Todd and Gigerenzer begin their response by addressing the ideals that the adaptive toolbox conflicts with. These idealistic assumptions are:
- More information is always better – Sternberg argues “you are always better off using as much information as possible when time allows” for consequential decisions. Todd and Gigerenzer argue this ideal is misleading and “in order to make sound decisions in an uncertain world, one must ignore some of the available information”. Engel points out that “frugality conflicts with legal systems, which often run on the defensive vision that more is always better” (767).
- Optimization is always better – Shanks & Lagnado argue that fast and frugal heuristics cannot account for human behavior because they do not optimize and human behavior can be optimal. Todd and Gigerenzer emphasize the distinction between optimizing processes (heuristics do not employ) and optimal outcome (heuristics can reach). Furthermore, using optimization does not guarantee an optimal outcome.
- Complex environments demand complex reasoning strategies – Allen suggests that social environments are “responsive” rather than “passive” and therefore, are so complex that they require demonic reasoning abilities. Todd and Gigerenzer respond that “simple social exchange rules can coordinate social interactions without logical reasoning” (768). Furthermore, logic can make one predictable and therefore more open to exploitation.
- Search can be ignored – Oaksford argues “information is usually integrated in decision making by pointing to examples (such as speech perception and sentence processing) where the necessary information is available simultaneously, obviating search” (768). Todd and Gigerenzer emphasize the requirement of searching for cues in many decisions situations.
Where do heuristics come from?
In this section, Todd and Gigerenzer address clarification questions about the origins of heuristics. They give three answers for where heuristics come from.
First, heuristics can arise through evolution. Baguley & Robertson express concerns about how heuristics get into the adaptive toolbox. Todd and Gigerenzer clarify they had no intention of giving the impression that evolution and learning are mutually exclusive. They further explain that “there would be strong selective advantages in coming into the world with a least some heuristics already wired into the nervous system” (768). Houston mentions that there is growing literature surrounding the use of heuristic by animals in specific environments. Hammerstein states the difference between an optimizing process and an optimal outcome. He discusses evolution as an optimizing process for how decision mechanisms come about in biology. He also argues that “a heuristic that was evolved for adaptive use in one environment may be misapplied in another” (769).
Secondly, heuristics can arise through individual and social learning. Baguley & Robertson claim that certain heuristics can be learned. Todd and Gigerenzer agree with this claim but emphasize the distinction between adding a new tools to the adaptive toolbox and learning to use old tools in a new way. Solomon distinguishes two developmental changes that apply to this distinction. Changes in core “theories”, concepts, and reasoning principles may be called the tools in the toolbox while changes in “expertise” with age may change how or when tools are used. Furthermore, many developmental questions arise from the adaptive toolbox perspective. Barrett & Henzi question if fast and frugal heuristics are specific to human reasoning. Todd and Gigerenzer claim that humans rely heavily on social learning of heuristics which enables rapid addition of heuristics into the adaptive toolbox and is the foundation for culture. Gorman also describes how both heuristics and decisions themselves can be obtained from other individuals.
Finally, heuristics can arise through the recombination of building blocks in the adaptive toolbox. Huber purposes that the adaptive toolbox is made up of many “partial” heuristics and multiple “partial” heuristics are used most decisions.Todd and Gigerenzer claim Huber overgeneralizes from the artificial lottery problem of risky choice and his assertion sets up a false opposition. The adaptive toolbox can hold both “partial” and “global” heuristics.
How are heuristics selected from the adaptive toolbox?
Many commentators propose ideas about heuristic selection. Todd and Gigerenzer point out that “heuristic selection may not always be a problem” (770). Feeny claims that there are situations when heuristic selection is not needed. Heuristics in the adaptive toolbox are designed for specific tasks and this reduces the selection problem. Furthermore, the knowledge available reduces the set of possible heuristics when making a decision.
However, even after making the set of possible heuristics smaller, there still may be multiple heuristics applicable for a given situation. Morton suggests “a meta-heuristic which chooses between heuristics using the same principles as the fast and frugal heuristics themselves”. Feeney fears an infinite regress of meta- and meta-meta-strategies for determining the best heuristic and meta-heuristic for a given situation. Cooper and Feeney also worry that meta-heuristics won’t always pick the best heuristic. Todd and Gigerenzer respond with the claim that “the whole point of the adaptive toolbox approach is not aiming at optimization” (770). Furthermore, since more than one heuristic can be used in many situation, the choice between them isn’t critical.
Cooper questions the conditions required for selecting certain heuristics. Todd and Gigerenzer respond that most heuristic-relevant conditions haven’t been discovered yet. Margolis fears that heuristics that adapt to certain conditions may be harmful when significant changes to an environment occur. Todd and Gigerenzer point out that these maladaptive heuristics may be “replaced by new more appropriate heuristics acquired through individual or social innovation and learning” (772).
How can environment structure be characterized?
Todd and Gigerenzer stress the importance of environmental structure in order to understand cognition and behavior in terms of adaptation, as these are shaped by previous environments. Two approaches to studying the structure of environments are the search for environmental/ecological texture and the search for invariants in the visual environment. However, both these approaches overlooked the relationship between heuristics and environment. Simple Heuristics focuses on a few types of environment structures: environments in which lack of recognition is informative; noncompensatory information; scarce information; J-shaped distributions; and decreasing choice sets.
Allen and Sternberg touch on important points about cost-benefit analysis, stating that there are cases in which the cost of making a wrong decision outweighs the benefit of simple heuristics and therefore it is worth the extra time and analyses of the more consequential decisions. Todd and Gigerenzer respond by taking into account the significance structure of the decision, using the example of avoiding poisonous mushrooms differently when they are lethal or not. They conclude that while consequential decisions are often overthought and require justification, it is likely that the decision is reached very quickly, and much of the following thought processes are used to justify the decision that was made originally.
The difference between friendly and unfriendly environments, as Shanteau & Thomas point out, is that “friendly” envs. can all be positively correlated, while a subset of “unfriendly” envs. can be negatively correlated. Todd and Gigerenzer welcome this, further explaining that unfriendly envs. contain tradeoffs to simple heuristics. As the number of cues increases in an unfriendly environment, the performance of fast and frugal heuristics decreases. To mitigate this, we can combine partial heuristics to eliminate poor options first, then use a lexicographic process on the remaining.
An important area of adaptation to these heuristics is social rationality, and the authors agree with commenters calling for research to combine psychology and game theory for the understanding of interactive strategies. Simple heuristics may be exploited by more complex thought. They may also be exploited in J-shaped distributions, which occur in environments of power laws and Poisson processes.
Heuristics monitor and assess valid environmental cues at the surface level covarying with the decision variable rather than cues causally linked, since both are caused by the same process. It is possible a decision can be made with only one surface level cue, in comparison to the several or more casual cues necessary.
How can we study which heuristics people use?
It is difficult to obtain empirical evidence for heuristic use, but Todd and Gigerenzer stand firm in the belief that it is possible especially in the case of new predictions. In situations where there is little prior knowledge, it is easy to diagnose how they come to simple conclusions on new information. They criticize hypothesis testing in psychology, stating that there is no hypotheses other than a null hypothesis, and that we cannot assume how people use heuristics and use aggregated means. Instead, the adaptive toolbox is necessary to study heuristic usage, which includes (1) specifying potential heuristics, (2) deriving predictions for each, and (3) testing each participant’s judgement against those predictions. This encourages precise predictions based on individual differences in knowledge.
Solomon proposes that this methodology is important in ontogenic development, following pre-schoolers through childhood, adolescence, and into adulthood. Chater additionally speaks on the complementary studies of simple heuristics and rational analysis, which finds optimal solutions through elimination processes of simplifications.
What is the evidence for fast and frugal heuristics?
While it is widely accepted that animals use simple heuristics in their environments, it is more difficult to analyze human usage. Cooper and Harries & Dhami claim there is little evidence that humans use fast and frugal heuristics; however, Todd and Gigerenzer stand by their piece and empirical evidence along with other studies who found similar results. Humans often look up only one or two cues in order to avoid finding conflicting evidence. Cue dependency can be ignored in suitable environments for accurate strategies. Despite their hesitations, Harries & Dhami conclude that simple heuristics can even be used in legal or medical decisions.
The authors push back on Allen’s note that there is no evidence that people make inconsistent, intransitive judgments, using another study in which some people incorrectly inferred that A is greater than B, B is greater than C, BUT C is greater than A (which is intransitive).
Kainen asks for examples of heuristics in other fields such as perception, engineering, and language processing, but Todd and Gigerenzer stress that we must be aware of artificial mechanisms working similarly to the human mind.
What criteria should be used to evaluate the performance of heuristics?
Todd and Gigerenzer focus on multiple corresponding criteria rather than internal coherence. Since many people often violate first order logic (see the cities example) and ignore laws of probability, the authors question whether cognitive psychology properly evaluate human reasoning and cognition. Instead, they believe we should focus on correspondence between heuristics and environments.
Allen and Fuller note the tension between coherence and correspondence to the real world, despite the ability for a belief to satisfy both criteria. However, we cannot have complete coherence given our bounded rationality. Simple heuristics focus on inferences, where coherence and correspondence may diverge. Bermudez questions the justifiability of simple heuristics evaluated on success alone; the authors push back saying that the process of evolution and learning use success to improve processes for future decision making.
Many responses extend the discussion of performance criteria of heuristics in societal and legal settings, raising fundamental questions. What are the implications of using simple heuristics with regard to the law?
Conclusion
This study of bounded rationality and its multidisciplinary results have relevance for many sciences attempting to understand organisms’ behaviors. Using the adaptive toolbox to find what lies inside is difficult, but fast and frugal heuristics allow us to reevaluate some underlying assumptions of world representations. We must extend our understanding of ecological rationality- how environment structures and heuristic mechanisms work together.
Questions:
Other than quick decisions, where and when can fast and frugal heuristics be effective?
It is likely that animals use fast and frugal heuristics most of the time. Humans obviously have capabilities much greater than this. However, are the fight or flight type decisions something that animals are superior to us in?
Where is the line between decisions and beliefs, especially if we are using simple heuristics
What are implications of using simple heuristics with regard to legal processes?