Fast and Frugal Heuristics for Making Decisions Under Uncertainty

We all make decisions every day, usually a lot of them, some more important than others. Almost none of these choices are made under optimal decision-making conditions.

One condition that can complicate decision-making is uncertainty.

Sometimes we can’t reliably predict the results of our decisions. We can’t figure out the probable outcome. We don’t know all of the possible alternatives, and we can’t estimate the probabilities. Important information may be lacking, and a strategy that may have been optimal in the past might not work now.

Some situations may also simply be too novel to provide any useful data, so we can’t rely on probability or statistics to guide us. Any analytical approach we attempt to take to the problem will likely lead to significant prediction errors.

Uncertainty inherently involves inaccurate or incomplete information. Therefore, to make fast, accurate decisions under uncertainty, we may need to rely on cognitive strategies that deliberately ignore some information.


A heuristic is a cognitive problem-solving approach that doesn’t necessarily lead us to an optimal solution, but still allows us to accomplish a task or achieve a short-term goal.

Herbert A. Simon, a pioneer of heuristics research, explained that heuristics “are not optimizing techniques, but methods for arriving at satisfactory solutions with modest amounts of computation.” [1]

Basically, heuristics are simple rules that we can use instead of more complex decision-making processes.

Psychologists Gerd Gigerenzer and Wolfgang Gaissmaier define a heuristic as “a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods.” [2]

Konstantinos Katsikopoulos further explains heuristics as models for making decisions that exhibit the following criteria:[3]

  • They rely heavily on core human capacities.
  • They do not necessarily use all available information.
  • They process information using simple computations.
  • They are easy to understand, apply, and explain.

Additionally, psychologists Anuj Shah and Daniel Oppenheimer proposed an effort reduction framework that offers some insight into how heuristics simplify cognitive processing. All heuristics, they say, rely on one or more of the following effort-reduction methods:[4]

  • Examining fewer cues.
  • Reducing the difficulty associated with retrieving and storing cue values.
  • Simplifying the weighting principles for cues.
  • Integrating less information.
  • Examining fewer alternatives.

Ecological rationality

Heuristics can be used to make better decisions with less information. But the selection of a given heuristic is most appropriate and effective if that heuristic is adapted to the environment.

This match between heuristic and information in the environment is known as ecological rationality. We can also view this as matching a decision strategy to the requirements of a particular task.

According to Gigerenzer and Gaissmaier, the study of ecological rationality addresses two primary questions:[5]

  • How does cognition exploit environmental structures? These structures typically include uncertainty, redundancy, sample size, and variability in weights.
  • How does cognition deal with error? A cognitive system must find a balance between bias and variance, rather than simply trying to eliminate bias.

Ecological rationality is an extension of Herbert A. Simon’s idea of bounded rationality, which suggests that when making decisions, our rationality is limited by the information we have, our own cognitive limitations, and the time available to make a choice.

Fast and frugal heuristics

We know that heuristics are fast, but how are they “frugal”?

The frugality of a heuristic can usually be measured by the number of cues that it searches.[6]

Generally, heuristics can be constructed using the following three building blocks:[7]

  • Search rules specify the direction in which the search proceeds through the search space.
  • Stopping rules specify when to stop the search.
  • Decision rules specify how the final decision is reached.

A heuristic that stops after searching two cues is more frugal than a heuristic that stops after searching four.

However, since heuristics by definition ignore some information, all heuristics are faster and more frugal than complex statistical methods such as regression analysis or Bayesian inference.

Fast and frugal heuristics are not meant to be logically consistent. They are simple, require little effort, and are focused on generating ecologically rational decisions.

In situations of uncertainty, accurate decisions do not always require a lot of effort or complexity. If we can apply a suitable heuristic to our decision-making environment, we can make efficient decisions with little time and effort.

Some useful heuristics

Let’s consider some specific heuristics and when they might prove useful.

Recognition heuristic

The recognition heuristic assumes that if we only recognize one of two alternatives, it likely has the higher value.

An oft-cited example of the recognition heuristic is a study that asked a group of U.S. students and a group of German students to choose which city they thought was larger, Detroit or Milwaukee.

The U.S. students likely engaged in an analytical approach.

They may have known that just a half-century ago, Detroit was the fifth-largest city in the U.S., only surpassed by New York, Chicago, Los Angeles, and Philadelphia.

They would have known that Detroit was famous for cars and its rich musical history, and that Milwaukee was known for motorcycles, power tools, and beer. Milwaukee was also the setting for two hugely popular television sitcoms: Happy Days and its spinoff, Laverne and Shirley.

They also knew that Detroit had suffered tremendous economic hardship and population loss over the past several decades, and they likely had a mental image of the city as a shell of its former glorious self, now littered with abandoned properties.

Maybe, just maybe, after all of that drainage of money and people from Detroit, Milwaukee might now be the larger city.

The German students, on the other hand, mostly relied on the faster, easier way to make their selection. They had just heard more about Detroit over the years than they had about Milwaukee, and assumed that Detroit must therefore be the larger city.

In most cases, they had simply heard of Detroit but not Milwaukee.

Approximately 90 percent of the German students said that Detroit was larger, while only 40 percent of the U.S. students agreed.[8]

The German students were of course correct. Most of the U.S. students over-analyzed their response at the expense of simplicity and accuracy. The German students used the recognition heuristic, whereas the U.S. students could not because they knew a little too much about each city.

The recognition heuristic relies on a single cue: which one of two available options is recognized. It is ecologically rational when there is a correlation between recognizing one of the options and the criteria for judgment.[9]

Fluency heuristic

The fluency heuristic is similar to the recognition heuristic, but unlike the recognition heuristic, it can be used when both of two alternatives are recognized.

Using the fluency heuristic, when both alternatives are recognized, the one that is recognized fastest can be inferred to have the higher value.

The fluency heuristic is ecologically rational when the judgment criteria correlates to speed of recognition.[10]


The take-the-best heuristic uses the following algorithm:[11]

  1. Search rule: Search through cues in order of their validity.
  2. Stopping rule: Stop on finding the first cue that discriminates between the alternatives.
  3. Decision rule: Infer that the alternative with the positive cue value has the higher criterion value.

Take-the-best stops after the first discriminating cue, and orders cues unconditionally according to validity.

Take-the-best has been shown to predict more accurately than linear multiple regression models and complex nonlinear strategies. It is most ecologically rational in environments of moderate-to-high cue redundancy and moderate-to-high variability in cue weights.[12]


The tallying heuristic involves simply counting the number of cues that favor one alternative over others.

The tallying heuristic uses the following algorithm:[13]

  1. Search rule: Search through cues in any order.
  2. Stopping rule: If the number of positive cues is the same for both alternatives, search for another cue. If no more cues are found, guess.
  3. Decision rule: Decide for the alternative that is favored by more cues.

A simple tallying heuristic decision aid has proven to be one of the most effective avalanche forecasting methods.

Skiers, snowboarders, and snowmobilers can use a checklist of seven obvious clues of avalanche danger to perform a quick hazard assessment. The list includes cues such as recent avalanche activity, unstable snowpack, and recent sudden thawing. If three or more of these conditions are present, there is avalanche danger.

Analysis indicates that using this simple, systematic method of identifying obvious hazard conditions could have prevented 92 percent of historical avalanche accidents.[14]

1/N rule

The 1/N rule is a simple heuristic where resources are allocated equally to each of N alternatives.

This heuristic is commonly applied to naïve investment portfolio diversification, where an investor might simply divide their capital evenly among a number of available assets.

One study used the 1/N heuristic as a benchmark to test the performance of 14 other asset-allocation models, many of which are highly touted “optimal” portfolio strategies.

Using 10 years of historical stock data to predict the performance over the following month, none of the optimization models were consistently better than 1/N.[15]

The 1/N heuristic appears to be ecologically rational when N is large, sample size is small, and uncertainty is high.[16]

Other common heuristics

Here are a few other well-known heuristics and a brief description of each:[17]

  • Default heuristic. If there is a default, follow it.
  • Imitate the majority. Imitate the behavior of the majority of people in your group.
  • Imitate the successful. Imitate the behavior of the most successful person in your group.
  • One-bounce rule. Continue searching as long as options improve. At the first downturn, stop searching and take the previous best option.
  • Satisficing. Search through alternatives and choose the first one that exceeds your aspiration level.

Practical considerations

Heuristics can simplify and expedite our decisions. However, heuristics can be prone to bias and errors in judgment, and the decision may not be optimal.

Under conditions of uncertainty, though, we often do not need to make an optimal choice, but merely a suitable one.

Heuristics may be used consciously or nonconsciously, and may be the basis of some of our intuitive judgments.

The accuracy or success of any heuristic will depend on our own cognitive capacities, as well as the structure of the decision-making environment. And as with most things, we can learn to choose appropriate heuristics with practice over time.

Real-world conditions don’t always allow for the application of rational decision models. And though heuristics may be suboptimal at times, they are still often more accurate than more complex decision strategies.


1. Herbert A. Simon, “Invariants of Human Behavior,” Annual Review of Psychology 41, (1990): 11.

2. Gerd Gigerenzer and Wolfgang Gaissmaier, “Heuristic Decision Making,” Annual Review of Psychology 62, (2011): 454.

3. Konstantinos V. Katsikopoulos, “Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice,” Decision Analysis 8, no. 1 (2010): 3.

4. Anuj K. Shah and Daniel M. Oppenheimer, “Heuristics Made Easy: An Effort-Reduction Framework,” Psychological Bulletin 134, no. 2 (2008): 209.

5. Gigerenzer and Gaissmaier, “Heuristic Decision Making,” 457-458.

6. Ibid, 455.

7. Ibid, 456.

8. Daniel G. Goldstein, “Heuristics,” in The Oxford Handbook of Analytical Sociology, edited by Peter Hedstrom and Peter Bearman (Oxford, UK: Oxford University Press, 2009), 145.

9. Shabnam Mousavi and Gerd Gigerenzer, “Risk, Uncertainty, and Heuristics,” Journal of Business Research 67, no. 8 (March 2014): 1674.

10. Gigerenzer and Gaissmaier, “Heuristic Decision Making,” 462.

11. Ibid, 464.

12. Ibid- 464-465.

13. Ibid, 469.

14. Ian McCammon and Pascal Hageli, “An Evaluation of Rule-based Decision Tools for Travel in Avalanche Terrain,” Cold Regions Science and Technology 47, (2007): 196-201.

15. Victor DeMiguel, Lorenzo Garlappi, and Raman Uppal, “Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?” The Review of Financial Studies 22, no. 5 (2009): 1947.

16. Mousavi and Gigerenzer, “Risk, Uncertainty, and Heuristics,” 1677.

17. Ibid, 1675.

Scroll to Top