Adaptive Toolbox

The Adaptive toolbox is the ability of an institution or individual to make expedient decisions. The concept was originated by Gerd Gigerenzer. The adaptive toolbox employs the theory of ecological rationality.^[1] Businesses or individuals employ the adaptive toolbox in order to handle situations of uncertainty involving limited time, computational resources and information. The content of the adaptive toolbox is shaped by evolution, learning, and culture for specific domains of inference and reasoning, as well as changes across the life stages .^[2] In short, it is a decision-making method that uses an individual's past experiences and problem solving skills to make decisions in an unfamiliar or high-stress environment. Heuristics is another term for these decision-making models.

The adaptive toolbox consists of:

1. The collection of elements -- such as search rules, stopping rules, and decision rules for constructing heuristics.
2. Core mental capacities that building blocks exploit -- such as recognition memory, depth perception, frequency monitoring, object tracking, and the ability to imitate.
3. A specific group of rules or heuristics rather than a general-purpose decision-making algorithm. These heuristics are fast, frugal, and computationally cheap, but are less consistent, coherent, and general.^[3]

Common examples include:^[3][4]

a) Recognition-based heuristics (e.g. recognition heuristic, fluency heuristic)

b) One-reason decision-making (e.g. Take-the-best, Fast and Frugal Trees)

c) Trade-off heuristics (e.g. 1/N, Tallying)

d) Satisficing heuristics

e) Social heuristics (e.g. tit for tat, imitate-the-majority, imitate-the-successful, default heuristic, social circle heuristic,^[5] averaging, choosing^[6]).

The extent to which humans, and other species, share heuristics depends on the extent to which those humans experience the same adaptive problems, environmental structures, and core capacities. For example, "while the absence of language production from the adaptive toolbox of other animals means they cannot use name recognition to make inferences about their world, some animal species can use other capacities such as taste and smell recognition as input for the recognition heuristic."^[7]

The selection of heuristics

The assumption that individuals are equipped with a repertoire of heuristics raises the question of how they select strategies in a given context. Scholars have proposed two main methods to explain how individuals select strategies from the adaptive toolbox:

• the cognitive niches approach;^[8] and

• the strategy selection learning theory.^[9]

According to the idea of cognitive niches, environmental structure, strategy, and cognitive capacity limit the application of specific heuristics.^[8] The result is cognitive niches for different heuristics. For example, when purchasing a mobile phone, a consumer relying on recognition heuristics will choose the familiar brand. However, unfamiliar brands force the consumer to compare all the features, weighing each model and using tallying, or, if lacking time or cognitive capacity, make use of a few important features to compare the two models using take-the-best.

The strategy selection learning theory, in contrast, argues that people select appropriate strategies based on learning. It assumes that individuals form subjective expectations for the strategies they have, select strategies proportionally to these expectations, and update their expectations after the use of the selected strategy.^[9] For example, in the case of selecting a strategy for choosing among different options during the purchase of a mobile phone, the strategy selection learning theory proposes that individuals first assess how well each of the available strategies would perform in terms of making the right decision. Then, based on this judgment, they would use the strategy with the best expected outcome. After using a particular strategy to make the purchase of the mobile phone, the choice outcome is evaluated and expectations about the performance of that executed strategy are updated and reinforced as an information for future purchase decisions.

Alternative views

This concept departs from the idea of a single strategy being universally superior as put forward by Gottfried Wilhelm Leibniz. Leibniz^[10] proposed to replace all reasoning with a universal logical language, the Universal Characteristic. "The multitude of simple concepts constituting Leibniz’s alphabet of human thought were all to be operated on by a single general-purpose tool such as probability theory”.^[3] Today, a number of approaches exist that assume a universal strategy: for example rational choice theory, the Bayesian approach to cognition,^[11] Parallel constraint satisfaction processes (PCS),^[12] sequential-sampling process models such as the adaptive spanner perspective^[5] and decision field theory.^[13]

See also

• Heuristics
• Heuristics in judgment and decision making
• Ecological rationality
• Decision making
• Gerd Gigerenzer

References