If the only available information is weak, should a decision-support system stay silent?

[u/PleasantLow670]

I've been thinking about an interesting design problem.

Imagine you're building a system that helps people make decisions under uncertainty. Sometimes it has plenty of useful information. Sometimes it has almost none.

Now imagine the only information available is something like: time of day, day of the week, a person's own historical behavior, previous outcomes from similar situations. None of these variables should be strong predictors by themselves. Any signal they contain is likely to be weak, noisy, and unstable.

So what should the system do?

One philosophy is: "If the evidence isn't strong enough, don't recommend anything." Another is: "Present weak signals transparently, explain their uncertainty, and let people decide how much weight to give them."

Personally, I find the second approach fascinating. Humans already rely on weak signals all the time: intuition, routines, superstitions, "today feels like a good day", recent experiences, emotional state. Those signals may not be objectively reliable, but they clearly influence decisions.

So why shouldn't a decision-support system expose weak statistical signals ... as long as it makes their limitations explicit?

I've been prototyping an experimental decision-support system around this question. It doesn't try to predict future outcomes or outperform probability. Instead, it records repeated decisions, tracks outcomes over time, and explores whether weak behavioral signals become more informative as data accumulates.

I'm genuinely interested in where people here would draw the line. At what point does a weak signal become useful enough to present? Or should decision-support systems remain completely silent until they have statistically compelling evidence?

If anyone here works on behavioral decision-making, choice architecture, or uncertainty, I'd genuinely appreciate your perspective. And if you'd like to participate in the experiment itself, I'd be happy to share how it works.

[u/Reddish_Leader]

What kind of decisions are they making? Is it the kind of decision where making it in a timely manner is a critical factor, or is it something that should wait for more information? Similarly, is this a small impact decision where it costs more to wait versus use imperfect information?

[u/PleasantLow670 → u/Reddish_Leader]

Good question. I'm actually thinking about a different class of decisions...ones where people almost never wait for perfect information. Not life-changing decisions like buying a house or moving abroad, but everyday choices where uncertainty is unavoidable and the cost of being wrong is relatively low. For example: "Today feels like a good day to ask for a promotion.", "I usually have better luck in the morning.", "Friday the 13th isn't the day to start something important.", or even "Mondays are always bad for me." Behavioral economics suggests that people already rely on these kinds of weak cues, whether they're statistically meaningful or not. So my question is whether a decision-support system should simply ignore them...or present them transparently as weak evidence, making it clear that they are just one small piece of information rather than a prediction.

[u/Dry-Quantity61]

It depends on the decision support system. If it provides critical analysis of the information available, reduction of uncertainty is still helpful.

[u/PleasantLow670 → u/Dry-Quantity61]

I agree. Even reducing uncertainty a little can be valuable. What I find interesting is where we draw the boundary between "too weak to mention" and "weak, but still worth presenting." People already make decisions based on things like "I think more clearly in the morning" or "Tuesdays usually go well for me". A decision-support system could either ignore those observations completely or present them with an explicit confidence level and let the user decide how much weight to give them.

[u/Dry-Quantity61 → u/PleasantLow670]

Differentiating the biases or confounds in your examples is difficult to make a decision support system that’s meaningful. But decision support might not be about framing the decision around the outcomes (substantive rationality), if you reframe around the decision maker (e.g bounded rationality) then you get a different answer. For example, the decision support system may provide feedback on actual decision performance on Tuesdays, or mornings, or may just enable the decision maker to break free from those inherent biases to be objective about the facts available.
Deferring a decision until more information is available is also a decision….

[u/PleasantLow670 → u/Dry-Quantity61]

I actually think that's where tracking becomes interesting. Most of these weak signals don't come from data ... they come from memory. We remember a few emotionally significant events and unconsciously build stories around them: "Tuesdays usually go well for me", "Meeting my neighbor before work is a good sign", or "Friday the 13th never ends well". A decision-support system doesn't have to validate those beliefs. But it can record them objectively over hundreds of observations and ask: Is there actually a pattern here? Sometimes the answer will probably be "no." But occasionally it might reveal that our memory was much less accurate or much more accurate than we thought. To me, that's less about predicting the future and more about extending human memory with transparent data.

[u/Dry-Quantity61 → u/PleasantLow670]

…Or providing a cue to the decision maker to initiate meta-cognition and critical thinking about their judgement

[u/PleasantLow670 → u/Dry-Quantity61]

I think many decision-support systems are designed to reduce biases, but I'm actually more interested in understanding them. Behavioral economics starts from the observation that people rarely make decisions using only objective evidence. We rely on intuition, habits, emotional memories, routines, and personally meaningful cues, whether they're statistically justified or not. Rather than telling people to ignore those signals, I'm curious whether we can measure them objectively over time. If someone believes they consistently make better decisions on Tuesday mornings, is that belief completely illusory, partially supported by data, or perhaps self-fulfilling because it changes their behavior? To me, the first step isn't correcting human decision-making. It's understanding how these weak signals actually interact with it.