A Critical Analysis on “Will Sociology Ever be a Normal Science?”
Introduction
Thomas Kuhn coined the concept of a Normal Science, a quiet and felicitous state of affairs
where the (supposedly unique and unitarian) scientific community corresponding to a discipline
believes faithfully in a unique paradigm (Boudon, 2001). In the essay Will Sociology Ever be a
Normal Science, Boudon applies this concept to the discipline of sociology. He posits that there
are several binary fragmentations that keep this discipline from having such a unique paradigm.
Furthermore, these binarities will never be resolved, as there are no unconditionally better or
worse approaches. This critical analysis will argue that such a unique paradigm may however
become possible by unifying the binarities through the use of computer models.
Critical Analysis
Can Sociology be Objective?
A first fragmentation that Boudon elaborates on are the different opinions of intellectuals
regarding the objectivity of sociology. One of these opinions is from Mannheim, who argues that
sociology generally produces theories or interpretations that belong to the relational type,
meaning that the value of them is context-dependent. Statements can therefore be meaningless
in relation to the social and historical context within which the social actor endorses them. The
given example is interests on loans not being present in certain historical contexts. This
contrasts the contemporary belief that loans generally do involve interests. I will argue that this
is an example of Epistemological Monism, there is a distinction between “universal” sociological
theories and “context-dependent” social outcomes. Though interests on loans are a
contemporary social reality, they are an outcome of social actors following predictable
behavioral laws within a given context. To further illustrate this point, imagine two individuals
that are the exact copy of each other. Now imagine one on an island that harbors only small
mammals as a food source. Now imagine the second individual on an island with no animals,
but many edible plants. It can be predicted that the first person will hunt and set traps to feed
themself, while the other one will pick fruits and dig up roots to feed themself. The exact same
person, assumably following the exact same behavioral laws (at least initially), ends up in
differing outcomes. This metaphor serves to drive home that a heterogeneity of outcomes does
not imply a heterogeneity of laws. There remains a universal theory (behavioral tendencies of
the individual) despite the differing context-dependent outcomes (food gathering and diet). Now
the question remains whether these universal laws are actually objective. It can be argued that
everything experienced through a human individual is by definition subjective. Even if a law in
physics could be universal and objective, it becomes subjective when thought about by an
individual. This critical analysis does not intend to dive into this fundamental debate within
epistemology. Therefore, this Kantonian Gap will be set aside by utilizing a different concept of
objectivity. In this analysis, a deterministic and materialistic version of objectivity will be used. If
a law is by all verifiable measures true for our shared reality, then it is considered objective. For
example, this way the interaction between two atoms can be objectively expressed in terms of
universal physical laws. Though, sociology still remains split up. Namely by, as Boudon
describes as a classical distinction, the binarity between questions of interpretation (Auslegung)
and/or explanation (Erklärung). It is useful to consider only the explanations as part of the
sociology paradigm, while interpretations are, intuitively, just interpretations of sociological
phenomena separate from its actual body of theory. A metaphor for this is a dog biting another
dog. This can be explained through objective bio-physical mechanisms such as its brain activity.
While interpreting these mental processes and/or deeming this dog to be “angry” is simply an
interpretation of these biological phenomena. So, the Mannheimian notion of relational
statements seems to me more a distinction between universal sociological laws and differing
context-dependent sociological outcomes.
Boudon remarks Simmel’s observation that there are subjects without unique truths, such as
biographies. While Boudon argues that the selection and ranking of information when writing a
biography inherently makes it subjective. He further notes that new data may keep being dug
up. This suggests that biographies involve a hermeneutic circle, constantly reformulating
subjective interpretations as new data arises. However, he does not make the argument that an
individual has a finite time on and interaction with the world, thus making an all-encompassing
biography possible in theory. Since the information on this individual is thus assumably finite.
Boudon appears to purposefully make a pragmatic argument, rather than a theoretical one. As
the creation of such a perfect biography might never be a reality.
Should Sociology be Individualistic or Holistic?
Secondly, Boudon mentions the distinction between individualistic and holistic explanations.
Some phenomena may only be explained by, for example, an individualistic approach, while
others only by holistic approaches. This way the discipline requires both an individualistic and a
holistic paradigm. The distinction created in Boudon’s essay seems to be that holistic causes
are due to social surroundings, while individualistic causes are due to the reasons and
motivations of a single individual. Boudon illustrates that this leads to equifinality, different
explanations for a unique outcome. The example he gives is that an economic crisis can be
explained by aggregating all individual decisions, or as an effect of a number of factors playing
on the societal level. I will now argue that the individualistic and holistic perspective do not
render each other redundant, but are both aspects of a single unique paradigm. Social
surroundings exert their effect through the individuals they influence, who make decisions based
on the aggregate of internal and external motivators and influences. Boudon’s case of economic
development that he deems as having two separate explanations becomes one unique
explanation. It is the aggregated effect of individual actions and decisions, that were influenced
by their social surroundings (consider these as social factors). Deeming these as separate
paradigms can be compared to deeming the internal workings of a racecar and the external
conditions on the track as separate disciplines, while they are both pertaining to racing physics.
Similarly, answering questions that only apply to either individual reasons or social factors does
not mean there is being dealt with separate disciplines.
Is the Social Actor Rational or Irrational?
The debate on the rationality of individual actors may become resolved as the workings of the
brain are further uncovered. If the brain, or generally the entirety of the human body, behaves as
a steady state machine, it becomes imaginable that one day science exhaustively understands
the causalities for individual behavior. The brain is in a dynamic state and constantly receives
and processes information. It then outputs, among others, observable behavior. Through
measurement and abstraction of the brain’s processes it may then be concluded whether the
human mind is rational, irrational, or perhaps something inbetween.
The Computer Model Paradigm
I will propose a theoretical sociological paradigm and argue how this would be a unique
paradigm, unifying the binarities that Boudon mentions. The paradigm relies on computer
models that simulate the behavior of an individual or group in a certain social context. These
simulations would likely rely on modelling the literal atomic structure (or even smaller building
blocks of matter) of individuals and their surroundings. Thus basing the simulations on laws
governing particle interactions, rather than requiring a priori sociological theories. These
simulations are comparable to those of nonliving processes, such as how particles behave in
the sun. At least in the sense that the models are simply simulating physical events.
Sociological laws can then be abstracted as emerging phenomena from these physical
processes. Imagine the following simplified example. A brain consists of two neurons, one that
signals when the environment is cold and one that makes the individual build a fire when a cold
environment is signalled. By modelling this individual with its brain that has two neurons and
simulating it in several warm and cold environments, a behavioral pattern emerges. The
behavioral law can then be interpreted that the individual builds a fire when the environment is
cold. This was thus found using a simulation of physics, requiring no a priori behavioral theory in
the model itself. Remark my use of “interpreted”, rather than “explained”, since the most
objective explanation here would only involve the interactions between particles and their
physical laws. The interpretation makes the behavioral law an abstraction-dependent truth,
since human concepts such as fire-building and reacting to cold environments lose their
relevance below the individual-level (e.g., the level of atomic interactions).
Assumptions and Limitations
The proposed paradigm requires several substantial assumptions concerning its achievability.
For starters, in the case of an atomic-interactions model it assumes that physics can reach the
point where a given initial state of reality can be accurately modelled at this atomic level. Here, a
potential problem arises in the form of quantum uncertainties. As the Heisenberg Uncertainty
Principle states, there are pairs of physical properties that cannot simultaneously be completely
known. For example, the position and momentum of an atom. Though this atomic-level issue
may turn out irrelevant for higher level processes. Even if the model cannot guarantee a perfect
replica of the atoms, interactions on, for example, the level of neurons are not being influenced
by these tiny deviations. A possible way to bypass this problem altogether is the abstraction of
atoms into bigger functional structures. Modelling, for example, the entirety of a neuron instead
of the atoms that it is made of. This would shift the model from a purely physical one to more of
a bio-physical model. This abstraction would still need to be an accurate simulation of reality.
A bigger potential problem looms however. It could be the case that these deviations from the
real makeup of individuals and their surroundings lead to swiftly diverging outcomes of the
simulation. This concept, known as Chaos Theory, might make the proposed Generative
Computer Model approach ill-advised. To make perfect predictions, the model, similar to
Laplace’s Demon, would require a representation of reality with an accuracy that is inherently
impossible. It would otherwise be doomed to make probabilistic predictions with, in the worst
case, a hugely extensive list of possible outcomes. Depending on the amount of these possible
outcomes, the approach may, or may no longer, be useful. Remark that this only becomes a
problem if science manages to even reach the point of such measuring accuracy, as to measure
the atomic makeup of individuals.
A similar question is whether the brain behaves deterministically. Though more of a spiritual
debate, it can be wondered whether something besides the bio-physical processes has any
influences on the brain’s workings. There are arguments, such as the measurement of a
Readiness Potential that argue against such influences, but the physical determinism of the
brain remains unproven for now.
Another pragmatic limitation is the amount of computing power that such a model would require.
Even a contemporary special-purpose supercomputer can only simulate a few microsecond of a
tiny protein per day. Simulating groups, or let alone single individuals, would likely take
unimaginable amounts of time and energy, even in the future. Hope is that abstractions, such as
mentioned earlier for the neuron-level, allow for near-perfect predictions. This opens the door to
what may be called a multi-resolution model paradigma. Instead of relying on the same type of
simulations, it may handle different possible model resolutions, depending on the task at hand.
Making it a Multi-Resolution Paradigm
The purpose of this would be that resources can be saved in cases where a lower-resolution
model would suffice in predicting real outcomes, as a coarser grain of objects should lower
computational requirements.
A possible multi-resolution implementation of the paradigm could be a Mechanistic Agent-Based
Model at the highest resolution level. Agent-Based in the sense that it models individuals
interacting with others and their surroundings and Mechanistic in the sense that every agent is
individually modelled at a lower-level (e.g., neuronal-level for the brain). On the lowest
resolution could then be a model based on Non-Ideal Sociophysics. This would simulate groups
of individuals and their surrounding by using global estimations, similar to how the Ideal Gas
Law abstracts the underlying gas-particle interactions. Finally, inbetween these two levels could
be a Complex Systems Model, modelling meso-level structures (e.g., how individuals form
hierarchies, markets, etc.). These intermediate-resolution models would allow more intricate
workings of societies to be simulated than Non-Ideal Sociophysics models would.
The Fragmentations Revisited
Can Sociology Now be Objective?
A question remains on the objectivity of sociology, what are now the so-called universal
sociological laws? Though the computer models are based on, what is called in this analysis,
objective laws, how can sociological laws be abstracted from them? And if such laws are
abstracted, will they be objective? One possibility is that there will be tools used for simulation
analysis. These would systematically abstract such behavioral and social laws, as they might
understand causalities in the data better than any human may ever do. It is hard to imagine an
algorithm being able to abstract laws that are readily interpretable by a human mind, but as this
is a future perspective, it could be the case. Another possibility is using these models for
in-silico experiments. Instead of relying on quasi-experiments or post-facto explanations as is
now the case in sociology, scientists could utilize rigorous research methods. Though this would
likely still require an abstraction of bio-physical processes into behavioral and social concepts, it
will now be more verifiable and falsifiable, at least internally within the models.
All this relies of course on the assumption that these simulations robustly predict reality
accurately, which is no small requirement.
Should Sociology Now be Individualistic or Holistic?
As discussed, there is a role for both internal and external motivators or influences. Remark
that, depending on the question at hand, the Multi-Resolution paradigm can provide approaches
that differ in their focus on either individualism or holism. A Non-Ideal Sociophysics model could
rely strongly on external social factors. An example could be the lay-out of a park causing
people to systematically cut corners to win time. Agent-Based models on the other hand would
base their simulation mostly on the processes taking place in each individual. If reality is, as
seems intuitive, a mixture of individualistic and holistic causes, then these models should prove
it.
Is the Social Actor Now Rational or Irrational?
Similarly to the individualistic versus holistic debate, the answer to this question will emerge
from the models. Either through interpretation or abstraction of the simulation, or by performing
in-silico experiments.
Discussion
In this discussion the perspectives of several relevant intellectuals will be evaluated for the new
computer model paradigm.
Giddens
Giddens (2001) argues that organizations play a central role in the coordination of human
activities, at least in modern societies. This serves as an argument for the inclusion of the social
surroundings of individuals into the models. As leaving them out would, Giddens’s would likely
argue, will not give an accurate representation of reality. I agree with this, at least for
contemporary societies, as organizations have become more than shared beliefs. It is also the
infrastructure that makes up cityscapes, but also rural areas. Behavior is strongly guided by
reacting to the external environment and this environment is not arbitrary. Something to
visualize this concept is the behavior of an individual when they are hungry and there is a tree
bearing edible fruits nearby. The environment around the tree may dissuade the individual from
picking the fruits by, for example, having a fence around the tree. Many infrastructural influences
on the individual may of course be more subtle, as is the case for nudging, but the concept
remains. Besides the point of organizations having a central role in human activities, Giddens
adds to this that these organizations are inherently dynamic. This intuitively true statement, as
well as the role of organizations, can be verified using the models.
Simmel
Simmel (1909) states that sociology’s mission is to abstract the forms of social interactions from
their specific contents. Content would be the motivators for human action, while form is the
social interaction that emerges between individuals, due to the forms. As the model should
include the abstract content on a lower-level (e.g., atomic or neuronal level), the forms that
Simmel speaks of would emerge in the simulation. Just as Giddens remarked in terms of
organizations, Simmel also remarks a dynamic nature of, in this case, societies in general.
Elias
In his essay, The Sociologist as a Destroyer of Myths, Elias (1978) remarks the need for
sociology to move away from unproven social myths and towards reality-congruent theories.
These theories should be testable, verifiable and correctable. For this he stresses the need for
knowledge to become ever more in agreement with observable data. If the model is able to
accurately simulate reality, the in-silico experiments will be a fitting tool for this shift to rigorous
science. Elias might not agree with the bottom-up model approach utilizing laws from the natural
sciences to achieve an accurate model, as he argues that the social field should be relatively
autonomous from the physical and biological levels.
Bauman
Bauman (2000) posits that social forms, institutions, relationships, etc. are no longer solid
concepts. Instead they are under continuous change. He coins this concept Liquid Modernity.
He adds to this the concept of zombie categories. These are concepts that once accurately
described an aspect of reality, but no longer do. This stresses the role of language in the
theoretical body of sociology and sciences in general. While the model does not necessarily use
any of these a priori concepts in its simulations, they may be utilized during the interpretation or
abstraction of the processes.
References
Bauman, Z. (2000). Liquid modernity. Polity Press.
Boudon, R. (2001). Sociology as a normal science. European Sociological
Review, 17(4), 451–469. https://doi.org/10.1093/esr/17.4.451
Elias, N. (1978). The sociologist as a destroyer of myths. In What is sociology?
(pp. 50–70). Columbia University Press.
Giddens, A. (2001). Organizations and networks. In Sociology (4th ed., pp.
637–680). Polity Press.
Simmel, G. (1909). The problem of sociology. American Journal of Sociology,
15(3), 289–320. https://doi.org/10.1086/211792
Further Reading
This section contains sources for further reading on concepts that I found interesting and
relevant and therefore included in my critical analysis.
The Readiness Potential
Libet, B., Gleason, C. A., Wright, E. W., & Pearl, D. K. (1983). Time of conscious
intention to act in relation to onset of cerebral activity (readiness-potential): The unconscious
initiation of a freely voluntary act. Brain, 106(3), 623–642.
https://doi.org/10.1093/brain/106.3.623
Anton Supercomputer Series
Shaw, D. E., Deneroff, M. M., Dror, R. O., Kuskin, J. S., Larson, R. H., Salmon, J.
K., Young, C., Batson, B., Bowers, K. J., Chao, J. C., Eastwood, M. P., Gagliardo, J., Grossman,
J. P., Ho, C. R., Ierardi, D. J., Kolossváry, I., Klepeis, J. L., Layman, T., McLeavey, C., ... Wang,
S. C. (2008). Anton, a special-purpose machine for molecular dynamics simulation.
Communications of the ACM, 51(7), 91–97. https://doi.org/10.1145/1364782.1364802
The Heisenberg Uncertainty Principle
Heisenberg, W. (1927). Über den anschaulichen Inhalt der quantentheoretischen
Kinematik und Mechanik [On the physical content of quantum theoretical kinematics and
mechanics]. Zeitschrift für Physik, 43(3-4), 172–198. https://doi.org/10.1007/BF01397280
Laplace’s Demon
Laplace, P.-S. (1902). A philosophical essay on probabilities (F. W. Truscott & F.
L. Emory, Trans.). John Wiley & Sons. (Original work published 1814)
The Kantonian Gap
Kant, I. (1998). Critique of pure reason (P. Guyer & A. W. Wood, Eds. & Trans.).
Cambridge University Press. (Original work published 1781)
Epistemological Monism
Stubenberg, L. (2018). Neutral monism. In E. N. Zalta (Ed.), The Stanford
Encyclopedia of Philosophy (Fall 2018 ed.). Metaphysics Research Lab, Stanford University.
https://plato.stanford.edu/archives/fall2018/entries/neutral-monism/
Chaos Theory
Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric
Sciences, 20(2), 130–141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
Non-Ideal Sociophysics
Sen, P., & Chakrabarti, B. K. (2014). Sociophysics: An introduction. Oxford
University Press.
The Ideal Gas Law
Clapeyron, E. (1834). Mémoire sur la puissance motrice de la chaleur [Memoir on
the motive power of heat]. Journal de l'École Polytechnique, 14, 153–190.
Complex Systems Model
Mitchell, M. (2009). Complexity: A guided tour. Oxford University Press.
In-Silico Experiments
Wiersma, Y.F. (2022). In Silico Experiments. In: Experimental Landscape
Ecology. Landscape Series, vol 29. Springer, Cham.
https://doi.org/10.1007/978-3-030-95189-4_10