Estrategia comercial Definición e importancia

Normative rationality (or ‘rationality’ simpliciter) says that behaviour or reasoning is rational if it conforms to the prescriptions of a formal normative model (e.g. inference

is unbiased - and so this notion of rationality doesn’t draw any clear distinction between what it means to be biased and what it means to be irrational.

One first way in which people might disagree about what is rational is, therefore, that they might disagree about what the correct normative model is. As Elqayam and Evans

(2011) point out, it is becoming increasingly rare to find ‘single norm paradigms’ in reasoning and decision making research - tasks where a single normative model is undis-

puted. Evans (1993) refers to this as the ‘normative system problem’, and Stanovich (2011) similarly talks of the ‘inappropriate norm argument’. This fuels some of the

disagreement I discussed in chapter two - around what the correct normative standard against which to judge confirmation bias is.

To say that a reasoning strategy isepistemically rationalis to say that it reliably leads one to form accurate beliefs about the world (Stanovich et al., 2008). This essentially

adds the importance of accuracy to the basic notion of normative rationality - in addition to asking whether a strategy leads to systematic deviation from a normative model, we

also want to ask whether those deviations come at a cost to accuracy. As I discussed in the earlier section on bias, because there is a tradeoff between bias and variance,

it’s possible for a reasoning strategy to be systematically biased and yet still be more accurate than alternative, higher variance, strategies.

Another notion of rationality commonly discussed is that ofinstrumental rationality, which is based on the idea that rationality should take into account the various different

goals an agent may have (Stanovich et al., 2008). To determine whether a strategy is instrumentally rational, therefore, we need to ask not just whether it deviates from

some normative model, but whether it actually comes at a cost to important goals. A heuristic might seem to result in bias relative to an abstract normative model, but

yet be highly effective at achieving certain goals. For example, though a tendency to ignore or underweight unpleasant information might be viewed as irrational by Bayesian

standards, it might be instrumentally rational if the goal is to maximise personal utility, at least in the short term. Buchak (2010) argues that it is not always instrumentally

rational for a risk averse decision maker to seek out more information before making a decision - even though seeking more information might be considered the normatively

Of course, there’s a lot of room for disagreement here about what the relevant goals are, and perhaps what goals people should have in different situations. Our reasoning and decision making strategies face conflicting goals on multiple levels - there’s often a conflict between my personal goals and the ‘goals’ of my genes (what is evolutionarily

adaptive isn’t necessarily what’s best for me - see Stanovich, 2009), a conflict between my short-term and long-term interests, and conflicts between the goals of individuals

and larger groups. Which of these goals we should define ‘rational’ behaviour relative to when conflicts arise is not an easy question to answer.

Discussions of bounded rationality (Gigerenzer and Goldstein, 1996, Simon, 2000) emphasise the importance of taking into account the cognitive constraints people face

when making judgements and decisions: we clearly do not have the computational power, or time, to always be calculating Bayes’ rule, and so proponents of bounded rationality

argue that many normative models are simply inappropriate standards by which to judge reasoning. A given strategy is boundedly rational if it is effective at achieving the

relevant goals (whether accuracy or other goals) given these cognitive constraints - or put differently, if there is not clearly any alternative strategy that would do better that

people could feasibly use. So even if I err towards interpreting ambiguous information in ways that favor my current hypothesis, the only way for me to avoid this may be to

consider multiple different counterfactuals and alternative hypotheses, going far beyond the cognitive capacity and time I have available.

Proponents of the closely-related notion of ecological rationality similarly suggest that the normative models typically used are inappropriate standards by which to judge

human rationality, because they fail to take into account the relevant features of the environments in which people are making judgements and decisions (Todd et al., 2000,

Todd and Gigerenzer, 2007). Proponents of an ecological theory of rationality suggest that we need to study human behaviour and judgement in real-world domains, observe

what heuristics and processes they use, and then assess whether this enables them to get the ‘correct answer’, and/or to successfully attain their goals in those domains.

This contrasts with the classic heuristics and biases approach, which typically studies judgement and decision making in much more general and abstract contexts, and in

comparison to much stricter normative standards.

for evolutionary purposes: a given strategy might look ‘biased’ relative to some nor- mative model, but be rational from the standpoint of the genes (Tooby and Cosmides,

1992). For example, some have argued that a kind of confirmation bias might have been beneficial in the ancestral environment: much better to believe that a predator is around

the corner and be wrong than the opposite.

Finally, prescriptive rationalityasks not just whether reasoning deviates from some normative standard in theory, but whether in practice there are alternative strategies people could actually use to move closer to these normative standards (Stanovich and

West, 2000). Just because Bayesian probability theory provides the correct formal so- lution to the kinds of inference problems people are often trying to solve, doesn’t mean

people should literally use Bayes’ rule when drawing inferences - the cost of the time and effort involved might well outweigh the benefits (in this sense, this is closely related

to bounded rationality.) What we can ask, however, is whether there are strategies people can use that are different from those currently or automatically employed, which

might bring judgements closer to these normative standards - and these would provide prescriptive standards for rationality. For example, the prescription to ‘consider the

opposite’ under some circumstances might help people to more accurately assess the diagnosticity of a given piece of information, by helping them to consider how likely it

is under an alternative hypothesis that they might otherwise ignore (Lord et al., 1984).

One useful way to understand the distinction between prescriptive and normative ratio-

nality is by saying they operate at differentlevels of description (Marr, 1982): normative rationality operates at the ‘computational level’ (i.e. it describes the kinds of problems

reasoning is trying to solve, without saying anything about the actual algorithms that implement those solutions in the brain), where prescriptive rationality operates at the

‘algorithmic level’ (trying to say something about the actual algorithms or strategies people might use to solve problems.)