Informed Practice and Superforecasting: Taking Your Forecasts to the Next Level

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Informed Practice and Superforecasting: Taking Your Forecasts to the Next Level

“Not all practice improves skill. It needs to be informed practice.”
– Phil Tetlock & Dan Gardner in Superforecasting

In any area of decision-making where uncertainty looms large, accuracy is the gold standard. However, many decision makers often find themselves in a frustrating cycle—sometimes they make the right call, but other times they miss the mark entirely. Inconsistency can be costly. So, what separates those who occasionally succeed from those who reliably deliver top-notch forecasts? The answer lies in informed practice—one of the concepts at the heart of Superforecasting.

What Is Informed Practice?

Informed practice is not just repetition. It’s a deliberate and thoughtful process of learning from each forecast, refining techniques, and continuously updating one’s beliefs based on new information. It’s about approaching forecasting with a Superforecaster’s mindset—an outlook geared toward improvement, with a consistent effort to mitigate one’s cognitive biases.

What Can Forecasters Learn from Superforecasters?

Superforecasters, known for their uncanny forecasting accuracy, exemplify informed practice. They don’t pull numbers out of a hat or look into a crystal ball for answers. For every question they face, they engage in a rigorous process of analysis, reflection, and adjustment. Here’s how informed practice gives them the edge:

1. Learning from Feedback: Superforecasters thrive on feedback. They meticulously track their forecasts, comparing them against the outcomes to identify where they went right and where they missed the mark. This feedback loop is crucial. It allows them to recalibrate their approach and avoid making the same mistakes twice. Over time, this leads to more refined and accurate forecasts.

2. Understanding Probability: A key aspect of informed practice is the understanding and effective use of probability. Superforecasters don’t think in black-and-white, yes-or-no terms. They consider a range of possible outcomes and assign probabilities to each. They also update these probabilities as new information becomes available, a process known as Bayesian reasoning. This probabilistic thinking helps them navigate uncertainty with greater precision.

3. Continuous Learning: The world is constantly changing, and so too are the variables that influence forecasts. Superforecasters are voracious learners, continuously updating their knowledge base. They stay informed about the latest developments in multiple areas, thus grounding their forecasts in the most current data and insights.

4. Mitigating Cognitive Biases: Cognitive biases can cloud judgment and lead to poor forecasts. Superforecasters are keenly aware of these biases and actively work to mitigate their impact. Through informed practice, they develop strategies to counteract such biases as overconfidence, anchoring, confirmation bias, and more, to make well-calibrated forecasts.

What Is the Role of Collaboration in This?

Informed practice is not a solitary endeavor. Collaboration with other forecasters is a powerful tool for improving accuracy and keeping track. By engaging in discussions, comparing notes, and challenging each other’s assumptions, forecasters can gain new perspectives and insights. Good Judgment’s Superforecasters work in teams, leveraging the collective intelligence of the group to arrive at superior forecasts.

What Practical Steps Can I Take?

1. Keep Track: Keep a record of your forecasts and compare them with the outcomes. Analyze your hits and misses to identify patterns and areas for improvement.

2. Seek Feedback: Seek out feedback from peers or through forecasting platforms such as GJ Open, which provides performance metrics. Use this feedback to refine your approach.

3. Diversify Your Sources of Information: Regularly update your knowledge on the topics you forecast and seek out diverse sources. This includes staying current with news, research, and expert opinions, including those you disagree with.

4. Practice Probabilistic Thinking: Assign probabilities to your forecasts and be willing to adjust them as new information emerges. This helps you avoid the trap of binary thinking.

5. Challenge Your Assumptions: Regularly question your assumptions and be open to changing your mind. This flexibility is crucial in a rapidly changing world.

6. Get a Head Start with GJ Superforecasting Workshops: Consider enrolling in a Superforecasting workshop. Good Judgment’s workshops, led by Superforecasters and GJ data scientists, leverage our years of experience in the field of elite forecasting as well as new developments in the art and science of decision-making to provide you with structured guidance on improving your forecasting skills. Our practical exercises will boost your informed practice, offering you lifelong benefits.

Informed practice is the cornerstone of good forecasting and one of the secrets behind the success of Superforecasters. By diligently applying the above principles, you can enhance your forecasting skills and make better-informed decisions. See the workshops we offer to help you and your team take your forecasting success to the next level.

Interview with the winner of the “Right!” said FRED Challenge

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Interview with the winner of the “Right!” said FRED Challenge

In this interview, we sit down with the winner of the “Right!” said FRED Challenge for Q2 2024, Sigitas Keras. Known on GJ Open as sigis, Sigitas is an experienced quant and trader who decided to explore the world of forecasting after an impressive 25-year career in finance. With a PhD in mathematics and a natural curiosity about the world, he shares insights into the unique challenge he has taken on to forecast every question on GJO in 2024 and the strategies that helped him excel on the platform. Originally from Lithuania, Sigitas currently lives in Canada.

GJO: What is your background, and how did you first become interested in forecasting?

I was born in Lithuania, have a PhD in maths, but, as many others with a similar background, ended up in finance industry. After almost 25 years as a quant and a trader, I recently retired, which freed up a lot of time for other things. I tried forecasting on GJO for the first time a couple years ago. It seemed like an interesting challenge where I could combine analytical skills and general curiosity about the world.

GJO: How did you learn about GJ Open? How would you describe your experience on the platform so far?

I read Tetlock’s book Superforecasting, so likely that was an initial prompt, but to be honest I don’t remember full details anymore. Rightly or wrongly, I am one of the few forecasters who decided to forecast every question in 2024. It was very enjoyable, and I feel I learnt a lot both about forecasting and about various topics, but I have to admit this is getting too difficult to maintain. I don’t think I’ll continue doing all questions next year, and most likely will just focus on a few challenges, but I still like to maintain a good mixture of various topics.

GJO: What was your approach to the “Right!” said FRED Challenge? What do you think helped you come out on top?

I like questions that have good supporting data. In that sense, the FRED challenge is perfect for me. Whenever there is good data available, I try to use some mathematical model. Having a background in finance industry helps a bit with that, although I don’t think I use anything that requires more than FRED and other publicly available data and a Google spreadsheet. I also try to update my forecasts regularly, typically once a week. I think consistency is another important component of successful forecasting.

GJO: What topics would you consider of particular interest to forecast for 2025 and beyond?

I tend to forecast better when there is good data available for analysis. On the other hand, geopolitical questions are often much more challenging, so perhaps I will focus on improving there. My goal is to improve my score in the Superforecasting Workshops challenge!

GJO: Is there anything you would like to add that would be of particular interest to other forecasters on GJ Open?

I feel I am still very new to forecasting and to the community. One thing I hope is to learn more about other forecasters, their backgrounds, their approaches to forecasting. And if anyone has any questions for me, feel free to reach out.

See the latest forecasting challenges on GJ Open and try your hand at forecasting!

Common Questions about Good Judgment Inc and Superforecasters

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A Primer on Good Judgment Inc and Superforecasters

At Good Judgment Inc (GJI), the official home of Superforecasting®, we pride ourselves on our ability to provide well-calibrated and insightful forecasts. As we continue to partner with clients and media worldwide, it is worthwhile to address some of the common questions we receive about our work. Here is a primer on our story, probabilistic forecasts, and our team of Superforecasters.

What’s in a Name? GJP, GJI, and GJ Open

The Good Judgment Project (GJP)
In 2011, the Intelligence Advanced Research Projects Activity (IARPA) launched a massive tournament to identify the most effective methods for forecasting geopolitical events. Four years, 500 questions, and over a million forecasts later, the Good Judgment Project (GJP), led by Philip Tetlock and Barbara Mellers at the University of Pennsylvania, emerged as the clear winner of the tournament. The research project concluded in 2015, but its legacy lives on. The GJP is credited with the discovery of Superforecasters, people who are exceptionally skilled at assigning realistic probabilities to possible outcomes even on topics outside their primary subject-matter training.

Good Judgment Inc (GJI)
GJI is the commercial successor to the GJP and the official home of Superforecasting® today. We leverage the lessons learned during the IARPA tournament and insights gained in our subsequent work with Phil Tetlock and his research colleagues as well as with leading companies, academic institutions, governments, and non-governmental organizations to provide the best and the latest in forecasting and training services. Our goal is to help organizations make better decisions by harnessing the power of accurate forecasts. GJI relies on a team of Superforecasters, as well as data and decision scientists, to provide forecasting and training to clients.

Good Judgment Open (GJ Open)
GJO, or GJ Open, is our public platform, open to anyone interested in making forecasts. Unlike GJI, which involves professional Superforecasters, GJO welcomes participation from the public. The “Open” in GJ Open not only signifies that it’s accessible to all but also draws a parallel to golf tournaments. Forecasting questions vary in their complexity, so there is no absolute score to indicate a “good” forecast. We use the median of participants’ scores as a benchmark, similar to par in golf, where lower scores indicate better performance.

A Note on Spelling
You may have noticed that “judgment” is spelled without an “e” on all our platforms. This is a consistent choice across GJP, GJI, and GJ Open, reflecting our preference for the parsimonious American spelling of the word.

Understanding Probabilistic Forecasts
Sample forecast on FutureFirst™, 12 July 2024

Our forecasts are not polls. They are aggregated probabilistic predictions about specific events. For instance, Superforecasters gave Joe Biden an 82% chance of winning the 2020 US presidential election. This means that if the election were held 100 times, Biden would win in 82 of those instances.

A common misconception is interpreting a probabilistic forecast as “X% of Superforecasters say a particular outcome will happen.” In reality, each Superforecaster provides their own probabilistic forecast, and we aggregate these individual predictions to reach a collective forecast. Therefore, an 82% forecast does not mean 82% of Superforecasters believe a certain outcome will occur. It is an aggregated probability of the outcome (an 82% probability of it occurring and an 18% probability of a different outcome) based on all individual forecasts.

Understanding Superforecasters’ Backgrounds

Good Judgment works with some 180 Superforecasters from around the world whose forecasting accuracy placed them in the top 1-2% of the more than 100,000 forecasters who took part in the GJP or qualified on GJ Open. Our Superforecasters come from a wide range of professional fields, including finance, IT, humanities, social sciences, engineering, and more. This allows them to approach forecasting questions in a well-rounded way, combining their exceptional forecasting skills with specialized knowledge in different areas.

Age and Geographic Diversity
Superforecasters range in age from their 20s to their 70s and hail from different parts of the world. This geographic and demographic diversity helps to ensure that our forecasts are informed by a broad spectrum of experiences and viewpoints.

The Wisdom of the Crowd
We emphasize the importance of the wisdom of the crowd. Our Superforecasters read different publications in various languages and bring diverse perspectives to the table. To borrow terminology from Tetlock’s training materials in the GJP, some are Intuitive Scientists, others are Intuitive Historians, while still others are Intuitive Data Scientists.

Collaborative Nature of Forecasting
Forecasting at GJI is a team effort. We focus on collective intelligence. It’s not about individual forecasting superheroes tackling challenges alone but about identifying people who bring unique strengths to the table as a team of Superforecasters.

Get actionable early insights on top-of-mind questions by subscribing to our forecasting monitoring tool, FutureFirst™!