We will summarize the text, offer our perspective on the author’s’ main points and pose some questions.
Unbounded Rationality
Todd and Gigerenzer propose two types of rationality: Bounded Rationality and Demonic (Divine) Reasoning. The latter assumes that “the human mind has essentially unlimited demonic or supernatural reasoning power” and is divided further into:
- Unbounded rationality
- Optimization under constraints.
Unbounded rationality represents a system that is omniscient and capable of certainty with unlimited access to information, however such a model is not realistic as the human mind has limited access to knowledge, time, money, and attention, when making decision.
The constrained optimization model requires a “stopping point” in the otherwise endless pursuit of knowledge. This point is reached when the costs of continuing to search for information outweigh the benefits of the additional knowledge that may be gained. This process also assumes almost unlimited time and computational power, as calculating the potential cost and benefit of each piece of further information would be hugely expensive and ultimately impossible.
The authors argue that “reasoning can be powerful and accurate without requiring unlimited time and knowledge”, which is why they move from the demonic model to the bounded model. Many philosophers and economists, however, argue that bounded rationality models “use a greater number of parameters and become more demanding mathematically” (730) than their unbounded alternatives, and for this reason they choose to stick with unbounded rationality. Todd and Gigerenzer respond to these critics by saying that “rationality need not be optimization, and bounds need not be constraints”(730).
Bounded Rationality
Thus, they present a notion of “Bounded Rationality” which presents the process of decision making as that of “a bounded [human] mind reaching into an adaptive toolbox filled with fast and frugal heuristics”(729). Bounded rationality accounts for both “the limitation of the human mind” and “the structure of the environment in which the mind operates”. The latter is a central theme of “Ecological rationality”, which shows the extent to which a heuristic is adapted to the structure of its operating environment. Subsequently, the authors present notions of “Satisficing” and “Fast and Frugal Heuristics”.
Satisficing is a method of making a choice from a set of options encountered sequentially when one “does not know much about the possibilities in advance”. It utilizes an “aspiration level” and stops searching for alternatives once an option that exceeds the aspiration level is found. However, difficulties arise during both the computation of the aspiration level and its relationship with each encountered option.
Fast and Frugal Heuristics limit the search of objects by using “easily computable stopping rules”, and make choices with “easily computable decision rules”. This represents Bounded Rationality in its purest form insofar as some fast and frugal heuristics make “satisficing sequential option decisions” and some do not. An example of a fast and frugal heuristic is One Reason decision making where one single piece of information is used to make a choice. This sharply contrasts with heuristics of unbounded rationality which attempt to assimilate unlimited information in different decision making environments.
The authors explain the fast and frugal heuristic principles that:
- Guide search
- Stop search
- Make decisions based on (1) and (2)
Regarding Search guidance, fast and frugal heuristics use ‘simple search strategies’, such as cue orderings, which are easy to compute and ignore dependencies between cues. This contrasts with ‘active search strategies’ which use extensive computations or knowledge to find the next cue or alternative to search.
Regarding Stopping search, fast and frugal heuristics employ simple rules. For example: cease searching and make a decision as soon as a reason that favors one alternative is found. For alternatives, simple aspiration level stopping rules can be used.
Regarding principles for decision making(3), decisions can be based on only one reason, regardless of the total number of cues found during search. Also, cues are not weighed or combined into a “common currency”. Rather, a simple elimination process is employed until one final choice remains.
The authors note that the components of fast and frugal heuristics are constructed by combining building blocks, such as One Decision making, or nesting existing heuristics within each other, such as a Recognition Heuristic that works on the basis of an elementary cognitive capacity and recognition memory.
Fast and frugal heuristics are divided into four classes, which are :
- Ignorance Based Decision making
- One Reason Decision making
- Elimination Heuristics for Multiple-option choices
- Satisficing Heuristics
First, Ignorance based decision making ensures that, in two-option situations, decision makers choose recognized options over unrecognized options. The authors give an example in which humans are more likely to choose recognizable ham over “odd colored” eggs. Such ignorance based recognizing heuristics perform better than random choice in environments where a positive correlation exists between the decision maker’s exposure to different possibilities and the ranking of these possibilities along the decision criteria being used. In other words, the more recognizable an option is, the more likely it is that it will get chosen.
Second, One Reason decision making ensures that, when multiple cues exist, a stopping rule is activated once enough information has been gathered to make a decision. In other words, the decision maker stops looking for cues as soon as a cue is found that differentiates between the options considered. Hence, such heuristics include both a stopping rule, and a decision cue. Moreover, cues can be searched by the following methods:
- i) In order of validity (Take the Best heuristic)
- ii) According to the order determined by their past successes in stopping search
iii) In random order (Minimalist heuristic)
The authors note that such heuristics are non-compensatory as “once they have used a simple cue to make a decision, no further cues in any combination can undo or compensate for that one cue’s effect”.
Third, Elimination Heuristics for multiple option choices are employed when each available cue “dimension” has fewer values than the number of available alternatives. One Reason decision making is insufficient in such cases because a single cue cannot distinguish between all alternatives. For example, knowing whether or not 15 cities contain a river is not enough to decide which is the most habitable. Moreover, elimination heuristics employ both stopping rules and elimination. The former ensures that cues are only sought until enough is known to make a decision while the latter ensures that successive cues are used to eliminate options until a single option remains.
Fourth, unlike other heuristics that assume all possible options are available to the decision maker, Satisficing heuristics find alternatives sequentially over an extended period or spatial region. This is done by setting an aspiration level and the search for alternatives is stopped once the selection criterion is met.
The authors posit that organisms deal with their environments. Hence, different environments can have different specific fast and frugal heuristics that exploit the information structures present to make adaptive decisions. However, this could lead to a multitude of specific fast and frugal heuristics. Thus, the authors propose the following information structures which make fast and frugal heuristics “ecologically rational”:
- Non-compensatory Information
- Scarce Information
- J-shaped distributions
- Decreasing Populations
Regarding Non-compensatory information, the potential contribution of each new cue falls rapidly. In such cases, the Take the Best heuristic is ecologically rational.
Regarding Scarce information, the Take the Best heuristic outperforms a class of linear models on average when few cues are known relative to the number of objects.
In J-Shaped distributions there are many small values and few large values. In such cases, the QuickEst heuristic, which takes values of objects along the same criterion using as little information as possible, is ecologically rational.
Regarding decreasing populations where the set of options is constantly shrinking, a satisficing heuristic is ecologically rational.
Furthermore, the authors propose that fast and frugal heuristics should be evaluated based on correspondence criteria regarding speed, frugality and accuracy. This contrasts with coherence criteria which are primarily concerned with the internal logical coherence of judgements. Moreover, this allows for the comparison of heuristics within the actual requirements of its environment which may include, using a minimal amount of information or making accurate decisions in a minimal amount of time.
Lastly, the the authors conceive of the mind as an “adaptive toolbox” which is a collection of specialized cognitive mechanisms that evolution has built into the human mind for specific domains of inference and reasoning. These contain psychological adaptations and include perceptual and memory processes. However, higher order cognitive processes are better modeled by simpler algorithms than lower order mechanisms.
Conclusion
The authors conclude by stating the following challenges to be addressed by their research:-
- Cognitive tasks such as planning, perceptual mechanisms, and problem solving
- Adaptive problems regarding domain specificity and the organization of the mind
- Social norms and emotions
- Ecological irrationality regarding the aspects that shape the design and performance of decision heuristics
- Performance criteria, that is, is there a role for coherence criteria?
- Selecting heuristics, in other words, how does the mind know which heuristic to use?
- Multiple methodologies regarding the prevalence of simple heuristics in human and animal reasoning
The authors’ conception of fast and frugal heuristics, although probable, seems to be lacking in both empirical evidence and in the complexity of the human decision making process. Regarding the former, it is acknowledged that empirical means of testing search processes during human decision making are either non-existent or not utilisable. This is problematic as it leaves one wondering what theory of knowledge and categorization of epistemology fast and frugal heuristics adhere to. Regarding the latter, they seem to overlook the complexity of nesting heuristics, calculating the validity of cues, and the distinction between conscious and unconscious rationality, if existent. Lastly, one is forced to ask whether the authors are trying to understand cognition in the context of modeling rational systems, and/or understand how humans reason. In other words, do humans inherently embody ecological rationality in ways that artificial systems cannot (example: early vision and visual recognition).
Questions
- Does this paper serve as an argument in support of the naturalisation of epistemology? If so, is it sufficiently supported by empirical findings? If not, is it supported by an externalist, coherence, or foundationalist theory of knowledge?
- At what point does the nesting of different fast and frugal heuristics require extensive computation, time, and knowledge? Think about making simple vs complex decisions
- How do we measure the validity of a cue in relation to other cues? Do Todd and Gigerenzer oversimplify the cue selection process?