Cooperation and trust in extra-legal contexts
I explore how agents sustain mutually beneficial cooperative relations in adverse contexts. Illegal online marketplaces (cryptomarkets) present a unique context for studying the emergence of cooperation in a context where legal controls are absent, social actors are highly anonymous, and the overall levels of trust are low. With collaborators, I study how socio-technical solutions (such as reputation systems) and informal mechanisms of social control (such as gossip) allow individuals to establish trade in such adverse extra-legal environments.
The Moral Embeddedness of Illegal
Markets With Wojtek Przepiorka, Under
Review. In this work, we explore how reputation-based
online markets have shifted the role that psychological mechanisms play
in promoting mutually cooperative market exchange from the stage of
exchange to the stage of sharing information about other traders in the
market. We use text mining to infer reasons traders had for sharing
reputation information about other traders in illegal online
marketplaces and zoom in on the essential tole of moral norms in solving
the second-order cooperation problem in large-scale anonymous internet
marketplaces. The Importance of Gossip in the Presence of
Formalized Reputation Measures in Online Markets
With Wojtek Przepiorka and Vincent Buskens, In
preparation.
Click for an overview of my projects on this
topic.
Norm emergence and change
I am interested in how norms emerge to promote or block sucesfull cooperation in societies. In a project with collaborators, I use agent-based models to explore how norms that signal group belonging emerge to solve cooperation problems in contexts where multiple groups with conflicting interests encounter each other. In such contexts, signalling one’s group behaviour can help establish parochial cooperation and benefit exploited minority groups. I am further interested in understanding conditions under which such signalling norms become inefficient and damage the well-being of the groups that enforce them.
Signals of Belonging: Emergence of
Signalling Norms as Facilitators of Parochial Cooperation
With Milena Tsvetkova, Wojtek Przepiorka, and
Vincent Buskens, In preparation. Mechanisms of social control at times reinforce
norms that appear individually, or even collectively, harmful or
“wasteful” – such as mutilation practices or extensive body tattoos.
This project explores the suggestion that such norms emerge as outcomes
of signalling games and promote parochial cooperation in contexts where
different groups have conflicting interests. To understand when
signalling norms emerge as solutions to the trust problem arising in
such environments, we build an agent-based model based on the trust game
with signalling: in this game, signalling can help individuals reveal
their group membership to others they are encountering – and thus
enforce and sustain parochial cooperation.
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topic.
Networks, culture, innovation and inequality
I am intrigued by how the structure of social networks affects outcomes in different social groups and communities. On the one hand, I am interested in how social network structure and cultural integration affect the capacity of (online) communities to develop shared culture and innovate. On the other hand, I am also intrigued by how numerical properties of different social groups engaging ininteractions with each other can lead to structural inequalities.
“I Heard it on the (Silk)Road”: Structural
and Cultural Pathways to Novelty Introduction in an Online Community
With Damiano Morando, In
preparation. Online communities play a crucial role in
generating and spreading innovation which affects both the online and
offline world. Yet, we still know relatively little about the members
who introduce novelty into the community - innovators. We explore the
structural and cultural embeddedness of members who introduce linguistic
and knowledge-based innovations into a large online forum. First, we
find that the effect of being structurally embedded in the community is
moderated by the content one is exposed to. That is, less embedded
individuals (i.e., brokers) benefit from access to diverse content in
the online community;while embedded individuals benefit from seeing
similar content.Second, we show that being culturally embedded in the
community has an inverse U shape effect over the likelihood introducing
an innovation. We find evidence for the tension between the
norm-following required for meaningful innovations and the norm-breaking
that pushes one to “think outside of the box”. Minority group size moderates
inequity-reducing strategies in homophilic networks
With Sam Zhang and Travis Holmes, In
preparation. Minorities are often disadvantaged in social
networks, and their disadvantage can arise in the presence of two
ubiquitous features of social networks: homophily and preferential
attachment. This disadvantage often translates to less visibility and
lower access to valuable social capital of minority group members. In
this paper, we use the directed network model with preferential
attachment and homophily (DPAH) to evaluate how minority groups fare in
the presence of additional groups that could bridge the
majority/minority gap, such as majority group members who support the
minority group (allies), and minority group members that are
incorporated into the majority group. Our results show that the marginal
benefit of majority group members becoming allies increases with
minority group size, and larger minority groups reach equity with a
smaller proportion of incorporated members. These results suggest that
interventions on structural inequities on networks can depend
sensitively on the relative sizes of the groups involved. Our models
also reveal the increasing difficulty of a minority group achieving
parity as the group shrinks.
Click for an overview of my projects on this
topic.
Text mining for sociological research
I am interested in better understanding how meaning can be extracted from large amounts of textual data and used in sociological theory building and testing. I explore how automatic text analysis can be used by social scientist and explore the benefits and limitations of working with big data sets and computational methods for text analysis.
Text mining for social science – The state
and the future of computational text analysis in sociology
Article published in Social Science Research available here.
The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in sociological research. Recent work in sociology of culture, science, and economic sociology has shown how computational text analysis can be used in theory building and testing. This review starts with an introduction of the history of computer-assisted text analysis in sociology and then proceeds to discuss five families of computational methods used in contemporary research. Using exemplary studies, it shows how dictionary methods, semantic and network analysis tools, language models, unsupervised, and supervised machine learning can assist sociologists with different analytical tasks. After presenting recent methodological developments, this review summarizes several important implications of using large datasets and computational methods to infer complex meaning in texts. Finally, it calls researchers from different methodological traditions to adopt text mining tools while remaining mindful of lessons learned from working with conventional data and methods.
Text mining individual states in short
texts
With Wojtek Przepiorka, Under Review. Preprint available here.
Sociologists have successfully used text mining to investigate discourse using news articles, official documents, and other sources. Yet, the potential of exploring millions of short texts generated spontaneously by individuals in online environments has remained untapped within the field. To fill this gap, we show how such texts can inform sociologists about individual internal states such as norms, motives, and stances, which thus far have been mainly elicited using surveys. We assess the performance of 581 variations of three text mining approaches–dictionary methods, supervised, and unsupervised machine learning–against the benchmark of texts coded by humans for complex schemes capturing individuals’ internal states. Our analysis includes coding feedback texts from an online market for motives for leaving feedback (N = 2,000) and tweet texts for moral values expressed in text (N = 3,832). We describe challenges arising with these different approaches and provide best-practice advice for future applications.