Home | Archives | About | Login | Submissions | Notify | Contact | Search
 ES Home > Vol. 5, No. 2 > Art. 4

Copyright © 2001 by the author(s). Published here under license by The Resilience Alliance.

The following is the established format for referencing this article:
Anderies, J. M. 2001. G. Gigerenzer, P. M. Todd, and the ABC Research Group. 2001. Simple heuristics that make us smart. Oxford University Press, Oxford, UK. Conservation Ecology 5(2): 4. [online] URL: http://www.consecol.org/vol5/iss2/art4/

Book Review

G. Gigerenzer, P. M. Todd, and the ABC Research Group. 2000. Simple Heuristics That Make Us Smart. Oxford University Press, Oxford, UK.

J. Marty Anderies


Published: October 29, 2001

When I saw the title of the book by Gigerenzer et al. (2000), Simple Heuristics That Make Us Smart, I thought “Fantastic! This book is going to be about how to construct simple heuristics to make good decisions in complex environments.” I also thought that such a book would be very useful to the Conservation Ecology audience. My excitement waned as I discovered that this was not what this book is about. Rather, it focuses on the tension between scientific approaches that model behavior as either unboundedly or boundedly rational.

My level of excitement was still fairly high after finishing the first chapter. Surely a better understanding of how people think and make decisions is important for integrative science and fundamental policy research. The book opens with “This book is an invitation to participate in a journey into largely unknown territory...into a land of rationality different from the familiar one we know from many stories in cognitive science and economics... .” It is a long journey, some 365 pages with a relatively small font.

Chapter 1 sets the theme of the book: the authors (18 of them) do not believe that humans have the unlimited computational power assumed in models of human cognition and decision processes from cognitive science and economics. The authors propose that “...much of human reasoning and decision making can be modeled by fast and frugal heuristics that make inferences with limited time and knowledge.” The next 13 chapters then set out to support this claim in a variety of contexts including binary choices, categorization, mate choice, and parental investment.

I sympathize with the basic theme of the book. However, as the authors note, “Proponents of unbounded rationality generally acknowledge that their models assume unrealistic mental abilities, but nevertheless defend them by arguing that humans act as if they were unboundedly rational.” Indeed, if the aim is to understand animal behavior, especially those behaviors under selective pressure, such a view is quite reasonable. If evolution can cook up such complex physical specimens, it can surely solve some pretty complex optimization problems, i.e., it can perform some very complex calculations and hardwire them into the brains and bodies of organisms. This optimizing process proceeds, of course, subject to historical contingencies and the physical limits to the construction of organisms. The study of this optimizing process under these physical and historical constraints forms the basis of much of behavioral ecology.

The authors are interested in more than explaining behavior, though. They state that “The greatest weakness of unbounded rationality is that it does not describe the way real people think.” The distinction between behavior (possibly unconscious) and conscious thinking and decision making is clear. The authors believe that, in many cases, people use (very simple) heuristics to think and make decisions. The approach used throughout much of the book to illustrate this point is to compare simple and complex approaches to predicting a “criterion” based on a set of “cues,” e.g., predicting which of two German cities has a higher population, based on cues such as whether a city is a national capital, has a soccer team in the major leagues, has a university, and so on. The performance of heuristics using individual cues and “stopping rules” that dictate when to stop looking up cues (gathering information) and make a decision is compared to the performance of methods, such as multiple regression, that use all the cues and “integrate” across them. The different methods are compared on the basis of “frugality” (using as few cues as possible) and accuracy.

An example of a heuristic used in the book is “The Minimalist,” which looks up cues until it finds one that distinguishes between the two samples and makes a decision. It begins with the recognition cue. If the minimalist recognizes one city and not another, it predicts that the one it recognizes has a higher population. If it recognizes both cities, it goes on to the next cue (e.g., is a city a national capital?). The city that is a national capital is predicted to have a larger population than the city that is not.

In many cases, the heuristic can do almost as well as much more complex algorithms, such as picking a stock for a portfolio based solely on whether or not you recognize its name. In the examples in the book, the performance of the heuristics is pretty impressive. However, it is easy to understand the circumstances in which a heuristic will compare well to more complex algorithms:

   1) The task is to predict which of two samples scores higher on some criterion rather than to predict the actual score (i.e., coarse-grained prediction).

   2) One or two of the cues have strong predictive value (or strong “ecological rationality” in the language of the book).

   3) The cues are highly correlated.

This became clear to me as I read about the “recognition” heuristic in Chapter 3 being used to pick stocks. The fact that a company name is recognized brings with it a lot of information, i.e., is highly correlated with other cues (for example, advertising expenditure, market share, and so on). These circumstances in which heuristics would do well lingered in my mind through all of the examples presented in Chapters 2–5.

In Chapter 4, the authors explore “Take the Best (TTB),” a heuristic that attempts to use the cue with the highest validity to predict which of two samples scores higher. It is not surprising that it does fairly well against multiple regression when asked which of two cities has a higher population. Note that “Take the Best” was not tested against multiple regression in predicting the actual population.

In Chapter 6, the authors discuss the conditions under which simple heuristics do well: the three points that I mentioned previously. They confirmed my hunch that the effectiveness of a heuristic depends on the environment, i.e., the nature of the cues. They sum up the result using a simple mathematical formula. Suppose we are given an ordered set (in terms of their predictive capacity) of cues, C1, C2, ...Cn. The cues are

  •   “noncompensatory” for a strategy (heuristic) if the weight (information content, or predictive capacity) of Cj outweighs any combination of cues with indices greater than j. For a linear model, this result can be stated very concisely: for a given set of weights W1, W2, ...Wn, the cues are noncompensatory if

    The result is: the performance of “Take the Best” is equivalent to a linear regression model with noncompensatory cues. The more condition (1) is violated, the more the performance of the heuristic may deteriorate.

    Chapters 7 and 8 focus more on the psychology of decision making rather than on the performance of particular heuristics, namely trying to understand when people use heuristics, and a comparison of heuristics to another model of decision making, the Bayesian network. I took two messages away from these chapters, namely, (1) the more people are pressed for time, the more they use heuristics; and (2) the performance of heuristics does not deteriorate as much as the Bayesian network when generalized.

    The idea that people use heuristics when they are pressed for time seems to undermine the book's claim that people do not use complex algorithms to solve problems. Chapter 7 suggests that the more time people have to think, the more they try to understand correlations, integrate across cues, and use other complicated decision algorithms. Indeed, the authors refer to a study demonstrating that “participants who have to account for their decisions more frequently select strategies that require considerable effort but offer a higher probability of a correct decision.” Near the end of Chapter 7, the authors state that “Even if people use a large amount of information and do integrate it, they seem to use simple cognitive operations.” This chapter failed to convince me that there is a meaningful separation between more complex cognitive processes and heuristics.

    Chapter 8 highlights a strong point of heuristics: they are less subject to overfitting than more complex algorithms. A Bayesian network falls prey to this when it is trained on a subpopulation in which correlations are stronger than in the population from which it was drawn. The way the text is written suggests that this result should be surprising. As they put it, “Simple one-reason decision making, as employed by ‘Take the Best,’ is almost as accurate as the computationally expensive Bayesian network.” Note that the Bayesian network does do better in most cases than TTB, but not by much. I was unconvinced that this was not the result of a mismatch between the complexity of the prediction and the complexity (or cost) of algorithm used to make it.

    Chapters 9–14 focus on more complex decision processes than binary choice, namely memory, estimation, and categorization, inferring intention from motion, mate selection, and parental investment. Chapter 9 explores how “hindsight bias” (the tendency to believe falsely, after the fact, that one would have predicted the outcome of an event) may be the by-product of a heuristic for memory updating and inference, rather than an error of human information processing. Chapter 10 develops a heuristic to estimate the populations of cities that exploits the nature of the j-shaped underlying distribution. Chapter 11 compares a heuristic for Categorization by Elimination with more complex algorithms including a neural network. Each of these chapters provides a nice connection to the existing literature, allowing the tasks to be put in context.

    Chapter 12 compares the performance of the “Categorization by Elimination” (CBE) heuristic to more complex algorithms, including a neural network to categorize intent (pursuit, evasion, fighting, courting, being courted, and playing) from motion cues (relative distance, relative angle, relative heading, absolute velocity, relative velocity, absolute vorticity, and relative vorticity) of two bugs simulated on a computer. What struck me here was that the difference in performance between the neural network and CBE was more marked than in other cases. The discussion focuses on how CBE does almost as well as two other linear integrative strategies: Dawe's rule which simply adds up the cue values, and Franklin's rule which is a weighted sum with the weights based on the data (training set), with less information. I would have found a discussion on why the neural net did much better in this case very interesting, i.e., what is it about the environment in this example that improved the performance of the neural network?

    Chapters 13 and 14 apply heuristics to topics prevalent in the biological literature: mate choice and parental investment. In Chapter 13, a variant of TTB is pitted against the optimal rule for a simple model of mate selection. The optimal rule guarantees the highest chance of picking the best candidate. If you are interested in picking not the best, but within the top 25% of candidates, the heuristic performed well. As with previous examples, for coarse-grained predictions, heuristics perform well. In Chapter 14, six simple heuristics for feeding chicks were discussed. The best of these simple heuristics was then compared to three constrained optimization models. According to the authors, the simple heuristic outperformed all three optimization models. The importance of this result was diminished by the fact that the optimization models to which the heuristic was compared were somewhat ad hoc.

    The final two chapters attempt to pull together all of the lines of thought developed in the book. Chapter 15, titled “Demons Versus Heuristics in Artificial Intelligence, Behavioral Ecology, and Economics” explores the role heuristics might play in understanding behavior in these fields. What caught my attention most in this section was the discussion of rationality in animals. The authors discuss optimization techniques in foraging theory and the deviation of actual behavior from “optimal.” The author states that “If behavioral ecologists have always been aware of constraints [which cause deviations from actual behavior] they have not always taken them into account in trying to explain behavior.” I question the accuracy of this statement in general. In my experience with stochastic dynamic programming models in behavioral ecology, a careful discussion of constraints and their influence on the explanation of behaviour is an essential part of the modeling process.

    The authors go on to suggest that the inclusion of constraints into optimization models falls short of “true” models of bounded rationality such as “satisficing.” In the latter, organisms are not assumed to be optimizing, but rather, attempting to achieve a minimum threshold. Any improvement beyond this is comparatively unimportant. The importance of the separation between heuristics and constrained optimization is overstated. For one, it is straightforward to alter the objective function to incorporate thresholds (i.e., the Stone-Geary utility function in economics), and thus capture satisficing-like behavior in an optimizing framework. Next, the heuristic itself might well be the optimal solution for the given problem. I do not see the value or importance of making such a strong separation here. Indeed, the authors later note that “One promising explanation is that aspiration levels and rules of thumb themselves are the result of evolutionary selection.” As with my earlier comment regarding behavioral ecology, heuristics may be (approximate) solutions to optimization problems given physiological constraints and historical contingencies.

    In summary, although the book provides some interesting discussions of decision processes, it is too long. Too many examples are explored that share the same basic structure, i.e., that in particular environments where the aim is a coarse-grain estimate of some criterion, heuristics can be effective. This seems to me to be a simple example of the trade-off between the value of information and the cost of obtaining it. No examples were presented in which heuristics performed very poorly. Those that are presented are not related to well-developed, standard notation from probability and statistics, i.e., the examples are about predicting random variables, given a set of independent variables. The increased predictive power of additional independent variables in a model will depend on the correlation between those variables. This is all well known. A more direct approach of investigating under what circumstances heuristics perform well in the context of well-developed theory would have been more enlightening than the approach of the book: the presentation of several examples in which heuristics perform well as surprising and counterintuitive. Finally, the authors make too much of the distinction between heuristics and constrained optimization. I was not convinced that heuristics provide a better model for human (or animal) behavior than do other more complex models such as Bayesian networks or constrained optimization. I was left to believe that people may apply a range of decision processes (including complex ones that integrate cues) to different problems in different environments. Whether we are conscious that we used a more complex decision rule, perhaps one hard-wired by natural selection, is another story. I expect that few Conservation Ecology readers would find this book very useful in their research. Those interested in behavioral ecology, human sociobiology, or psychology might find the perspective presented in the book of some interest.


    Gigerenzer, G., P. M. Todd, and the ABC [Adaptive Behavior and Cognition] Research Group. 2000. Simple Heuristics that Make Us Smart. Oxford University Press, Oxford, UK. 416 pp., paperback, US$19.95. ISBN 0195143817.


    Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a comment, follow this link. To read comments already accepted, follow this link.


    I would like to thank an anonymous referee for comments that significantly improved this book review.


    Gigerenzer, G., P. M. Todd, and the ABC Research Group. 2000. Simple heuristics that make us smart. Oxford University Press, Oxford, UK.

    Address of Correspondent:
    J. Marty Anderies
    GPO Box 284
    Canberra, ACT 2601
    Phone: +61 2 6242 1662
    Fax: +61 2 6242 1565

  • Home | Archives | About | Login | Submissions | Notify | Contact | Search