## 1. Origins



The method of agent-based modeling was originally developed in the
1970s and '80s with models of social segregation (Schelling
1971; Sakoda 1971; see also Hegselmann 2017) and cooperation (Axelrod
& Hamilton 1981) in social sciences, and under the name of
"individual-based modeling" in ecology (for an overview
see Grimm & Railsback 2005). Following this tradition, ABMs drew
the interest of scholars studying social aspects of scientific
inquiry. By representing scientists as agents equipped with rules for
reasoning and decision-making, agent-based modeling could be used to
study the social dynamics of scientific research. As a result, ABMs of
science have been developed across various disciplines that include
science in their subject domain: from sociology of science,
organizational sciences, cultural evolution theory, the
interdisciplinary field of meta-science (or "science of
science"), to social epistemology and philosophy of science.
While ABMs developed in philosophy of science often tackle themes that
are similar or related to those examined by ABMs of science in other
domains, they are motivated by philosophical questions--issues
embedded in the broader literature in philosophy of science. Their
introduction was influenced by several parallel research lines:
analytical modeling in philosophy of science, computational modeling
in related philosophical domains, and agent-based modeling in social
sciences. In the following, we take a brief look at each of these
precursors.



One of the central ideas behind the development of formal social
epistemology of science is succinctly expressed by Philip Kitcher in
his *The Advancement of Science*:



> 
> 
> The general problem of social epistemology, as I conceive it, is to
> identify the properties of epistemically well-designed social systems,
> that is, to specify the conditions under which a group of individuals,
> operating according to various rules for modifying their individual
> practices, succeed, through their interactions, in generating a
> progressive sequence of consensus practices. (Kitcher 1993: 303) 
> 
> 
> 



Such a perspective on social epistemology of science highlighted the
need for a better understanding of the relationship between individual
and group inquiry. Following the tradition of formal modeling in
economics, philosophers introduced analytical models to study tensions
inherent to this relationship, such as the tension between individual
and group rationality. In this way, they sought to answer the
question: how can individual scientists, who may be driven by
non-epistemic incentives, jointly form a community that achieves
epistemic goals? Most prominently, Goldman and Shaked (1991) developed
a model that examines the relationship between the goal of promoting
one's professional success and the promotion of
truth-acquisition, whereas Kitcher (1990, 1993) proposed a model of
the division of cognitive labor, showing that a community consisting
of scientists driven by non-epistemic interests may achieve an optimal
distribution of research efforts. This work was followed by a number
of other contributions (e.g., Zamora Bonilla 1999; Strevens 2003).
Analytic models developed in this tradition endorsed economic
approaches to the study of science, rooted in the idea of a
"generous invisible hand", according to which individuals
interacting in a given community can bring about consequences that are
beneficial for the goals of the community without necessarily aiming
at those consequences (Maki 2005).



Around the same time, computational methods entered the philosophical
study of rational deliberation and cooperation in the context of game
theory (Skyrms 1990, 1996; Grim, Mar, & St. Denis 1998), theory
evaluation in philosophy of science (Thagard 1988) and the study of
opinion dynamics in social epistemology (Hegselmann & Krause 2002,
2005; Deffuant, Amblard, Weisbuch, & Faure 2002). Computational
models introduced in this literature included ABMs: for instance, a
cellular automata model of the Prisoner's Dilemma, or models
examining how opinions change within a group of agents.



Agent-based modeling was first applied to the study of science in
sociology of science, with the model developed by Nigel Gilbert (1997)
(cf. Payette 2011). Gilbert's ABM aimed at reproducing
regularities that had previously been identified in quantitative
sociological research on indicators of scientific growth (such as the
growth rate of publications and the distribution of citations per
paper). The model followed an already established tradition of
simulations of artificial societies in social sciences (cf. Epstein
& Axtell 1996). In contrast to abstract and highly-idealized
models developed in other social sciences (such as economics and
archaeology), ABMs in sociology of science tended towards an
integration of simulations and empirical studies used for their
validation (cf. Gilbert & Troitzsch 2005).



Soon after, ABMs were introduced to the philosophy of science through
pioneering works by Zollman (2007), Weisberg and Muldoon (2009), Grim
(2009), Douven (2010)--to mention some of the most prominent
examples. In contrast to ABMs developed in sociology of science, these
ABMs followed the tradition of abstract and highly-idealized modeling.
Similar to analytical models, they were introduced to study how
various properties of individual scientists--such as their
reasoning, decision-making, actions and relations--bring about
phenomena characterizing the scientific community--such as a
success or a failure to acquire knowledge. By representing inquiry in
an abstract and idealized way, they facilitated insights into the
relationship between some aspects of individual inquiry and its impact
on the community while abstracting away from numerous factors that
occur in actual scientific practice. But in contrast to analytical
models, ABMs proved to be suitable for scenarios often too complex for
analytical approaches. These scenarios include heterogeneous
scientific communities, with individual scientists differing in their
beliefs, research heuristics, social networks, goals of inquiry, and
so forth. Each of these properties can change over time, depending on
the agents' local interactions. In this way ABMs can show how
certain features characterizing individual inquiry suffice to generate
population-level phenomena under a variety of initial conditions.
Indeed, the introduction of ABMs to philosophy of science largely
followed the central idea of generative social science: to explain the
emergence of a macroscopic regularity we need to show how
decentralized local interactions of heterogeneous autonomous agents
can generate it. As Joshua Epstein summed it up: "If you
didn't grow it, you didn't explain its emergence"
(2006).


## 2. Central Research Questions



ABMs of science typically model the impact of certain aspects of
individual inquiry on some measure of epistemic performance of the
scientific community. This section surveys some of the central
research questions investigated in this way. Its aim is to explain why
ABMs were introduced to study philosophical questions, and how their
introduction relates to the broader literature in philosophy of
science.


### 2.1 Theoretical diversity and the incentive structure of science



How does a community of scientists make sure to hedge its bets on
fruitful lines of inquiry, instead of only pursuing suboptimal ones?
Answering this question is a matter of coordination and organization
of cognitive labor which can generate an optimal diversity of pursued
theories. The importance of a synchronous pursuit of a plurality of
theories in a given domain has long been recognized in the
philosophical literature (Mill 1859; Kuhn 1977; Feyerabend 1975;
Kitcher 1993; Longino 2002; Chang 2012). But how does a scientific
community achieve an optimal distribution of research efforts? Which
factors influence scientists to divide and coordinate their labor in a
way that stimulates theoretical diversity? In short, how is
theoretical diversity achieved and maintained?



One way to address this question is by examining how different
incentives of scientists impact their division of labor. To see the
relevance of this question, consider a community of scientists all of
whom are driven by the same epistemic incentives. As Kitcher (1990)
argued, in such a community everyone might end up pursuing the same,
initially most promising line of inquiry, resulting in little to no
diversity. Traditionally, philosophers of science tried to address
this worry by arguing that a diversity in theory choice may result
from diverse methodological approaches (Feyerabend 1975), diverse
applications of epistemic values (Kuhn 1977), or from different
cognitive attitudes towards theories, such as acceptance and
pursuit-worthiness (Laudan 1977). Kitcher, however, wondered whether
non-epistemic incentives, such as fame and fortune--usually
thought of as interfering with the epistemic goals of
science--might actually be beneficial for the community by
encouraging scientists to deviate from dominant lines of inquiry.



The idea that scientists are rewarded for their achievements through
credit, which impacts their research choices, had previously been
recognized by Merton (1973) and Hull (1978, 1988). For example, a
scientist may receive recognition for being the first to make a
discovery (known as the "priority rule"), which may
incentivize a specific approach to research. Yet, such non-epistemic
incentives could also fail to promote an optimal kind of diversity.
For instance, they may result in too much research being spent on
futile hypotheses and/or in too few scientists investigating the best
theories. Moreover, an incentive structure will have undesirable
effects if rewards are misallocated to scientists that are
well-networked rather than assigned to those who are actually first to
make discoveries, or if credit-driven science lowers the quality of
scientific output. This raises the question: which incentive
structures promote an optimal division of labor, without having
epistemically or morally harmful effects?



ABMs provide an apt ground for studying these issues: by modeling
individual scientists as driven by specific incentives, we can examine
their division of labor and the resulting communal inquiry. We will
look at the models studying these issues in
 Section 4.1.


### 2.2 Theoretical diversity and the communication structure of science



Another way to study theoretical diversity is by focusing on the
communication structure of scientific communities. In this case we are
interested in how the information flow among scientists impacts their
distribution of research across different rival hypotheses. The
importance of scientific interaction for the production of scientific
knowledge has traditionally been emphasized in social epistemology
(Goldman 1999). But how exactly does the structure of communication
impact scientists' generation of knowledge? Are scientists
better off communicating within strongly connected social networks, or
rather within less connected ones, and under which conditions of
inquiry? These and related questions belong to the field of
*network epistemology*, which studies the impact of
communication networks on the process of knowledge acquisition.
Network epistemology has its origin in economics, sociology and
organizational sciences (e.g., Granovetter 1973; Burt 1992; Jackson
& Wolinsky 1996; Bala & Goyal 1998) and it was first combined
with agent-based modeling in the philosophical literature by Zollman
(2007) (see also Zollman 2013).



Simulations of scientific interaction originated in the idea that
different communication networks among scientists, characterized by
varying degrees of connectedness (see
 Figure 1),
 may have a different impact on the balance between
"exploration" and "exploitation" of scientific
ideas. Suppose a scientist is trying to find an optimal treatment for
a certain disease, since the existing one is insufficiently effective.
On the one hand, she could pursue the currently dominant hypothesis
concerning the causes of the disease, hoping that it will eventually
lead to better results. On the other hand, she could explore novel
ideas hoping to have a breakthrough leading to a more successful cure
for the disease. The scientist thus faces a trade-off between
exploitation as the use of existing ideas and exploration as the
search of new possibilities, long studied in theories of formal
learning and in organizational sciences (March 1991). The information
flow among scientists could impact this trade-off in the following
way: if an initially misleading idea is shared too quickly throughout
the community, scientists may lock-in on researching it, prematurely
abandoning the search for better solutions. Alternatively, if the
information flow is slow and sparse, important insights gained by some
scientists, which could lead to an optimal solution, may remain
undetected by the rest of the community for a lengthy period of time.
ABMs were introduced to investigate whether and in which circumstances
the information flow could have either of these effects. For instance,
if scientists are assumed to be rational agents, could a tightly
connected community end up exploring too little and miss out on
significant lines of inquiry?



Besides studying communities consisting of "epistemically
uncompromised" scientists--that is, agents whose inquiry
and actions are directed at discovering and promoting the
truth--similar questions can be posed about communities in which
epistemic interests have been overridden. For instance, the impact of
industrial interest groups on science may lead to biased or deceptive
practices, which may sway the scientific community away from its
epistemic goals (Holman & Elliott 2018). While recent
philosophical discussions on this problem have largely focused on the
role of non-epistemic values in science (Douglas 2009; Holman &
Wilholt 2022; Bueter 2022; Peters 2021), ABMs were introduced to
examine how epistemically pernicious strategies can impact the process
of inquiry, as well as to identify interventions that can be used to
mitigate their harmful effects.



In addition to the problem of theoretical diversity, network
epistemology has been applied to a number of other themes, such as
optimal forms of collaboration, factors leading to scientific
polarization, effects of conformity on the collective inquiry, effects
of demographic diversity, the position of minorities, optimal
regulations of dual-use research, argumentation dynamics, and so
forth. We will look at the models studying theoretical diversity and
the communication structure of science in
 Section 4.2
 and
 Section 4.6.




![10 nodes distributed evenly in a circle; each node is connected by a line, aka edge, to each of its two neighbors.](figure-communication-a.svg)

(a)




![similar to (a) except there is also a node in the center and each node on the circle is also connected by a line, aka edge, to the center node as well as its neighbors.](figure-communication-b.svg)

(b)




![similar to (a) except each node on the circle is connected to all the other nodes by a line, aka edge.](figure-communication-c.svg)

(c)




Figure 1: Three types of idealized
communication networks, representing an increasing degree of
connectedness: (a) a cycle, (b) a wheel, and (c) a complete graph. The
nodes in each graph stand for scientists, while edges between the
nodes stand for information channels between two scientists.



### 2.3 Cognitive diversity



A diversity of cognitive features of individuals can be beneficial in
various problem-solving situations, including business and
policy-making (Page 2017). But how does the diversity of cognitive
features of scientists, including their background beliefs, reasoning
styles, research preferences, heuristics and strategies impact the
inquiry of the scientific community? In philosophy of science, this
issue gained traction with Kuhn's distinction between normal and
revolutionary science (Kuhn 1962), suggesting that different
propensities may push scientists towards one type of research rather
than another (see also Hull 1988). This raises the question: how does
the distribution of risk-seeking, maverick scientists and risk-averse
ones impact the inquiry of the community? Put more generally: do some
ways of dividing labor across different research heuristics result in
a more successful collective inquiry than others?



By equipping agents with different cognitive features we can use ABMs
to represent different cognitively diverse (or uniform) populations,
and to study their impact on some measure of success of the communal
inquiry. We will look at the models studying these issues in
 Section 4.3.


### 2.4 Social diversity



A scientific community is socially diverse when its members endorse
different non-epistemic values, such as moral and political ones, or
when they have different social locations, such as gender, race and
other aspects of demography (Rolin 2019). The importance of social
diversity has long been emphasized in feminist epistemology, both for
ethical and epistemic reasons. For instance, many have pointed out
that social diversity is an important catalyst for cognitive
diversity, which in turn is vital for the diversity of perspectives,
and therefore for scientific objectivity (Longino 1990, 2002; Haraway
1989; Wylie 1992, 2002; Grasswick 2011; Anderson 1995; for a
discussion on different notions of diversity in the context of
scientific inquiry see Steel et al. 2018). 



Moreover, in the field of social psychology and organizational
sciences, it has been argued that social diversity is epistemically
beneficial even if it doesn't promote cognitive diversity.
Instead, it may counteract epistemically pernicious tendencies of
homogeneous groups, such as unwarranted trust in each other's
testimony or unwillingness to share dissenting opinions (for an
overview of the literature see Page 2017; Fazelpour & Steel 2022).
While these hypotheses have received support in virtue of empirical
studies, ABMs have proved a complementary testing ground, allowing for
an investigation of minimal sets of conditions which need to hold for
social diversity to be epistemically beneficial.



Another problem tackled by means of ABMs concerns factors that can
undermine social diversity or disadvantage members of minorities in
science. For instance, how does one's minority status impact
one's position in a collaborative environment, given the norms
of collaboration that can emerge in scientific communities? Or how
does one's social identity impact the uptake of their ideas? We
will look at the models studying these issues in
 Section 4.4
 and
 Section 4.7.


### 2.5 Peer-disagreement in science



Scientific disagreements are commonly considered vital for scientific
progress (Kuhn 1977; Longino 2002; Solomon 2006). They typically go
hand in hand with theoretical diversity (see
 Section 2.1
 and
 2.2)
 and stimulate critical interaction among scientists, important for
the achievement of scientific objectivity. Nevertheless, an inadequate
response to disagreements may lead to premature rejections of fruitful
inquiries, to fragmentation of scientific domains and hence to
consequences that are counterproductive for the progress of science.
This raises the question: how should scientists respond to
disagreements with their peers, to lower the chance of a hindered
inquiry? Which epistemic and methodological norms should they follow
in such situations?



This issue has been discussed in the context of the more general
debate on peer-disagreement in social epistemology. The problem of
peer disagreement concerns the question: what is an adequate doxastic
attitude towards *p*, upon recognizing that one's peer
disagrees on *p*? Should one follow a "Conciliatory
Norm", demanding, for instance, to lower the confidence in
*p*, split the difference by taking the middle ground between the
opponent's belief and one's own on the issue, or to
suspend one's judgment on *p*? Or should one rather follow
a "Steadfast Norm" demanding to stick to one's guns
and keep the same belief with the same confidence as before
encountering a disagreeing peer? (For initial arguments in favor of
the Conciliatory Norm see, e.g., Elga 2007, Christensen 2010, Feldman
2006; for arguments in favor of the Steadfast Norm see, e.g., De Cruz
& De Smedt 2013, Kelp & Douven 2012; for reasons why norms are
context-dependent see, e.g., Kelly 2010 [2011], Konigsberg 2013,
Christensen 2010, Douven 2010; for a recent critical review of the
debate as applied to scientific practice see Longino 2022.)



Similarly, in case of scientific disagreements and controversies we
can ask: should a scientist who is involved in a peer disagreement
strive towards weakening her stance by means of a conciliatory norm,
or should she remain steadfast? What makes this issue particularly
challenging in the context of scientific inquiry is that we are not
only interested in the epistemic question of an adequate doxastic
response to a disagreement, but also in the methodological (or
inquisitive) question of how the norms impact the success of
collective inquiry as a process. In particular, if scientists
encounter multiple disagreements throughout their research on a
certain topic, will their collective inquiry benefit more from
individuals adopting conciliatory attitudes or steadfast ones? ABMs
naturally lend themselves as a method for investigating these issues:
by modeling scientists as guided by different normative responses to a
disagreement, we can study the impact of the norms on the communal
inquiry. We will look at the models studying these issues in
 Section 4.5.


### 2.6 Scientific polarization



Closely related to the issue of scientific disagreements is the
problem of scientific polarization. While scientific controversies
typically resolve over time, they may include periods of polarization,
with different parts of the community maintaining mutually conflicting
attitudes even after an extensive debate. But how and why does
polarization emerge? Do scientific communities polarize only if
scientists are too dogmatic or biased towards their viewpoints, or can
polarization emerge even among rational agents?



Following a range of formal models in social and political sciences
addressing a similar issue in society at large (for a review see
Bramson et al. 2017), ABMs have been used to examine the emergence of
polarization in the context of science. What makes agent-based
modeling particularly apt for this task is not only that we can model
different aspects of individual inquiry that may contribute to the
emergence of polarization (such as different background beliefs,
different communication networks, different trust relationships, and
so on), but we can also observe the formation of polarized states,
their duration (as stable or temporary states throughout the inquiry)
and their features (such as the distribution of scientists across the
opposing views). We will look at the models studying these issues in
 Section 4.6.


### 2.7 Scientific collaboration



As acquiring and analyzing scientific evidence can be highly
resource-demanding for any individual scientist, scientific
collaboration is a widespread form of group inquiry. Of course, this
is not the only reason why scientists collaborate: incentives leading
to collaborations range from epistemic ones (such as increasing the
quality of research) to non-epistemic ones (such as striving for
recognition). This raises the question: when is collaborating
beneficial, and which challenges may occur in collaborative research?
Inspired by these questions, philosophers of science have investigated
why collaborations are beneficial (Wray 2002), which challenges they
pose on epistemic trust and accountability (Kukla 2012; Wagenknecht
2015), what kind of knowledge emerges through collaborative research
(such as collective beliefs or acceptances, (M. Gilbert 2000; Wray
2007; Andersen 2010), which values are at stake in collaborations
(Rolin 2015), what an optimal structure of collaborations is
(Perovic, Radovanovic, Sikimic, & Berber 2016), and
so on.



ABMs of collaboration were introduced to study the above and related
questions, focusing on how collaborating can impact inquiry. While
collaborations can indeed be beneficial, determining conditions under
which they are such is not straightforward. For instance, depending on
how scientists engage in collaborations, minorities in the community
may end up disadvantaged. We will look at the models studying these
issues in
 Section 4.7.


### 2.8 Summing up



Beside the above themes, ABMs have been applied to the study of
numerous other topics in philosophy of science: from the allocation of
research funding, testimonial norms, strategic behavior of scientists,
all the way to different procedures for theory-choice (see
 Section 4.8
 where we list models studying additional themes). Moreover, one ABM
can simultaneously address multiple questions (for example, social
diversity and scientific collaboration are often inquired
together).



To study the questions presented in this section, philosophers have
utilized different representational frameworks. Even if models are
aimed at the same research question, they are often based on different
modeling approaches. For instance, individual scientists may be
represented as Bayesian reasoners, as agents with limited memory, as
agents searching for peaks on an epistemic landscape, as agents that
form their opinions by averaging information they receive from other
scientists and from their own inquiry, or as agents that are equipped
with argumentative reasoning. Similarly, the process of evidence
gathering may be represented in terms of pulls from probability
distributions, as foraging on an epistemic or an argumentation
landscape, or as receiving signals from others and from the world. We
now look into some of the common modeling frameworks employed in the
study of the above questions.


## 3. Common Modeling Frameworks



When developing a model examining certain aspects of scientific
inquiry, one first has to decide on a number of relevant
representational assumptions, such as: 


1. How to represent the process of *inquiry* and *evidence
gathering*?
2. What do *agents* in the simulation stand for (e.g.,
individual scientists, research groups, scientific labs, etc.)?
3. What are the *units of appraisal* in scientific inquiry
(e.g., hypotheses, theories, research programs, methods, etc.)?
4. How do scientists *reason* and evaluate their units of
appraisal?
5. How do scientists *exchange information*?



Similarly, if we wish to model a scenario in which scientists bargain
about the division of tasks or resources, we will have to decide how
to represent their *interactions* and *rewards* they get
out of them. These modeling choices are guided by the research
question the model aims to tackle, as well as the epistemic aim of the
model.



The majority of ABMs developed in philosophy of science are built as
simple and highly idealized models. The simpler a model is, the easier
it is to understand and analyze mechanisms behind the results of
simulations (we return to this issue in
 Section 5).
 In this section, we delve into several common modeling frameworks
that have been used in this way. Each framework offers a different
take on the above representational choices and has served as the basis
for a variety of ABMs. Particular models will not be discussed just
yet--we leave this for
 Section 4.


### 3.1 Epistemic landscape models



Modeling the process of inquiry as an exploration of an epistemic
landscape draws its roots from models of fitness landscapes in
evolutionary biology, first introduced by Sewall Wright (1932). By
representing a genotype as a point on a multidimensional landscape,
where the "height" of the landscape corresponds to its
fitness, the model has been used to study evolutionary paths of
populations.



The idea of *epistemic* landscapes entered philosophy of
science with the work of Weisberg and Muldoon (2009) and Grim (2009).
In this reinterpretation of the model, the landscape represents a
research topic, consisting of multiple projects or multiple
hypotheses. A research topic can be understood either in a narrow
sense (e.g., the study of treatments for a certain disease) or in a
broader sense (e.g., the field of astrophysics). A point on the
landscape stands for a certain hypothesis or a specific approach to
investigating the topic. Approaches can vary in terms of different
background assumptions, methods of inquiry, research questions, etc.
Accordingly, the landscape can be modeled in terms of \(n\)
dimensions, where \(n-1\) dimensions represent different aspects of
scientific approaches, while the \(n^{th}\) dimension (visualized as
the "height" of the landscape) stands for some measure of
epistemic value an agent gets by pursuing the corresponding approach.
For instance, in case of a three-dimensional landscape, the *x*
and *y* coordinates form a two-dimensional disciplinary matrix in
which approaches are situated, while *z* coordinate measures
their "epistemic significance" (see
 Figure 2).
 The latter can be understood in line with Kitcher's idea that
significant approaches are those that enable the conceptual and
explanatory progress of science (Kitcher 1993: 95).



ABMs of science have utilized two-dimensional and three-dimensional
landscapes, as well as the generalized framework of NK-landscapes, in
which the number of dimensions and the ruggedness of the landscape are
parameters of the
 model.[1]
 Scientists are modeled as agents who explore the landscape, trying to
find its peak(s), that is, the epistemically most significant points.
The framework allows for different ways of measuring the success and
efficiency of inquiry: in terms of the success of the community in
discovering the peak(s) of the landscape (rather than getting stuck in
local maxima), the success in discovering any of the areas of non-zero
vicinity, the time required for such discoveries, and so forth.



![two pictures mostly described in the caption. The three-dimensional one has two cones rising from the plain with the upper right one seemingly a bit higher while the two-dimensional one has two circular regions on a black surface gradually getting lighter gray towards their respective centers with the upper right region having a whiter center.](ep_landscape-wm.png)

Figure 2: Two representations of
Weisberg and Muldoon's epistemic landscape: a three-dimensional
representation on the left and a two-dimensional representation of the
same landscape on the right, where the height of the landscape is
represented by different shades of gray: the lighter the shade, the
more significant the point on the landscape (adapted from Weisberg
& Muldoon 2009). 



#### 3.1.1 Application to the problem of cognitive diversity



The framework of epistemic landscapes has been applied to a variety of
research questions in philosophy of science. Weisberg and Muldoon
(2009) introduced it to examine the impact of cognitive diversity on
the performance of scientific communities. What makes the epistemic
landscape framework particularly attractive for the study of cognitive
diversity and the division of labor is its capacity to represent
various research strategies (as different heuristics of exploring the
landscape), as well as a coordinated distribution of research efforts
(cf. Poyhonen 2017).



Weisberg and Muldoon's ABM employs a three-dimensional landscape
(see
 Figure 2),
 built on a discrete toroidal grid, with two peaks representing the
highest points of epistemic significance. To study cognitive
diversity, the model examines three research strategies, implemented
as three types of agents:


* The "controls" who aim to find a higher point on the
landscape than their current location, while ignoring the exploration
of other agents.
* The "followers" who aim to find already explored
approaches in their direct neighborhood, which have a higher
significance than their current location.
* The "mavericks" who also aim to find points of higher
significance given previously explored points, but rather than
following in the footsteps of other scientists, they prioritize the
discovery of new, so far unvisited, points.



The control strategy represents individual learning that disregards
social information, while the follower and the maverick strategies
represent different ways of taking the latter into account. The model
examines how homogeneous populations of each type of explorers, or
heterogeneous populations consisting of diverse groups of explorers,
impact the efficiency of the community in discovering the highest
points on the landscape, and in covering all points of non-zero
significance.



Following Weisberg and Muldoon's contribution, the framework of
epistemic landscapes became highly influential, resulting in various
refinements and extensions of the model. For instance, Alexander,
Himmelreich, and Thompson (2015) introduced the "swarm"
strategy, which describes scientists who can with certain probability
identify points of higher significance in their surrounding and adjust
their approach so that it is similar, but different from approaches
pursued by others who are close to them. Thoma (2015) introduced
"explorer" and "extractor" strategies, the
former of which describes a scientist seeking approaches very
different from those pursued by others, while the latter (similar to
Alexander and colleagues' swarm researcher) seeks approaches
that are similar to, yet distinct from those pursued by others.
Moreover, Fernandez Pinto and Fernandez Pinto (2018)
examined alternative rules for the follower strategy. Finally,
Poyhonen (2017) introduced a "dynamic"
landscape, such that the exploration of patches "depletes"
their significance.


#### 3.1.2 Applications to network epistemology



Another application domain of the epistemic landscape framework is the
communication structure of science and its impact on communal inquiry.
To study these issues Grim and Singer (Grim 2009; Grim, Singer,
Fisher, et al. 2013) developed ABMs employing a two-dimensional
landscape, where points on the *x*-axis represent alternative
hypotheses for a given domain of phenomena, while the *y*-axis
indicates the "epistemic goodness" of each hypothesis or
the epistemic payoff an agents gets by pursuing it. Depending on how
well the best hypothesis is hidden, the shape of the epistemic terrain
will represent a more or less "fiendish" research problem
(see
 Figure 3).
 For instance, if the best hypothesis is a narrow peak in the
landscape (as in
 Figure 3c)
 finding it will resemble a search for a needle in a haystack and
therefore a fiendish research
 problem.[2]
 An inquiry is considered successful if any scientist (eventually
followed by the rest of the community) discovers the highest peak on
the landscape, while it is unsuccessful if the community converges on
a local maximum. The model is used to study how different social
networks with varying degrees of connectedness (such as those in
 Figure 1),
 and different epistemic landscapes with different degrees of
fiendishness impact the success of inquiry.



![Graph, titled: the epistemic landscape, with the y-axis, epistemic payoff, ranging from 0 to 100 and the x-axis, hypothesis, ranging also from 0 to 100. A sine-like curve goes from a point approximate (0,50) up to (25,75) down to (80,25) and up to (100,30). ](grim1.png)

(a)




![Similar to the previous graph except the y-axis ranges from -23 to 100. The curve is more complex starting at about (0,20) down to (20,-15) up to (40,20) ambles along more or less horizontally to (60,20) then up to (85,75) then down to (100,50).](grim2.png)

(b)




![Similar to the previous graph except the y-axis ranges from -11 to 100. The curve is no longer smooth. Starts at about (0,20) down to a point (15,-9) straight line up to a point at (17,100) straight line down to a point at (20,-11) then curve up to (40,20) ambles along more or less horizontally to (60,20) then up to (85,75) then down to (100,50). ](grim3.png)

(c)



Figure 3: Epistemic landscapes with an
increasing degree of fiendishness (adapted from Grim, Singer, Fisher,
et al. 2013: 443)



#### 3.1.3 Other applications



Beside the above themes, the framework of epistemic landscapes has
appeared in many other variants. For instance, De Langhe (2014a)
proposed an ABM aimed at studying different notions of scientific
progress, which makes use of a "moving epistemic
landscape": a landscape in which the significance of approaches
can change as a result of exploration of other approaches. Balietti,
Mas, and Helbing (2015) developed an epistemic landscape model to
study the relationship between fragmentation in scientific communities
and scientific progress, in which agents explore a two-dimensional
landscape, representing a space of possible answers to a certain
research question, with the correct answer located at the center of
the landscape. Currie and Avin (2019) proposed a reinterpretation of
the three-dimensional landscape aimed at studying the diversity of
scientific methods, in which the *x* and *y* axis stand for
different investigative techniques or methods of acquiring evidence,
and the *z* axis for the "sharpness" of the obtained
evidence. Sharpness here refers to the relationship between research
results and a hypothesis, where the more the evidence increases
one's credence in the hypothesis, the sharper it is. The model
also represents the "independence" of evidence as the
distance between two points on the landscape according to the *x*
and *y* coordinates: the further apart two methods are, the less
overlapping their background theories are, and the more independent
the evidence obtained by them is.



Finally, epistemic landscape modeling has inspired related landscape
models. The argumentation-based model of scientific inquiry by Borg,
Frey, Seselja, and Strasser (2018, 2019) is one
such example. The model, inspired by abstract argumentation frameworks
(Dung 1995), employs an "argumentation landscape". The
landscape is composed of "argumentation trees" that
represent rival research programs (see
 Figure 4).
 Each program is a rooted tree, with nodes as arguments and edges as a
"discovery relation", representing a path agents take to
move from one argument to another. Arguments in one theory may attack
arguments in another theory. In contrast to epistemic landscapes,
argumentation landscapes aim to capture the dialectic dimension of
inquiry where some points on the landscape, assumed to be acceptable
arguments, may subsequently be rejected as undefendable. This allows
for an explicit representation of false positives (accepting a false
hypothesis) as an argument on the landscape an agent accepts without
knowing there exists an attack on it, and false negatives (rejecting a
true hypothesis) as an argument an agent rejects, without knowing that
it can be defended. The model has been used to study how argumentative
dynamics among scientists, pursuing rival research programs, impacts
their efficiency in acquiring knowledge under various conditions of
inquiry (such as different social networks, different degrees of
cautiousness in decision making, and so on).



![two collections of nodes with one collection colored orange (bright and dark nodes) and the other blue (bright and dark). The largest node in each collection is titled 'Research Program 1' and 'Research Program 2' respectively. 2 arrows point from dark orange nodes to dark blue nodes; 1 arrow points from a dark blue node to a dark orange node. ](arg-abm.png)

Figure 4: Argumentation-based ABM by
Borg, Frey, Seselja, and Strasser (2019) employing
an argumentative landscape. The landscape represents two rival
research programs (RP), with darker shaded nodes standing for
arguments that have been explored by agents and are thus visible to
them; brighter shaded nodes stand for arguments that are not visible
to agents. The biggest node in each RP is the root argument, from
which agents start their exploration via the discovery relation,
connecting arguments within one RP. Arrows stand for attacks from an
argument in one RP to an argument in another RP.



### 3.2 Bandit models



In the previous section we saw how epistemic landscape models have
been used to represent inquiries involving mutually compatible
research projects (as in Weisberg and Muldoon's model) as well
as inquiries involving rival hypotheses (as in Grim and Singer's
model). Another framework used to study the latter scenario is based
on "bandit problems".



The name of bandit models comes from the term "one-armed
bandit"--a colloquial name for a slot machine. Multi-armed
bandit problems, introduced in statistics (Robbins 1952; Berry &
Fristedt 1985) and studied in economics (Bala & Goyal 1998; Bolton
& Harris 1999), are decision-making problems that concern the
following kind of scenario: suppose a gambler is about to play several
slot machines. Each machine gives a random payoff according to a
probability distribution unknown to the gambler. To determine which
machine will give a higher reward in the long run, the gambler has to
experiment by pulling arms of the machines. This will allow her to
learn from the obtained results. But which strategy should she use?
For instance, should she alternate between the machines for the first
couple of pulls, and then play only the machine that has given the
highest reward during the initial test run? Or should she rather have
a lengthy test phase before deciding which machine is better? While in
the first case she might start *exploiting* her current
knowledge before she has sufficiently *explored*, in the second
case she might explore for too long. In this way the gambler faces a
trade-off between exploitation (playing the machine that has so far
given the best payoff) and exploration (continued testing of different
machines). The challenge is thus to come up with the strategy that
provides an optimal balance between exploration and exploitation, so
as to maximize one's total winnings.



As we have seen above (see
 Section 2.2),
 the exploration/exploitation trade-off may also occur in the context
of scientific research. Whether scientists are attempting to determine
if a novel treatment for a disease is superior to an existing one, or
selecting between two novel methods of evidence gathering with varying
success rates, they may encounter the exploration/exploitation
trade-off. In other words, they may have to decide when to cease
exploring alternatives and begin exploiting the one that appears most
suitable for the task.



ABMs based on bandit problems were first introduced to philosophy of
science by Zollman (2007, 2010). A bandit ABM usually looks as
follows. In analogy to slot machines, each scientific theory (or
hypothesis, or method) is represented as having a designated
probability of success. Scientists are typically modeled as
"myopic" agents in the sense that they always pursue a
theory they believe to give a higher payoff. They gather evidence for
a theory by making a random draw from a probability distribution.
Subsequently, they update their beliefs in a Bayesian way, on the
basis of results of their own research (the gathered evidence) as well
as results of neighboring scientists in their social network (such as
those in
 Figure 1).
 Scientists are thus not modeled as passive observers of evidence for
all the available theories, but rather as agents who actively
determine the type of evidence they gather by choosing which theory to
pursue. The inquiry of the community is considered successful if, for
example, scientists reach a consensus on the better of the two
theories.



Bandit models of this kind build on the analytical framework developed
by economists (Bala & Goyal 1998) who examined the relationship
between different communication networks in infinite populations and
the process of social learning. Applied to the context of science,
this variation of bandit problems concerns the puzzle mentioned above
in
 Section 2.2:
 assuming a scientific community is trying to determine which of the
two available hypotheses offers a better treatment for a given
disease, could the structure of the information flow among the
scientists impact their chances of converging on the better
hypothesis? By applying Bala and Goyal's framework to the
context of science and scenarios involving finite populations, Zollman
initiated the field of network epistemology (see Zollman 2013), which
studies the impact of social networks on the process of knowledge
acquisition.



Beside the aim of examining network effects on the formation of
scientific consensus, bandit models of scientific interaction have
been applied to a number of other topics, such as the impact of
preferential attachment on social learning (Alexander 2013), bias and
deception in science (Holman & Bruner 2015, 2017; Weatherall,
O'Connor, & Bruner 2020), optimal forms of collaboration
(Zollman 2017), factors leading to scientific polarization
(O'Connor & Weatherall 2018; Weatherall & O'Connor
2021b), effects of conformity (Weatherall & O'Connor 2021a),
effects of demographic diversity (Fazelpour & Steel 2022),
regulations of dual-use research (Wagner & Herington 2021), social
learning in which a dominant group ignores or devalues testimony from
a marginalized group (Wu 2023), or disagreements on the diagnosticity
of evidence for a certain hypothesis (Michelini & Osorio et al.
forthcoming).


### 3.3 Bounded confidence models of opinion dynamics



As we have seen above, the performance of a scientific community is
sometimes assessed in terms of its success in achieving consensus on
the true hypothesis. The question how consensus is formed and which
factors benefit or hinder its emergence is also studied within the
theme of *opinion dynamics* in epistemic communities. Models of
opinion dynamics aim at investigating how opinions form and change in
a group of agents who adjust their views (or beliefs) over a number of
rounds or time-steps, resulting in the formation of
*consensus*, *polarization*, or a *plurality* of
views. A modeling framework that has been particularly influential in
this context originates from the work of Hegselmann and Krause (2002,
2005, 2006), drawing its roots from analytical models of consensus
formation (French 1956; DeGroot 1974; Lehrer & Wagner
 1981).[3]




The basic model functions as follows. At the start of the simulation
(that is, at time \(t=0\)) each agent \(x\_i\) is assigned an opinion
on a certain issue, expressed by a real number \(x\_i(0) \in {(0,1]}\).
Agents then exchange their opinions (or beliefs) with others and
adjust them by taking the average of those beliefs that are "not
too far away" from their own. These are opinions that fall
within a "confidence interval" of size \(\epsilon\), which
is a parameter of the model. In this way, agents have bounded
confidence in opinions of others. By iterating this process, the model
simulates the social dynamics of the opinion formation (see
 Figure 5a).



Applied to the context of scientific inquiry, scientists are usually
represented as truth seeking agents who are trying to determine the
value of a certain parameter \(\tau\) for which they only know that it
lies in the interval \((0,1]\) (Hegselmann & Krause 2006). At the
start of the simulation each agent is assigned a random initial
belief. As the model runs, agents adjust their beliefs by receiving a
(noisy) signal about \(\tau\) and by learning opinions of others who
fall within their confidence interval. For instance, an agent's
belief can be updated in terms of the weighted average of
others' opinions and the signal from the
 world.[4]
 Such dynamics represent scientists as agents who are able to generate
evidence that points in the direction of \(\tau\), though their
updates are also influenced by their prior beliefs and the beliefs of
their peers (see
 Figure 5b
 &
 Figure 5c).



![No truth-seekers: The 100 agents' opinions spread along the y axis quickly collapse to 8 opinions which don't change over time and are only incidentally near the truth. ](HK06_1.png)

(a)




![All truth-seekers: The 100 agents' opinions quickly collapse to 6 and then 2 then 1 which over time becomes nearer to the truth.](HK06_2.png)

(b)




![Half truth seekers: The 100 agents' opinions also collapses but takes a bit longer to reach 1; however, that 1 is also near the truth.](HK06_3.png)

(c)



Figure 5: Examples of runs of Hegselmann
and Krause's model, with the *x*-axis representing time
steps in the simulation and the *y*-axis opinions of 100 agents.
The change of each agent's opinion is represented with a colored
line. The value of \(\tau\) (the assumed position of the truth) is
represented as a black dotted line. Figure (a) shows opinion dynamics
in a community without any truth-seeking agents, resulting in a
plurality of diverging views. Figure (b) shows the opinion dynamics in
a community in which all agents are truth-seekers, which achieves a
consensus close to the truth. Figure (c) shows the opinion dynamics in
a community in which only half of the population are truth-seekers,
which also achieves a consensus close to the truth (adapted from
Hegselmann & Krause 2006; for other parameters used in the
simulations see the original article).




An early application of the bounded confidence model was the problem
of the division of cognitive labor (Hegselmann & Krause 2006).
Since agents can be modeled as updating their beliefs only on the
basis of social information (that is, by averaging over opinions of
others who fall in one's confidence interval) or on the basis of
both social information and the signal from the world, the model can
be used to study the opinion dynamics in a community in which not
everyone is a "truth-seeker". The model then examines what
kind of division of labor between truth-seekers and agents who only
receive social information allows the community to converge to the
truth.



Subsequent applications of the framework in social epistemology and
philosophy of science focused on the study of scientific disagreements
and the question: what is the impact of different norms guiding
disagreeing scientists on the efficiency of collective inquiry (Douven
2010; De Langhe 2013)? If agents update their beliefs both in view of
their own research and in view of other agents' opinions they
represent scientists that follow a Conciliatory Norm by
"splitting the difference" between their own view and the
views of their peers (see above
 Section 2.5).
 In contrast, if they update their beliefs only in view of their own
research they represent scientists that follow a Steadfast Norm. Other
application themes of this framework include the impact of noisy data
on opinion dynamics (Douven & Riegler 2010), opinion dynamics
concerning complex belief states, such as beliefs about scientific
theories (Riegler & Douven 2009), updating via an inference to the
best explanation in a social setting (Douven & Wenmackers 2017),
deceit and spread of disinformation (Douven & Hegselmann 2021),
network effects and theoretical diversity in scientific communities
(Douven & Hegselmann 2022), and so forth.


### 3.4 Evolutionary game-theoretic models of bargaining



Game theory studies situations in which the outcome of one's
action depends not only on one's choice of an action, but also
on actions of others. A "game" in this sense is a model of
a strategic interaction between agents ("players"), each
of whom has a set of available actions or strategies. Each combination
of the players' strategic responses has a designated outcome or
a "payoff". In contrast to traditional game theory, which
focuses on agents' rational decision-making aimed at maximizing
their payoff in one-off interactions, the evolutionary approach
focuses on repeated interactions in a population. A game is assumed to
be played over and over again by players who are randomly drawn from a
large population. While agents start with a certain strategic
behavior, they learn and gradually adjust their responses according to
specific rules called "dynamics" (for example, by
imitating other players or by considering their own past
interactions). As a result, successful strategies will diffuse across
the community. In this way, evolutionary game-theoretic models can be
used to explain how a distribution of strategies across the population
changes over time as an outcome of long-term population-level
processes. While the standard approach to game theory has primarily
focused on combinations of players' strategies that lead to a
"stable" state, such as the Nash equilibrium--a state
in which no player can improve their payoff by unilaterally changing
their strategy--the evolutionary approach has been used to study
how equilibria emerge in a community (see the entries on
 game theory
 and on
 evolutionary game theory).



Evolutionary game theory was originally introduced in biology
(Lewontin 1961; Maynard Smith 1982). It subsequently gained interest
of social scientists and philosophers as a tool for studying cultural
evolution, that is, for investigations into how beliefs and norms
change over time (Axelrod 1984; Skyrms 1996). The models can be
implemented using a mathematical treatment based on differential
equations, or using agent-based modeling. While the former approach
employs certain idealizations, such as an infinite population size or
perfect mixing of populations, ABMs were introduced to study scenarios
in which such assumptions are relaxed (see, e.g., Adami, Schossau,
& Hintze 2016).



Applications of evolutionary game theory to social epistemology of
science were especially inspired by models of bargaining, studying how
different bargaining norms emerge from local interactions of
individuals (Skyrms 1996; Axtell, Epstein, & Young 2001). The
framework was introduced to the study of epistemic communities by
O'Connor and Bruner (2019), building up on Bruner's (2019)
model of cultural
 interactions.[5]




The basic idea of bargaining models is as follows: agents bargain over
shares of available resources, where their demands and expectations
about others' demands evolve endogenously in view of their
previous interactions. Applied to the context of science, bargaining
concerns not only explicit negotiations over financial resources, but
also situations in which scientists need to agree how to divide their
workload in joint projects (O'Connor & Bruner 2019). For
example, if two scientists are working on a joint paper or if they are
organizing a conference together they will have to agree on how much
time and effort each of them will devote to it. The norms determining
such a division of labor may not be fair. For example, if scientist
*A* puts much less effort into the project than scientist
*B*, but they both get the same recognition for its success,
*B* will be disadvantaged. Similarly, if they agree on *A*
being the first author in a collaborative paper, while *B* ends
up working much more on it, the outcome will again be unfair. Such
norms may become entrenched in the scientific community, especially in
the context of interactions between members of majority and minority
groups in academia. But how do such norms emerge? Are biases favoring
members of certain groups over others necessary for the emergence of
such discriminatory patterns, or can they become entrenched due to
other, perhaps more surprising factors?



Evolutionary game-theoretic models have been used to study these and
related questions. Bargaining is represented as a strategic
interaction between two agents, each of whom makes a demand concerning
the issue at hand (for instance, a certain amount of workload, the
order of authors' names in a joint publication, and so on).
Depending on each agent's demand, each gets a certain payoff.
For instance, suppose *A* and *B* wish to organize a
conference and they start by negotiating who will cover which tasks.
If they both make a high demand in the sense that each is willing to
put only a minimal effort into the project while expecting the other
to cover the rest of the tasks, they will fail to organize the event.
If *B*, on the other hand, makes a low demand (by taking on a
larger portion of the work) while *A* makes a high demand, they
will be able to organize the conference, though the division of labor
will be unfair (assuming they both get equal credit for successfully
realizing the project).



The game, originating in the work of John Nash (1950), is called the
"Nash demand game" (or the "mini-Nash demand
game"). Each player in the game makes their demand (Low, Med or
High). If the demands do not jointly exceed a given resource, each
player gets what they asked for. If they do exceed the available
resource, no-one gets anything. In the example above, High can be
interpreted as demanding to work less than the other on the
organization of the conference, or demanding the first authorship in a
joint paper while putting relatively lower effort into it. Similarly,
Low corresponds to the willingness to take on a larger portion of the
work (relative to the order of authors in case of the joint paper),
while Med corresponds to demanding a fair distribution of labor.
 Table 1
 displays the payoffs in such a game. Any combination of demands that
gives a joint payoff of 10 is a Nash equilibrium, which means that
either player's strategy is the best response to the other
player's one. While a Nash equilibrium may correspond to a fair
distribution of resources (if both players demand Med), it may also
correspond to an unfair one (if one player demands Low and the other
one High). This raises the question: which equilibrium will the
community achieve if agents learn from their previous interactions? In
particular, if the individuals are divided into sub-groups (which may
be of different sizes), where their membership can be identified by
means of markers visible to other agents, they can develop strategies
conditional on the group membership of their co-players. Which
equilibrium state will such a community evolve to? To study such
questions, evolutionary models employ rules or dynamics that determine
how players update their strategies and how the distribution of
strategies across the community changes over
 time.[6]






  
|  | Low | Med | High |
| --- | --- | --- | --- |
| Low | L,L | L,5 | L,H |
| Med | 5,L | 5,5 | 0,0 |
| High | H,L | 0,0 | 0,0 |



Table 1: A payoff table in the Nash
demand game. The rows show the strategic options of Player1 and the
columns the options of Player2. Each cell shows the payoff Player1
gets for the given combination of options, followed by the payoff for
Player2. Players can make three demands: Low, Med and High for the
total resource of 10. The payoffs are represented as L, M and H, where
\(\mathrm{M}= 5\), \(\mathrm{L} < 5 < \mathrm{H}\), and
\(\mathrm{L} + \mathrm{H} = 10\). (cf. O'Connor & Bruner
2019; Rubin & O'Connor 2018) 




The modeling framework based on bargaining has been used to study
norms in scientific collaborations and inequalities that may emerge
through them. For example, O'Connor and Bruner (2019)
investigate the emergence of discriminatory norms in academic
interactions between minority and majority members. Rubin and
O'Connor (2018) study the emergence of discriminatory patterns
and their effects on diversity in collaboration networks, while
Ventura (2023) examines the impact of the structure of collaborative
networks on the emergence of discriminatory norms even if there are no
visible markers of an agent's membership to a sub-group.
Moreover, Klein, Marx, and Scheller (2020) use a similar framework to
study the relationship between rationality and inequality, that is,
the success of different strategies for bargaining (such as maximizing
expected utility) and their impact on the emergence of inequality.


### 3.5 Summing up



Besides the above frameworks, numerous additional approaches have been
used to build simulations in social epistemology and philosophy of
science. Some prominent frameworks not mentioned above include the
Bayesian framework "Laputa" developed by Angere
(2010--in Other Internet Resources) and Olsson (2011), aimed at
studying social networks of information and trust, the model of
argumentation dynamics by Betz (2013), or the influential framework by
Hong and Page (2004) utilized in the study of cognitive diversity (for
more on these frameworks see the entry on
 computational philosophy).
 Another evolutionary framework used in philosophy of science was
proposed by Smaldino and McElreath (2016). The model represents a
scientific community as a population consisting of scientific labs
that employ culturally transmitted methodological practices, where the
practices undergo natural selection from one generation of scientists
to the next, and it has been employed, for example, to study the
selection of conservative and risk-taking science (O'Connor
2019). In the next section we take a look at some of the central
results obtained by ABMs in philosophy of science, based on the above
and some other frameworks.


## 4. Central Results



To provide an overview of the main findings obtained by means of ABMs
in philosophy of science, we will revisit the research questions
discussed in
 Section 2
 and look at how they have been answered through specific models.


### 4.1 Theoretical diversity and the incentive structure of science



Before we survey ABMs that study the incentive structure of science,
we first look into the results of some analytical models which
inspired the development of simulations. To get a more precise grip on
how individual incentives shape epistemic coordination and theoretical
diversity, philosophers introduced formal analytical models, inspired
by research in economics. One of the central results from this body of
literature is that the optimal distribution of labor can be achieved
when scientists act according to their self-interest rather than
following epistemic ends (e.g., Kitcher 1990; Brock & Durlauf
1999; Strevens 2003). More precisely, the models show that if we
assume scientists aim at maximizing rewards from making discoveries,
they will succeed to optimally distribute their research efforts if
they take into account the probability of success of each research
line and how many other scientists currently pursue it. Assuming that
all scientists evaluate theories in the same way, their interest in
fame and fortune, rather than epistemic goals alone, will lead some of
them to select avenues that initially appear less
 promising.[7]




ABMs were introduced to address similar questions, but assuming more
complex scenarios. For example, Muldoon and Weisberg (2011) developed
an epistemic landscape model (see
 Section 3.1)
 to examine the robustness of Kitcher's and Strevens'
results under the assumption that scientists have varying access to
information about the pursued research projects in their community and
the future success of those projects. Their results indicate that once
scientists have limited information about what others are working on,
or about the degree to which projects are likely to succeed, their
self-organized division of labor fails to be optimal. Another example
is the model by De Langhe & Greiff (2010) who generalize
Kitcher's model to a situation with multiple epistemic standards
determining the background assumptions of scientists, acceptable
methods, acceptable puzzles, and so on. The simulations show that once
scientific practice is modeled as based on multiple standards, the
incentive to compete fails to provide an optimal division of labor.




A closely related question concerns the "priority
rule"--a norm that allocates credit for a scientific
discovery to the first one to make it (Strevens 2003, 2011)--and
its impact on the division of labor. While Kitcher's and
Strevens's models suggested that the priority rule incentivizes
the optimal distribution of scientists across rival research programs,
a range of formal models, including ABMs, were developed to reexamine
these results and shed additional light on this norm. For instance,
Rubin and Schneider (2021) examine what happens if credit is assigned
by individuals, rather than by the scientific community as a whole, as
in Strevens' model. They further suppose that news about
simultaneous discoveries by two scientists spreads through a networked
community. The simulations show that more connected scientists are
more likely to gain credit than the less connected ones, which may, on
the one hand, disadvantage minority members in the community, and on
the other hand, undermine the role of the priority rule as an
incentive resulting in the optimal division of labor. Besides the
question of how the priority rule impacts the division of labor, ABMs
have also been used to study other effects of the priority rule. For
example, Tiokhin, Yan, and Morgan (2021) develop an evolutionary ABM
showing that the priority rule leads the scientific community to
evolve towards research based on smaller sample sizes, which in turn
reduces the reliability of published findings.



The impact of incentives on the division of labor in science has also
been analyzed in terms of incentives to "exploit" existing
projects in contrast to incentives to "explore" novel
ideas. For instance, De Langhe (2014b) developed a generalized version
of Kitcher and Strevens' model in which agents achieve the
optimal division of labor by weighing up relative costs and benefits
of exploiting available theories and exploring new ones. Within the
framework of bandit models and network epistemology (see
 Section 3.2),
 Kummerfeld and Zollman (2016) proposed an ABM that examines a
scenario in which scientists face two rival hypotheses, one of which
is better though the agents don't know which one. While agents
always choose to pursue (or exploit) a hypothesis that seems more
promising, they may also occasionally research (and thereby explore)
the alternative one. The simulations show that if the community is
left to be self-organized in the sense that each scientist explores to
the extent that they consider individually optimal, agents will be
incentivized to leave exploration to others. As a result, scientists
will fail to develop a sufficiently high incentive for exploring novel
ideas, that is, an incentive which would be optimal from the
perspective of their community at large.


### 4.2 Theoretical diversity and the communication structure of science


#### 4.2.1 The "Zollman effect"



The study of theoretical diversity in terms of network epistemology
led to a novel hypothesis: that the communication structure of a
scientific community can promote or hinder the emergence of
theoretical diversity and thereby impact the division of cognitive
labor. The idea was first demonstrated by bandit models developed by
Zollman (2007, 2010; see
 Section 3.2)
 and came to be known as the "Zollman effect" (Rosenstock,
Bruner, & O'Connor 2017). ABMs by Grim (2009) Grim, Singer,
Fisher, and colleagues (2013), and Angere & Olsson (2017) produced
similar findings based on different modeling
 frameworks.[8]
 These models show that in highly connected communities early
erroneous results may spread quickly among scientists, leading them to
investigate a sub-optimal line of inquiry. As a result, scientists may
prematurely abandon the exploration of different hypotheses, and
instead exploit the inferior ones. In light of these findings, Zollman
(2010) emphasized that for an inquiry to be successful it needs a
property of "transient diversity": a process in which a
community engages in a parallel exploration of different theories,
which lasts sufficiently long to prevent a premature abandonment of
the best theory, but which eventually gets replaced by a consensus on
it. Besides the result that connectivity can be harmful, it has also
been shown that learning in less connected networks is slower, which
indicates a trade-off between accuracy and speed in the context of
social learning (Zollman 2007; Grim, Singer, Fisher, et al. 2013).



Subsequent studies showed, however, that the Zollman effect is not
very robust within the parameter space of the original model
(Rosenstock et al. 2017). In particular, the result holds for those
parameters that can be considered characteristic of difficult inquiry:
scenarios in which there is a relatively small number of scientists,
the evidence is gathered in relatively small batches, and the
difference between the objective success of the rival hypotheses is
relatively small. Moreover, additional models showed that if the
diversity (and hence, exploration) of pursued hypotheses is generated
in some other way, more connected communities may outperform less
connected ones. For instance, Kummerfeld and Zollman (2016) showed
that relaxing the trade-off between exploration and exploitation by
allowing agents to occasionally gain information about the hypothesis
they are not currently pursuing is a way to generate diversity,
leading a fully connected community to perform better than less
connected ones. Another way of generating diversity was examined by
Frey and Seselja (2020): they show that if scientists
have a dose of caution or "rational inertia" when deciding
whether to abandon their current theory and start pursuing the rival,
a fully connected community gets a sufficient degree of exploration to
outperform less connected groups. Similar points have been made with
ABMs based on other modeling frameworks, such as the bounded
confidence model by Douven and Hegselmann (2022), or an
argumentation-based ABM by Borg, Frey, Seselja, and
Strasser (2018), each of which shows a different way of
preserving transient diversity in spite of a high degree of
connectivity. 


#### 4.2.2 The spread of disinformation



ABMs studying epistemically pernicious strategies in scientific
communities have largely employed network epistemology bandit models
(see
 Section 3.2).
 For instance, a model by Holman and Bruner (2015) examines how an
interference of industry-sponsored agents may impact the information
flow in the medical community, and which strategies scientists could
employ to protect themselves from such a pernicious influence. For
this purpose, they consider a scenario in which medical doctors
regularly communicate with industry-sponsored agents about the
efficacy of a certain pharmaceutical product as a treatment for a
given disease. Since the industry-sponsored agents are motivated by
financial rather than epistemic interests and they are unlikely to
change their minds no matter how much opposing evidence they receive,
they are not merely biased, but "intransigently biased".
Their simulations indicate two ways in which a scientific community
can protect itself from the pernicious influence of the intransigently
biased agent: first, by increasing their connectivity, and second, by
learning how to reorganize their social network on the basis of
trustworthiness, which leads them to eventually ignore the biased
agent. In a follow-up model, Holman and Bruner (2017) also show how
the industry can bias a scientific community without corrupting any of
the individual scientists that compose it, but by simply helping
industry-friendly scientists to have successful careers.



Using a similar network-epistemology approach, Weatherall,
O'Connor and Bruner (2020) developed a bandit model to study the
"Tobacco strategy", employed by the tobacco industry in
the second half of the twentieth century to mislead the public about
the negative health effects of smoking (analyzed in detail by Oreskes
& Conway 2010). In particular, the model examines how certain
deceptive practices can mislead public opinion without even
interfering with (epistemically driven) scientific research. The
authors look into two such propagandist strategies: a "selective
sharing" of research results that fit the industry's
preferred position, and a "biased production" of research
results where additional research gets funded, but only suitable
findings get published. The results show that both strategies are
effective in misleading policymakers about the scientific output under
various examined parameters since in both cases the policymakers
update their beliefs on the basis of a biased sample of results,
skewed towards the worse theory. The authors also look into strategies
employed by journalists reporting on scientific findings and show that
incautiously aiming to be "fair" by reporting an equal
number of results from both sides of the controversy may result in the
spread of misleading information.



Another example of ABMs developed to examine deception in science is
an argumentation-based model by Borg, Frey, Seselja, and
Strasser (2017, 2018; see
 Section 3.1.3).
 Assuming the context of rival research programs where scientists have
to identify the best out of three available ones, deceptive
communication is represented in terms of agents sharing only positive
findings about their theory, while withholding news about potential
anomalies. The underlying idea is that deception consists in providing
some (true) information while at the same time withholding other
information, which leads the receiver to make a wrong inference
(Caminada 2009). Unlike the previous two models discussed in this
section, where not all agents display biased or deceptive behavior,
Borg et al. study network effects in a population consisting entirely
of deceptive scientists. Such a scenario represents a community that
is driven, for instance, by confirmation bias and an incentive to
shield one's research line from critical scrutiny. The
simulations show that, first, reliable communities (consisting of no
deceivers) are significantly more successful than the deceptive ones,
and second, increasing the connectivity makes it more likely that
deceptive populations converge on the best theory.


### 4.3 Cognitive diversity



As we have seen in
 Section 2.1,
 the problem of cognitive diversity concerns the relation between the
diversity of cognitive features of scientists (including their
background beliefs, reasoning styles, research preferences, heuristics
and strategies) and the inquiry of the scientific community.
Philosophers of science have especially been interested in how the
division of labor across different research heuristics impacts the
performance of the community.



A particularly influential ABM tackling this issue is the epistemic
landscape model by Weisberg and Muldoon (2009). The model examines the
division of labor catalyzed by different research strategies, where
scientists can act as the "controls",
"followers" or "mavericks" (see above
 Section 3.1).
 In view of the simulations, Weisberg and Muldoon argue that, first,
mavericks outperform other research strategies. Second, if we consider
the maverick strategy to be costly in terms of the necessary
resources, then introducing mavericks to populations of followers can
lead to an optimal division of labor. While Weisberg and
Muldoon's ABM eventually turned out to include a coding error
(Alexander et al. 2015), their claim that cognitive diversity improves
the productivity of scientists received support from adjusted versions
of the model, albeit with some qualifications.



First, Thoma's (2015) model showed that cognitively diverse
groups outperform homogeneous ones if scientists are sufficiently
flexible to change their current approach and sufficiently informed
about the research conducted by others in the community. Second,
Poyhonen (2017) confirmed that if we consider the maverick
strategy to be slightly more time-consuming, mixed populations of
mavericks and followers may outperform communities consisting only of
mavericks in terms of the average epistemic significance of the
obtained results. According to Poyhonen, another condition
that needs to be satisfied if cognitive diversity is to be beneficial
concerns the topology of the landscape: diverse populations outperform
homogeneous ones only in case of more challenging inquiries
(represented in terms of rugged epistemic landscapes), but not in case
of easy research problems (represented by landscapes such as Weisberg
and Muldoon's one,
 Figure 2).
 The importance of the topology of the landscape was also emphasized
by Alexander and colleagues (2015) who use NK-landscapes to show that
whether social learning is beneficial or not crucially depends on the
landscape's topology. Finally, there are other research
strategies (such as Alexander and colleagues'
"swarm" one, see
 Section 3.1.1)
 that outperform Weisberg and Muldoon's mavericks, while small
changes in the follower strategy may significantly improve its
performance (Fernandez Pinto & Fernandez Pinto
2018).



Another aspect of cognitive diversity that received attention in the
modeling literature concerns the relationship between diversity and
expertise. This issue was first studied in economics by Hong and Page
(2004). The model examined how heuristically diverse groups,
consisting of agents with diverse problem-solving approaches, compare
with groups consisting solely of experts with respect to finding a
solution to a particular problem. Hong and Page's original
result suggested that "diversity trumps ability" in the
sense that groups consisting of individuals employing diverse
heuristic approaches outperfom groups consisting solely of experts,
that is, agents who are the best "problem-solvers". While
this finding became quite influential, subsequent studies showed that
it does not hold robustly once more realistic assumptions about
expertise are added to the model (Grim, Singer, Bramson, et al. 2019;
Reijula & Kuorikoski 2021; see also Singer 2019).



The problem of cognitive diversity and the division of labor in
scientific communities was also studied by Hegselmann and
Krause's bounded-confidence model (Hegselmann & Krause 2006;
see above
 Section 3.3).
 The ABM examines opinion dynamics in a community that is diverse in
the sense that only some individuals are active truth seekers, while
others adjust their beliefs by exchanging opinions with those agents
who have sufficiently similar beliefs to their own. Hegselmann and
Krause investigate conditions under which such a community can reach
consensus on the truth, combining agent-based modeling and analytical
methods. They show that, on the one hand, if all agents in the model
are truth seekers, they achieve a consensus on the truth. On the other
hand, if the community divides labor, its ability to reach a consensus
on the truth will depend on the number of truth seeking agents, the
position of the truth relative to the agents' opinions, the
degree of confidence determining the scope of opinion exchange and the
relative weight of the truth signal (in contrast to the weight of
social information). For instance, under certain parameter settings
even a single truth-seeking agent will lead the rest of the community
to the truth.


### 4.4 Social diversity



As we have seen in
 Section 2.4,
 ABMs were introduced to study two issues that concern social
diversity in the context of science: first, epistemic effects of
socially diverse scientific groups, and second, factors that can
undermine social diversity or disadvantage members of minorities in
science.



Concerning the former question, Fazelpour and Steel (2022) developed a
network epistemology bandit model (see
 Section 3.2)
 to study effects of social (or demographic) diversity on collective
inquiry. The model examines how different degrees of trust between
socially distinct sub-groups impact the performance of the scientific
community. The simulations show that social diversity can improve
group inquiry by reducing the excessive trust scientists may place in
each other's findings. This is particularly relevant in cases of
difficult inquiry in tightly connected communities, where a high
degree of trust may lead to a premature endorsement of an objectively
false hypothesis. The authors also demonstrate how social diversity
can counteract negative effects of conformity in the choice of
research paths, under the condition that the tendency to conform
isn't too high. Using a similar bandit framework, Wu (2023)
examines how a marginalized group learns in environments in which
members of the dominant group ignore or devalue results coming from
the former. She shows that in such situations, members of the
marginalized group can achieve an epistemic advantage in the sense of
forming more accurate beliefs than the members of the dominant group.
In this way, the model aims to explain the "inversion
thesis" from standpoint epistemology, according to which
marginalized groups may have an epistemically privileged status (Wylie
2003).



With regard to the latter question, we previously mentioned the model
by Rubin and Schneider (2021), which shows how minority members can
become disadvantaged due to the priority rule in science and the
structure of the scientific community
 (Section 4.1).
 In a follow-up model, Rubin (2022) demonstrates how the
organizational structure of the scientific community can also
influence the emergence of "citation gaps", where
publications by members of underrepresented and minority groups are
cited less than those by members of the majority. The impact of one's
minority status on one's position in scientific collaborations was
mainly studied by means of evolutionary game-theoretic models of
bargaining (see
 Section 3.4).
 The models show different ways in which minorities can become
discriminated against merely due to their minority status and the
impact of such a status on the bargaining and collaborative practices
(Rubin & O'Connor 2018; O'Connor & Bruner 2019;
Ventura 2023). Moreover, the results show that such an outcome may
have a negative impact on scientific progress, and it may help to
explain why gender and racial minorities tend to cluster in academic
subdisciplines (O'Connor & Bruner 2019; Rubin &
O'Connor 2018). We will revisit these models below in
 Section 4.7,
 devoted to the issue of scientific collaboration.


### 4.5 Peer-disagreement in science



As we have seen in
 Section 2.5,
 peer disagreement debate highlighted different norms that can guide
scientists. ABMs were introduced to examine the impact of these norms
on the communal inquiry. To this end, Douven (2010) and De Langhe
(2013) enhanced the bounded confidence model by Hegselmann and Krause
(2006; see
 Section 3.3)
 to study the impact of the Conciliatory and Steadfast Norms on the
goals of
 inquiry.[9]




Both models suggest that the impact of these norms is context
dependent. In Douven's model, when data received through inquiry
is not very noisy--in the sense that it is indicative of the
truth rather than resulting from measurement errors--conciliating
populations will be faster in discovering the truth than the steadfast
ones. Nevertheless, if the data becomes noisy, the simulations show a
trade-off between accuracy and speed. While steadfast populations get
within a moderate distance of the truth relatively quickly, they
don't improve their accuracy in the subsequent rounds of the
simulation. In contrast, conciliating populations end up closer to the
truth but it takes them relatively longer to do so. This indicates
that whether a norm is optimal for the communal goals may largely
depend on contextual issues of inquiry, such as obtaining noise-free
evidence. De Langhe comes to a similar conclusion, using a model that
represents longstanding scientific disagreements and an inquiry
involving multiple rivaling epistemic systems. The model is inspired
by Goldman's (2010) idea that even though disagreeing peers may
share the evidence concerning the issue in question, they may not
share the evidence for the epistemic system within which they evaluate
the former kind of evidence. His simulations suggest that while
conciliating within one's epistemic system is beneficial, that
is not the case when it comes to disagreements between different
epistemic
 systems.[10]



### 4.6 Scientific polarization



The initial work on ABMs of polarization in scientific communities is
based on Hegselmann and Krause's bounded confidence model
(Hegselmann & Krause 2006; see
 Section 3.3).[11]
 The model shows that a community can polarize when some agents form
their opinions by disregarding the evidence coming from the world and
by instead taking into account only what they learn from others, who
have sufficiently similar views. Subsequent models, based on different
frameworks, sought to examine whether polarization can emerge even if
all agents are truth-seekers, that is, rational agents who form their
opinions only in view of epistemic considerations.



For example, a model by Singer et al. (2019) shows the emergence of
polarization in groups of deliberating agents who share reasons for
their beliefs, and who merely use a coherence-based approach to manage
their limited memory (by forgetting those reasons that conflict with
the view supported by most of their previous considerations). Using
the Bayesian modeling framework Laputa, Olsson (2013) shows how
polarization can emerge over the course of deliberation if agents
assign different degrees of trust to the testimony of others,
depending on how similar views they hold. In another Bayesian
framework based on bandit models, O'Connor and Weatherall (2018)
show how a community of scientists who share not only their testimony,
but unbiased evidence, can end up in a polarized state if they treat
evidence obtained by other scientists, whose beliefs are too far off
from their own, as uncertain. Moreover, Michelini, Osorio and
colleagues (forthcoming) combine bandit models with the
bounded-confidence framework to study polarization in communities that
disagree on the diagnostic value of evidence for a certain hypothesis
(modeled by the Bayes factor). Their results indicate that an initial
disagreement on the diagnosticity of evidence can lead to
polarization, depending on the sample size of the performed studies
and the confidence interval within which scientists share their
opinions.



Polarization in epistemic communities has also been studied by ABMs
simulating argumentative dynamics. For example, using the framework by
Betz (2013), Kopecky (2022) proposes a model that shows how certain
ways in which rational agents engage in argumentative exchange in an
open debate forum can result in polarized communities, even if agents
don't have a preference for whom they argue with.


### 4.7 Scientific collaboration



As discussed in
 Section 2.7,
 ABMs have been used to study when collaborating is beneficial and how
it impacts inquiry. To examine why collaborations emerge and when they
are beneficial, Boyer-Kassem and Imbert (2015) developed a
computational model in which scientists collaborate by sharing
intermediate results. They study collaborative groups in a larger
competitive community, driven by the priority rule, where
collaborators have to equally share the reward for making a discovery.
Their simulations suggest that collaborating is beneficial both for
communal and individual inquiry. When scientists collaborate, they
proceed faster with their inquiry, making it more likely that they
will belong to a team that is first to make a discovery. Moreover,
while the scientific community may profit from all scientists fully
collaborating (since solutions found by some scientists will be shared
with everyone), for individual scientists other collaborative
constellations may be better (see also Boyer-Kassem & Imbert
forthcoming). 



To examine conditions for optimal collaboration Zollman (2017)
proposed a network-epistemology model. The model starts from the
assumption that by collaborating scientists teach each other different
conceptual schemes aimed at solving scientific problems, and that
collaboration comes at a cost. His findings suggest that reducing the
costs of collaborations and enlarging the size of the collaborative
group benefits those involved, though encouraging scientists to engage
with different collaborative groups may not lead to efficient
inquiry.



While the above models studies socially uniform communities, ABMs have
also been employed to study socially diverse collaborative
environments and discriminatory patterns that can endogenously emerge
in them. These models are typically based on evolutionary
game-theoretic frameworks examining bargaining norms that accompany
academic collaborations (see
 Section 3.4).
 Bruner (2019) initially used this framework to show that in cultural
interactions involving a division of resources, minority members can
become disadvantaged merely due to the smaller size of their
 group.[12]
 O'Connor and Bruner (2019) use this approach to study the
emergence of similar patterns in epistemic communities. The
simulations show that minorities can become disadvantaged in
scientific collaborations due to the size of their group, where the
effect is increased the smaller their group is. Rubin and
O'Connor (2018) use a similar framework to examine diversity and
discrimination in epistemic collaboration networks, with a special
focus on the role of homophily--the tendency to preferentially
establish links with members of one's own social group in
contrast to members of the outgroup. Their results suggest that
discriminatory norms, likely to emerge in academic interaction, may
promote homophily and decrease social diversity in collaborative
networks. Furthermore, Ventura (2023) shows how inequality can emerge
in collaboration networks even in the absence of demographic
categories since scientists can become disadvantaged merely due to the
structure of the collaboration network and their specific position in
it.


### 4.8 Additional themes



As mentioned in
 Section 2.8,
 ABMs have been applied to the study of many additional themes in
philosophy of science: the problem of allocating funding to research
grants (Harnagel 2019; Avin 2019a,b), examining Popperian and Kuhnian
models of scientific progress (De Langhe 2014a), the dynamics of
normal and revolutionary science (De Langhe 2017), pluralism of
scientific methods (Currie & Avin 2019), the impact of methods
guiding research design and data analysis on the quality of scientific
findings (Smaldino & McElreath 2016), strategic behavior of
scientists as a motivated exchange of beliefs (Merdes 2021),
journals' publishing strategies (Zollman 2009), meta-inductive
learning (Schurz 2009, 2012), the impact of evidential strength on the
accuracy of scientific consensus (Santana 2018), the management and
structure of scientific groups (Sikimic & Herud-Sikimic
2022), the reliability of testimonial norms, which guide how one
should change beliefs in light of others' claims (Mayo-Wilson
2014), the assessment of source reliability (Merdes, Von Sydow, &
Hahn 2021), the impact of "epistemic stubbornness" as an
unwillingness to change one's scientific stance in face of
countervailing evidence (Santana 2021), the impact of different
theory-choice evaluations on the efficiency of inquiry (Borg et al.
2019), and so forth.


## 5. Epistemology of Agent-Based Modeling



In the previous section we delved into various results obtained by
means of agent-based modeling. But how seriously should we take each
of these findings? What exactly do they tell us about science? Given
that one of the main cognitive functions of models in science is to
help us to learn about the world (cf. the entry on
 models in science),
 this raises the question: what exactly can we learn with ABMs, and
which methodological steps do we have to follow to acquire such
knowledge? These issues are a matter of the epistemology of
agent-based
 modeling.[13]



### 5.1 The challenge of abstract modeling



In an early discussion on simulations in social sciences, Boero and
Squazzoni (2005) proposed a classification of ABMs into case-based
models, typifications, and theoretical abstractions--based on the
properties of the represented target. While a case-based model
represents an empirical scenario that is delineated in time and space,
typifications represent classes of empirical phenomena. Finally,
theoretical abstractions, as their name suggests, abstract away from
various features of empirical targets and aim at simplified and
general representation of social phenomena.



Following the tradition of abstract ABMs in social sciences, the
majority of simulations in philosophy of science have been developed
within the so-called "KISS" (*Keep it Simple,
Stupid*)
 approach.[14]
 As such, they belong to the third type of ABMs listed above. The main
advantage of constructing simple models is that they allow for an
easier understanding of the represented causal mechanisms than complex
models do. However, this also raises the question: what can we learn
from these models about real science, given their highly idealized
character? For instance, do results of such simulations increase our
understanding of scientific communities? Can we use them to provide
potential explanations of certain episodes from the history of
science, or to formulate normative recommendations for how scientific
inquiry should be organized? These questions have been a matter of
philosophical debate, closely related to the discussion on the
epistemic function of highly idealized or toy-models across empirical
 sciences.[15]
 In particular, critics have argued that if abstract ABMs are to be
informative of empirical targets, they first need to be verified and
empirically validated.


### 5.2 Verification and validation: from exploratory to explanatory functions of ABMs



In the context of agent-based modeling, verification is a method of
evaluating the accuracy of the computer program underlying an ABM
relative to its conceptual design. Validation, on the other hand,
concerns the evaluation of the link between the model and its
purported target. Irrespective of the purpose for which the model was
built, it always requires some degree of verification to ensure that
its simulation code does not suffer from bugs and other unintended
issues. The type of required validation depends on the purpose of the
model and its intended target. As Mayo-Wilson and Zollman (2021) have
argued, validation isn't always necessary for some modeling
purposes, such as illustrating that certain events or situations are
theoretically possible. Models can instead be justified by
"plausibility arguments" and in view of stylized
historical case studies. According to Mayo-Wilson and Zollman, ABMs
play a role analogous to thought experiments in that they can be used
to evoke normative intuitions, to justify counterfactual claims, to
illustrate possibilities and impossibilities, and so on. Moreover,
when it comes to questions concerning the dynamics of social systems,
they are more apt for the task than mere thought experiments due to
the complexity of such target phenomena.



Empirical validation is also not required if the function of an ABM is
to provide a proof-of-concept or a how-possibly explanation. A model
provides a *proof-of-concept* (sometimes also called a
"proof-of-possibility" or
"proof-of-principle") if it merely demonstrates a
theoretical possibility of a certain phenomenon (cf. Arnold 2008). The
target phenomenon may be represented in an abstract, idealized way,
disregarding the empirical adequacy of the modeling assumptions.
Gelfert (2016: 85-86) distinguishes two specific functions of
the proof-of-concept modeling. First, a model may exemplify how an
approach or methodology can generate a potential representation of a
given target phenomenon. For instance, if an ABM shows how the
framework of epistemic landscapes can be used to represent cognitive
diversity, it provides a proof-of-concept in this sense of the term.
Second, a model may provide results showing that a certain causal
mechanism can be found within the model-world. For instance, if a
highly idealized simulation shows that a certain kind of cognitive
diversity results in efficient inquiry of the modeled community, it
provides a proof-of-concept in the latter sense. While
proof-of-concept modeling is sometimes taken to be just a preliminary
step in the development of more realistic models (see for instance
Grabner 2018), it has also been considered as providing valuable
philosophical insights already on its own (see for example
Seselja 2021a).



Similarly, simulations provide *how-possibly explanations*
(HPEs) if they show how a certain target phenomenon can possibly
result from certain preconditions (Rosenstock et al. 2017;
Grabner 2018; Frey & Seselja 2018;
Seselja 2022b). In contrast to how-actually
explanations, or explanations simpliciter, which are accounts of how
phenomena actually occur, HPEs (sometimes also called "potential
explanations") cover accounts of possible ways in which
phenomena can
 occur.[16]
 According to a broad reading of the notion proposed by
Verreault-Julien (2019), HPEs are propositions that have the form
"It is possible that 'p because q'.", where
*p* is the explanandum, *q* the explanans, and the
possibility may refer to various types of modalities such as
epistemic, logical, causal, and so forth.



The above epistemic functions--providing a proof-of-concept or an
HPE--are often considered a kind of exploratory modeling, where
the represented target can be an abstract, theoretical phenomenon
(Ylikoski & Aydinonat 2014; Seselja 2021a). In
contrast, to provide explanations of empirical phenomena, many have
argued that ABMs need to be empirically validated. This has been
emphasized as important in case models are supposed to explain certain
patterns from actual scientific practice, to provide evidence
supporting existing empirical (including historical) hypotheses, or to
provide suggestions for interventions through science policy (Arnold
2014; Martini & Fernandez Pinto 2017; Thicke 2020;
Seselja 2021a).



Validation of ABMs consists in examining whether the model is a
reasonable representation of the target, where
"reasonable" may refer to different aspects of the model
(Grabner 2018). For instance, we may test how well mechanisms
represented in the model conform to our empirical knowledge about
them, whether the exogenous inputs for the model are empirically
plausible, to what extent the output of the model replicates existing
knowledge about the target, or whether it can predict its future
states. Different authors have suggested different elaborations of
these points with respect to ABMs in philosophy of science (see Thicke
2020; Seselja 2022b; Bedessem 2019; Politi 2021; Pesonen
2022).



While some of these steps may be challenging, others may be more
feasible. For instance Martini and Fernandez Pinto (2017) argue
that ABMs of science can be calibrated on empirical data. Harnagel
(2019) exemplifies how this could be done by calibrating an epistemic
landscape model with bibliometric data.



Others have argued that results of simulations should at least be
analyzed in terms of their *robustness*. Robustness analysis
(RA) includes: 


1. parameter RA, examining the stability of results with respect to
changes in parameter values of the model, usually studied by means of
sensitivity
 analysis;[17]
2. structural RA, which focuses on the stability of results under
changes in structural features of the model and its underlying
assumptions;
3. representational RA, which studies the stability of the results
with respect to changes in the representational framework, modelling
technique, or modelling medium (Weisberg 2013: Chapter 9; Houkes,
Seselja, & Vaesen forthcoming).



The main purpose of RA in the context of ABMs is to help with
understanding conditions (in the model world) under which results of
the model hold: whether they depend on specific parameter values or on
specific assumptions about the causal factors involved in the
represented target, or whether the result is just an artifact of
certain idealizing assumptions in the model. This can benefit our
understanding of the model and its results in at least two ways. On
the one hand, if an RA shows that the result holds under various
changes in parameter values, structural and representational
assumptions, this may increase our confidence that the result is not a
mere artifact of idealizations in the model (see, e.g., Kuorikoski,
Lehtinen, & Marchionni
 2012).[18]
 On the other hand, if the result holds only for specific parameter
values or under specific structural or representational assumptions,
this can help to delineate the context of application of the model
(for example, the context of difficult inquiry, the context of inquiry
involving a small scientific community, and so on).


### 5.3 The "family of models perspective"



The importance of robustness analysis suggests that determining the
epistemic function of a single ABM in isolation may be difficult. In
order to assess whether the results of an ABM are explanatory of a
certain empirical target, it is useful to check whether they are
robust under changes in the idealizing modeling assumptions. One way
to do so is to construct a new model by replacing some assumptions in
the previous one. Moreover, using a model based on an entirely
different representational framework may additionally help in such
robustness studies. This is why the value of using multiple models to
study the same research question has been increasingly emphasized in
the modeling literature.



For instance, Aydinonat, Reijula, and Ylikoski (2021) have argued for
the importance of the "family-of-models perspective". The
authors consider models as argumentative devices, where an argument
supported by a given model can be strengthened by means of analyses
based on subsequent models. The approach according to which phenomena
should be studied by means of multiple models has also been endorsed
in the broader context of modeling in social sciences (cf. Page 2018;
Kuhlmann 2021).



In sum, even though ABMs in philosophy of science tend to be highly
idealized, they can have exploratory roles, such as identifying
possible causal mechanisms underlying scientific inquiry, offering
how-possibly explanations, or providing conjectures and novel
perspectives on historical case-studies (cf. Seselja
2022a). In addition, they can play more challenging epistemic roles
(such as providing explanations of empirical phenomena or evidence for
empirical hypotheses) if accompanied by validation procedures, which
may benefit from looking into classes of models aimed at the same
target phenomenon.


## 6. Conclusion and Outlook



This entry has provided an overview of applications of agent-based
modeling to issues studied by philosophers of science. Since such
applications are primarily concerned with social aspects of scientific
inquiry, ABMs in philosophy of science are mainly developed within the
subfield of formal social epistemology of science. While agent-based
modeling has experienced rapid growth in this domain, it remains to
stand the test of time. In particular, foundational issues concerning
the epistemic status of abstract models and their methodological
underpinnings (for instance, the KISS vs. the KIDS approach, see
 footnote 14)
 remain open. Having said that, theoretical achievements made with the
existing models have been remarkable and they provide a firm ground
for further growth of agent-based modeling as a philosophical
method.



One avenue that has received relatively little attention is the
combination of agent-based modeling and empirical methods. Although
early philosophical discussions on ABMs emphasized the fruitful
combination of experimental and computational methods (see, e.g.,
Hartmann, Lisciandra, & Machery 2013), this area has not been
explored extensively, leaving ample opportunities for future research.
For instance, highly idealized ABMs can benefit from validation in
terms of experimental studies (as exemplified by Mohseni et al. 2021).
Moreover, qualitative research, such as ethnographic studies of
scientific communities, can help to empirically inform assumptions
used to build models (see, e.g., Ghorbani, Dijkema, & Schrauwen
2015).



Another emerging line of inquiry comes from the recent developments in
artificial intelligence. In particular, big data and machine-learning
models can be a fruitful way of generating input for ABMs, such as the
behavior of agents (see, e.g., Kavak, Padilla, Lynch, & Diallo
2018; Zhang, Valencia, & Chang 2023). For instance, natural
language processing technology has been used to represent agents who
express their arguments in terms of natural language (Betz 2022). Such
enhancements provide novel opportunities for highly idealized models:
from their robustness analyses in which certain assumptions are
de-idealized to the exploration of novel questions and phenomena.