sign up to receive custom content delivered to your inbox quarterly, from Riverbank:


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