Gerd-Gigerenzer

Gerd Gigerenzer

Thinker

The Heuristics Revolutionary

Intro

Gerd Gigerenzer is a German psychologist, behavioral scientist, statistician, writer, and educator who shaped our understanding of heuristics. Gigerenzer mainly studied the use of bounded rationality and heuristics in decision-making.

Together with Daniel Goldstein, he was the first to theorize the recognition heuristic and the take-the-best heuristic. He did so by providing evidence that a lack of familiarity with a topic can actually result in an individual making more accurate inferences.1

Gigerenzer has been a strong critic of Daniel Kahneman and Amos Tversky’s work, arguing that heuristics should not lead us to believe that human thinking is biased. Instead, Gigerenzer believes that we should think of rationality as an adaptive tool that is not consistent with the rules of logic.2

Today, Gigerenzer is the director of the Harding Center for Risk Literacy at the University of Potsdam. He has been able to apply his ideas outside of academia as the founder of Simply Rational, an institution that investigates decision-making. Gigerenzer also holds a director emeritus position at the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development in Berlin.

Intelligent decision making entails knowing what tool to use for what problem.

– Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions

Subscribe to get notified when a new podcast episode is out

Heuristics Revolution

The Recognition Heuristic

The recognition heuristic informs how we make decisions when we are not familiar with all the alternatives. Gigerenzer and Goldstein found that we tend to choose what we recognize over what we don’t because we perceive the recognizable object to be more valuable.

Gigerenzer and Goldstein supported the recognition heuristic with their well-known 2002 experiment. Students from the United States and Germany were asked a series of questions about the populations of both American and German cities. They were given pairs of city names and were asked to identify the more populous city. When the participants only knew about one city, they used the recognition heuristic in their response 89% of the time,3 which was followed by a correct response 71% of the time.

One of the city pairs used was San Antonio and San Diego. Only half of the German participants had heard of San Antonio, but all of them had heard of San Diego. All the German participants correctly guessed that San Diego was the more populous city, in comparison to only 60% of American participants choosing correctly – those who were previously familiar with both cities.4 These results have been replicated in similar experiments.1

Despite American and German participants only recognizing a small number of the cities, American participants scored higher on German cities, while German participants scored higher on American cities.3 Gigerenzer argues that this result is caused by the less-is-more effect:1 sometimes, knowing less allows us to make better inferences than knowing more. Americans knew less about Germany, and Germans knew less about the United States; but the less-is-more effect allowed them to draw better conclusions.

Critics of Gigerenzer’s recognition heuristic argue that the evidence used to support it comes from static environments which remain stable without experiencing rapid change – city populations tend to stay relatively stable over time. However, the recognition heuristic has also been successfully applied in the prediction of sports events such as Wimbledon5 and elections.6

One study showed that when laypeople and amateur tennis players used the recognition heuristic to predict the outcome of Wimbledon matches, they were able to correctly predict the winner up to 92% of the time.7 The recognition heuristic’s ability to successfully predict outcomes in a tennis tournament shows that it can be effectively applied to a dynamic environment, where the performance and momentum of tennis players can rise and fall in short periods of time.8

There are also important implications in marketing science: research shows that recognition-based heuristics can help consumers decide which brands to choose when buying a frequently purchased product.9 For instance, recognition might make a difference when you’re choosing between different pasta brands at the supermarket.

The Take-The-Best Heuristic

The take-the-best heuristic is a straightforward shortcut that helps an individual choose between multiple alternatives. The decision-maker selects an attribute they think distinguishes desirable alternatives from less desirable alternatives. Then, based only on that cue, the decision-maker takes the best option.

For example, someone deciding between Paris and Tokyo for a vacation would start by ranking the most important attributes. It could include the quality of food, affordability, weather, and the availability of flights to the city. Paris and Tokyo both have excellent food options, are similarly expensive, and have similar weather. However, there are no direct flights to Paris for our traveller, whereas Tokyo can be reached in a single flight. This is the first and highest ranked attribute that allows the individual to distinguish between the two alternatives. Based only on this cue, the take-the-best heuristic means Tokyo wins out over Paris.

Gigerenzer and Goldstein made this scientific discovery as part of their research on human decision-making.10 Using computer simulations, they were able to compare the take-the-best heuristic with rational methods of inference. Unlike Gigerenzer and Goldstein’s heuristic, these rational methods of inference assumed that humans had unlimited time and knowledge. The computer simulations showed that the take-the-best heuristic led to inferences which were as good as or better than those made with other inferential strategies.11

Recent studies demonstrate the effectiveness of the take-the-best heuristic in making accurate inferences in real life. A 2012 study looked into voters’ opinions on how US presidential candidates would handle the issue they thought was most important, such as foreign policy or unemployment. Researchers were then able to design a model based on a single issue—assuming that potential voters would use a take-the-best heuristic—that correctly chose the winner of the popular vote in 97% of all predictions.12

The results closely matched forecasts from models that included significantly more information. Candidates can use this knowledge to quickly identify issues and policies that they should emphasize during their campaign.12

Since the take-the-best heuristic was introduced by Gigerenzer and Goldstein, it has been adapted and applied to political forecasting, artificial intelligence and medicine. The heuristic even accurately predicts how airport customs officers10 and professional burglars13 make decisions.

Historical Biography

Gerd Gigerenzer was born in 1947 in Wallersdorf, Germany. Gigerenzer received his PhD in 1977 from the University of Munich, where he then became a professor of psychology. He moved to the University of Konstanz in 1984, before leaving for Austria to work at the University of Salzburg in 1990.

In 1992, Gigerenzer moved to the University of Chicago as a Professor of Psychology. It was around this time that Daniel Goldstein started pursuing a PhD in Cognitive Psychology, and Gigerenzer took on the role as Goldstein’s doctoral advisor. In 1995, Gigerenzer returned to Germany, becoming the director of the Max Planck Institute for Psychological Research in Munich and founding its Center for Adaptive Behavior and Cognition. The research center existed until 2017, when Gigerenzer retired.14

After his return to Germany in 1995, Gigerenzer published some of his groundbreaking discoveries, contributing to literature in heuristics. Together with Goldstein, he introduced the take-the-best heuristic in 1996, which was derived from their recognition principle, an early version of the recognition heuristic.11 Gigerenzer later realized that the recognition principle could exist as a stand-alone model.15 Working again with Goldstein, he formally theorized this principle in 1999, and renamed it the recognition heuristic.16

Following his revolutionary work in behavioral science, Gigerenzer also applied his expertise in the real world. Gigerenzer founded Simply Rational in 2015, a consulting institution that applies behavioral science, data science, and artificial intelligence to create recommendations in fields such as medicine, politics, society, and economics.

Outside of academia, Gigerenzer is an avid jazz and Dixieland musician. In fact, he was a member of The Munich Beefeaters Dixieland Band, playing the banjo in a television advertisement for Volkswagen Golf in 1974.

Relevant Quotes

“An intuition is neither caprice nor a sixth sense but a form of unconscious intelligence.”

― Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions

 

“Knowledge is the antidote to fear.”

― Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions

 

“Make everything as simple as possible, but not simpler.”

― Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions

 

“Great thinkers often learn, to their surprise, that new ideas are less than welcome.”

― Gerd Gigerenzer, Adaptive Thinking: Rationality in the Real World

 

“The quest for certainty is the biggest obstacle to becoming risk savvy. While there are things we can know, we must also be able to recognize when we cannot know something.”

― Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions

 

“We have to learn to live with uncertainty.”

― Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions

 

“There is strong evidence that intuitions are based on simple, smart rules that take into account only some of the available information.”

― Gerd Gigerenzer, Risk Savvy: How To Make Good Decisions

 

“In Western countries, for instance, it is legal for physicians to receive “bribes” in the form of cash by pharmaceutical companies for every new patient they put on their drugs.”

― Gerd Gigerenzer, Risk Savvy: How To Make Good Decisions

Other Content by Gigerenzer

Outside of his academic publications, Gigerenzer has also published books suited for a lay audience. Reckoning with Risk: Learning to Live with Uncertainty explores how ordinary people reason with risk and explains why we over-exaggerate the probability of unwanted scenarios. In Gut Feelings: The Intelligence of the Unconscious, Gigerenzer claims that our gut feelings are better prepared to help us make decisions, whilst also emphasizing that reflection and reason can be overrated. Both books have been translated into 18 languages.

Another popular Gigerenzer book, Risk Savvy: How to Make Good Decisions, provides an accessible guide to efficient decision-making. Gigerenzer incorporates the less-is-more effect in this book, concluding that the best results can come from considering less information and gut feeling.

At the 2018 Summer Institute on Bounded Rationality, hosted by the Max Planck Institute for Human Development, Gigerenzer gives a detailed talk on the heuristics revolution and its impact on decision-making, touching on some of his groundbreaking ideas.

References

  1. Pachur, T., Todd, P. M., Gigerenzer, G., Schooler, L. J., & Goldstein, D. G. (2011). The recognition heuristic: A review of theory and tests. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00147
  2. Vranas, P. B. M. (2000). Gigerenzer’s normative critique of Kahneman and Tversky. Cognition, 76(3), 179–193. https://doi.org/10.1016/S0010-0277(99)00084-0
  3. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109(1), 75-90. https://doi.org/10.1037/0033-295x.109.1.75
  4. Ayton, P., Önkal, D., & McReynolds, L. (2011). Effects of ignorance and information on judgments and decisions. Judgment and Decision Making, 6(5), 381–391.
  5. Pachur, T., & Biele, G. (2007). Forecasting from Ignorance: The use and usefulness of recognition in lay predictions of sports events. Acta Psychologica, 125(1), 99-116. https://doi.org/10.1016/j.actpsy.2006.07.002
  6. Gaissmaier, W., & Marewski, J. N. (2011). Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls. Judgment and Decision Making, 6(1), 73-88.
  7. Scheibehenne, B., & Bröder, A. (2007). Predicting Wimbledon 2005 tennis results by mere player name recognition. International Journal of Forecasting, 23(3), 415-426. https://doi.org/10.1016/j.ijforecast.2007.05.006
  8. Serwe, S., & Frings, C. (2006). Who will win Wimbledon? The recognition heuristic in predicting sports events. Journal of Behavioral Decision Making, 19(4), 321-332. https://doi.org/10.1002/bdm.530
  9. Hauser, J. R. (2011). A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm). Judgment and Decision Making, 6(5), 396-408.
  10. Cuofano, G. (2020, December 19). Take-the-Best heuristic in a nutshell. FourWeekMBA. https://fourweekmba.com/take-the-best-heuristic/
  11. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669. https://doi.org/10.1037/0033-295X.103.4.650
  12. Graefe, A., & Armstrong, J. S. (2012). Predicting elections from the most important issue: A test of the take-the-best heuristic. Journal of Behavioral Decision Making, 25(1), 41-48. https://doi.org/10.1002/bdm.710
  13. Garcia-Retamero, R., & Dhami, M. (2009). Take-the-best in expert–novice decision strategies for residential burglary. Psychonomic Bulletin & Review, 16(1), 163–169. https://doi.org/10.3758/PBR.16.1.163
  14. Adaptive Behavior and Cognition. (n.d.). Max Planck Institute for Human Development. https://www.mpib-berlin.mpg.de/research/concluded-areas/center-for-adaptive-behavior-and-cognition
  15. Gigerenzer, G., & Goldstein, D. G. (2011). The recognition heuristic: A decade of research. Judgment and Decision Making, 6(1), 100-121.
  16. Gigerenzer, G., Todd, P. M., & ABC Research Group. (1999). Simple Heuristics That Make Us Smart. Oxford University Press.