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Uncertainty: History of the Concept
Joshua Wakeham, University of Alabama, Tuscaloosa, AL, USA
Ó2015 Elsevier Ltd. All rights reserved.
Abstract
This article explores the various understandings of the concept of uncertainty in the social sciences. First, it examines the idea
of uncertainty and related concepts. Then, it explores the use of the concept in economics, psychology, and sociology.
Within the social sciences, the concept of uncertainty has long
been of interest, particularly in areas concerned with decision-
making and knowledge. In very broad terms, uncertainty occurs
at the limits of knowledge. In those social sciences that take an
information-processing perspective on the problem of knowl-
edge and cognition, such as economics, organizational studies,
and some parts of psychology, some might describe uncertainty
as a fundamental problem, even in places where it is not
explicitly discussed. Uncertainty lurks in the background of any
seemingly rational decision that is of interest to social scien-
tists. A lack of certainty about the outcome of any action colors
the way individuals or groups evaluate the world and their
choices. Yet this is not the only understanding of the concept
of uncertainty. One can find references to the problem of
uncertainty –and related concepts –scattered throughout
economics, psychology, sociology, anthropology, and related
subfields. Both across and within the social science disciplines,
there are differing understandings of the concept of uncer-
tainty, as well as differing interpretations of its significance for
social behavior and social science.
In order to better illuminate the history of the concept of
uncertainty in the social sciences, this article sets out to do two
things. First, it provides some conceptual discussion of uncer-
tainty. Second, it provides historical overviews of the concept of
uncertainty in each of the social sciences.
Conceptual Considerations
The Dimensions of Uncertainty
Broadly speaking, uncertainty refers to an epistemic state at the
limits of knowledge. It concerns what is known or believed
without certainty. It also concerns what is not known. If being
certain means having clarity with regard to the truth, then being
uncertain means having an obscured view of the truth. The
problem of uncertainty points not only to the limits of what
any individual might know, but also to the ultimate limits of
what is knowable.
To put it another way, there are subjective and objective
dimensions to uncertainty (Tannert et al., 2007). First, there are
the subjective dimensions of uncertainty. An individual may
experience uncertainty with regard to his or her own knowledge
about some particular topic. Some experience or some piece of
evidence might challenge an individual’s once firmly held
belief, causing them to have doubts about this belief. An
individual may experience uncertainty about the possible
outcome of some course of action. In important ways, this
subjective, phenomenological experience of uncertainty is both
cognitive and emotional. As such, it has obvious relevance to
social behavior. There are questions of not only how the
experience of uncertainty drives or influences behavior, but
also how social context shapes the perception and experience of
this cognitive and emotional uncertainty.
Second, there are the objective dimensions of uncertainty,
which presume a world out there that is knowable only to
a degree. Uncertainty, in this sense, is not necessarily some-
thing felt, but rather a feature of living in a complex world. It is
useful to distinguish between epistemological and ontological
uncertainty (Van Asselt and Rotmans, 2002). In the case of
epistemological uncertainty, something that is potentially
knowable remains unknown. Further research or searching for
more information can reduce such uncertainty. In the case of
ontological uncertainty, something remains unknown because
it is ultimately unknowable. This may be due to the sheer
complexity of the problem, the stochastic nature of the situa-
tion, or because natural processes obscure access to the rele-
vant knowledge (such as time-obscuring access to historical
knowledge). Thinking about uncertainty as an objective
feature of the world is related to social scientificworkattwo
levels. At the first level is the thinking about how other people
encounter and deal with the uncertainty of the world around
them. Problems of epistemological uncertainty, for example,
and the practical problem of distinguishing between episte-
mological and ontological uncertainty are directly relevant to
the social production of knowledge in the sciences, the law,
medicine, business, and other venues. More broadly, how
people manage ontological uncertainty, particularly as it
relates to the complex social, political, technological, and
economic systems that make up modernity, is of great interest
for the social sciences. At the second level is the thinking about
716 International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Volume 24 http://dx.doi.org/10.1016/B978-0-08-097086-8.03175-5
how the social sciences themselves encounter uncertainty as
a feature of the world. This has direct bearing on the limits of
proposed models of human behavior, particularly on their
predictive validity. Uncertainty, in this sense, is a technical
problem that social scientists must account for in their models
and explanations of human behavior.
Presumably, most of the time, the subjective experience of
uncertainty and the reality of an uncertain world line up, so
speaking of the uncertainty in the market or surrounding
a particular policy decision or about some organizational
strategy practically conflates these two analytic dimensions.
Nonetheless, within the social sciences, researchers have
emphasized different aspects of uncertainty, depending on
their field and their research subject. This expanded view of
uncertainty as having both subjective and objective dimen-
sions provides a sufficiently broad framework for capturing the
various ways the social sciences have used the term. However,
it is also important to recognize that these different points of
conceptual emphasis result in research projects that do not
necessarily speak to one another.
Related Concepts
Within the social sciences, uncertainty is rarely considered on
its own. It is often paired with other ideas related to challenges
and problems in knowledge. This includes terms like risk,
ambiguity, and ignorance. A brief discussion of how uncer-
tainty has been associated with each of these terms is useful for
further conceptual clarity.
Risk and Uncertainty
Uncertainty has most commonly been paired with risk in the
social sciences. This is particularly true in economics, where it
goes back to Knight’s early work (1921), but it is also common
in the more recent sociological work on risk and organizations,
as well as work on the ‘risk society’(Douglas and Wildavsky,
1983;Beck, 1992,2009).
Risks are generally understood to be the known potential
negative outcomes of a course of action. Risks include things
like a loss on an investment, a nuclear plant having a melt-
down, or the environmental and human health impact of
widespread use of automobiles. A more technical under-
standing of risk typically includes some measure or guess of
the probability of that negative-outcome happening multi-
plied by some measure of the presumed cost of that nega-
tive outcome. As risks are known potential outcomes,
individuals or organizations presumably make efforts to
mitigate or manage them, and avoid risks with costs that are
too severe.
Uncertainty may be understood as the state of mind of
someone deciding on a course of action without a clear
outcome. In this subjective sense of the term, an uncertain
individual consider the benefits and the risks, or potentially
negative outcomes, of any course of action. Perceptions of
costly risks are more likely to induce feelings of uncertainty
about a particular course of action. However, when paired with
risk, uncertainty has been more often understood in the more
objective sense of the term.
If risks are known potential negative outcomes, uncertainty,
in an objective sense, refers to the broader set of unknowns that
may affect the course of action and its outcome. Risks, in
theory, can be accounted for, but uncertainty cannot. A deeper
critique of theories of risk management (in business, in
management, or in other fields; or social sciences, such as
economics), is that in the real world, the real probabilities of
negative outcomes are never truly known, meaning that real
uncertainty is more pervasive than any models of risk reveal
(Taleb, 2010).
Afinal distinction between risk and uncertainty worth
noting is the evaluative sense in which they are used. Risk bears
a distinctly negative connotation. Objective uncertainty, by
contrast, is neutral. Unknown factors may have a positive or
negative effect on any course of action.
Ambiguity and Uncertainty
An ambiguous situation or problem is one that invites multiple
interpretations. There is a lack of clarity about what it really is.
Ambiguity has often been lumped together or even conflated
with uncertainty. The amount of overlap between the two
concepts, of course, depends on what definition one gives
each idea.
Those who take information-processing approach to the
problems of knowledge and cognition tend to define ambi-
guity, like uncertainty, more technically. Ambiguity, like
uncertainty, has to do with missing information that is relevant
to the outcome of an action (Galbraith, 1973;Frisch and
Baron, 1988). Some economists, building on Ellsberg’s
paradox (Ellsberg, 1961), define ambiguity as uncertainty
about the probability of possible outcomes rather than uncer-
tainty about the outcome itself (Camerer and Weber, 1992;
Dequech, 2000). Others in organizational studies (Schrader
et al., 1993) have argued that in an ambiguous situation, the
structure of the problem itself –i.e., the relationship between
the potentially relevant variables –is unknown or unsettled,
while in an uncertain situation, the structure of the problem is
settled, but the value of the relevant variables is not. In these
kinds of distinctions, one finds attempts to elucidate not just
how the experience of ambiguity differs from uncertainty, but
how dealing with ambiguity in order to solve a problem
requires different strategies than dealing with uncertainty.
Ambiguity, conceived as a subjective phenomenon, has
similarities with the experience of uncertainty. Encountering
ambiguity is a troubling cognitive and emotional experience
that pushes people to seek out clarity, even at an unconscious
level. From experiments on perception in cognitive psychology
(Hoffman, 1998) to sociological studies of culture (Zerubavel,
1993), social scientists have demonstrated how people, at the
individual or collective level, appear motivated to resolve
ambiguity, imposing a sense of clarity. Research using this
conception of ambiguity tends to focus on the cognitive and
social processes through which individuals and groups
perceive and define problems and construct knowledge.
Interpretive approaches to social science, particularly in
anthropology and sociology, tend to focus on this kind of
ambiguity, often conflating it with uncertainty. Arguably, the
focus on social and cognitive processes and the production of
knowledge rather than the outcomes of actions, where
uncertainty has greater practical salience, may account for this
emphasis on ambiguity and the underdeveloped sense of
uncertainty as a distinctive notion.
Uncertainty: History of the Concept 717
Ignorance and Uncertainty
Some social scientists have also been interested in the larger
problem of ignorance. Ignorance refers to the broad set of
unknowns. Ignorance is of interest not only because it points to
the limits of knowledge, but also because of its dynamic role in
human affairs, as both a productive and destructive force
(Gross, 2007). To that end, some have offered typologies that
make distinctions between different kinds of ignorance. Merton
(1987), for example, distinguishes between unrecognized and
specified ignorance within scientific communities. Unrecog-
nized ignorance refers to ‘unknown unknowns’(Kerwin, 1993)
or ‘meta-ignorance’(Smithson, 1989)–ignorance of what one
does not know. Specified ignorance refers to the acknowledged
limitations of knowledge, which often drive the search for
knowledge (in science and in other fields). Several related terms
like Nichtwissen, nonknowledge, negative knowledge (Knorr-
Cetina, 1999), and nescience (Simmel, 1906) are used in
similar ways.
Smithson’s taxonomy of ignorance (1989) makes multiple
levels of distinction. The first level of distinction is between
irrelevance and error. Things that are irrelevant are those that
are deemed unworthy of further consideration. Error includes
both distortions of information (confusion and inaccuracy)
and incompleteness of information. Incompleteness is broken
down into absence (e.g., missing piece of information) and
uncertainty. Uncertainty includes vagueness, probability, and
ambiguity. In this typology, uncertainty is thus a subtype of
ignorance related to states where one has partial information.
Recent social scientific interest in the problem of ignorance
tends to frame the social production of ignorance as process
that is complementary to the production of knowledge
(Proctor and Schiebinger, 2008). Cultural norms and social
structures play a key role in placing and enforcing limits on
what people know about the world around them. Within this
framework, uncertainty is often treated as a political tool. For
example, politically motivated propagandists use the scientific
community’s provisional sense of the truth to instill a false
sense of uncertainty about the status of official scientific claims
regarding things such as cigarette smoking or environmental
degradation (Oreskes and Conway, 2011).
Uncertainty in the Social Sciences
Uncertainty in Economics
Of all the social sciences, economics has given the most
attention to the concept of uncertainty. Given economics’
interest in successfully modeling rational action, uncertainty
poses a real problem. Uncertainty, as either an objective or
subjective phenomenon, challenges many of the basic
assumptions of such modeling efforts. The history of the
concept of uncertainty in economics is very much one of
attempting to ‘tame and domesticate uncertainty’(Quiggin,
2009, p. 195).
For uncertainty in economics, 1921 was an important year.
Both British economist John Maynard Keynes and American
economist Frank M. Knight published books that dealt, in part,
with the problem of uncertainty. In his Treatise on Probability,
Keynes (1921) lays out an argument about demonstrating
logical relationships between two propositions based on both
a subjective probabilistic assessment of certainty in one’s belief
and the accuracy of that belief. While some relationships
between two propositions can be established probabilistically,
there are some instances where no numerical relationship can
be established. Keynes considers these instances to reflect
a kind of fundamental uncertainty about the world (Lawson,
1985;Dequech, 2000). Knight’sRisk, Uncertainty, and Profit
(1921) offers a basic definitional distinction between risk and
uncertainty. Risk is measurable. The probabilities of the various
outcomes are known. Uncertainty refers to those unknowns
that cannot be quantified or measured. For both Keynes and
Knight, the looming uncertainty of the world also explains
some seemingly irrational social behavior (for Keynes, reliance
on conventional thinking in the face of long-term uncertainty;
for Knight, development of large, hierarchical organizations),
as such behaviors have formed to help deal with or manage it.
Keynes and Knight’s similar understandings of uncertainty had
influence on the post-Keynesian and Austrian schools of
economics, respectively, but really failed to take hold in
mainstream economics despite being cited often (Kelsey and
Quiggin, 1992).
Uncertainty as an inescapable, objective feature of the world
cannot effectively accounted for in traditional economic
models, so most economists ignored or abandoned these
particular definitions of uncertainty, treating them as heterodox.
Instead, alternative understandings of uncertainty as a subjective
phenomenon became more prominent. Being able to incorpo-
rate subjective estimates of probability –expressed or revealed –
without regard to their accuracy (that is, essentially ignoring
whether these estimates reflected objective features of the
world) into economic models was a key. This is where devel-
opment of expected utility theory comes in. Von Neumann and
Morgenstern (1944) demonstrated that people evaluate
outcomes by their expected utility –and that expected utility
was subject to variation. This allowed economists to explain
why people were often risk averse under conditions of uncer-
tainty. This model influenced several lines of work, including
game theory and several variations on the expected utility
model. This includes Savage’s (1954) work deriving subjective
probabilities and expected utility from people’s preferences, as
well as Arrow and Debreau’s (1954) demonstration of ‘suffi-
cient conditions’–namely, market completeness, or a market in
which all future outcomes are accounted for by assuming
a‘frictionless’market for all current goods –“for the existence of
general equilibrium .under conditions of uncertainty”
(Quiggin, 2009, p. 196). In what would become the prevailing
microeconomic model, this meant that uncertainty, as it was
conceived, could be effectively accounted for. Much attention
turned to developing ways to better account for subjective
probabilities in the models, such as Bayesian approaches
(Hirshleifer and Riley, 1992), or variations on expected utility
theory like rank-dependent expected utility theory (Kelsey and
Quiggin, 1992).
Since the 1970s, a growing body of empirical findings has
shown that people consistently violated the predictions of the
expected utility theory. In particular, the work of psychologists
Daniel Kahneman and Amos Tversky and their collaborators
(Kahneman and Tversky, 1979;Kahneman et al., 1982;Tversky
and Kahneman, 1992) demonstrated that under conditions of
risk and uncertainty, people violated the predictions of the
718 Uncertainty: History of the Concept
expected utility model, but in consistent ways. In place of
expected utility theory, they propose prospect theory, which
takes into account the psychological dimensions of decision
making. Prospect theory breaks choices down into two pha-
ses: editing/framing and evaluation. In the editing/framing
phrase, people simplify available options and outcomes
through a variety of heuristics, or cognitive shortcuts, which are
determined by the context or formulation of the problem. Such
editing or framing makes the evaluation phase easier. During
this phase, people evaluate the outcomes or prospects of
decision, following several psychological rules, such as using
one’s starting position to judge gains or losses, overweighing
small probabilities or underweighing large probabilities
depending on the risks, and loss aversion. The key lesson from
all of this is that under conditions of uncertainty, people’s
behavior is not consistent with the rational actor model, but
they do behave in relatively predictable ways. Essentially,
people’s minds draw on even the flimsiest pieces of evidence
from the context in order to impose a subjective sense of
certainty in a situation with objective uncertainty. At times, this
leads people to overestimate the potential costs and underes-
timate the benefits (or vice versa) of such risky decisions. This
work, of course, contributed directly to the development of the
new fields, behavioral economics and behavioral finance.
These developments in the field of economics, along with
the Great Recession of 2008, have led some (e.g., Taleb, 2010)
to question the wisdom of economics effectively bracketing off
the problem of objective uncertainty. However, the problem of
effectively modeling or theorizing about unforeseen events and
other exogenous shocks to economic markets still remains.
Uncertainty in Psychology
Uncertainty, usually as a subjective experience, often lingers
in the background of various theories in psychology.
Psychologists have connected uncertainty to the concepts of
personality, emotion, and cognition, and the problem of
decision making. Although work with these concepts often
interacts, they map roughly onto the three distinct normative
approaches to the problem of uncertainty that Smithson
(2009) identifies.
The first approach comes out of the politicized psychoana-
lytic tradition of the mid-twentieth century. Tolerance of
ambiguity and uncertainty are taken as signs of a positive,
well-adjusted personality type, whereas intolerance of such
epistemic conditions is often taken as signs of an authoritarian
personality (Adorno et al., 1950). While the politics of such an
approach have largely been toned down, interest in the concept
of personality and relationship to the psychological experience
of uncertainty have persisted. At the heart of this research is the
idea that there are measurable differences in how individuals
respond to uncertainty (Sorrentino et al., 1984;Sorrentino and
Short, 1986;Sorrentino and Roney, 2000). Freeston et al.
(1994) even developed an Intolerance for Uncertainty scale to
measure such individual variation. Differences in tolerance for
uncertainty are in turn measured against other psychometric
data, including now widely accepted ‘big five’measures of
personality –openness, conscientiousness, extraversion, agree-
ableness, and neuroticism (Berenbaum et al., 2008). Even within
this more recent work, a lack of tolerance for uncertainty –as an
inevitable fact of life –is seen as potentially problematic from
the individual psychological point of view.
The second approach to uncertainty focuses the cognitive,
emotional, and physiological effects of ‘uncertainty, unpre-
dictability, and uncontrollability’(Smithson, 2009, p. 206).
Uncertainty, in this sense, is a generally experienced problem.
As such, it may even be a variable induced in laboratory
settings. In such research, psychologists typically assume that
uncertainty induces psychological distress or strain, including
anxiety and motivation to seek certainty. For example, Berger
and Calabrese (1975) put forth an ‘uncertainty reduction
theory’of social interaction and communication, arguing that
in the initial stages of successful social interaction depends on
reducing uncertainty among participants. Social psychologists
have been interested in not only how uncertainty affects social
interaction and communication (Gao and Gudykunst, 1990),
but also social judgment and evaluation (van de Boss 2001,
2009). Recent work in neuroscience and cognitive psychology
suggests that the drive for certainty works even at an uncon-
scious level (Burton, 2008). The general lesson is that the
experience of uncertainty often drives people into behaviors
and beliefs that are potentially problematic because of an
underlying cognitive and/or emotional need for certainty.
Wilson et al. (2005) have challenged this largely negative
assessment of uncertainty, demonstrating that the drive for
certainty sometimes lessens the positive emotional experience
of a positive event and that uncertainty about a positive event
can lengthen the experience of positive emotions.
The third approach to uncertainty that Smithson identifies
comes from research focused on decision making. This
approach takes an information-processing perspective on
cognition (Smithson, 1989). It is often found in cognitive
psychology, but it is also related to other approaches to decision-
making outside of psychology, including organizational
studies, economics, and behavioral economics. Kanheman
and Tversky’s (1982) work is probably the best example of
this approach. Researchers taking this approach tend to treat
uncertainty like economists do, often conflating it with notions
of probability or risk that can be accounted for in models of
decision-making. The central concern is understanding and
identifying cognitive biases and heuristics that people rely on
when making decisions under conditions of uncertainty.
Uncertainty in Sociology
Considerations of uncertainty figure less prominently within
the field of sociology. There are some areas, however, where the
problem of uncertainty garners some explicit attention. In
particular, in the field of organizational studies and in the
social theoretical work on risk culture and society.
Organizational Studies
Organizational sociologists, and others under the broader
umbrella of organizational studies, have long been interested
in how uncertainty drives organizational behavior. The
problem is, like in the other social sciences, the term uncer-
tainty has often been used to refer to slightly different things.
For example, of general interest is the uncertainty an organi-
zation faces in its task environment, yet environmental uncer-
tainty has been conceived of as a state or an objective feature of
Uncertainty: History of the Concept 719
the world, as largely a matter of the perception of organiza-
tional actors, or having to do with the unknown effects of an
action, or the unknown responses of the environment
(Milliken, 1987). Because of such differences in understanding
the concept, scholars have used the problem of uncertainty to
account for an array of organizational behaviors.
The Carnegie School’s early work on the cognitive-
behavioral approach to organizational decision making, for
example, focuses on the ways in which practical limits
constrain or bound rational behavior (March and Simon
[1958]1993). In the face of uncertainty, organizational actors
often rely on standardization of behavior and responses (Cyert
and March [1964]1992). Uncertainty thus contributes to the
kind of suboptimal, bounded rationality observed in organi-
zational decisions.
Uncertainty has also played a central role in the work on
organizational design (Thompson, 1967;Lawrence and Lorsch
1967;Galbraith, 1973). Organizations cannot have full
knowledge of their environment, so they must anticipate the
unforeseen. One of the goals in organizational design is not
necessarily to eliminate uncertainty (this is impossible), but to
cope with it. This means not only paying attention to various
sources of information (through the division of labor or
specialization), but also designing an organization that is
responsive to unanticipated information (through the
successful integration or coordination of the parts). Attempting
to cope with such uncertainty has been invoked to explain
macrolevel organizational behaviors from a wide array of
theoretical perspectives, including population ecology
(Hannan and Freeman, 1977), resource dependency theory
(Pfeffer and Salancik, 1978), and neoinstitutionalism
(DiMaggio and Powell, 1983). Uncertainty, as either an
objective feature of the environment or as a subjective
perception of key organizational actors, can thus possibly
explain differences in organizations across environment (more
or less certain), as well as seemingly irrational or inefficient
organizational behaviors and forms.
Beyond theories of organizations, the problem of uncertainty
also plays a central role in the practical considerations of the
literature on organizations operating in high-risk environments.
The complexity of both technologies and large organizations
make accurate assessment of the situations, actions, or conse-
quences more difficult, if not impossible. With the threat of
serious human and environmental costs for being wrong,
managing risks and uncertainty is paramount. In his normal
accident theory, Perrow ([1984]1999) argues that major disas-
ters are an inevitable by-product of working under such
epistemic conditions, so some high technological endeavors
should be abandoned. In contrast, drawing on cases of
successful organizational management of high-risk endeavors,
the high-reliability organization theory out of Berkeley (Weick,
1987;Roberts, 1989) proposes that uncertainties and risks can
be effectively managed with the right kind of management,
organizational culture, and approach to teamwork and organi-
zational learning and knowledge. Arguably, what separates these
two different approaches to risk management is an assessment
of the nature of uncertainty and our ability or inability to
anticipate problems. In the high-reliability approach and in the
broader literature on organizational risk management, one finds
an understandable focus on preventing foreseeable mistakes and
disasters. This places more focus on epistemological uncertainty.
Perrow’s more cautious approach places greater emphasis on the
idea of ontological uncertainty –that is, there are things that are
going to happen that could not be predicted.
Risk Culture and Society
The other area within sociology where the concept of uncer-
tainty receives some explicit attention is in the social theoretical
work concerned with the role of risk in modernity. Within this
area of research, there has been emphasis on both the cultural
and structural elements related to problems of risk. Douglas
and Wildavsky’s (1983) work on the culture of risk explores
how perceptions of risk are tied to cultural ideals about what
constitutes a good society. Fears and concerns about certain
dangers do not necessarily reflect real risks, but rather culturally
framed anxieties embedded within a particular social organi-
zation. More recently, Cerulo (2006) argues that culture plays
a role in preventing people from anticipating the worst possible
outcomes, skewing their perceptions to be overly optimistic.
While this work emphasizes the problem of risk perception, the
problem of uncertainty looms in the background. Culture
shapes what areas of uncertainty receive more attention within
a given social context, and thus, what areas of uncertainty
people may fail to see coming.
Beck (1992, 2009) argues that one of the hallmarks of the
postindustrial, globalized world is the unequal distribution of
risks and hazards. Scientific and technological advances have
brought with them the potential for untold human and envi-
ronmental costs; yet those costs are not born out equally,
especially in a globalized economy. This political dynamic, in
effect, means that the powerful are able to minimize negative
uncertainties in their lives, while those on the bottom have
lives exposed to even more uncertainty.
Conclusion
The concept of uncertainty has had a varied, if somewhat
inconsistent, history in the social sciences. At the heart of the
matter is a conceptual confusion. Is uncertainty a characteristic
of the knowledge itself or a characteristic of the knower? For
better or worse, the social sciences have treated it as both.
Uncertainty refers to an inevitable feature of the chaotic,
unpredictable world, yet it also refers to a psychological state or
phenomenological experience. How uncertainty has been
conceived of in the social sciences depends on the agenda,
methods, and theoretical assumptions of the researcher.
Economists, for example, turned away from the idea of
uncertainty as a kind of fundamental unpredictability because
it essentially stands outside of the models and thus, the basic
assumptions of economists. Uncertainty as a subjective expe-
rience holds greater sway in psychology and sociology because
it is seen as a fundamental driver for an array of seemingly
nonrational individual and collective behaviors. Yet this drive
for certainty in an uncertain world does not eliminate the
intrusion of unpredictable events in human affairs.
The lack of conceptual clarity and consistency does not
undermine the importance of this idea to the social sciences.
Rather it points to the challenges and the potential intellectual
richness of working at the limits of human knowledge.
720 Uncertainty: History of the Concept
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