Reliabilist epistemology meets bounded rationality

Abstract

Epistemic reliabilism holds that a belief is justified if and only if it is produced by a reliable or truth-conducive process. I argue that reliabilism offers an epistemology for bounded rationality. This latter concept refers to normative and descriptive accounts of real-world reasoning instead of some ideal reasoning. However, as initially formulated, reliabilism involves an absolute, context-independent assessment of rationality that does not do justice to the fact that several processes are reliable in some reasoning environments but not in others, as is widely reported in the cognitive sciences literature. I consider possible solutions to this problem. Resorting to ‘normality reliabilism’, a variant of the theory, is one; but I find it insufficient. Therefore, in addition, I propose to relativise the reliability assessment to reasoning environments. This novel version of reliabilism fits bounded rationality better than the original one does.

Full text

Synthese (2024) 203:115
https://doi.org/10.1007/s11229-024-04525-y
ORIGINAL RESEARCH
Reliabilist epistemology meets bounded rationality
Giovanni Dusi1
Received: 29 July 2023 / Accepted: 6 February 2024 / Published online: 3 April 2024
© The Author(s) 2024
Abstract
Epistemic reliabilism holds that a belief is justified if and only if it is produced by a
reliable or truth-conducive process. I argue that reliabilism offers an epistemology for
bounded rationality. This latter concept refers to normative and descriptive accounts of
real-world reasoning instead of some ideal reasoning. However, as initially formulated,
reliabilism involves an absolute, context-independent assessment of rationality that
does not do justice to the fact that several processes are reliable in some reasoning
environments but not in others, as is widely reported in the cognitive sciences literature.
I consider possible solutions to this problem. Resorting to ‘normality reliabilism’, a
variant of the theory, is one; but I find it insufficient. Therefore, in addition, I propose
to relativise the reliability assessment to reasoning environments. This novel version
of reliabilism fits bounded rationality better than the original one does.
Keywords Process reliabilism ·Ecological rationality ·Heuristics and biases ·
Normality reliabilism ·Heuristics ·Reasoning environments
1 Introduction
According to reliabilism, the epistemic theory first introduced by Alvin Goldman, a
belief is justified if and only if it is produced by a reliable belief-forming process, which
is one that usually generates true beliefs rather than false ones. In this essay, I argue
that reliabilism offers an epistemology for bounded rationality, a term used to refer
to a broad spectrum of normative and descriptive accounts of real-world reasoning
instead of some ideal reasoning. However, my argument continues, reliabilism can
only provide this service as long as it is subject to changes that lend a more contextual
character to the judgement of reliability. Indeed, no version of the theory, neither the
original one nor those proposed later, can account for reasoning processes that are
BGiovanni Dusi
Giovanni.Dusi@uab.cat
1Department of Philosophy, Autonomous University of Barcelona, Bellaterra (Barcelona), Spain
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115 Page 2 of 21 Synthese (2024) 203 :115
reliable in certain types of reasoning environments and unreliable in others, while that
this does occur is an empirical fact well-known in cognitive sciences.
In my discussion, I will assume that there is a close connection between epis-
temological investigation and empirical sciences. This naturalistic approach to
epistemology has been adopted in several philosophical inquiries into justification
and rationality conducted taking into consideration the findings of the psychology of
reasoning (for instance, Bishop & Trout, 2005; Goldman, 1986,2008; Kornblith, 2014;
Sturm, 2020). The idea is that the psychological sciences offer empirical results about
how we actually think and reason that might be useful or essential to make progress
in addressing epistemic questions (cf. Feldman’s [2012] ‘cooperative naturalism’).
It should be noted that reliabilist epistemology and bounded rationality have already
met. The venue was a colloquium on the foundation of the norms of rationality between
leading philosophers and psychologists.1On that occasion, Alvin Goldman gave a talk
on epistemological and psychological perspectives on human rationality. In his arti-
cle which followed the colloquium, Goldman acknowledged that: “In fact, each of
the other contributors to this symposium, Gerd Gigerenzer and Michael Bishop, has
made roughly this proposal; each regards truth, or accuracy, as (at least) one type of
consequence that is pertinent to a rationality appraisal.” (2008, p. 240). Gerd Gigeren-
zer is a cognitive psychologist who has propounded an original approach to bounded
rationality called ‘ecological rationality’. His reply to Goldman arrived later in a 2019
article, where he claimed that “ecological rationality can… be evaluated by goals
beyond coherence, such as predictive accuracy, frugality, and efficiency” (p. 3547)
and that: “The extension of goals of rationality from coherence to performance… has
much in common with existing approaches, such as Kitcher’s (1992) naturalism and
Goldman’s (1999) epistemological reliabilism.” (p. 3556).
The take-home message from this exchange over the years seems to be that process
reliabilism (PR, henceforth) fits Gigerenzer’s ecological approach to bounded ratio-
nality (EBR, from now on) pretty well. Assessing this match is my primary goal here.
To make this assessment possible, I will need to go into the details of both standpoints.
Thus, in Sect. 2, I introduce bounded rationality, focusing on EBR. I then (Sect. 3)
present reliabilism and Goldman’s paradigmatic examples of reliable and unreliable
processes. A relevant difference between PR and EBR will manifest itself at that stage.
In Sect. 4, I will show how Goldman’s original, simple version of reliabilism involves
an absolute assessment of rationality, which does not do justice to reasoning processes
that are reliable in some contexts but not in others. This is problematic since the psy-
chological sciences have shown how the performance of several reasoning strategies
changes from one environment to another. In Sect. 5, I start searching for solutions to
this problem which preserve the match between PR and EBR by appealing to a variant
of PR called normality reliabilism or normal conditions reliabilism. Despite specify-
ing ‘normal’ and ‘abnormal’ domains for the reliability assessment, I will claim that
normality reliabilism fails to solve the problem of absolute reliability. Even so, the
normality condition proves helpful to block some of the standard objections to reli-
abilism, so we should keep it rather than drop it. In the following section, I advance
1More precisely, the colloquium was part of the Sixth International Conference organised by the German
Society for Analytic Philosophy (GAP) which took place in 2006 in Berlin.
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Synthese (2024) 203 :115 Page 3 of 21 115
my proposal to relativise the reliability assessment to reasoning environments. The
novel version of PR that will emerge can account for differences in the performance of
one and the same process between one reasoning environment and another. Thus, my
reliabilist epistemology offers a theory of rational belief that fits bounded rationality
better than the original version does. In Sect. 7, I draw some conclusions.
2 Bounded rationality
The conditions in which human and non-human agents find themselves when drawing
inferences or making decisions are often less than ideal. For instance, agents could
evaluate alternative options having only scarce information about the alternatives.
Similarly, they could need to forecast uncertain future events that are beyond easy
prediction or lack the time needed to consider all relevant factors and be forced to come
to an answer quickly. Moreover, both animal and machine computational capabilities
have intrinsic limitations. For example, the storage capacity of human workingmemory
is restricted to a small number of meaningful pieces of information. For these and
similar reasons, the term ‘bounded rationality’ was introduced to qualify the reasoning
capacities of agents who draw inferences and make decisions under environmental and
cognitive constraints. Economist Herbert Simon initially proposed it as a departure
from the conception of rationality common in neoclassical economics (Simon, 1957).
Since then, it has been used in numerous other fields, including computer science,
decision sciences, cognitive psychology, neuropsychology, biology, and philosophy.
Research on bounded rationality encompasses several accounts of behaviour which
deviates from exhibiting ideal rationality. To begin with, it is possible to distinguish
between descriptive and normative theories. Moreover, specific instances of these two
kinds of theories are sometimes combined in distinct views on human reasoning. In
the following two subsections, I briefly present the main accounts of such behaviour
and then focus on EBR, one specific view.
2.1 Normative and descriptive accounts
Part of the inquiry concerning bounded rationality addresses the normative standards
against which judgements and decisions are evaluated. These standards are those
people are expected to reason by. Broadly speaking, there are two main views. The
first one, the coherence-based view, takes compliance with rules of logic, probability,
and decision theory as the rationality criterion. Dubbed by Stein (1996) the ‘standard
picture of rationality’, this view considers reasoning to be at fault whenever it does
not comply with these rules (cf., for instance, Piattelli-Palmarini, 1994).
Alternatively, the process-based view understands rationality as the use of appro-
priate reasoning processes (cf., for instance, Simon, 1976). The appropriateness of
processes is often evaluated in consequentialist ways, such as promoting one or more
epistemic values. Accuracy—the production of true beliefs while not producing false
ones—is the value most often mentioned in this regard. Process-based theories of ratio-
nality that prioritise accuracy hold that reasoning is rational if it employs processes that
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allow it to arrive at the correct output in a cognitive task. When the rationality criterion
is that it applies accurate processes, reasoning is evaluated on its performance.
Regarding empirical research, cognitive psychology is the discipline that has incited
most debate about the bounds of reasoning. Here, it is possible to recognise two
dominant lines of thought. The first trend is associated with the Heuristics and Biases
programme (H&B, henceforth; see Kahneman et al., 1982; Gilovich et al., 2002).
According to this view, reasoners’ cognitive limitations make it challenging to reason
according to the rules of logic, probability, and decision theory. Thus, people frequently
use heuristics to reduce complex tasks, such as assessing probabilities and predicting
values, to simpler judgemental operations. However, heuristics often depart from the
standard norms and tend to make people fall into systematic reasoning mistakes called
biases. For example, people are prone to commit the conjunction fallacy when they
evaluate the probability of two co-occurring events (Tversky & Kahneman, 1983), or
they tend to neglect relevant base-rate information when predicting uncertain events
(Kahneman & Tversky, 1973).
The Ecological Rationality programme promotes the second prominent view in this
debate (see Todd & Gigerenzer, 2012; Gigerenzer et al., 1999). Supporters of this view
have drawn attention to the capacity of animal and artificial agents to take advantage
of environmental structures to draw intelligent inferences and make smart decisions.
People can adapt their cognitive strategies to the context in which reasoning occurs,
using those strategies that fit the features of the environment and result in good rea-
soning. Most notably, it has been empirically demonstrated that simple heuristics can
enable people to draw correct inferences, and sometimes even more correct inferences
than those produced by complex strategies that obey logical and probabilistic norms
(see Gigerenzer et al., 2011). For instance, simple recognition heuristic can outper-
form Wimbledon experts and ATP rankings in predicting the outcomes of Wimbledon
matches (Serwe & Frings, 2006); and fast and frugal decision trees can be more accu-
rate than logistic regressions and other complex statistical models in several tasks,
such as predicting bank failure (Aikman et al., 2014) or in medical diagnoses such as
detecting depressed mood (Jenny et al., 2013).
Some combinations of normative and descriptive accounts might have already
become evident to the reader. On the one hand, H&B subscribes to the coherence-
based theory of rationality and evaluates reasoning on its respect for standard rules.
On the other hand, EBR assesses as rational those inferences and decisions which fulfil
the epistemic goals, embracing the process-based and consequentialist standpoint (cf.,
for instance, Schurz & Hertwig, 2019).
The differences between the two schools of thought are substantial. They represent
two cultures of research on bounded rationality, telling two different stories about ratio-
nality (Katsikopoulos, 2014). These differences fostered debate within the psychology
domain in the 1990s, sometimes leading to harsh clashes.2The two psychology pro-
grammes portray images of human rationality in different shades of colour: H&B
2The debate between Tversky and Kahneman of H&B on one side and Gigerenzer of EBR on the other
gave rise to a considerable body of literature. The discussion started from an adversarial exchange which has
become a classic in the field of theories of rationality (see Gigerenzer, 1991; Kahneman & Tversky, 1996;
Gigerenzer, 1996). In philosophy, several authors have attempted to frame and tame the disagreement. See
Vranas (2000), Samuels et al. (2002), and Sturm (2012,2019) for alternative ways to address the issue.
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Synthese (2024) 203 :115 Page 5 of 21 115
shows numerous ways humans fail to conform to the standard rules of reasoning,
while EBR points out many instances of adaptive and successful behaviour.
So far, I have distinguished between normative and descriptive accounts of bounded
rationality and illustrated how they can be combined in existing research programmes.
I will now focus on EBR, given its compatibility with epistemic reliabilism.
2.2 The ecological approach to bounded rationality
EBR is committed to explaining how animal and artificial agents cope with the bounds
of reasoning. The empirical research conducted within this programme encompasses
cases of both theoretical and practical reasoning. For example, it studies how reason-
ers form beliefs when deciding between two alternatives (e.g., which city has more
inhabitants, Milan or Brescia?) and how agents choose the course of action to take
(e.g., how should money be invested in a certain number of assets?). However, here I
will only look at cases of theoretical reasoning and consider bounded rationality only
as a form of epistemic rationality.3
According to the ecological approach, every agent facing an inferential or deci-
sional task has recourse to a plurality of reasoning strategies for addressing the task.
These are algorithmic procedures of different kinds. Some are complex and make
extensive calculations; others, the heuristics, are fast and frugal processes that pro-
duce outcomes in little time and using few pieces of information. Altogether, they
constitute the ‘cognitive toolbox’, the repertoire of tools for drawing inferences and
making decisions. It is crucial that the reasoner selects the strategy that best suits the
reasoning task and the context in which the cognitive performance takes place. Indeed,
using the appropriate reasoning method substantially contributes to reasoning success
(cf. Gigerenzer & Sturm, 2012, pp. 251–252; Hertwig et al., 2021, pp. 17–18).
Ecological rationality builds on Herbert Simon’s adaptive view of rational
behaviour. As Simon puts it, “Human rational behavior… is shaped by a scissors [sic]
whose two blades are the structure of the task environments and the computational
capabilities of the actor” (Simon, 1990, p. 7). This claim does not have to be under-
stood in descriptive terms only. Indeed, it contains an important normative message:
what is to be considered rational behaviour depends on the relationship between the
reasoning environment and the cognitive capacities of the reasoner. Ecological ratio-
nality emerges when the structure of reasoning mechanisms matches the structure of
the environment and the cognitive setup of the reasoner. Therefore, no judgement of
rationality can be made without reference to the context in which the reasoning is
performed.
Ecological rationality, normatively understood, is about the success of reasoning
strategies in the world, assessed by the following principle (Brighton & Todd, 2009,
p. 337):
3These choices need to be justified. The reason for the first deals with the scope of my research. I aim
to delineate an account of bounded epistemic rationality. In other words, I want to examine what it means
for reasoning to be epistemically rational under constraints of information, time, cognitive resources, etc.
Moreover, the instances of bounded reasoning I am interested in can be evaluated on their epistemic merits
without considering any practical virtues. This means it is possible to assess their normative status without
referring to practical issues; hence the second choice.
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ECOLOGICAL RATIONALITY
A mechanism M is ecologically rational in environment E in comparison to some
other mechanism M’ when M outperforms M’ on some criterion, or currency,
of comparison.
The criterion of comparison most often used is the accuracy of judgement. However,
other criteria are also sometimes adopted, such as speed (how fast an inference can
be drawn or a decision made), frugality (how many pieces of information need to be
searched before a judgement can be made), etc.
It might be helpful to see an example from the EBR literature to understand how
this principle works. Consider this inferential task:
Creditworthiness
of two companies, you have to choose the more creditworthy.
To draw the appropriate inference,4agents usually use information gathered from
different cues such as the company’s financial flexibility, its efficiency, the qualifica-
tions of the employees, etc. Psychological research has shown that these cues can be
processed in somewhat different ways (Gigerenzer & Goldstein, 1996). Suppose all
the cues are binary (0 and 1), where 1 is the higher criterion value, and they can be
ranked according to their validity. By cue validity, we mean the probability of draw-
ing a correct inference on the condition that the cue used in drawing that inference
discriminates between the options. The higher the cue validity, the higher the prob-
ability of drawing a correct inference when relying on that cue. In our example, this
is the probability of correctly inferring which of two companies is more creditworthy
relying on a cue whose value is positive (1) for one company and non-positive (0 or
unknown) for the other.5The first strategy people might choose from their cognitive
toolbox is the linear weighted additive strategy (WADD). For each company, WADD
calculates the sum of all cue values multiplied by the cue’s validity and then selects
the alternative with the largest sum. Alternatively, there is evidence that people rely
on a simpler method known as the take-the-best heuristic (TTB). TTB is a one-reason
decision-making method that decides which of two objects scores higher on a criterion
solely on the basis of the most valid cue that discriminates between the two objects.
This strategy consists of three steps (Gigerenzer, 2008, p. 32):
1. Search rule: Search through cues in order of their validity. Look up the cue values
of the cue with the highest validity first.
4Someone might see this as a case of decision-making rather than belief formation. However, this can
also be a case of belief formation, as one might think that when I face such a reasoning task, in addition to
choosing between two alternatives, I also form a belief as to which company is more creditworthy. This is
the sense in which I am interested in this case.
5More precisely, the validity vof a cue is estimated as:
vC/(C+W),
where Cis the number of correct inferences when a cue discriminates, and Wis the number of wrong
inferences, all estimated from samples (see Gigerenzer, 2019, p. 3559).
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2. Stopping rule: If one object has a positive cue value and the other does not (or is
unknown), stop the search and proceed to Step 3. Otherwise, ignore this cue and
return to Step 1. If no more cues are found, guess.
3. Decision rule: Predict that the object with the positive cue value has the higher
value on the criterion.
How each strategy performs is a matter of empirical research, and the evidence
gathered by proponents of EBR shows something quite surprising. Despite its sim-
plicity, TTB matches alternative, more complex and cognitively demanding strategies,
including WADD, in inferential accuracy. This happens when specific circumstances
obtain, for example, when the information available to draw the inference is non-
compensatory. This concept refers to the fact that each cue available for choosing
between the two alternatives cannot be outweighed by any combination of less valid
cues. To see what this means, consider the following explanation from Gigerenzer
(2008, p. 37). Assume an environment with Mbinary cues ordered according to their
weights Wj, with 1≤j≤M. A set of cue weights is non-compensatory if the following
property holds for each cue’s weight:
Wj>
k>j
Wk
As an example, take a set of weights that decreases exponentially, such as 1, 1/2,
1/4, 1/8, and so on. The sum of the cue weights to the right of a cue can never be
larger than this cue’s own weight. In environments with this feature, namely, non-
compensatory environments, no linear strategy can outperform the faster and more
frugal TTB, achieving at most the same level of accuracy as this simple heuristic6
(Martignon & Hoffrage, 2002).
Furthermore, TTB can also outperform WADD when the non-compensatory
condition holds (Brighton & Gigerenzer, 2015). This phenomenon is called the less-
can-be-more effect and is one of the most astonishing findings of EBR research.7Under
non-compensatory conditions, using TTB is ecologically rational. Alternatively, if this
does not hold, and for instance we have compensatory conditions, then TTB performs
poorly and is usually outperformed by WADD. TTB is not ecologically rational in
compensatory environments, and people would be better off relying on other strate-
gies, such as WADD.
This example shows how rationality is assessed in the ecological framework. Each
of the two strategies for solving Creditworthiness is more accurate than the other
in certain environments. Therefore, given the definition of ecological rationality, it is
rational to use it in such circumstances. This rationality assessment in terms of accuracy
6Non-compensatoriness is an environmental feature prevalent in natural environments to such a high
degree that inferences guided by this feature approach (and occasionally exceed) the inferences drawn
using multiple linear regression in predictive accuracy (¸Sim¸sek, 2013).
7The reason why a simple method can be more accurate than complex ones deals with the nature of the
predictive error. The total error of predictive models is, roughly speaking, composed of two parts: bias
and variance. When cues and cue values have the properties we assumed here, and the information is non-
compensatory, TTB and WADD have the same bias, but TTB tends to have a smaller variance. Hence,
TTB’s total error can be less than that of WADD (for further details, see Wheeler, 2020).
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reveals the consequentialist nature of EBR. This is where bounded rationality meets
epistemic reliabilism.
3 Epistemic reliabilism
In the late 1970s, Alvin Goldman put forth his first reliabilist theory. Since then, he and
other authors have kept developing that original proposal, which has been amended by
numerous alternative accounts.8Initially introduced as a normative theory of justifi-
cation, it has also been presented as a theory of rationality (Goldman, 2008, p. 240). A
common feature of the original ‘simple’ theory and more sophisticated versions is that
the rationality of a belief depends on the reliability of the processes which give rise
to the belief in question. In this sense, PR is a historical or genetic theory that makes
the rationality of a belief depend on the belief’s provenance, namely, the process that
generates it. This also makes it an indirect form of epistemic consequentialism: the
rightness of belief is determined by the reliability of the process which generates the
belief and not by its consequences, as maintained instead by direct consequentialism
(cf. Ahlstrom-Vij & Dunn, 2018,p.6).
Epistemic reliabilism rests on process reliability. In the words of Goldman, “a
reasoning process is rational if and only if it is reliable—i.e., usually generates true
beliefs rather than false ones” (Goldman, 2008, p. 240). This property characterises all
rational processes and distinguishes them from irrational ones. More fundamentally,
reliabilism assumes that accuracy, or truth-conduciveness, is the primary epistemic
goal the reasoner ought to pursue. Beliefs owe their epistemic goodness to the ratio-
nality of their belief-forming processes. Thus, simple PR consists of the following
claim:
A belief is rational if and only if it is the output of a belief-forming process that
is reliable—i.e., one that usually generates true beliefs rather than false ones.
Belief-forming processes confer rationality if they have a high truth ratio, that is,
most of the beliefs they produce are true. Precisely how high the truth ratio must be
is left vague. The truth-ratio threshold need not be as high as 1, but it must be greater
(presumably much greater) than 0.50.
PR distinguishes between justified and unjustified beliefs according to the reliability
of the processes that generate them. In his writings, Goldman provides the following
examples, we can assume without claiming completeness for them:
Unreliable processes: Confused reasoning, wishful thinking, reliance on emo-
tional attachment, mere hunch or guesswork, hasty generalisation (Goldman,
1979, p. 9), and failure to take account of all one’s relevant evidence (or failure
to take account of obviously relevant evidence) (Goldman, 1986, p. 104).
Reliable processes: Standard perceptual processes, remembering, good reason-
ing, introspection (Goldman, 1979, p. 10), and certain patterns of deductive and
inductive reasoning (Goldman, 1986, pp. 103–104).
8For an overview of reliabilist epistemology, see Goldman and Beddor (2021).
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By using these and similar reliable processes and avoiding the use of unreliable
ones, the reasoner satisfies the necessary condition for producing rational beliefs. By
doing so, reasoners will draw more accurate inferences than they would have done by
relying on non-truth-conducive tools.
These are the essential features of simple PR. Epistemologists have raised objections
to it, and more elaborated versions have been proposed to evade them. To assess the
match of PR with bounded rationality, we can, for the moment, stick to Goldman’s
simple version. Later, I will introduce normality reliabilism, one of its developments,
and show how it copes with some objections to the original proposal.
Now, a tempting inference is that every agent reasoning along the lines prescribed by
this theory also fulfils the principle of ecological rationality presented above. Indeed,
we should expect reliable processes to perform better than unreliable ones, scoring
higher on accuracy. However, the distance between simple PR and EBR is not as
short as one might imagine or hope. Some findings of the psychological sciences
regarding the working and effectiveness of inferential methods are hard to square with
the prototypical instances of reliable and unreliable processes. In particular, inferential
processes that do not consider all available evidencecan produce true beliefs contrary to
what is assumed by simple reliabilists. This points to a significant normative difference
between simple PR and EBR.
4 Absolute versus contextual assessment of rationality
In the previous sections, I described the main characteristics of EBR and PR.Tosum
up: (i) both programmes advance a consequentialist account of epistemic rationality,
understood as the achievement of some epistemic values; and (ii) among these values,
accuracy, or truth-conduciveness, is the most relevant. At this point, someone might
object that accuracy is not all that matters for bounded rationality theorists. This is
indeed the case: they also measure the success of cognitive strategies in the world by
other criteria, such as speed and frugality. This point requires clarification.
EBR theorists make rationality assessments using a variety of criteria. Looking at
these assessments, one notices two recurring points. First, some criteria, such as speed
and frugality, are used only occasionally. Moreover, one criterion is never absent in
evaluations of rationality: accuracy. Hence, it seems plausible to infer that accuracy is
a necessary condition for ecological epistemic rationality, whereas speed and frugality
are not. If this is true, we should conclude that accuracy is the most relevant rationality
criterion for EBR.9
We can also add summary point (iii): both theories of rationality are based upon
the use of appropriate reasoning methods, referred to as ‘processes’ by reliabilists and
‘strategies’ by psychologists of bounded rationality. These methods are algorithmic
procedures for deriving inferences and making decisions. This is to say, the methods
9This is why a monist epistemology seems most suitable for EBR. However, I do not deny that a pluralist
epistemology would fit bounded rationality equally well; one might claim, for instance, that accuracy is
insufficient and give the other criteria more relevance. This would require explaining whether there is an
open or closed list of criteria and the relationships between them, among other things. There is room for
future research here.
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arrive at outcomes by following rule-based patterns of reasoning. This is the case for
complex, highly resource-consuming methods and also for fast and frugal heuristics.10
These are the relevant similarities between PR and EBR; but we can now ask: Does
PR perfectly fit EBR? In response, it is possible to point out at least two dissimilarities
between the two. If we recall the definitions of ecological rationality and process
reliability, we will notice the first difference. The former is a comparative notion of
rationality: a strategy is ecologically rational compared to some other strategy when
the former outperforms the latter on some evaluation criterion. For example, when used
in non-compensatory environments, TTB is ecologically rational compared to other
inference models, such as WADD. By contrast, process reliability does not require
comparison: a reasoning process is rational if and only if it is reliable.11 Therefore,
strictly speaking, a reliable process is not ecologically rational unless there is evidence
that it outperforms rival processes. But what about the reverse: Is an ecologically
rational process also reliable? The definition of ecological rationality does not allow
us to say that it is: ecological rationality poses no requirement regarding the truth-
conduciveness of a process. However, I think we must say that ecologically rational
processes are also truth conducive, otherwise ecological rationality would get into
trouble. Let me illustrate this claim with an example.
Imagine two reasoning strategies, Sand S’, whose truth ratios are 0.30 and 0.20,
respectively. If ecological rationality were merely a matter of outperforming, then we
should conclude that Sis (ecologically) rational, for Sproduces more true beliefs than
S’. However, nobody would evaluate Sas rational, given its low level of accuracy. Thus,
there seems to be a hidden condition that ecological rationality requires a minimum
level of accuracy, and plausibly this must be much greater than 0.50, as it is for PR.
Further support comes from the study of simple heuristics. By scrutinising the EBR
literature, one can see that no heuristic with an average accuracy of less than 0.50 (or
probably less than 0.60 or 0.70) has been put forward. When assessed as ecologically
rational, heuristics are always remarkably accurate (for a recent systematic review of
the literature, see Katsikopoulos et al., 2018).
So, if ecologically rational processes must reach a high truth-ratio threshold, they
are reliable. However, the opposite cannot automatically be said to be the case: not all
reliable processes are ecologically rational. This discrepancy does not have trouble-
some consequences for the match between PR and EBR; and bearing this asymmetry
in mind, we can safely defend a strong compatibility between the two programmes.
10 Heuristics are sometimes seen as mere hunches or anarchic reasoning under the influence of some local
stimulus and therefore prone to produce bad outcomes. Although never characterised in these terms, this
is partly the image of heuristics that H&B has popularised. However, this is not the understanding I adopt
here. The example I provided above of the TTB heuristic solving Creditworthiness depicts a different image
according to which heuristics are step-by-step procedures for computing certain functions, with each step
itself being a function. Moreover, heuristics can draw accurate inferences, sometimes even more accurate
than those produced by more complex and sophisticated methods.
11 Notice that there is a sense in which reliability is a comparative notion: certain processes produce more
correct beliefs than others. For instance, the visual belief that a dog is in front of me formed from detailed and
unhurried scanning is plausibly more correct than the visual belief formed from quick and hasty scanning.
Accordingly, the former belief is more justified than the latter (Goldman, 1979,p.10).However,thisis
not the sense of comparativeness I am using here; my point is that a process can be reliable without this
property being established by any comparison with other processes.
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Synthese (2024) 203 :115 Page 11 of 21 115
There is a second difference between PR and EBR. Recall the original definition of
reliability again: it is the tendency of a process to generate true beliefs rather than false
ones. Where this tendency needs to be shown or in which reasoning context, is a piece of
information that the definition of simple reliability does not require. Simple reliabilism
is based on a non-located notion of rationality. Now, recall how ecological rationality
is defined: a reasoning strategy is ecologically rational in environment E compared to
some other mechanism when it outperforms this other mechanism on some criterion
of comparison. Here, the rationality assessment is relativised to a specific context
or environment of reasoning; whereas, as we have just recalled, this relativisation is
absent in Goldman’s reliabilism.
This discrepancy creates trouble. To see why the non-locatedness of PR is a problem
for its match with EBR, consider once again Creditworthiness. When inferring which
of the two companies is the more creditworthy, people can either use WADD, which
processes all the relevant cues about the two companies, or TTB, which only relies
on a fraction of the available data. Evaluating these processes according to simple
reliabilism, one should conclude that TTB is irrational. Indeed, as we saw before, not
taking into account all one’s relevant evidence is considered a paradigmatic example of
an unreliable process. However, empirical research in cognitive psychology has shown
that TTB can predict which company is more creditworthy as accurately as WADD
can, and sometimes even more in non-compensatory environments, thus proving to
be ecologically rational. Therefore, PR considers irrational a reasoning process that is
considered rational in a specific context by EBR, although the two accounts start from
a very similar concept of rationality.12
This dissimilarity highlights how simple PR adopts a relatively rigid notion of
reliability. According to this, a reasoning process is rational if and only if it tends to
produce true beliefs rather than false ones. Apparently, it is tacitly assumed that the
process ought to do so wherever it is used. I will call this an absolute understanding
of process reliability. By not specifying where a process needs to produce true beliefs
to be assessed as rational and assuming that this is in every place it is used, simple PR
precludes the possibility that a method is truth-conducive in specific reasoning contexts
and not truth-conducive in others. However, the reliability of a process can change
from one context to another, as shown by Creditworthiness. Therefore, simple PR is
not equipped to account for such changes in the performance of reasoning processes
from one environment to another. This is a substantive theoretical issue which deserves
attention.
Although PR does not perfectly fit EBR, I believe that something can be done to
remedy this; something that is worth doing. Indeed, there are good reasons for pre-
serving their match. On the one hand, PR can strengthen the theoretical underpinnings
of EBR, securing it from puzzling rationality assessments. As I have shown, the purely
12 The empirical evidence gathered by proponents of EBR about the accuracy of TTB proves Goldman’s
assumption that one needs to “take account of all one’s relevant evidence” incorrect in situations of uncer-
tainty, where some possible states, their consequences, or their probabilities are not known for sure. Here,
less evidence can be more beneficial. However, Goldman’s assumption is correct in situations of risk, where
all possible states, their consequences, and their probabilities are known. See Sect. 6.1 for more on the
distinction between uncertainty and risk. I thank an anonymous reviewer for suggesting this caveat.
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115 Page 12 of 21 Synthese (2024) 203 :115
comparative evaluation EBR offers allows for cases of ecologically rational but non-
truth-conducive processes. To avoid this pitfall, EBR should also require a high truth
ratio. This leads to a hybrid definition of ecological rationality according to which
a reasoning mechanism M is ecologically rational in environment E in comparison
to some other mechanism M’ only if M is more accurate than M’, and M exceeds a
relatively high accuracy threshold.
On the other hand, the psychology research pursued by the ecological project might
be seen as a practical implementation of reliabilism. Moreover, EBR can also help to
improve some conceptual aspects of the epistemic theory proposed by Goldman and
other reliabilists, and contribute to overcoming the problem of absolute reliability, as
we will see in Sect. 6.
So, to preserve the match, simple PR should be modified. It seems clear that it needs
to acquire the capacity to relativise reliability assessment to the contexts in which the
belief-forming processes are used. I will now consider a couple of ways to do this.
The first possible solution I contemplate is using a tool the reliabilist family already
possesses.
5 Reliability under normal conditions
One of the most disputed questions facing reliabilism concerns the domain in which
a process is assessed for reliability. Much of the dispute originated from the so-called
‘new evil demon problem’ (Cohen, 1984). According to this problem, it is possible
to imagine a world where an evil demon creates non-veridical perceptions of physical
objects in everybody’s mind, which are qualitatively identical to our own, but false
in the imagined world. Therefore, the perceptual processes of the inhabitants of this
world are unreliable, and their beliefs so caused are unjustified. However, given that
their perceptual experiences are qualitatively identical to ours, those very beliefs in the
demon world should be justified. Recently, some authors proposed a solution to this
problem by taking the domain in which reliability is assessed as the ‘normal conditions’
for using a given process. Since these conditions are free from evil demons, perceptual
and other processes are indeed reliable in these domains.
Normality-based versions of reliabilism as a theory of justified belief can be found,
for instance, in Leplin (2007,2009) and Graham (2012,2017). Meanwhile, Beddor
and Pavese (2020) introduced a ‘normal conditions’ variant of the reliabilist account
of knowledge. This account offers a simple and effective tool to handle those cases
in which knowledge is generated in contexts we would be hard pushed to consider
suitable for its production. With some adjustment, this tool can also be used in a
theory of rational belief. Moreover, it might offer a solution to the problem of absolute
reliability.
Consider the following example by Beddor and Pavese, adapted to a theory of
rationality. Temp forms beliefs about the room temperature by consulting a broken
thermometer. Unbeknownst to him, Temp has a guardian angel in the room who
manipulates the thermostat, ensuring that the room’s temperature matches the reading
displayed on the thermometer. Any belief Temp forms about the temperature will be
true. However, intuitively, Temp’s thermal beliefs are not rational.
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Synthese (2024) 203 :115 Page 13 of 21 115
This case represents an objection to the sufficiency of a reliability condition for
rational belief. This objection can be blocked by restricting the domain of reliability
assessment to normal conditions, understood by Beddor and Pavese as those we would
consider ‘fair’ for performing and assessing the task.13 So, to defend reliabilism, we
might want to modify simple reliability by adding the normality condition:
NORMAL CONDITIONS RELIABILITY
A reasoning process is rational if and only if it is reliable in normal condition-
s—i.e., it usually generates true beliefs rather than false ones in such conditions.
Accordingly, Normal Conditions Reliabilism (NCR, from now on) holds that a belief
is rational if and only if produced by a process which is reliable in normal conditions.
Now, let us consider Temp once more. The task being performed is forming beliefs
about the temperature on the basis of a thermometer. The reasoning process used is
consulting a broken thermometer. Intuitively, having a hidden helper manipulating the
thermostat is a highly abnormal circumstance for Temp’s task. However, in normal
conditions, namely, in helper-free worlds, consulting a broken thermometer leads
to false beliefs. So, Temp’s reasoning process is unreliable in normal conditions.
Therefore, NCR deems Temp’s beliefs to be irrational.
Unlike Temp’s case, the new evil demon problem challenges the necessity of the
reliability condition for rational belief. Can NCR block this criticism as well? Consider
Danny, a subject deceived by the evil demon, who possesses false perceptions of
physical objects identical to ours (Danny could alternatively be a subject in a vat whose
brain is stimulated by a supercomputer which generates non-veridical representations
of the world). The task Danny is performing is forming beliefs on the basis of visual
stimuli or simply forming visual beliefs. To do so, the perceptions of physical objects
are relied upon. Intuitively, the normal conditions for Danny’s task are free from the
demon’s interventions. Thus, Danny is in highly abnormal conditions for performing
and assessing the task. In the demon world, Danny’s reasoning process is unreliable,
and the resultant beliefs are false. In contrast, Danny’s reasoning process is reliable
in normal conditions. Hence, NCR deems Danny’s beliefs in the demon world to be
rational (as also for the brain-in-vat case).
Normality-based reliabilism is a variant of the traditional theory that Goldman has
welcomed (Goldman & Beddor, 2021, pp. 10–11). Its novelty consists in relativising
the assessment of process reliability to normal conditions, understood as those we
would consider fair for performing and assessing a given task. Now, the questions
for us are: Can normality reliabilism account for the change in performance of a
reasoning method from one environment to another? Can it explain why using TTB in
non-compensatory environments is rational but it is irrational in compensatory ones?
And: Does NCR succeed where simple reliabilism fails?
Empirical and analytical research has shown that the performance of many reason-
ing strategies is influenced by features of the reasoning context and the cognitive setup
of the reasoner. Non-compensatoriness, for instance, makes TTB as accurate as WADD
13 Those authors note that it would be difficult to give a precise and non-circular analysis of what these
conditions consist of. However, intuition about cases might suffice here. Indeed, they argue, we often
manifest our tacit conception of fair conditions when we evaluate reasoning performance.
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115 Page 14 of 21 Synthese (2024) 203 :115
and sometimes even more accurate. Should we conclude that non-compensatory envi-
ronments are normal conditions for TTB? These conditions are those we consider fair
for performing and assessing a reasoning task. For instance, under these conditions, no
manipulation has occurred to make an unreliable process truth-conducive (as occurred
in the Temp case) or to make a reliable process non-truth-conducive (as in the evil
demon case). However, the normality condition does not seem to solve the TTB case.
Indeed, the change in performance of TTB from one environment to another is not
due to any manipulation of the reliability of the process. Instead, we would need to
say that the performance of TTB is rational in an environment having a specific fea-
ture, namely, non-compensatoriness. Fairness for performance and assessment seems
to be irrelevant here. Hence, NCR fails to offer a solution to the problem of absolute
reliability.
Notice that this failure does not mandate abandoning the normality condition, which
blocks the new evil demon and the guardian angel objections to simple PR. We should
not renounce this achievement, given that we propose PR as an epistemology for
bounded rationality. Therefore, we should embed the normality condition in our defi-
nition of process reliability and continue the search for a way to complete the account
and solve the problem of absolute reliability.
6 A contextual approach to reliability
In light of the foregoing considerations, relativising reliability assessment is the way
to go. Here, in addition to normality reliabilism, inspiration can be found in a recent
dispute in the reliabilist camp. The so-called ‘temporality problem’ for reliabilism
(Frise, 2018; Tolly, 2019) consists of the fact that a process can be more reliable at one
time than another. For instance, weather forecasting has improved over time. Thus,
the process type ‘forming a belief based on the forecast’ is more reliable now than
it was twenty years ago. This raises a question about the temporal parameters we
should use when evaluating reliability. Imagine we are evaluating whether belief Bis
justified at time t. Should we focus on whether the belief-forming process responsible
for Bis reliable at t? Or should we consider whether it has always been reliable up
until t; or at all times that are temporally close to t? Or something else? According
to Frise (2018), the temporality problem generates insurmountable difficulties for
reliabilism, whereas Tolly (2019) rejects this conclusion and shows that there are
reasonable temporal parameters for the reliabilist to adopt.
I do not need to enter into the details of the issue. Nevertheless, it provides helpful
insight when framing a solution to the problem of absolute reliability. First, it seems
to support the idea that an absolute conception of reliability is implausible: it may be
the case that most reasoning processes are more or less reliable depending on some
contextual features, such as the time or the place of their use. Hence, it encourages
the relativisation of reliability. Moreover, the issue suggests that, by analogy with the
temporality problem, reliabilism could also have a spatiality problem: a process can
be more reliable in one environment than another. Thus, a possible way to tackle the
question of absolute reliability might be to introduce a spatial parameter E which
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Synthese (2024) 203 :115 Page 15 of 21 115
relativises the reliability assessment to specific environments. Reliabilism could bor-
row this parameter from ecological rationality, which already possesses one. Hence,
as a first approximation, an environment is generated by the interplay between fea-
tures of the reasoning scenario (for instance, available information, time pressure,
etc.) and features of the cognitive setup of the reasoner (for example, memory states,
computational faculties, etc.).
I propose that the assessment of process reliability should be a function of the
process truth ratio, the conditions for performing and assessing the reasoning task
(i.e., whether they are normal or abnormal), and the environment in which the process
is used. Therefore, the following principle should replace simple reliability:
NORMAL ENVIRONMENT RELIABILITY
A reasoning process is rational in environment Eif and only if it is reliable in
environment Eunder normal conditions—i.e., it usually generates true beliefs
rather than false ones in this environment under normal conditions.
To illustrate this principle, recall the task of assessing the creditworthiness of two
companies. The cognitive goal is to make the most accurate judgement based on the
available cues. Two reasoning strategies are available to achieve this: TTB and WADD.
Cognitive psychology has shown that the former performs as well as the latter and
sometimes even better in environments characterised by non-compensatory informa-
tion. In contrast, TTB performs poorly and is usually outperformed by WADD in
environments characterised by compensatory information. The normative conclusion
is that TTB, an inferential process that ignores part of the available evidence, is rational
in non-compensatory environments since it is reliable there—i.e., it tends to produce
true beliefs rather than false ones in these environments. In contrast, it is irrational
in compensatory environments since it is unreliable there—i.e., it tends to produce
false beliefs rather than true ones in these environments. Thus, normal environment
reliability can account for the change in the performance of this reasoning process
from some environments to others.
With this new notion of reliability, we can now introduce the corresponding epis-
temic theory called Normal Environments Reliabilism (NER, henceforth), according
to which a belief is rational in environment Eif and only if it is the output of a
belief-forming process that is reliable in environment Eunder normal condition-
s—i.e., it usually generates true beliefs rather than false ones in this environment
under normal conditions. This is a candidate for an epistemology for EBR. Notice that
EBR only offers a theory of rational reasoning strategy: an account of what makes a
belief-forming process rational. In this sense, ecological rationality parallels process
reliability. However, EBR provides no fully fledged epistemology intended as a theory
of rational belief . A further step is needed, and reliabilism seems helpful here. The
beliefs an agent produces under environmental and cognitive constraints are rational in
the context of production if and only if they are generated by belief-forming processes
that, in normal conditions, are reliable in that context.
Let us consider the main features of the new account. First, it should be clear that
normal environment reliability is not more demanding than absolute reliability, as it
does not introduce any further rationality criterion. Accuracy, or truth-conduciveness,
remains the only standard for evaluating the performance of a reasoning process.
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Second, normal environment reliability is a located notion of rationality: it refers to
the tendency of a reasoning process to be truth-conducive in specific contexts. Thus,
reference must be made to environments when assessing this kind of reliability. To
do this, the assessor should use knowledge about where a strategy performs well and
where it does not.
Moreover, the parameter Eand the normality condition play different functions, and
we need both. Eindividuates reasoning contexts with features that play out in favour
of or against the performance of a reasoning process, such as non-compensatoriness
and compensatoriness for TTB. Meanwhile, normality discriminates between normal
and abnormal conditions for the performance and assessment of a task. Conditions
are normal when nothing has altered the reliability of a process that can be applied to
tackle the task. In Creditworthiness, conditions are normal if evil-demon-like factors
have not modified the reliability of TTB and WADD. Imagine instead that an agent
is assisted by her guardian angel who, whenever and wherever she uses TTB, ensures
that her prediction matches the actual relative creditworthiness of the two companies.
Suppose also that the agent is in a compensatory environment. The reasoner will draw
many true inferences thanks to the aid of the guardian angel. Will they be rational?
Under normal, guardian-angel-free conditions, TTB is unreliable in compensatory
environments. Hence, the agent’s inferences produced by TTB will not be rational.
Suppose now that the agent is instead in a non-compensatory environment. In this
case, the agent’s inferences produced by TTB will be rational because TTB is reliable
in non-compensatory environments.
Finally, NER is more descriptively plausible than the simple, absolute version.
Indeed, it allows us to distinguish where a process is reliable and where it is not, and
it does not oblige us to consider a process reliable or unreliable wherever it is used.
This brings justice to the empirical evidence gathered by EBR.
Reliability can now be relativised to environments where reasoning methods are
used or usable, making the environment a central notion in the new epistemic frame-
work. So, at this point, it might be opportune to clarify how this notion should be
understood.
6.1 Reasoning environments
Reasoning environments are contexts within which and upon which the reasoner acts.
Their features affect the agent’s thoughts and actions in various ways. To begin with,
they provide the input processed by reasoners and shape the tools available for reaching
their goals. Moreover, reasoning environments influence process reliability. As we
saw in Creditworthiness, the reliability of processes such as TTB and WADD sensibly
varies depending on the environment in which the reasoning takes place. Therefore,
the reliability assessment cannot neglect environmental factors.
Offering a complete characterisation of reasoning environments might be difficult,
but it is possible to highlight several important structures. Gigerenzer and Sturm (2012)
isolated three: the degree of uncertainty, the number of alternatives, and the learning
sample size. This list is not meant to be complete; other structural features might be
added. Time pressure is a candidate here. For instance, Marewski and Schooler (2011)
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Synthese (2024) 203 :115 Page 17 of 21 115
show that the inferential accuracy of heuristics varies depending on whether people
can take all the time they want to draw the inference or are given a short timeframe.
But I will now briefly address the three items already on the list.
Uncertainty. The degree of uncertainty refers to the extent to which available cues
can predict a criterion. Some predictions are more uncertain than others, depend-
ing on what is predicted. For instance, the future performance of stocks and funds
is highly unpredictable; heart attacks are slightly more predictable; and tomorrow’s
weather is even more predictable. Uncertainty characterises most reasoning environ-
ments in which humans can find themselves. According to the definition introduced
by economist Frank Knight (1921), it applies to all those situations where we cannot
know all the information about a future event and, therefore, cannot calculate its prob-
ability accurately. It must be distinguished from risk, which is the feature of those
situations where, although we do not know the outcome of a future event, we can
accurately measure its probability. A pure game of chance in a casino is an example
of a risky situation.
Number of alternatives. When drawing inferences or making decisions, people can
deal with different numbers of alternatives. These can refer to individual objects (such
as companies) or sequences (such as moves or pathways). This environmental structure
becomes particularly relevant when the alternatives are many. Algorithmic procedures
for estimating a criterion might run into the problem of computational intractability,
the impossibility of estimating the criterion optimally, given a massive number of
alternatives. Consider, for instance, the game of chess. Although an optimal, best
sequence of moves does, in theory, exist, no mind or computer can determine it. This
is one of the reasons why both machines and humans rely on simpler, non-optimisation
techniques, including heuristics.
Sample size. This structural feature of the environment constitutes the number
of sampling units included in the sample. Statistical methods for predicting future
events estimate parameter values from past data. Sample size directly influences the
performance of predictive strategies. For example, consider two models, one complex
with many free parameters and a heuristic with few parameters, predicting an uncertain
future event using a small sample of data. In this circumstance, the resulting error
due to ‘variance’ committed by the complex model may exceed the error due to the
heuristic’s ‘bias’, making the latter strategy preferable (Gigerenzer & Brighton, 2009).
Thus, different sample sizes can call for different predictive methods.
Notice that ‘environment’ is not used here to indicate only the physical environ-
ment. For instance, the degree of uncertainty reflects the environment as well as the
limited understanding of the mind. Hence, the degree of uncertainty is located in the
mind–environment system (Gigerenzer & Sturm, 2012, pp. 256–258). More generally,
the environment here is to be understood as the totality of the relevant physical features
of the world that, combined with those of the reasoner’s cognitive system, favour the
performance of some reasoning strategies over that of others.
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7 Conclusion
I have explored the relationship between reliabilist epistemology and the ecological
approach to bounded rationality. The two accounts have been recognised as largely
compatible by the authors who have contributed most to their development. However, I
first show that the comparative definition of ecological rationality allows an unreliable
process to be assessed as rational. To avoid this problem, I suggest that EBR might
adopt a reliability condition. Furthermore, I highlight how the absolute character of
Goldman’s simple reliabilism does not square with the located notion of rationality
adopted by EBR.SimplePR cannot account for processes that are reliable in one
reasoning context and unreliable in others, the existence of which is an empirical fact
widely reported in the psychology literature.
A first attempt to save the relationship between the two theories resorted to normal-
ity reliabilism, which relativises process reliability to normal conditions, understood
as ‘fair’ domains for performing and assessing a reasoning task. Although the normal-
ity condition solves the new evil demon problem for reliabilism, it does not capture
those environmental features that play out in favour of or against a reasoning strategy.
Thus, I proposed solving the problem of absolute reliability by relativising process
reliability to reasoning environments. Normal environment reliability adds the nor-
mality condition and the environmental parameter Eto simple reliability. The former
blocks some standard objections to simple PR14; the latter individuates reasoning
environments whose features influence process reliability. A new theory of rational
beliefs emerges, NER, which, as opposed to the original reliabilist view, does jus-
tice to those reasoning processes that are truth-conducive in some environments and
non-truth-conducive in others, such as TTB in non-compensatory and compensatory
environments, respectively.
We can see an advantage of NER when it comes to heuristic reasoning. As we
saw at the beginning, there is disagreement about the pros and cons of heuristics.
We can interpret the ongoing debate as a controversy about the reliability of heuris-
tics, since scholars disagree about the tendency of heuristics to produce true beliefs
rather than false ones. For instance, Goldman expressed his sympathy for the line
of thought according to which heuristics are “highly error-prone, or indeed biased
toward error”, the “quick and dirty” view often associated with Tversky and Kahne-
man’s H&B approach (Goldman, 2017, p. 24). However, this view clashes with the
empirical findings of Gigerenzer and his colleagues of the EBR, which have instead
shown when heuristics perform well and when they do not. Adopting a contextual
approach to epistemic rationality might contribute to solving the dispute about heuris-
tics since relativising rationality assessment to reasoning environments leads to more
14 The normality condition might not block all objections to simple reliabilism. Consider Bonjour’s (1980)
clairvoyance problem: Norman, a completely reliable clairvoyantwith no evidence of his clairvoyant powers,
does not seem to be justified in his beliefs. However, the conditions for performing and assessing his
reasoning task seem normal, so NER must conclude that Norman’s beliefs are rational. Several solutions to
the clairvoyance problem have been proposed, including combining reliabilism with evidentialist elements,
appealing to primal systems, and attribution theories (see Goldman & Beddor [2021] for an overview of
these solutions). Here I will neither choose one of these solutions nor offer my own: defending reliabilism
from all criticism goes beyond the goal of this paper, which is to sketch a reliabilist account of bounded
rationality. However, future research might strengthen the current account.
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Synthese (2024) 203 :115 Page 19 of 21 115
balanced conclusions. Indeed, using normal environment reliability, it is possible to
express that a particular heuristic is reliable in some environments and unreliable
in others. Accordingly, agents are rational if they use such a heuristic in the former
environments and irrational if they do so in the latter.
On the one hand, I hope to have contributed to strengthening the philosophical
underpinnings of EBR by exploring and critically assessing its relationship with PR.
On the other hand, I believe that reliabilism can now account for empirical facts that
are well-known in cognitive sciences. If this is indeed the case, this work can represent
a starting point for future research across philosophy and psychology of reasoning.
Acknowledgements I am grateful to Konstantinos Katsikopoulos, Thomas Sturm, and David Thorstad
for their comments on earlier drafts of this paper. Special thanks to Nick Hughes for his comments and
crucial discussions. Thanks also to Michele Palmira and Sven Rosenkranz for helpful conversations and
to the audiences of the Logos Epistemology Reading Group, the Logos Graduate Reading Group, and
the Adaptive Behavior and Cognition Workshop for incisive questions. Thanks to Christopher Evans for
proofreading the final version.
Funding Open Access Funding provided by Universitat Autonoma de Barcelona. This work was supported
by the Secretariat for Universities and Research of the Catalonian Department of Business and Knowledge
and the European Social Fund.
Declarations
Conflict of interest The author has no relevant financial or non-financial interests to disclose.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use
is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/
by/4.0/.
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