Quantitative Scientist

Jesper Armouti-Hansen

Economics PhD applying theoretical modelling, statistical modelling, and machine learning to understand data, evaluate models, and support better decisions.

Illustrated portrait of Jesper Armouti-Hansen

Focus

  • Econometrics and statistical modelling
  • Machine learning model evaluation
  • Behavioral and organizational research

Profile

Quantitative work grounded in economic research

My work connects applied data science with formal research training: building models, evaluating evidence, and communicating uncertainty clearly enough to support decisions.

Quantitative modelling

Statistical models, econometrics, model comparison, and hypothesis-driven analysis of structured data.

Applied data science

Machine learning workflows with attention to confidence, error analysis, and the practical conditions under which model outputs are useful.

Research depth

Academic work on behavioral economics, incentives, social preferences, and organizational decision-making.

Selected work

Projects

Retrieval and evaluation GitHub project

RAG Search Engine

Python search system combining BM25, Sentence Transformers, CLIP, reciprocal rank fusion, query enhancement, multimodal search, caching, and evaluation workflows.

  • Python
  • RAG
  • BM25
  • Sentence Transformers
  • CLIP
View project
Applied data science Case study

CitiBike Demand, Risk, and Net Flow Analysis

Exploratory and risk-oriented analysis of CitiBike usage, bike-type mix, membership patterns, station-level risk, and net-flow dynamics using trip and collision data.

  • Python
  • geospatial matching
  • risk analysis
  • visualization
View project
Research code GitHub project

Economic Theories and Machine Learning

Analysis code connected to research on evaluating economic theories using machine learning techniques.

  • Jupyter Notebook
  • machine learning
  • economic theory
  • model evaluation
View project
All projects

Research

Selected publications

Publication 2024

Efficiency Wages with Motivated Agents

Jesper Armouti-Hansen, Lea Cassar, Anna Dereky, and Florian Engl

Games and Economic Behavior, 145, pp. 66-83

Abstract

Many jobs serve a social purpose beyond profit maximization. This paper uses a modified principal-agent gift-exchange game with positive externality to study how workers' prosocial motivation interacts with efficiency wages in stimulating effort. The results show that prosocial motivation and efficiency wages are independent in stimulating effort, while principals offer higher wages in the prosocial treatment because they underestimate reciprocity in the standard gift-exchange environment.

Publication 2024

Managing Anticipation and Reference-Dependent Choice

Jesper Armouti-Hansen and Christopher Kops

Journal of Mathematical Economics, 112, 102988

Abstract

The paper develops a model of reference-dependent choice in which the reference point may be any convex combination of possible outcomes under a consumption lottery. It introduces solution concepts, characterizes them on choice data, and identifies the model's parameters.

Publication 2020

Optimal Contracting with Endogenous Project Mission

Jesper Armouti-Hansen and Lea Cassar

Journal of the European Economic Association, 18(5), pp. 2647-2676

Abstract

The paper studies how organizations can choose a project mission to attract, incentivize, and screen workers. It analyzes how contractual environments shape the optimal distance between the organization's preferred mission and the agents' preferred mission.

All publications

Toolkit

Methods and tools

Quantitative

  • Statistical modelling
  • Econometrics
  • Experimental data analysis
  • Theoretical modelling
  • Model evaluation
  • Hypothesis testing
  • Error analysis

Programming

  • Python
  • pandas
  • NumPy
  • scikit-learn
  • statsmodels
  • SQL
  • R
  • Stata
  • Git
  • Linux

Research

  • Behavioral economics
  • Personnel economics
  • Organizational economics
  • Contract theory
  • Social preferences
  • Incentives and motivation