Four Steps to Integrate Probabilities into Decisions

Written by

Four Steps to Integrate Probabilities into Decisions

Picnic scene and wedding scene side by side with a weather icon
The true yes/no boundary for action is rarely 50%.

Decision makers often want a simple “yes” or “no.” This creates a challenge when you’re in the business of providing probabilities. If you tell them there’s a 76% chance of Event X happening by Date Y, you might get this response: “So that’s a yes!” When you point out the remaining 24% chance it won’t happen, you might get: “So that’s a no?”

Busy leaders need to get straight to action. They often simplify the process by treating anything above 50% as a “yes” and anything below as a “no.” However, it’s rarely the case that the yes/no boundary is 50% in the real world. The true yes/no boundary, which we call the decision threshold, depends on the nature of the decision, the cost, and the stakes involved.

Say there’s a 28% chance of rain. For a casual picnic, you might accept the risk and go anyway. But for an outdoor wedding reception, your threshold for action is probably much lower. You may set up tents just in case.

The decision threshold is fundamentally about cost-benefit analysis: How many false positives are you willing to accept in order to avoid missing a real threat or opportunity?

For example, if you risk losing $100 and the cost of mitigation is $35, it may be worth taking action (mitigating the risk) if the probability of loss exceeds 35%.

The Four-Step Framework

Instead of simply delivering a forecast for the decision maker to interpret, we reverse the process by defining the yes/no boundary first. Here’s our four-step process for decision makers to use forecasts with clear thresholds, leading to better, faster judgments.

1. Identify the core decision. Begin by clearly stating the decision the organization faces. Here are a few examples:

  • Committing to an innovation cycle to mitigate the risk of a future regulatory ban on a key product.
  • Preparing for a possible increase in US tariffs on a critical base metal import.
  • Validating the underlying assumptions of a new investment thesis before allocating capital.

2. Set the decision threshold. The yes/no boundary can only be set by the decision maker. Will they act at 20%? 40%? 60%? By focusing on the cost and stakes, they avoid defaulting to the simple (and often misguided) 50% threshold.

3. Pose the question to the forecasters. Ideally, they should be unaware of the specific decision or the decision maker’s identity. This firewall ensures the probability estimate remains independent and unbiased. But if anonymity can’t be provided, treat the forecasting as a separate process as much as possible. Remember: Forecasts focus on how the world will be while decisions often reflect what we want the world to be.

4. Deliver the actionable forecast. With the yes/no boundary already in place, the decision maker can use the probability estimate immediately to make a call. With this robust framework, decision makers are able to use probabilities effectively to arrive at better decisions faster.

Learn more about FutureFirst and see ahead of the crowd.

What’s a month?

Written by

What’s a month?

Why question wording must be exact in forecasting

Superforecaster Ryan Adler turns a live CNBC disagreement about Tesla shares into a quick guide on clarity. Good forecasting starts with shared definitions.

On Monday morning (4 August 2025), I was pounding away on my keyboard with CNBC playing in the background. Living in the Mountain time zone, morning meant the Halftime Report, hosted by Scott “The Judge” Wapner. I was loosely listening in when it became clear that Wapner and “Investment Committee” member Joe Terranova were having a disagreement over whether Tesla shares were up or down over the past month. The exchange was cordial but awkward, as Wapner insisted that Tesla shares were down in the past month based on where the stock was trading that morning, but Terranova was very confident that it was up in the past month. They eventually went to commercial and came back having discovered the source of discrepancy. The problem wasn’t that one was right and the other wrong. The problem was that they were each defining “month” differently.

A month before 4 August 2025 would have been 4 July 2025, a market holiday. The chart CNBC showed related back to the closing price of Tesla on 3 July (about $315). Terranova, on the other hand, was using the opening price as of the opening bell on 7 July 2025, four weeks previous, when the price was a bit under $300. The two talked past each other for a bit until the reason for the difference was identified.

Ambiguity Kills Forecasts

What does this have to do with forecasting? Everything!

Among the many lessons that came out of the Good Judgment Project, it was clear that the fight against ambiguity is essential and never-ending. While others may give this fight a lower priority, it is front-and-center on our minds at Good Judgment with every question drafted and reviewed.

If a term or clause could be interpreted reasonably in different ways, we define that term and include examples as needed. And even if someone interprets something in an arguably unreasonable way, such as asserting that the death of a country’s president doesn’t mean that the person stops being that country’s president (it’s happened repeatedly, for some reason), we clarify.

We aren’t perfect, and the world sometimes creates situations that weren’t on anyone’s radar when a germane question was launched beforehand. That said, we know that everybody must be contemplating the same elements of an event they are asked to forecast. Leaning on Potter Stewart’s concurrence in Jacobellis v. Ohio, where he said, “I know it when I see it,” may work when deciding that a movie is not obscene, but it is no way to set a threshold for a forecasting question. Otherwise, we would invite static from the crowd instead of signal.

Bottom line: The CNBC confusion shows how ambiguity kills forecasts. Define upfront what counts, when it counts, and who decides, and leave as little as possible to interpretation. Good forecasting starts with good question writing.

Do you have what it takes to be a Superforecaster? Find out on GJ Open!

* Ryan Adler is a Superforecaster, GJ managing director, and leader of Good Judgment’s question team

When AI Becomes a False Prophet: A Cautionary Tale for Forecasters

Written by

When AI Becomes a False Prophet: A Cautionary Tale for Forecasters

With a nod to Taylor Swift and Travis Kelce, Superforecaster Ryan Adler discusses the gospel according to AI and why forecasters should always verify their sources.

Google’s AI Overview references an AI-generated video to support a false claim.

The promises of artificial intelligence have set up camp in media headlines over the past few years. ChatGPT has become a household name, billions are being spent just to power the equipment to run these programs and models, and the cutting-edge technology is front and center in ongoing tensions between the US and China. It hasn’t left any aspect of human activity untouched, including forecasting.

To be sure, the impacts already felt cannot be understated. We are looking at the front end in what I’m confident will be a seismic shift in society, with large swaths of labor markets around the globe being shaken to their core. That said, we aren’t there yet.

Here’s a recent example of how AI took itself out at the knees regarding a recent forecasting question on Good Judgment Open. In late April 2025, the time came to close a question regarding potential nuptials between Kansas City Chiefs star Travis Kelce and pop superstar Taylor Swift: “Before 19 April 2025, will Travis Kelce and Taylor Swift announce or acknowledge that they are engaged to be married?” (It’s not my favorite subject matter, but we try to maintain a diverse pool of questions.)

As a moderately rabid Chiefs fan myself, I was confident the answer was no, because that would have made headlines across media outlets. However, a key part of the job of running a forecasting platform is being in the habit of double and triple checking. So, I checked with Google. I entered “Are Travis Kelce and…” into the search field, which immediately autofilled to “are travis and taylor engaged?” (The first-name thing with pop culture stars annoys me to no end, but I digress.) To my surprise, Google’s AI preview popped up immediately.

“Yes, according to reports, Travis Kelce and Taylor Swift are engaged.”

“Trust, but verify”

Skeptical, I looked at what the experimental generative AI response was using as a reference to return such a statement. That’s when things got fun.

The first link of the cited material was a YouTube video. Keep in mind that Google, the search engine I used to start my research, owns YouTube. The account that posted the video? DangerousAI. That alone raises more red flags than a May Day parade in Moscow circa 1974. The brief video, dated 24 February 2025, purported to show Travis Kelce announcing that Swift and he “got engaged last week.” However, as the video progressed, the absurdity of Kelce’s putative announcement became perfectly clear.

To sum up, Google’s AI system linked to search was fooled by an AI product posted on another Google platform to give a patently false response.

I don’t highlight this incident as a criticism of Google. However, it should serve as a warning. I’ve seen some GJ Open forecasters take AI responses as gospel. I’m here to tell you that in matters of facts vs fiction, AI is very capable of being a false prophet. This is not to say that AI isn’t an incredibly valuable tool. It certainly is! We are finding more and more uses for it at Good Judgment, but we put it through its paces long before we deem it reliable for a particular role. As the Russian proverb instructs, “Trust, but verify.” (No, President Reagan didn’t say it first.) When it comes to AI and everything else you see online, my suggestion is that you just verify.

Do you have what it takes to be a Superforecaster? Find out on GJ Open!

* Ryan Adler is a Superforecaster, GJ managing director, and leader of Good Judgment’s question team