Exploring Rationality

Rationality, like beauty can be said to lie in the eye of the beholder, or, perhaps, the model. Often analyzed in the process of decision-making, the concept of rationality has been the subject of numerous models and theories seeking to describe, assess, and/or improve human judgement. While the wisdom or foolishness of a choice may be evaluated by analyzing the decision’s “potential outcomes, their probabilities, and their values to the decision maker at the time the decision is made,” a separate set of criteria is used to assess rationality, thus highlighting the difference between a ‘good’ decision and a rational one (Hastie and Dawes 16). Taking a more realistic approach, with an eye to environmental constraints, the rationality of a decision is assessed by its adherence to four criteria: basis on current assets, basis on possible consequences, use of probability theory when consequences are uncertain, and adaptability to probabilities and personal values (16). Though these criteria seem cut-and-dry, approaches to rationality models and theories vary in their assessment of human decision-making abilities, which are often susceptible to the pitfalls of cognitive constraints, heuristics, and biases.

To accurately describe what it means to be rational, an overview of the three preeminent models of rationality is needed. The normative model of rationality represents an unattainable standard in which humans are expected to constantly optimize the value of their decisions by thoroughly incorporating the probability and personal values attributed to outcomes. This is often accomplished through the use of Expected Utility Theory, which mathematically determines the ‘best’ choice by multiplying the probability and personal utility of each alternative. Though consistently making the ‘best’ decision would be ideal, cognitive limitations and environmental conditions often render the normative model highly impractical. Time restraints, for example, often prevent optimal decision-making. For instance, Kahoot games, often used for class reviews, require participants to answer a series of multiple choice or true/false questions within brief time limits. Though participants can use the full time allotment set by the instructor, the program incentivizes quick responses by awarding more points to those who answer correctly the fastest. Conditions like these are often conducive to the use of various heuristics and biases that either help or harm the decision-maker in various predictable ways. The Kahoot example could easily induce the use of an availability heuristic, like the bias of salience, where a student is prone to choose an answer based on its prominence in their memory, rather than the answer that best meets the question’s criteria.

The prescriptive model of rationality, on the other hand, accounts for cognitive limitations and environmental constraints in its aim to explain how people should make decisions. Though the prescriptive model may occasionally advocate for the use of Expected Utility Theory, it does not seek to indiscriminately and unrealistically apply the principles of probability or expected utility to all scenarios. For instance, in a decision-making scenario where alternative options abound, the prescriptive model might account for the possibility of negative consequences in exceeding the decision-maker’s cognitive load, as seen in maximizing and satisficing literature. These points can be illustrated by my own experiences with college visits and applications. In my search for the ‘best’ college, I maximized to the best of my ability: touring ten institutions and researching countless others over the span of two years. In the end, however, I accepted admission to the same university I would have attended had I satisficed, or chosen a ‘good enough’ option, from the beginning. Though I’m relatively satisfied with my final decision, I’ve often lamented the amount of time and energy I expended while searching for alternatives. Considering the correlations established in prior research between maximizing and negative affect, perhaps the prescriptive model would have suggested a less exhaustive approach (Iyengar et al., 2006).

Finally, the descriptive model of rationality seeks to delineate the ways in which people actually arrive at their decisions. In its understanding of human behavior, the descriptive model accounts for the numerous heuristics and biases used to overcome cognitive and environmental restraints. Though these mental shortcuts are often seen as violations of human rationality in the ‘traditional’ approach or normative model, the “fast and frugal” approach to heuristics and biases highlights the many ways in which they improve decision-making efficiency. For instance, the less-is-more effect, wherein an overabundance of information can lead to reduced inference accuracy, challenges the more traditional accuracy-effort trade-off approach where the amount of information considered is consistently positively correlated with decision accuracy. For example, instructors often recommend that students stick with their first choice when taking an assessment. This approach is thought to reduce the likelihood that students will ‘overthink’ the question’s criteria, thus preventing students from diminishing the accuracy of their choice by considering too much information and choosing an incorrect response.

The normative, prescriptive, and descriptive models of rationality are distinguished by their perspective on heuristics and biases. Rationality can therefore be described in three ways: a strict adherence to probability or expected utility principles, a prioritization of normative thinking where possible, or a realistic description of human decision-making, mental shortcuts and all. Though the use of heuristics and biases can undoubtably result in predictably erroneous conclusions, they exist to overcome cognitive and environmental limitations and enhance decision-making efficiency. To define rationality, the question therefore arises: can mental shortcuts be considered a viable part of the rational decision-making process?

References

Hastie, R. & Dawes, R. M. (2010). Rational choice in an uncertain world: The psychology of

judgement and decision making (2nd ed.). SAGE Publications, Inc.

Iyengar, S. S., Wells, R. E., & Schwartz, B. (2006). Doing better but feeling worse: Looking

for the “best” job undermines satisfaction. Psychological Science, 17(2), 143-150. https://doi.org/10.1111/j.1467-9280.2006.01677.x