SSRN Working Paper · 2026
Institutional Field Dynamics: A Force Field Model of Multi-Actor Governance
Formalises institutional dynamics as a force field system in which actors move
through configuration space under three types of force: intrinsic motivation,
social influence mediated by an asymmetric power matrix, and institutional
pressure imposed by policy architecture. Policy is modeled as transformation
of the field itself. The model generates diagnostic metrics for coherence,
friction, and power asymmetry, and enables attractor analysis that tests
whether a policy genuinely transforms the institutional landscape or merely
shifts positions within it. Demonstrated on Dutch education reform
(Passend Onderwijs), with applications to advisory governance,
regulatory design, and participatory democracy.
Status
Working paper
Year
2026
Institutional dynamics
Force field model
Multi-actor governance
Attractor analysis
Policy design
Master's Thesis · Computational Science
Using probabilistic methods for hierarchical visualization of single-cell RNA-seq data
Computational methods for making sense of high-dimensional single-cell RNA-seq data.
The focus is on probabilistic, hierarchical views that expose structure in cell
populations instead of drowning in raw expression matrices.
Institution
University of Amsterdam
Year
2020
Single-cell RNA-seq
Hierarchical visualization
Probabilistic modelling
Computational biology
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Master's Thesis · Information Studies (Data Science)
Using transfer learning to increase performance of multinomial text classification on documents of different municipalities
Transfer learning with TrAdaBoost on heterogeneous municipal document sets.
The work looks at how knowledge can be moved between domains to stabilise
performance when each city writes in its own bureaucratic dialect.
Institution
University of Amsterdam
Year
2018
Transfer learning
Text classification
TrAdaBoost
Naive Bayes
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