# An Agent-based Model to Evaluate Interventions on Online Dating Platforms to
Decrease Racial Homogamy

Stefania Ionescu<EMAIL_ADDRESS>University of ZürichAndreasstr.
15ZürichSwitzerland8050 ,  Anikó Hannák<EMAIL_ADDRESS>University of
ZürichAndreasstr. 15ZürichSwitzerland8050 and  Kenneth Joseph
<EMAIL_ADDRESS>University at BuffaloDavis HallBuffaloNY, USA14260

(2021)

# An Agent-based Model to Evaluate Interventions on Online Dating Platforms to
Decrease Racial Homogamy

Stefania Ionescu<EMAIL_ADDRESS>University of ZürichAndreasstr.
15ZürichSwitzerland8050 ,  Anikó Hannák<EMAIL_ADDRESS>University of
ZürichAndreasstr. 15ZürichSwitzerland8050 and  Kenneth Joseph
<EMAIL_ADDRESS>University at BuffaloDavis HallBuffaloNY, USA14260

(2021)

###### Abstract.

Perhaps the most controversial questions in the study of online platforms
today surround the extent to which platforms can intervene to reduce the
societal ills perpetrated on them. Up for debate is whether there exist _any_
effective and lasting interventions a platform can adopt to address, e.g.,
online bullying, or if other, more far-reaching change is necessary to address
such problems. Empirical work is critical to addressing such questions. But it
is also challenging, because it is time-consuming, expensive, and sometimes
limited to the questions companies are willing to ask. To help focus and
inform this empirical work, we here propose an agent-based modeling (ABM)
approach. As an application, we analyze the impact of a set of interventions
on a simulated online dating platform on the lack of long-term interracial
relationships in an artificial society. In the real world, a lack of
interracial relationships are a critical vehicle through which inequality is
maintained. Our work shows that many previously hypothesized interventions
online dating platforms could take to increase the number of interracial
relationships from their website have limited effects, and that the
effectiveness of any intervention is subject to assumptions about
sociocultural structure. Further, interventions that are effective in
increasing diversity in long-term relationships are at odds with platforms’
profit-oriented goals. At a general level, the present work shows the value of
using an ABM approach to help understand the potential effects and side
effects of different interventions that a platform could take.

racism, social media, dating platforms, agent-based modeling

††journalyear: 2021††copyright: acmcopyright††conference: Conference on
Fairness, Accountability, and Transparency; March 3–10, 2021; Virtual Event,
Canada††booktitle: Conference on Fairness, Accountability, and Transparency
(FAccT ’21), March 3–10, 2021, Virtual Event, Canada††price: 15.00††doi:
10.1145/3442188.3445904††isbn: 978-1-4503-8309-7/21/03††ccs: Applied computing
Sociology

##  1\. Introduction

Online platforms have been criticized for their role in a profusion of
societal ills, from the increased spread of political misinformation (Benham
et al., 2012), to extrajudicial killings in the Philippines (Pimentel,
[n.d.]). Platforms have sought to address these issues with various
_interventions_ , from attempts to filter or restrict various forms of content
(e.g. hate speech) (Hern, 2020) to attempts to decrease discriminatory
practices by users (Dickey, 2016). Few, if any, would argue that these
interventions have fully addressed the problems at hand. Underlying agreement
to this high-level assertion, however, lies a more debated question– _what are
the limits of a platform’s ability to intervene on society’s ills?_

Given the documented problems platforms have helped give rise to, it would be
wrong to assume that they have no role to play in positive change. At the same
time, however, we must be careful to ascribe too heavy of a causal arrow to
platforms. Doing so is destined to lead to an ill-informed aura of
technological determinism (Marx et al., 1994), where we search for solutions
to societal problems by endlessly tweaking the structure of our technology,
ignoring other root underlying causes (Fisman and Luca, 2016).

How might we understand the limits to a platform’s ability to intervene to
reduce a given societal ill? Empirical work, especially based on natural
experiments (Malik and Pfeffer, 2016) and using algorithmic auditing (Sandvig
et al., 2014; Hannak et al., 2013), is critical, but is restricted to studying
the platform as it currently exists. Exploration of the effects of
interventions is thus limited to what platforms are willing and able to
implement given their profit-oriented goals. Even beyond these profit-related
restrictions, there are ethical questions about the use of experimentation
without the consent of platform users (Siroker and Koomen, 2013).

Even still, scholars have pursued ethical empirical research not bound by
profit-oriented goals, for example, by constructing platforms of their own
(Chen et al., 2017; May et al., 2019). But doing so is time-consuming and
expensive; it would be useful if we could first scope potential solutions and
their potential side effects in a more rapid and expansive way. Social theory
can partially fulfill this role (Radford and Joseph, 2020), but it can be
difficult to theoretically characterize, and much less operationalize, the
myriad causal factors underlying sociotechnical systems.

What is needed is a methodology that exists in this middle ground between
difficult empirical work on the one hand, and often abstract theory on the
other. Simulation has long filled this role of bridging theory and empirical
work in the social sciences (Schelling, 1971; Gilbert and Troitzsch, 2005;
Epstein, 1999), and more recently has proved useful in the study of
algorithmic fairness. For example, (Lum and Isaac, 2016), and later (Ensign et
al., 2017), use simulation to show concretely how the abstract notion of
feedback loops in predictive policing can lead to settings where police
unfairly target one population over another, despite equal crime rates.
Simulation has the added benefit of being applied to hypothetical people,
allowing us to carefully and fully consider the harms that may be caused by a
particular intervention before it has any real effects.In the present work, we
use simulation, and specifically, an _agent-based model (ABM)_ , to make
concrete a set of proposed interventions to address a given societal ill on a
given online platform, and to provide new ideas about their implications and
side-effects.

##### Online Dating

Specifically, we construct an ABM of an online dating platform and assess how
interventions theorized recently by Hutson et al. in their award-winning CSCW
paper (Hutson et al., 2018) affect the degree of racial homogamy in a
simulated society. _Racial homogamy_ is defined as the _tendency of
individuals to seek out and engage in long-term relationships with individuals
who are the same race_.

Critically, we do not argue that racial homogamy is in and of itself a
societal ill; there are many valid reasons why individuals, particularly those
identifying with an oppressed racial group, may want to maintain racial
homogamy in their relationships. However, racial homogamy has been well-
established to contribute to the reinforcement of racial inequality by
increasing racial segregation (Baldassarri and Bearman, 2007) and racialized
income inequality (Grodsky and Pager, 2001). Hutson et al. (2018) argue that
racial homogamy is hence worthy of dating-platform intervention, as it is one
racializer of economic and social inequality (Hutson et al., 2018).

After detailing this connection between racial homogamy and racial inequality,
Hutson et al. (2018) explore an array mechanisms within online dating
platforms that present opportunities for interventions to reduce racial
homogamy. For example, “search, sort, and filter tools” allow users to perform
initial searches for romantic partners, and thus might encourage users to make
particular decisions about the race of their partner. Hutson et al. therefore
take, at least implicitly, the stance that platforms could construct
interventions that reduce racial homogamy. Others, however, dispute this
claim. In particular, Anderson et al. (2014) study racial bias in online
dating from a sociological perspective, and conclude that “merely altering the
structural environment [on the platform]—say, by creating more opportunities
for individuals of different races to interact—would not necessarily
ameliorate persistent patterns of racial homogamy in romantic relationships”
(pp. 38-39). This claim puts the ideas presented in Anderson et al.’s work in
contrast with those presented by Hutson et al., who explicitly argue that
structural changes to platforms can reduce racial homogamy.

These two papers therefore present a difference of opinion on the extent to
which interventions on online dating platforms can impact racial homogamy, and
ultimately racial inequality. However, empirical analyses of this debate is
difficult for the reasons listed above. We therefore employ a simulation model
to bridge the middle ground between Hutson et al. (2018) theorized
interventions and potential future empirical work. Our work helps to
operationalize the theoretical constructs and to explore their impacts and
potentially unintended consequences in an _artificial_ , but _empirically and
theoretically grounded_ , complex sociotechnical system.

##### Contributions

The present work makes three contributions:

  * •

We provide a blueprint (including an open-source modeling
framework)111Repository link: https://github.com/StefaniaI/ABM-for-Online-
Dating.git. for the use of agent-based modeling to help operationalize theory
and explore the potential impacts of interventions of online platforms

  * •

Specific to racial homogamy and online dating platforms, we find that various
interventions theorized by Hutson et al. (2018) indeed decrease racial
homogamy in our simulated society according to our selected measure by as much
as 10%

  * •

_However_ , the benefits of these interventions are dependent on specific
assumptions about the sociocultural structure of that society, and are often
at odds with societal outcomes and profit-oriented goals of the platform

Like many social science endeavors (Watts, 2011), our core findings present
claims that, once stated, seem quite obvious. However, given their motivation
from established differences of opinion in published literature by leading
scholars, our work serves a critical role in the path forward to understanding
the potential of platforms to intervene to reduce racial homogamy, and as a
broader methodology to address similar questions in other domains.

##  2\. Related Work

###  2.1. Online Dating

Online dating plays an increasingly central role in the process of finding
romantic partners, with 15% of Americans reporting to have used an online
dating site (Cacioppo et al., 2013). Indeed, at least for heterosexual
couples, online dating has become the predominant means of meeting romantic
partners (Rosenfeld et al., 2019). This change in the means for dating has
opened the door to a debate on whether platforms can disrupt patterns of
assortative mating, and in particular decrease racial homogamy. While Hutson
et al. (2018) argue that platforms have the means of decreasing racial
homogamy through interventions, Anderson et al. (2014) think that this would
likely not be the case as homogamy is partly caused by same-race preferences
which cannot be changed through opportunity alone. These preferences influence
and are influenced by social norms, and their formation is based on imitation
and recurrent behaviour (Opp, 1982) and enforced through social verification
(Hardin and Higgins, 1996). To capture this phenomena, we consider norms and
their impact on preferences. In alignment with Hutson et al. (2018), we
acknowledge that platforms can potentially intervene to change norms on the
platform, so, in our model, we differentiate between offline and online norms.

The process people go through while dating online is complex in multiple ways.
First, one goes through a variety of different “phases” of dating, from
seeking information about dating platforms to developing a long-term
relationship (Finkel et al., 2012). Second, the decision-making process
depends on distinct factors such as the perceived level of compatibility
(Bruch and Newman, 2018), how much we can validly infer about others online
(Frost et al., 2008), and one’s own personal stereotypes (Krueger, 1996).

The complexity of studying dating have naturally given rise to the use of
simulations. Previous agent-based models for dating have looked at, for
example, how the selection of partners is influenced by space and mobility
(Smaldino and Schank, 2012), and whether assortative mating patterns could
explain previously observed statistics on marriages and divorce (Hills and
Todd, 2008). We build upon these examples in our work, but focus on the novel
problem of understanding the role of platform interventions on racial homogamy
in the context of online dating.

###  2.2. Uses of Simulation to Study Fairness

Starting with the seminal work of Schelling (1971) on housing segregation,
simulation has been widely leveraged to explore how inequality and unfairness
can arise and be combated in social systems. These studies have provided
useful fodder for the more recent application of simulation to questions of
_algorithmic_ fairness. For example, Martin Jr. et al. (2020) use a type of
simulation called System Dynamics Modeling (Karnopp et al., 2012) to show how
simulation can help us to think through the complex impacts of different
interventions in the context of credit lending. Others have leveraged
different kinds of simulation models to study lending in similar ways, for
example, within a reinforcement learning paradigm (D’Amour et al., 2020).
Still others have used simulation beyond the context of lending. For example,
various forms of simulation have been used to study fairness and feedback
loops in the context of predictive policing (as noted above (Lum and Isaac,
2016; Ensign et al., 2017)) the effects of recommender systems (Bountouridis
et al., 2019), and income inequality for drivers on rideshare platforms
(Bokányi and Hannák, 2020).

Like Bokányi and Hannák (2020), we use an agent-based model here. Agent-based
modeling involves the construction of virtual worlds in which artificial
agents interact according to a set of pre-specified rules (Epstein, 1999).
While behavior is thus predictable at the individual level, ABMs are useful
for understanding how these pre-specified micro-level behaviors result in
emergent properties, e.g., social biases across social groups (Joseph et al.,
2014) or opinion polarization (Schweighofer et al., 2019).

Our work compliments existing literature using simulation modeling to study
questions of fairness and equality in three ways. First, we study a concrete,
contemporary, and contested question about the role that online dating
platforms can play in reducing racial homogamy. Second, rather than focus on
algorithmic fairness specifically, we emphasize other ways in which online
platforms can have impacts on society. Finally, and at the same time, we
develop an agent-based model that can be easily extended to study algorithmic
fairness, complementing existing System Dynamics and reinforcement learning
platforms provided by prior work (D’Amour et al., 2020; Martin Jr. et al.,
2020).

##  3\. Model

Our model contains a variety of parameters, listed in Table 1 alongside
references, where applicable, to were used to determine their values. Below,
we first provide an overview of the model, and then further details on each
major element of the model.

###  3.1. Model Overview

(a) Creating a profile

(b) Searching (using filters)

(c) Browsing the (filtered) list of potential partners

(d) Looking at (and potentially responding to) messages

Figure 1. The actions agents can take on the platform Figure 2. Overview of relationship phases and transitions in each turn of an iteration, explained through the blue agent’s choices and actions. Quote boxes represent decisions the blue agent makes, and the 0/1 vectors represent the agent’s attributes, or the blue agent’s knowledge of potential partner’s attributes. Table 1.  Tabular description of model parameters (Left) and the values taken in the virtual experiment (Right). For parameters with a red-coulured value, we simulate each value for all simulated interventions but keep the values of the other parameters to red values (see Section 4). Parameters |  Values Taken
---|---
Varied in Virtual Experiment |
Tolerance to un-successful search turns |  20/  25/ 30
Out-platform norms for protected attributes |  0/0.1/  0.2/0.3
Strength of updates: norms $\to$ pref.s |  0/  1%/ 5%
Initial degree of ethnocentrism |  0/  0.25/ 0.5/ 1
Correlations in attribute generation: $\beta,\gamma$ |  0/ 0.4/0.6/0.8,  0.2/0.6
Fixed in Virtual Experiment |
Strength of updates: interaction $\to$ pref. |  2%
Strength of updates: average pref. $\to$ norms |  5%
Relative importance of out-platform norms when generating initial preferences |  20%
Failure tolerance to unsuccessful relationships |  20
# initial acquaintances in-group & out-group |  300, 100 (McPherson et al., 2001; Tajfel et al., 1971; Joseph et al., 2014)
Weight of searchable vs experiential attributes |  1 - 3 (Frost et al., 2008)
Pr. searchable attribute specified on profile |  50% (Lewis, 2016)
Pr. learning searchable & experiental attributes |  50% (Finkel et al., 2012)
# rounds until offline & long-term |  7, 37 (Ramirez et al., 2015; Brym and Lenton, 2001)
Pr. to meet while offline |  14%(Brym and Lenton, 2001; Muñoz-Rivas et al., 2007)
Pr. to consider a message (while in search) |  50%(Finkel et al., 2012)
Maximum # agents considered in a search turn |  30 (Frost et al., 2008; Finkel et al., 2012)
# iterations |  2000
Initial population size |  300
# agents added per iteration |  4

_Agents_ in our model are people that have at some point used our simulated
dating website. Agents are assumed to carry out a series of activities on the
site in pursuit of a romantic partner. These activities include creating a
profile (Figure 1a), searching for a partner (b), browsing the list of
potential partners returned by that search (c), and messaging (d). Agents in
our model perceive other agents according to their _attributes_. As in most
ABMs (Gilbert, 2007), attributes are just a vector of 0s and 1s, but are
assumed to represent real-world traits that might be important in the context
of dating, e.g. partisanship. All agents also have a “protected attribute,”
that is, an attribute relevant to questions about discriminatory dating
practices. We will focus on race as the protected attribute here. Moreover, we
will focus on a binarized model of race, that is, agents can be only one of
two races in the model. In the context of prior work we discuss above, the two
races of interest here are White and Black. Section 3.2 provides full details
on how agents are modeled.

As shown in Figure 2, relationships between agents evolve over a series of
_phases_ , from finding (or being found by) a potential partner, to
interacting with each other on the platform via messages (online
relationship), and finally to an offline relationship (Finkel et al., 2012).
At any phase, a relationship might end, at which point the agents will resume
their search for another romantic partner, or exit the platform. If a
relationship continues for long enough, it becomes a _long-term_ relationship.
Our primary outcome measures focus on racial homogamy in these long-term
relationships. and Section 3.3 provides complete details on the different
relationships phases.

The simulation is carried out via a sequence of iterations, or _turns_ , with
one turn being loosely the equivalent of one day. On the first turn of the
simulation, 300 agents are initialized and become the population of users on
the online dating site. On each subsequent turn, we add four new agents,
modeling a constant flow of new users. After new agents are added, all agents
get a turn to act. During their turn, agents can 1) conduct a search,
potentially using a set of search _filters_ provided by the site, 2) send a
message, 3) start a new relationship, 4) continue or end an existing
relationship, or 5) leave the platform, either because they have found a long-
term partner, or because they have become frustrated with the site.

As shown in Figure 2 , which of these things agents decide to do is based on
which part on the relationship phase they are in, and on the _decision model_
we construct for agents. The decisions made by an agent are determined by 1)
their attributes, 2) the attributes of their potential (or current) partner,
3) the _stereotypes_ they hold of how different attributes are related, and 4)
their _preferences_ for others with certain attributes. These preferences are
themselves informed by _social norms_ that exist both on and off the platform.
Section 3.4 provides details on how agents make decisions.

Agent’s attributes are static in our model. However, their stereotypes and
preferences _evolve_ as they interact with others and move through
relationship phases. The social norms governing all agents also evolve
throughout the simulation. Section 3.5 details when and how these updates to
stereotypes, preferences, and norms occur.

###  3.2. Agents

As in all ABMs, our agents have state and can take actions. Agents’ states
encompass their _type_ , _attributes_ , _stereotypes_ , _preferences_ , and
_knowledge of others_. The actions agents take, based on these states, are a
set of _decisions_ , which we describe in Section 3.4.

####  3.2.1. Type

Agents can be of two types, and are only attracted to agents of the opposite
type. We thus loosely mimic a hetero- and mono-normative online dating
website, as was studied by Anderson et al. (2014). We return to this
limitation in our conclusion. Agents are assigned to the two modeled types
with equal probability.

####  3.2.2. Attributes

Each agent $i$ has a vector of binary attributes, $a^{(i)}$. We distinguish
between race, $a^{(i)}_{0}$, and $K$ other attributes. Following theory in the
online dating literature (Finkel et al., 2012; Bruch and Newman, 2018), we
make two additional distinctions among attributes. First, we distinguish
between _matching_ and _competing_ attributes. For matching attributes, agents
try to find partners with similar values, while for the competing attributes,
they try to find partners with the highest possible values. We assume that the
protected attribute is matching.

Second, we distinguish between attributes visible only offline as
_experiential_ attributes, and ones that are visible both online and offline
as _searchable_ attributes. Examples of searchable attributes include personal
interests and location. Sense of humor is an example of experiential attribute
(Finkel et al., 2012). Since people do not share everything about themselves
in their profiles (Lewis, 2016), agents have a probability of $50\%$ to
specify each searchable attribute on the platform. In the example from Figure
1, the agent chose to specify their music taste, but not their race. We
describe the complete process of generating attributes for new agents in the
Supplementary Materials.

####  3.2.3. Stereotypes

Agents’ _stereotypes_ encompass their beliefs of _which attribute combinations
are more likely to be encountered_ (Krueger, 1996). We model the stereotypes
of agent $i$ as a matrix $G^{(i)}$. This matrix is initialized via a
theoretically grounded model that is also based on prior work. Specifically,
when generating a new agent, we first generate a sample of $300$ agents having
the same race, and $100$ agents with a different race. The imbalance in sample
sizes is motivated by homophily: people are, in general, more likely to meet
and know people similar to themselves (McPherson et al., 2001). In addition to
homophily, we also model in-group favoritism, i.e. that people tend to look
favorably on people like them (Hastorf and Cantril, 1954; Tajfel et al.,
1971). To capture this in our model, we follow prior agent-based models
(Joseph et al., 2014) and assume that agents also have an initial degree of
ethnocentrism. For example, as in (Joseph et al., 2014), if the degree of
ethnocentrism is $0.3$, then the agent will, on average, expect 30% of the
attributes of agents in the other-race sample to have values that the agent
does not like.

The Supplementary Materials contain full details on stereotype initialization,
and Section 3.5 on how stereotypes evolve over time.

####  3.2.4. Preferences

An agent’s _preferences_ define which attributes they believe are most
important in a potential partner. We model the preferences of agent $i$ as a
weight vector, $w^{(i)}\in\Delta^{K}$. This vector of preferences is in the
$K$th simplex, i.e. its entries sum to one, thus capturing the _relative_
importance of different attributes in the eyes of the agent. For example, a
weight vector with $w^{(i)}_{j}=1$ and all other entries of $0$ corresponds to
an agent who only considers the $j$th attribute to be important in a potential
partner.

The preferences of agents influence and are influenced by two different types
of social norms, i.e. sociocultural regulations on one’s preferences (Lizardo
and Strand, 2010).222We discuss initialization of preferences and norms in the
supplementary material. First, while interacting online, an agent impacts and
is impacted by _platform norms_. Second, when in an offline relationship, the
agent preferences inform and are informed by off-platform norms. Critically,
then, in our model as in the real world (Lizardo and Strand, 2010), agent’s
preferences thus vary in part based on social norms, which change as they move
between the context of the platform and their offline social life. At the same
time, norms are aggregations of the preferences of individuals, except, as we
will discuss, when explicit efforts are made by the platform to fix norms.

Agent preferences are initialized based on a procedure common in the decision-
making literature (Cantwell et al., 2020). We note here that two parameters
govern important aspects of this process. First, a parameter $\beta$ is used
to capture the level of correlation between race and other attributes. Second,
$\gamma$ captures the level of correlation between the non-protected
attributes. Varying these two parameters changes the predictability of one
attribute, given another. In our virtual experiment, both $\beta$ and $\gamma$
are changed. Most importantly, $\beta$ is varied to reflect different degrees
to which race can be inferred by agents based on other available attributes
when the agent chooses not to display race in their online profile.

####  3.2.5. Knowledge of other agents

As they interact with another agent, agents can become aware of the values of
new attributes of that agent. This knowledge is used to make decisions about,
e.g., whether to continue relationships. How agents acquire this knowledge is
discussed in Section 3.3.

###  3.3. Relationship Phases

Agents can be in one of three relationship phases: 1) searching, 2) in an
online relationship, or 3) in an offline relationship.333We make the
simplifying assumption that an agent can only be in one of the before-
mentioned phases at one time, e.g. one cannot be both searching and dating
offline.

####  3.3.1. Searching phase

When first entering the platform, an agent is in the searching phase. An agent
can search in two ways. First, the agent can perform a query for potential
partners using a search interface. Second, the agent can look through messages
in their inbox that they have received from other agents. In one turn of the
simulation, agents can perform up to $30$ search actions, mimicking the
typical length of a user’s session on an online dating website (Frost et al.,
2008; Finkel et al., 2012). A search action represents looking at either a
single search result or a single message in the agent’s inbox. The agent
randomly decides which of these two actions to take. If the agent decides to
view a profile from their search result list, they look at the profile of the
next search result and decide whether or not they should send that person a
message. If the agent decides to view a message in their inbox, they look at
the oldest un-read message and consider whether or not they should reply.

Search results are a randomly ordered subset of other agents currently on the
platform. Importantly, however, the agent _decides which other agents will
appear in their search results_. They do so by _filtering_ for potential
dating partners. The agent filters on a single attribute, the one that, on
this turn, is most important to them (i.e. for agent $i$, the attribute with
the highest value of $w^{(i)}$). In Figure 1, for example, interest in music
is the most important factor for the blue agent, and they hence use this as a
filter. The blue agent is then only shown profiles of other agents having a
passion for music.

If the agent decides to reply to a message in their inbox, their search ends,
and the two agents involved proceed to an online relationship phase. However,
the agent can send as many initial messages to agents in their search results
as they would like. An _unsuccessful search_ is considered to be a turn in
which the agent is not interested in answering any of the messages they have
looked at, and in which they did not find at least half of the profiles
they’ve looked at interesting enough to send an initial message. Too many
unsuccessful search turns would make the agent exit the platform.

####  3.3.2. Online relationship phase

After answering or getting a reply to an initial message, the agent moves to
an Online Relationship Phase with another agent. While in this phase, in each
iteration the two agents within the relationship have an online interaction.
During this interaction, three things happen. First, agents may update their
knowledge about the attributes of the other. Going back to the example in
Figures 1 and 2, when entering the relationship, the blue agent only knows the
value of the green agent’s interest in music. After one interaction, they have
also learned the self-identified race of the green agent. Second, agents
update their stereotypes based on what they learn about the other; see Section
3.5. Finally, agents decide whether or not to continue the relationship. If
both decide to continue, the interaction is _positive_ , and the relationship
will continue with a new interaction in the next iteration. Otherwise, the
interaction is _negative_ , and the relationship ends.

After deciding on whether or not to continue the relationship, the preferences
of agents are updated based on whether the interaction was positive or
negative (see Section 3.5) and the turn of the agents ends. If the agents have
decided to end the relationship, their _tolerance to failed relationships
decreases_. Agents will exit the platform if this tolerance reaches $0$, and
otherwise they return to searching. If the agents decide to continue the
relationship, they either continue the online relationship, or, if the
relationship has lasted long enough, move to the Offline Relationship Phase.

####  3.3.3. Offline relationship phase

The turn of an agent in an Offline Relationship Phase progresses similarly to
the Online one, with two key differences. First, if the interaction was
positive, the agents either continue the relationship (as above); however, if
the relationship lasts for $30$ iterations, the agents then exit the platform
into a _long-term_ relationship. Second, while offline, in each iteration
there is a fixed $1/7$ chance of interacting. This means that, in expectation,
people in offline relationships meet once per week (Brym and Lenton, 2001;
Muñoz-Rivas et al., 2007).

###  3.4. Agent Decision-Making

In the Searching phase, agents answer the questions (posed to themselves),
”should I reply to/send an initial message to this agent?”. When in a
relationship, the agent answers questions of the type ”should I continue being
in a relationship with this agent?”. Both of these are yes/no questions, and
agents use the same decision function, described below, to answer them.

Suppose that agent $i$ must make a decision about sending a message or
continuing a relationship with agent $j$. We assume agent $i$ may know only an
incomplete vector of agent $j$’s attributes, which we call $\hat{a}^{(j)}$. We
use $\hat{a}^{(j)}\subseteq a$ if $a$ extends the incomplete vector of
attributes $\hat{a}^{(j)}$, i.e. $a$ is a complete vector that has the same
entries as $\hat{a}^{(j)}$ on the known attributes. Lastly, for the agents
$i,j$ there is some time value, $t$, capturing the amount of time spent in
that specific phase with one another (for somebody in the searching phase or
who just started an online or an offline relationship, $t=0$).

In making decisions, agents consider a) the time spent so far with the other
agent and b) how well the attributes of the other agent align with their own
attributes and preferences. Time is an important predictor for breakups, as
agents that have stayed longer in a relationship are less likely to interrupt
their relationship. For example, people tend to be especially selective in
their first offline date together (Long, 2010). Consequently, using the above
introduced notation, the probability that agent $i$ answers yes to a decision
question regarding agent $j$ is given by the following sigmoid function:

$\mathbb{P}(\text{yes})=\frac{1}{1+\exp\left(-\mathbb{E}\left[w^{(i)}\cdot
s^{(i)}(a)|\hat{a}^{(j)}\subseteq a\right]-t\right)}.$

Two aspects of the above equation require further explanation. First, the
expected value is taken over all possible attribute combinations extending the
incomplete information of $i$ over the attributes of $j$. The probability of
each combination is given by the current stereotypes of $i$. Thus, $i$’s
stereotypes impact what they _expect_ the unknown attributes of $j$ to be.
Second, the function $s^{(i)}(a)$ is an attribute scoring function that for a
given agent and complete attribute vector returns a vector with entries $1$,
$0$, and $-1$ depending on whether each attribute is viewed as good, neutral,
or bad. Agents see a matching attribute value as being good if it is the same
as their own and bad if it is different. Similarly, they see a competing
attribute value as good if it is larger than their own, neutral if it is
equal, and bad if it is lower. The Supplementary Material provides a
formalization of this scoring function.

###  3.5. Belief, Preferences, and Norm Updates

Agents’ stereotypes, preferences, and the norms of the platform change on each
iteration.

####  3.5.1. Agent Stereotype Updates

Recall that when generating an agent, we construct $G^{(i)}$, a sample of
attribute combinations they have encountered so far. After each interaction,
an agent observes a new combination of attributes. If that observation is a
complete list of attributes, they add it to the sample. Otherwise, they find
the probabilities of each possible complete combination of attributes that
could extend the observed one and adds a proportional fraction to each of
these categories. For example, consider somebody that observes the combination
$(1,0,\bot)$ after an interaction, and their sample was $25$ observations for
$(1,0,0)$ and $75$ for $(1,0,1)$. Post-interaction, their sample changes to
$25.25$ and $75.75$.

####  3.5.2. Norm and Preference Updates

Preferences and on-platform norms both change during each iteration. First,
agent preferences change according to their interactions based on a) whether
the interaction was positive or negative and b) the attributes of agents. For
example, a positive interaction with somebody of the same race will change the
preference of the agent by increasing the preference of an agent to date
someone of the same race. In contrast, a negative interaction with someone of
the same race will make the agent _less_ likely to seek out a same-race
partner in the future.

At the end of each simulation turn, norms and preferences are updated to
reflect their convergence (Lizardo and Strand, 2010). Specifically, the on-
platform norm gets closer to the average preference of agents on the platform,
and the preferences of agents are updated by getting closer to the respective
norm. Each update is carried out by taking convex combinations between the
original value of the norms (preferences) and the values of the influencing
preferences (norms). The extent to which norms determine preferences, and vice
versa, is set via model parameters. See the supplementary information for more
details on how these updates are made, as well as details on initialization.

##  4\. Virtual Experiment

We now describe how we use our model to explore three different classes of
_interventions_ outlined by Hutson et al. (2018). In Section 4.1, we
operationalize these interventions, which are also listed in Table 2. We then
discuss how we vary model parameters to evaluate the effectiveness of these
interventions under different assumptions about the underlying society.
Finally, we detail the outcome measures we use to understand the impacts of
these interventions.

###  4.1. Interventions

Hutson et al. (2018) suggest three places where platforms might intervene to
reduce bias and discrimination on intimate platforms: 1) at the search, sort,
and filter level, 2) at the matching algorithm level, and 3) at the community
policy and messaging level. Here, we focus on the first and the last, and
leave out the second for two reasons. First, not all platforms use matching
algorithms, and even when they do, the algorithm differs substantially.
Second, the literature on the possible algorithm to implement is vast (Gale
and Shapley, 1962; Shapley and Scarf, 1974; Abdulkadiroğlu et al., 2005;
Hitsch et al., 2010; Tu et al., 2014; Xia et al., 2015; Brozovsky and
Petricek, 2007). Consequently, there are many different alternatives to
substitute the existing matching algorithm with. A comparison between these
various procedures is beyond the scope of this paper.

####  4.1.1. Filtering Interventions

The ease of filtering out people based on their attributes when searching for
potential partners in online systems may exacerbate racial homogamy in two
ways: 1) it reduces the diversity in search results and 2) it makes race a
legitimate basis for decisions (Lewis, 2016; Hutson et al., 2018; Bowker and
Star, 2000). The first suggested solution to mitigate this problem by Hutson
et al. (2018) is to purposefully introduce diversity into search results, e.g.
by removing the ability to filter by certain attributes entirely. We refer to
any intervention along these lines as a _filter intervention_. We consider
four different filter interventions, comparing them to a baseline of
traditional platform behaviors.

As explained above, without any intervention, agents will filter their
searches for romantic partners by the attribute they consider to be most
important. We refer to this baseline option as _Strong filtering_. From this
baseline, Hutson et al. (2018) note that interventions to reduce racial
homogamy can be constructed along two dimensions. First, we can restrict the
possibility of filtering on race entirely. To signal such an intervention we
add a ”non-race” label to the filter name. Second, we can introduce diversity
by showing profiles of agents who did not provide the given attribute in their
profile. We label filtering strategies that only include the desired setting
of the given attribute as ”Strong,” and those that also include potential
partners who simply do not fill out a value for this attribute as ”Weak”.
Combining interventions on these two dimensions gives three more filtering
options, namely Strong non-race, Weak, and Weak non-race. We also add a fourth
intervention where no filtering is allowed at all, on any attribute, i.e. the
”Off” filtering option.

The five filtering conditions are most clearly stated with an example.
Consider the agents in Figure 1. Let us assume the platform opts to use
_Strong_ filtering. Further, let us assume that the blue agent’s strongest
preference is for a romantic partner that is the same race. In this case, the
blue agent will search for others with the same race, and will see only other
potential partners who publicly self-identified with that race on the
platform. If the platform had _Weak_ filtering, the agent would also see any
other agents who a) have the same self-identified race or b) do not specify
any race on the platform. If the platform employed _Strong non-race_
filtering, the platform would not allow the agent to search by race at all.
The agent would therefore use their second strongest preference (say, music),
to perform their search. Weak non-race follows similarly, and in the Off
condition, agents would simply not be able to filter at all. Finally, note
that we have described the process with a matching attribute; for competing
attributes, agents are always assumed to search for other agents who hold the
desired attribute.

Table 2. The three types of Interventions, the baselines for each, and the different intervention conditions we test. Intervention |  Description
---|---
Filtering Interventions
_Strong_ |  _Baseline. Only see others with the preferred attribute value on the most important attribute._
Strong non-race |  Strong filtering, if not on race. Otherwise, Strong filter on second most important.
Weak |  Also shows profiles with unknown values on most important attribute
Weak non-race |  Weak filtering, but not on Race.
Off |  No filtering allowed.
Attribute Intervention
_5 attributes_ |  _Each agent has the protected attribute, plus one of each kind of attribute_
9 attributes |  Each agent has the protected attribute, plus two of each kind of attribute
Norm Intervention
_Off_ |  _Baseline. On-platform norms are an average of agent preferences_
On |  On-platform norms are fixed to 0 on race, encouraging agents not to consider it

####  4.1.2. Attribute Intervention

Removing filtering options can make it harder for agents to find the partners
they desire. A suggested solution by Hutson et al. (2018) to mitigate this
problem is to add or give additional attention to new non-race attributes on
which agents can filter (e.g. political view or smoking preferences). We refer
to this intervention as an _Attribute Intervention_. In our model, this
intervention is implemented by increasing the number of dating-relevant
attributes from a baseline of 5 to a total of 9, i.e. from having one non-
racial attribute of each type, to having two of them.444As a reminder,
attributes differ along two dimensions matching-competing and searchable-
experiential, thus giving 4 possible types. Note also that Hutson et al.
(2018) additionally suggest a variant of this intervention, namely one based
on the minimal group paradigm in social psychology (Tajfel et al., 1971). We
provide results for this similar intervention in the supplemental material.

####  4.1.3. Norm Intervention

The final intervention from Hutson et al. (2018) that we model are attempts to
change or outline platform norms. Hutson et al. (2018) suggest that different
community policies, such as introducing pledges, educating users on the effect
of expressing racial preferences, and detailing the disparities faced by
racial minorities, can encourage social norms to date interracially. Norms, in
turn, are known to be perhaps the most powerful mechanism for changing
discriminatory behaviors (Tankard and Paluck, 2016). In our experiment, the
platform norm intervention fixes the value of the on-platform norm for the
race attribute to $0$. This means that agents, via the link between norms and
agent preferences, are encouraged not to consider race at all in their
decisions. Differently from the baseline condition, when on-platform norms are
updated to reflect the average of preferences, the value on the race dimension
does not change and continues to be 0 (see the Supplemental Material).

###  4.2. Assumptions about Society

Table 1 lists the model parameters we vary or keep constant in our virtual
experiment. As we will show, consideration of these parameters is critical to
understanding how well claims about intervention effects generalize to
different assumptions about society. At the same time, a full exploration of
the parameter space is infeasible. We take a three-pronged approach to
addressing this. First, as noted in the table, many of these parameter and
their values are theoretically grounded and/or empirically informed by prior
work. Second, we engaged in sensitivity testing, described in the appendix,
across a range of values for all parameters. Finally, for our virtual
experiment, we fixed parameters that did not appear to have significant
impacts on model outcomes. For parameters that did appear to have an effect,
we select a default value for each (coloured in red in Table 1); when varying
one such parameter, we keep the others at their default values.

As shown in Table 1, we vary five model parameters in our simulation. First,
tolerance to unsuccessful search turns, which allows us to see how results
change if agents are more or less tolerant to seeing undesired profiles and
messages while browsing. Second, we vary racial bias in the out-platform
norms, capturing how much importance society puts on same-race relationships
compared to the other attributes of individuals. Third, we vary the influence
norms have on preferences, i.e. the percentage of individual preferences
explained by social norms. Fourth, we alter the initial degree of
ethnocentrism, which intuitively corresponds to the extent to which agents are
likely to prefer same-race relationships. The last parameters we change are
the levels of correlation between attributes, i.e. $\beta$ and $\gamma$; we
test in turn scenarios with zero, low, mild, and high correlations between
race and the other attributes ($\beta$), and low and medium correlations
between the other non-race attributes ($\gamma$). Each simulation runs over
$2000$ iterations, which corresponds roughly to a period of over $5$ years.
Moreover, we use $20$ distinct random seeds for each scenario, thus having a
total of $3000$ runs.

###  4.3. Outcomes

We treat racial _hetero_ gamy–the opposite of racial homogamy—as the main
outcome variable when studying the impact of the simulated interventions on
racial homogamy.555We do so since we would like to emphasize the positive
nature of the proposed change. We operationalize racial heterogamy as the
percentage of long-term relationships that are interracial. However, a
proportion-based measurement can of course vary based on changes in the
numerator or denominator. Thus, an increase in racial heterogamy might be
caused by a decrease in efficiency, i.e. the overall number of long-term
relationships, or an increase in the number of interracial relationships. We
therefore investigate these two quantities separately as well.

Lastly, we provide two measures of user satisfaction. , which are important
for two reasons. First, profit-oriented decisions by platforms depend in part
on the number and, hence, also on the happiness of users. This in turn
suggests platforms may be less likely to implement interventions which
threaten these metrics. Second, even if a platform was somehow persuaded to
implement such interventions, the free nature of the market permits
unsatisfied users to easily move to a different dating platform. To capture
user satisfaction, we use two measures informed by literature on online dating
(Frost et al., 2008). First, we measure the percentage of time spent in an
offline relationship, relative to online dating phases. Second, we measure the
number of users who exit the platform because they have exhausted their
tolerance for unsuccessful online searches.

##  5\. Results

###  5.1. Average Effects on Racial Heterogamy

Each intervention proposed by (Hutson et al., 2018) increases racial
heterogamy. However, as shown in Figure 3, the magnitude of these effects
vary. The largest positive effect of any intervention is the Strong non-race
filtering condition, i.e. when our simulated platform removes the option of
filtering searches by race but allows agents to search on all other
attributes. Doing so increases racial heterogamy from 10.5% to 18.3%. In
contrast, a Weak filtering option increases the percentage by 3.4% over the
baseline.

Combining the Strong non-race filtering intervention with the norm and
attribute interventions further increase racial heterogamy. However, the
effects are non-additive. For example, the effect of the norm intervention
decreases after implementing a filtering intervention. Applying the norm
intervention to the baseline filtering option results in a 4% increase in
interracial relationships, while if it is applied on top of a Strong non-race
intervention, the impact reduces to less than 3%. Finally, adding dating-
relevant attributes produces an additional and relatively stable increase of
2% to 3%. These results suggest the potential of interventions on the
platform, but also that their impacts may quickly reach a saturation point.

Figure 3. Racial heterogamy (y-axis) given a particular filter intervention
(x-axis), and depending on whether or not the norm intervention is On (green
lines, Xs as points) or Off (blue lines, triangles as points) and whether or
not the attribute intervention is off (left subplot) or on (right subplot)

###  5.2. Effects on Total Number of Relationships

We also find that increases in racial heterogamy–the _percent_ of long term
interracial relationships–are not solely due to an increase in the _number_ of
interracial long-term relationships. Rather, as depicted in the first part of
Figure 4, most interventions are also paired with a significant decrease in
the overall number of long-term relationships.

With respect to the filtering interventions, removing the ability to filter by
race causes an almost 20% drop in the total number of long-term relationships.
This happens, in short, because agents, and people, often _want_ to filter by
race, whether they are conscious of that fact or not (Anderson et al., 2014).
Thus, removing this filter makes it harder to find partners that agents
desire. The attribute intervention causes this decrease for a separate, non-
obvious reason. Specifically, including additional attributes makes the search
space becomes more diverse, thus making it harder to find the “right” partner.
Most obviously, with more attributes, the chance of two agents matching on all
attributes decreases.

Finally, with respect to the norm intervention, influencing the preferences of
agents by changing on-platform norms produces fluctuating evaluation criteria,
especially when paired with racially biased offline norms. Agents enter the
simulation with their own potentially racially-biased preferences. As they
interact on the platform, their preferences slowly converge towards the
racially unbiased on-platform norm. However, when going through the offline
relationship phase, their preferences again adapt to the racially biased
societal (offline) norms, thus shifting the evaluation criteria of agents
again. These fluctuations means it takes longer to find suitable partners; so,
if the process extends over the tolerance of agents for either bad
recommendations or failed relationships, they will exit the platform without
entering a long-term relationship. In our model, as in modern cultural
sociological theory (Patterson, 2014), then, the impact of the norm
intervention is therefore contingent on the extent to which norms within the
context of the platform permeate to other normative context by way of
individual preferences.

###  5.3. Effects on User Satisfaction

Our simulations suggest that most interventions, on average, will decrease
user satisfaction, especially filter interventions that do not allow users to
filter on race. However, the magnitude, and in select cases, even the sign of
these effects vary according to the settings of the other interventions, and
across the two metrics considered. These findings present evidence that
interventions can potentially be carried out without decreasing user
satisfaction, but that these interventions typically are weaker (in terms of
reducing racial homogamy), that effects on one satisfaction metric may
contradict results on another, and that which combinations of interventions
are most effective is not obvious a priori.

Specifically, Figure 5(a) shows that filtering interventions, to varying
degrees, all decrease the percentage of agent’s time spent in offline
relationships, and in doing so likely decrease user satisfaction. The most
drastic impacts are from the Strong non-race intervention, where 19.0% of
agents’ time is spent offline, as opposed to 24.2% in the baseline condition.
This effect is, however, mitigated when paired with the attribute
intervention. This is likely because agents become more satisfied with the
non-race filtering options they have available to them. Other interventions,
while less capable of reducing racial homogamy than the Strong non-race
filter, have a much more limited effect on user satisfaction.

These observations for time spent offline also appear when measuring user
satisfaction in term of the percentage of users who leave the platform without
a partner. As shown in Figure 5, however, certain filter interventions can
actually _increase_ user satisfaction along this metric. Specifically, the use
of Weak or no filtering can increase satisfaction on this metric, because
agents often do not report all of their attributes, and thus including also
the profiles with unknown attribute-values lowers the chance of excluding
possibly interesting partners. Full descriptions of other patterns in Figures
5(a) and 5 are included in the Supplemental Material.

Figure 4. Number of total (left) and interracial (right) long-term
relationships (y-axis) for the filter conditions (x-axis) and the norm
intervention (Blue: off, Green: on). Results are only shown without the
attribute intervention

(a) The percentage of time spent in offline relationships.

(b) The percentage of exits due to exhausting the tolerance for un-successful
search turns.

Figure 5. Plots showing the level of user satisfaction with the application of
different interventions.

###  5.4. Intervention Impacts under Different Assumptions about Society

Figure 6. The first and second order marginal effects of the varied parameters
on the percentage of out-group long-term relationships, for the 10 effects of
strongest absolute value.

Finally, we explore how interventions fare under different assumptions about
society. Broadly speaking, we find, as reflected in Figure 6, that
interventions do have strong stand-alone beneficial effects, but that their
impact is diluted if certain, often reasonable, assumptions about society are
met.

The figure shows coefficients resulting from a linear regression with racial
heterogamy as the outcome variable, and all (centered and scaled) model
parameters in Table 1 _and their pairwise interactions_ as independent
variables. For readability, Figure 6 only includes the 10 coefficients with
the strongest absolute effect. The regression model fits the data quite well
($R^{2}=0.83$), indicating that first- and second-order effects explain much
of the dynamics of the complex system we model, but also that the model cannot
be fully explained by phenomenon enumerable by theory alone.666See the
Supplementary Material for full results of the first-order effects.

Filter interventions have the largest absolute effect on racial heterogamy. On
average, across all simulated societies, removing the possibility to filter by
race increases racial heterogamy by $7.65\%$ (CI = $(7.43,7.88)$)777Note that
confidence intervals are provided here but should be cautiously interpreted,
as they can be made infinitely small simply by running additional simulations.
The Norm intervention also has a considerable positive impact, as it alone
induces an average increase of $3.95\%$ (CI = $(3.73,4.17)$). The impact of
these interventions reflects their potential to have real and possibly lasting
changes on society.

Not all interventions we tested, however, increase racial homogamy in the
manner theorized by Hutson et al. (Hutson et al., 2018). Specifically, we find
that only _matching_ (as opposed to competing) and _searchable_ (as opposed to
experiential) attributes produce a meaningful increase in racial heterogamy.
Adding other types of attributes can actually have negative effects, either in
decreasing overall numbers of relationships while not impacting the number of
interracial relationships, or in simply leading to a decrease in racial
heterogamy.888See the Supplement for full results These findings underscore
the importance of using simulation to think through the operationalization of
potential interventions, especially by combining various theoretical models
(here, of intervention and attribute types) into a single simulation model.

Other results in Figure 6 show, unsurprisingly, that different assumptions
about society reflect drastically different baselines from which this
improvement might come. For example, we see that racial heterogamy is
significantly _reduced_ as correlations between race and the other attributes
increase, and with an increase in racial bias in off-platform norms. Perhaps
most importantly, however, Figure 6 reveals that intervention effects and
societal conditions can _interact_ to change the effectiveness of an
intervention. For example, the Norm intervention is particularly effective
when we make the assumption that agent preferences are strongly impacted by
social norms. But, adding a non-race filtering option is less effective with
increased values of the $\beta$ parameter. Put another way, the effectiveness
of not allowing individuals to search by race on an online platform is limited
by the extent to which race is signaled by, or correlated with, other
attributes that the platform _does_ allow one to search on.

##  6\. Conclusion

In their recent articles, Hutson et al. (2018) and Anderson et al. (2014)
present contrasting views on the extent to which online dating platforms can
intervene to impact long-term patterns of racial homogamy in online dating. In
the present work, we developed an agent-based modeling approach that
synthesizes modern perspectives in (cultural) sociology, the psychology of
online dating, and social computing to help us explore this debate.

Before discussing the implications of our work, we again explicitly call out
two critical assumptions that our model makes. While we allow race to co-vary
with other individual attributes, reflecting its multidimensional nature (Sen
and Wasow, 2016), our virtual experiment does ultimately assume that race, and
gender, are both binary, i.e. that one can easily and completely specify an
individual’s race or gender. Doing so lends an aura of racial and gender
determinism to our work that FAccT scholars are rightfully skeptical of (Hanna
et al., 2020). Our experiment also assumes that these variables are
independent, and thus ignores important intersectional identities. However, we
emphasize that these assumptions are made only in the present virtual
experiment, and are by no means hard-coded into our open source simulation
framework. We hope that we and others can therefore easily explore other
constructions of race, dating, gender, and sexual preference in future work.

These limitations in mind, our findings shed critical light on the
effectiveness and side effects of the interventions proposed by Hutson et al.
(2018) in reducing racial homogamy, and ultimately racial inequality. Our work
implies that the extent to which these interventions will be effective depends
on four things. First, it depends on what we assume about society. We find
that some of the proposed assumptions are effective only if we assume that
people are unable to infer (perceived) race from other cultural preferences
along which race can be performed online, an assumption that is potentially
untenable given what is known about correlations between cultural attributes
in general (DellaPosta et al., 2015) and perceptions of race in particular
(Freeman and Ambady, 2011). And, other interventions are effective only if we
assume that 1) the platform can significantly impact social norms towards
interracial dating on their site and that 2) these norms have more significant
effects on individuals than other “offline” normative structures (e.g. the
family) that have historically encouraged racial homogamy.

Second, it depends on what platforms, and society, are willing to accept as
sacrifice for progress on racial homogamy. Our model makes explicit these
potentially non-obvious tradeoffs, quantifying how certain interventions to
reduce racial homogamy have negative impacts on user satisfaction and rates of
overall long-term relationships. Third, it depends on how long one is willing
to wait. We find that changing on platform norms have a limited effect;
however, our model only studies short-term effects. Changes in large-scale
social norms can take much longer, and thus the payoff of these normative
interventions may exist only much further into the future as on-platform norms
influence agent preferences, which in turn slowly reshape norms in other
contexts. Finally, it depends on what one’s ultimate goal is. If one’s
ultimate goal is to increase racial homogamy to any degree, our results
provide evidence that this is possible under a fairly wide range of
assumptions. However, if one’s goal is to eradicate racial homogamy, then
interventions at the platform level seem to hit a saturation point well below
true equality. These technological interventions will therefore almost
certainly never be enough; they must be supplemented with structural and
normative social change beyond the platform.

While one could argue that these findings are post-hoc somewhat obvious, our
work is motivated by a disagreement in the existing literature over the
potential for platform intervention. This should serve as an important
reminder that everything is obvious once it is observed (Watts, 2011), and we
hope motivates the use of our approach to other domains. Especially when
unsuitable changes could have large negative effects, constructing a model and
simulating different possible outcomes could help explore potential problems
to those interventions that emerge within a complex sociotechnical system.

###### Acknowledgements.

We thank Yuhao Du, David Garcia, Atri Rudra, Christoph Stadtfeld, Aleksandra
Urman, and the FAccT reviewers for their valuable feedback on this work. KJ
was supported by an NSF award IIS-1939579, with partial support from Amazon.

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Lukas Malburg, Joscha Grüger, Ralph Bergmann 11institutetext: Artificial
Intelligence and Intelligent Information Systems,
University of Trier, 54296 Trier, Germany
11email<EMAIL_ADDRESS>
http://www.wi2.uni-trier.de 22institutetext: German Research Center for
Artificial Intelligence (DFKI)
Branch University of Trier, 54296 Trier, Germany
<EMAIL_ADDRESS>
# An IoT-Enriched Event Log for Process Mining in Smart Factories

Lukas Malburg 1122 Joscha Grüger 1122 Ralph Bergmann 1122

###### Abstract

Modern technologies such as the _Internet of Things (IoT)_ are becoming
increasingly important in various domains, including _Business Process
Management (BPM)_ research. One main research area in BPM is process mining,
which can be used to analyze event logs, e. g., for checking the conformance
of running processes. However, there are only a few IoT-based event logs
available for research purposes. Some of them are artificially generated and
the problem occurs that they do not always completely reflect the actual
physical properties of smart environments. In this paper, we present an IoT-
enriched XES event log that is generated by a physical smart factory. For this
purpose, we create the SensorStream XES extension for representing IoT-data in
event logs. Finally, we present some preliminary analysis and properties of
the log.

###### Keywords:

IoT-Enriched Event Log, SensorStream XES Extension, Process Mining, Physical
Smart Factory

##  1 Introduction

The combination of _Business Process Management (BPM)_ methods with the
_Internet of Things (IoT)_ promises several advantages for both sides
Janiesch.2020_Manifesto . The smart environment sensed and actuated by IoT-
devices can benefit from process modeling methods for controlling data
acquisition and actuation of resource functionalities
Seiger.2022_IntegratingProcessManagement ; Malburg.2020_FactoriesAndBPM .
Moreover, _Process Mining (PM)_ ProcessMiningManifesto  techniques can be
applied in smart environments Leotta.2015_ProcessMining  such as manufacturing
RinderleMa.2021_ProcessAutomationAndMining ;
Seiger.2022_IntegratingProcessManagement  to check conformance w. r. t. the
given process model or to adapt and optimize processes when runtime failures
occur. On the other hand, BPM can benefit from systematic data collection and
the variety of IoT-data, e. g., event data or context data. BPM research
artifacts can then be modified to achieve appropriate analysis results with
this more complex IoT-data. In current research, some event logs for PM in
smart environments have been proposed (e. g., stertz2020analyzing ;
Zisgen.2022_IoTEventLogGenerator ; Serral.2022_SupportingUsers ). Even though
these provide a good basis for research, they are mostly synthetically
generated, as acquiring real-world data for research purposes can be very
difficult Malburg.2020_FactoriesAndBPM ; Klein.2019_PredM . However,
artificially generated data does not always completely reflect the actual
physical properties of smart environments, such as runtime behavior and ad-hoc
interventions Malburg.2020_FactoriesAndBPM . Another current issue is the lack
of support by the _eXtensible Event Stream (XES)_ format gunther2014xes  to
represent IoT-data appropriately in the log. In previous work
Grueger.2022_SensorStreamXESExtension , we introduce a first step towards a
XES extension to enable representing IoT-enriched event logs. In this paper,
we present a concrete IoT-enriched event log produced by our physical smart
factory. The log is available at Malburg.2022_EnrichedEventLogDataset  with
documentation. The main advantage by using small-scale physical simulation
models is that it enables to conduct laboratory experiments while maintaining
real world environmental conditions of production lines. Thus, they provide
much more realistic data than synthetically generated data. A further
advantage is that developed research artifacts can be evaluated by using the
factory model. This strengthens the results in a scenario that is closer to
real-world production lines and, thus, facilitates the transfer to them
Malburg.2020_FactoriesAndBPM ; Seiger.2022_IntegratingProcessManagement .

In the following, Sect. 2 describes how the proposed IoT-enriched event log
has been generated in the physical smart factory. Afterwards, a detailed
description of the event log is given in Sect. 3. Preliminary analyses are
presented in Sect. 4 and, finally, Sect. 5 summarizes the paper.

##  2 Data Acquisition

To generate our proposed IoT-enriched event log, we use a _Fischertechnik
(FT)_ physical factory simulation model illustrated in Fig
1111https://iot.uni-trier.de. The factory represents two production lines that
work independently of each other but are connected for the exchange of
workpieces.

Fig. 1: Process-based Control of the Fischertechnik Factory Simulation Models.
Seiger.2022_IntegratingProcessManagement

Each shop floor consists of six identical machines. In addition, there are
individual machines on each shop floor, i. e., a _Punching Machine (PM)_ and a
_Human Workstation (HW)_ on the first shop floor and a _Drilling Machine (DM)_
on the second one. To enable BPM-related research, we use a service-based
architecture Seiger.2022_IntegratingProcessManagement ;
Malburg.2020_SemanticWebServices_IN4PL ; Malburg.2020_FactoriesAndBPM  to
control the production lines in a process-based fashion by _Workflow
Management Systems (WfMSs)_. The data produced by the machines is pushed to
Apache Kafka222https://kafka.apache.org/. Based on this endpoint, we extract
the IoT-data from Kafka and the data on process executions from the used web
server, which contains further concrete information compared to the log from
the WfMS. For example, besides the start and end time of each activity, the
reasons for runtime exceptions and the planned operation times are also logged
(see Sect. 3). We model 16 processes that are executed in the smart factory.
In total, we execute processes in the factory for more than 20 hours and
recorded its IoT-data consisting of data from sensors and actuators as well as
the process data. Based on the three data sources, we build the IoT-enriched
event log by applying a comprehensive pre-processing. We publish the log in
two versions, an original version in which harmonization of data, unification
of names, mapping of sensor data to events from the _Process Execution Engine
(PEE)_ , and a mapping of events, actuators, and sensors to entities in the
underlying FTOnto ontology Klein.2019b_FTOnto  of the factory were performed
(see figure 2). On the other hand, a corrected event log based on the pre-
processed original log is proposed. Here, in addition to the basic pre-
processing, errors in the data were also corrected. To this end, missing
events were restored, duplicates were removed, missing data was generated, and
errors caused by time shifts were corrected. Using the SensorStream XES
extension, the log was then generated Grueger.2022_SensorStreamXESExtension .

Fig. 2: Pre-processing of data From the Process Execution Engine (PEE), the
middleware and from the actuators and sensors. The dashed lines indicate which
steps were applied only to the corrected log.

##  3 Description of the IoT-Enriched Event Logs

All event logs are provided in XES format. The Concept, Identity, Time,
Lifecycle, and the SensorStream XES Grueger.2022_SensorStreamXESExtension
extensions are used to represent the data. The SensorStream XES extension
enriches the XES standard with the ability to integrate complex sensor data
into the event log. For this purpose, the extension introduces the
sensorstream schema. This uses the SOSA and SNN333https://www.w3.org/TR/vocab-
ssn/ ontology to semantically describe the sensor data, e. g., which sensor
produces the data and to which actuator this sensor is attached. In the event
logs provided, the extension is used to describe sensor data at the event
level, which forms the context of the event execution. For each sensor value
the data type, the interaction type (observation or actuation), the type of
the receiving system as well as the assignment in the underlying FTOnto
Klein.2019b_FTOnto  ontology, and the timestamp are described. The log
reflects several levels of granularity. For this purpose, events refer via the
_SubProcessID_ attribute to the subtrace, which describes the respective event
with finer granularity (see Fig. 3).

Fig. 3: Excerpt of a Process Instance of Workflow 109 Including Resolving the
Relationship to the Subprocess of the Event ”/ov/temper” in the Lifecycle
State ”(Inprogress)” via the SubProcessID.

First, we provide an unmodified and error-ridden IoT-enriched event log. The
errors in the event log are typical problems in real-world production shop
floors or, in general, in IoT domains: For example, events in the log are
delayed or completely missing due to communication problems, or are sent
multiple times and logged twice due to timeouts. In addition, the response
code of the web server is not contained in some events. For this reason, it is
not possible to obtain whether the corresponding activity was successfully
executed or not in the shop floor. The timestamps of IoT-sensor data and the
corresponding process data are sometimes shifted since a Network Time Protocol
server was out of service. Second, we provide the event log in a cleaned gold
standard. Therefore, all errors and _Data Quality Issues (DQIs)_ were removed
Bose2013-sb . Using our developed domain ontology FTOnto Klein.2019b_FTOnto
and separate recordings produced by Apache Kafka from the smart factory used
for logging, it was possible to correct the timestamps and to reconstruct
unrecorded, so-called invisible events. By providing these comprehensive IoT-
enriched event logs, we enable process mining approaches to be developed and
evaluated based on real-world data. In particular, the provision of a real
version of the dataset interspersed with DQIs and a cleaned dataset enables
the explicit targeting of DQIs. Thus, we provide the data for addressing the
process mining challenges C1) Finding, Merging, and Cleaning Event Data, C2)
Dealing with Complex Event Logs Having Diverse Characteristics, and C4)
Dealing with Concept Drifts ProcessMiningManifesto .

##  4 Preliminary Analysis

In the preliminary analysis, we analyze the event logs, their structure, and
content. Unless otherwise stated, the values refer to the original and
erroneous event log (see Tab. 1). Sensors are only present at the resource
level, and sensor values are directly associated with events in the subtraces.
The number of sensors and sensor values varies between resources. While _High
Bay Warehouse (HBW)_ sometimes records 160 sensor values per second or more,
_Human Workstation (HW)_ only records about 40 values.

In total, 16 workflows are shown. While on the _MainProcess_ level (Main log
in Tab. 1), all events represent the three lifecycle steps _assigned_ ,
_inProgress_ , and _success_ or _failure_. In the sublog (sublogs in Tab. 1),
no lifecycle attributes are present. All traces start with a _/hbw/unload_.
The end event is _/hbw/store_ in 212 traces and _/hbw/unload_ in 34 traces.
The end of the other traces is distributed over 14 further events. A closer
look at workflow 111 shows that each event has an edge to itself. These
represent the events in different lifecycle stages. It also shows that the
traces can end in any of the activities. This suggests that the process of
burning the workpiece in the oven, milling it in the milling machine, and then
deburring it in the milling machine contains errors.

Since the SensorStream extension has not yet been implemented in any process
mining tool, the sensor perspective could not be analyzed. Due to a lack of
space, the illustration of a process discovery was omitted.

Table 1: Overview Over the Logs (after preprocessing, corrected log). Traces in the main log reference on sublogs. Data Sets |  Events |  Cases |  Activities |  Resources |  Variants |  Actuators |  Sensors |  Data Points |  Trace Len (avg,min,max)
---|---|---|---|---|---|---|---|---|---
Main log | 9,471 | 301 | 21 | 15 | 231 | - | - | - |  31 |  3 |  69
Sublogs | 13,424 | 3,118 | 109 | 15 | 269 | 52 | 131 | 136,208,108 |  8 |  1 |  14
Total | 22,895 | 3,489 | 21 | 15 | 500 | 52 | 131 | 136,208,108 |  10 |  1 |  69

##  5 Conclusion

In this paper, we present an IoT-enriched event log for process mining
research in smart factories. Such event logs are currently rather rare and
mostly synthetically generated. We generate two versions of the event log with
a physical factory model (see Sect. 2): one version is the native log with
DQIs such as delayed or missing events etc. and a cleaned version without
these DQIs. The IoT-enriched event logs provide the basis for data-intensive
research involving IoT sensor data in the process mining field. However, the
current frameworks and tools for process mining cannot directly use the
additional data. In future work, we investigate to extend frameworks and tools
to facilitate this.

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