BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Vrije Universiteit Amsterdam//NONSGML v1.0//EN
NAME:ABRI Lunch Seminar Jonas Andersen
METHOD:PUBLISH
BEGIN:VEVENT
DTSTART:20260519T120000
DTEND:20260519T130000
DTSTAMP:20260519T120000
UID:abri-lunch-seminar-jonas-ander@8F96275E-9F55-4B3F-A143-836282E12573
CREATED:20260925T004324
LOCATION:VU Main Building, 1105, HG-05A36, De Boelelaan, 1081 HV, Amsterdam
SUMMARY:ABRI Lunch Seminar Jonas Andersen
X-ALT-DESC;FMTTYPE=text/html: We are happy to invi
te you to the ABRI Lunch Seminar "Understanding and Anticipating the
Emergent Outcomes of Algorithmic Matching on Digital Platforms" by Dr
. Jonas Andersen (Department of Management, Aarhus University) organi
zed by ABRI and the KIN Center for Digital Innovation.
The
seminar will take place on Tuesday, May 19th, from 12:00 to 13:00 (H
G-05A36).
This is a lunch seminar; please register your attend
ance by accepting/declining your emailed invitation by Friday, May 15
th at the latest (for catering).
Abstract
Algorithmic matching facilitates value creation on digital platforms
by connecting users to content, services, and to one another. Prior r
esearch on outcomes of algorithmic matching across different contexts
, however, typically conceptualizes matching outcomes as individual-
or population-level effects observed at a static point in time, assum
ing that these outcomes are non-emergent. We challenge this assumptio
n and argue that the core characteristics of algorithmic matching mak
e emergent, system-level outcomes not only possible but likely. We id
entify three foundational characteristics, data fidelity, reductionis
t interaction rules, and user intention heterogeneity, that jointly i
ncrease the likelihood of mismatches, triggering user adaptation. Dra
wing on complex adaptive systems as a meta-theoretical lens, we devel
op a multi-level theory that explains how three interacting feedback
mechanisms generate dynamic, path-dependent emergent outcomes: ecosys
tem-level growth and diversity feedback, platform-level design and go
vernance feedback, and user-level behavioral adaptation and predictio
n feedback. We demonstrate how these three feedback mechanisms can le
ad to emergent outcomes via an agent-based simulation of a dating pla
tform. Our theorizing advances research on matching and complexity in
digital platforms and offers a foundation for evaluating the long-ru
n implications of algorithmic design and governance choices.
DESCRIPTION: We are happy to invite you to the ABRI Lunch Seminar "Und
erstanding and Anticipating the Emergent Outcomes of Algorithmic Matc
hing on Digital Platforms" by Dr. Jonas Andersen (Department of Manag
ement, Aarhus University) organized by ABRI and the KIN Center for Di
gital Innovation. The seminar will take place on Tuesday, May 19th, f
rom 12:00 to 13:00 (HG-05A36).
This is a lunch seminar; please
register your attendance by accepting/declining your emailed invitat
ion by Friday, May 15th at the latest (for catering).
Abstract
Algorithmic matching facilitates value creation
on digital platforms by connecting users to content, services, and to
one another. Prior research on outcomes of algorithmic matching acro
ss different contexts, however, typically conceptualizes matching out
comes as individual- or population-level effects observed at a static
point in time, assuming that these outcomes are non-emergent. We cha
llenge this assumption and argue that the core characteristics of alg
orithmic matching make emergent, system-level outcomes not only possi
ble but likely. We identify three foundational characteristics, data
fidelity, reductionist interaction rules, and user intention heteroge
neity, that jointly increase the likelihood of mismatches, triggering
user adaptation. Drawing on complex adaptive systems as a meta-theor
etical lens, we develop a multi-level theory that explains how three
interacting feedback mechanisms generate dynamic, path-dependent emer
gent outcomes: ecosystem-level growth and diversity feedback, platfor
m-level design and governance feedback, and user-level behavioral ada
ptation and prediction feedback. We demonstrate how these three feedb
ack mechanisms can lead to emergent outcomes via an agent-based simul
ation of a dating platform. Our theorizing advances research on match
ing and complexity in digital platforms and offers a foundation for e
valuating the long-run implications of algorithmic design and governa
nce choices.
END:VEVENT
END:VCALENDAR