Reflective writing: ‘Superforecasting: The art and science of prediction

Reflective Journal: Chapters 7 and 8 in Superforecasting

Tetlock and Gardner (2016), in their book “Superforecasting: The art and science of prediction,” offer crucial insight into the concept of predictive analysis. Throughout the textbook, I learned a lot of concepts related to forecasting and how this art is significant in various aspects of life. Making forecasts is crucial in many ways, but it is challenging; the process must be approached sensitively to avoid making wrong decisions or conclusions. Chapters 7 and 8 provide more information on things surrounding forecasting. Among the three most important takeaways from the two chapters include the concept of updating predictions continuously, the notion of making mistakes, and the idea of continuous analysis and improvement.

The work does not end with making a forecast. It is necessary to always update the forecast every time there is a piece of additional information. Superforecasters tend to update their predictions more often than forecasters, and these updated forecasts tend to be more accurate because the forecaster is likely to be more informed. Yet, it is not simple to update a forecast. It is possible to underreact or overreact in the process of making the update. The authors say that it is crucial to distinguish whether the new information is relevant or not because people often present information according to their personal beliefs or emotions. Sometimes people think something is significant even when it is not. Irrelevant information can also cause confusion and bias. The trick is to remain consistent in the update and make it a continuous process.

In chapter 8, Tetlock and Gardner (2016) explain that mistakes are common in forecasts and they should not be taken as a dead end. It is okay to be wrong sometimes and making mistakes is considered part of the learning process as per the authors. It is crucial to learn from failure especially by knowing when and why we fail. So practicing should be followed with feedback, without which, we can assume we are doing well and end up getting overconfident. Without appropriate feedback, we can think inaccurately about our performance. Additionally, to avoid what the authors call hindsight bias, it is necessary to get feedback immediately when the event is still fresh in our minds. Understanding the problem in a forecast is only the beginning. It is important to ensure an unceasing process of analysis and improvement.

Another crucial takeaway from the chapters is the need for continuous analysis and improvement.  The authors explain the concept as perpetual beta. This concept does not only talk about programs or data analysis. It also applies to life. It is necessary to keep improving whether in learning new things or updating the information we already have on a particular concept. It is through this way we become updated and able to address novel problems in the future. Tetlock and Gardner (2016) state that perpetual beta – a state of continuous analysis and improvement – is way more important than intelligence. Of all the qualities popular to superforecasters, perpetual beta is considered the quality which does the best job in predicting who will become a superforecaster.

Similar to the previous chapters in this textbook, chapters 7 and 8 present ideas in a clear and easy-to-understand manner. The author simplifies concepts that would otherwise be hard to get using illustrations and examples that the reader can relate to. Forecasting is a crucial skill in life and understanding the very many significant concepts described in the textbook has been an excellent experience for me. Specifically, understanding why it is necessary to keep updating forecasts while keeping in mind the potential issues such as overreacting upon receiving new information seems relatable. We live in a world where the truth today is not always the truth tomorrow. This may require continuous analysis and improvement to capture any potential changes and make sure mistakes do not get repeated.

References

Tetlock, P. E., & Gardner, D. (2016). Superforecasting: The art and science of prediction. Random House.

Chapters 9 and 10 in Superforecasting: Reflective Journal

The textbook ‘Superforecasting: The art and science of prediction,’ offer significant insight into historical facts that help demonstrate attributes and concepts to increase the chances of being a successful forecaster. The authors made crucial illustrations and descriptions of concepts useful in forecasting. Chapters 9 and 10 are not short of this critical characteristic of the textbook, giving the reader a wonderful experience with well-arranged ideas and arguments throughout the chapters. The three takeaway points include the notion that the decision-making process can be altered for the better, the importance of group dynamics, and identifying the need to separate feelings and biases.

Tetlock and Gardner begin chapter 9 by explaining the history of the Bay of Pigs Invasion. The Kennedy administration lost credibility, but things changed during the Cuban Missile Crisis. Despite the same team managing the two events, the results differed significantly. After the Bay of Pigs, the president launched an investigation on what went wrong. It became known that the problem was decision-making. The team was a victim of groupthink as people wanted to get along. The team came up with a new method that involved questioning their assumptions. When the Cuban Missile Crisis came about, the team came up with better solutions. It demonstrates the significance of changing the decision-making process for the better whenever necessary.

The authors’ description of the Cuban Missile Crisis brings on the second important takeaway; the significance of group dynamics. Teams can make some people lazy, letting others do the work themselves. There is also the danger of groupthink. Yet, with group dynamics, possibilities are significant. The authors say that working in a team allows people to share information and perspectives, increasing their chances of success. According to the authors, superforecasters working in teams out-forecasted the prediction markets. This argument may not be an automatic indicator of success, but it highlights the importance of good group dynamics. Teams should have open minds, encouraging a culture of sharing. Every team should also have an aspect of diversity in it. Diverse people possess different perspectives. They bring more information to the table, increasing the chances of successful alternatives. The team can also identify solutions that require modifications to avoid biases.

The third crucial point from the chapters is the notion of identifying feelings and biases in decision-making. The authors give a historical illustration using the US Army. In the army, subordinates were not allowed to question their superiors. Whenever the seniors needed something done, the orders were often lengthy and detailed, containing every action they needed the subordinates to take. The authors say that this situation remained that way for a long time until 1980 when they had to learn a hard lesson. Today, the army is more decentralized. For instance, when the US invaded Iraq, the general was empowered to respond to the circumstances he found on the group to reduce resistance. He wanted his subordinates to think more flexibly and look at things from other perspectives. This idea is crucial in life, encouraging everyone not to adopt a single approach to everything and remain open-minded to change whenever necessary.

A decision making should change if the result does not address the problem at hand in the most effective manner. When working in groups, members should complement each other, helping arrive at a solution not impacted by groupthink. It is essential to understand when feelings and biases potentially influence the alternatives in group decision-making processes. Effective decision-making is the central point of the authors in the two chapters, illustrating this concept using historical examples.

References

Tetlock, P. E., & Gardner, D. (2016). Superforecasting: The art and science of prediction. Random House.