Selected work

Analysis, modeling, and research code

Each project appears once, with the question it addresses, the method, and the result.

Open source github.com/armoutihansen →

432 contributions in the last year Less More

Applied analysis

Data work aimed at a decision: pick the measure that matters, build the analysis, and state what it supports.

01 Case study · Operational analysis

CitiBike Demand, Risk, and Net Flow

Station- and trip-level analysis of demand, net flow, and collision-adjusted risk across New York's bike-share network.

Problem: Where does the network strain — heavy use, poor rebalancing, or higher risk per trip?
Approach: Joined trip records with collision data and built station-level measures that separate raw volume from exposure-adjusted risk.
Result: Three separate measures — demand, imbalance, and exposure-adjusted risk — that a single ranking would have conflated.

Python · pandas · feature engineering · risk analysis · forecasting

Station-level crash risk LowerMediumHigher

Each dot is a CitiBike station.

Modeling & tools

Search and ranking, choice-modeling code, and small tool-calling systems.

01 GitHub project · Retrieval system

RAG Search Engine

A hybrid movie-search engine combining lexical and semantic retrieval, reranking, image search, and RAG answers.

Problem: Plain search fails when a query is vague, visual, or more description than title.
Approach: Combined BM25, embeddings, reciprocal rank fusion, CLIP, reranking, and caching, with explicit checks at each retrieval stage.
Result: A pipeline with each retrieval stage measured separately, so ranking quality is attributable to a stage rather than the whole.

Python · RAG · BM25 · embeddings · CLIP · evaluation

Search pipeline

Hybrid retrieval, fused and reranked, then answered with citations.

02 Early-stage · Python package

choicekit

A Python package for reusable choice-modeling workflows, in development.

Problem: Choice-modeling code gets hard to trust when every project restarts from a loose script.
Approach: Move estimation machinery into tested package code and leave project files for data and interpretation.
Result: Early, but aimed at a clean boundary between modeling tools and one-off analysis.

Python · choice modeling · package design

03 GitHub project · AI tooling

Minimal Coding Agent

A small Gemini-powered coding agent that reads files, runs scripts, and edits code through explicit tools.

Problem: How much can a coding agent do with only a few explicit tools and no framework?
Approach: Implemented file inspection, script execution, and edits behind explicit, inspectable tool calls.
Result: A working agent that reads, runs, and edits code in a loop — and surfaces where it needs guardrails.

Python · tool-calling · Gemini API · CLI

Agent run

An example run — the agent finds a failing test and fixes it.

Research & replication

Research code: published replication packages and methods work in economics, tested and reproducible.

01 Manuscript + replication package · Inference & simulation

The Informativeness of Frequency-Report Scoring Rules

Manuscript, simulations, and replication code for belief elicitation from frequency reports.

Problem: A count report is observable; the belief behind it is not. How much does the report actually pin down?
Approach: Characterized the identified set of beliefs behind each report under three scoring rules, then checked the bounds with simulation.
Result: No rule dominates: which scoring rule gives the sharpest bounds depends on how concentrated the beliefs are.

Python · simulation · pytest · scoring rules

Sharpest-bound win share

Share of cases where each scoring rule gives the sharpest belief bounds, by belief concentration α.

02 GitHub project · Model comparison

Economic Theories and Machine Learning

Code comparing economic theories against machine-learning benchmarks on behavioral data.

Problem: When a theory predicts behavior, how much of the predictable variation does it actually capture?
Approach: Benchmarked theory-based specifications against machine-learning models and examined the remaining predictive gap.
Result: Theory specifications scored on out-of-sample prediction and compared against the machine-learning benchmark.

Python · machine learning · model evaluation

03 Published · GEB 2024 · Replication package

Efficiency Wages with Motivated Agents

Replication data and code for a published paper on wages, motivation, and effort.

Problem: Do wage incentives and prosocial motivation reinforce each other, or work through separate channels?
Approach: Built reproducible analyses around experimental data and documented the paper's online appendix.
Result: A peer-reviewed empirical project with a complete replication package.

Python · Stata · experimental data · replication

Chosen effort by wage

Mean chosen effort by offered wage, with 95% confidence bands.

Also on GitHub

Smaller builds from learning new tools — a notes site, a static-site generator, and a few games and CLIs.

Personal Knowledge System Static Site Generator BookBot Asteroids Maze Solver